阅读说明：本技术 负荷均衡的方法、装置、计算设备及计算机可读存储介质 (Load balancing method and device, computing equipment and computer readable storage medium ) 是由 高瑜鸿 张之栋 李连本 张亚男 贾磊 于 2020-05-20 设计创作，主要内容包括：本发明实施例涉及通信技术领域,公开了一种负荷均衡的方法、装置、计算设备及计算机可读存储介质,该方法包括：获取源小区及其邻区的特征数据；对源小区和任意一个邻区的特征数据进行相关性分析,确定目标小区；根据源小区和目标小区的频段确定负荷均衡的切换事件类别；确定各切换事件类别对应的迭代变量组合；对迭代变量组合进行多次迭代,根据每一次迭代后得到的迭代变量组合值计算利用率变化值以及覆盖率变化值；将使利用率变化值和覆盖率变化值之和最小的迭代变量组合值确定为目标迭代变量组合值；根据目标迭代变量组合值中的各目标参数值对源小区进行负荷均衡。通过上述方式,本发明实施例实现了负荷均衡。(The embodiment of the invention relates to the technical field of communication, and discloses a load balancing method, a load balancing device, computing equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring feature data of a source cell and adjacent cells thereof; performing correlation analysis on the characteristic data of the source cell and any one adjacent cell to determine a target cell; determining the switching event type of load balance according to the frequency bands of the source cell and the target cell; determining an iteration variable combination corresponding to each switching event type; performing multiple iterations on the iteration variable combination, and calculating a utilization rate change value and a coverage rate change value according to the iteration variable combination value obtained after each iteration; determining an iteration variable combination value which minimizes the sum of the utilization rate change value and the coverage rate change value as a target iteration variable combination value; and carrying out load balancing on the source cell according to each target parameter value in the target iteration variable combination value. Through the mode, the embodiment of the invention realizes load balancing.)

1. A method of load balancing, the method comprising:

acquiring feature data of a source cell and adjacent cells thereof;

performing correlation analysis on the feature data of the source cell and any one of the neighboring cells, and determining a target cell for load balancing in the neighboring cells according to an analysis result;

determining the switching event type of load balance according to the frequency band of the source cell and the frequency band of the target cell;

determining an iteration variable combination corresponding to each switching event category, wherein the iteration variable combination comprises a plurality of target parameters, and the target parameters are used for load balancing of the source cell;

performing multiple iterations on the iteration variable combination, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration;

determining an iteration variable combination value which minimizes the sum of the utilization rate change value and the coverage rate change value as a target iteration variable combination value;

and carrying out load balancing on the source cell according to each target parameter value in the target iteration variable combination value.

2. The method of claim 1, wherein the performing correlation analysis on the feature data of the source cell and any one of the neighboring cells, and determining a target cell for load balancing in the neighboring cells according to an analysis result comprises:

determining a first switching frequency of the source cell to each adjacent cell within a preset time period, and a second switching frequency of each adjacent cell to the source cell within the preset time period;

calculating a switch-out correlation coefficient and a switch-in correlation coefficient of the source cell and each neighboring cell according to the first switching times and the second switching times;

determining the adjacent cell with the switching-out correlation coefficient larger than a preset first threshold value as a pre-flow cell;

determining the adjacent cell of which the switching-in correlation coefficient is larger than a preset second threshold value as a pre-throttling cell;

and performing coverage correlation analysis on the pre-flow cell and the pre-flow cell, and determining a target cell with balanced load according to the result of the coverage correlation analysis.

3. The method according to claim 2, wherein the performing coverage correlation analysis on the pre-throttle cell and the pre-throttle cell, and determining a target cell for load balancing according to a result of the coverage correlation analysis comprises:

counting a first sampling point number of the level intensity of the first cell higher than the level intensity of the source cell in the preset time period, and a second sampling point number of the level intensity of the first cell lower than the level intensity of the source cell in the preset time period; the first cell is any one of all pre-throttling cells or the pre-throttling cell;

calculating a first ratio of the number of the first sampling points to the number of the second sampling points, and a second ratio of the number of the second sampling points to the number of the first sampling points;

determining the pre-distribution cell with the first ratio larger than a preset third threshold value as a distribution cell;

determining the pre-throttling cell with the second ratio being greater than a preset fourth threshold value as a throttling cell;

and determining the set of the shunting cell and the throttling cell as a target cell with balanced load.

4. The method of claim 2 or 3, wherein the determining a first number of handovers of the source cell to each neighboring cell within a preset time period and a second number of handovers of each neighboring cell to the source cell within the preset time period comprises:

determining the busy time interval of the source cell every day in a preset time period, wherein the busy time interval is the time interval of the maximum uplink PRB utilization rate or the maximum downlink PRB utilization rate of the source cell;

taking the sum of the switching times of the source cell to each adjacent cell in the busy period in the preset time period as the first switching time;

and taking the sum of the switching times of each adjacent cell to the source cell in the busy period in the preset time period as the second switching time.

5. The method of claim 1, wherein the determining the handover event class for load balancing according to the frequency band of the source cell and the frequency band of the target cell comprises:

and if the frequency band of the source cell and the frequency band of the target cell belong to the same level of preset priority, determining that the type of the switching event is a first switching event, otherwise, determining that the type of the switching event is a second switching event, wherein the first switching event and the second switching event are different switching event types.

6. The method according to claim 1, wherein the iterating the iterative variable combination for multiple times, and calculating a utilization rate change value after load balancing of the source cell and a coverage rate change value after load balancing of the target cell according to the iterative variable combination value obtained after each iteration, comprises:

randomly generating a plurality of groups of iteration variable combination values and update speed values corresponding to the iteration variable combination values, wherein each group of iteration variable combination values is used as an individual optimal value;

calculating a first sum of a first utilization rate change value and a first coverage rate change value corresponding to each group of iteration variable combination values;

determining a set of iterative variable combination values that minimizes the first sum as a population optimal value;

iterating the updating speed values according to the individual optimal values and the group optimal values to obtain corresponding first updating speed values;

iterating the corresponding iteration variable combination value according to the first updating speed value to obtain a corresponding first iteration variable combination value;

calculating a second sum of a second utilization rate change value and a second coverage rate change value corresponding to each group of first iteration variable combination values;

if any one of the second sums is smaller than the corresponding first sum, updating the corresponding individual optimal value to a corresponding first iterative variable combination value;

if the smallest second sum of all the second sums is smaller than the first sum corresponding to the group optimal value, updating the group optimal value to the first iteration variable combination value corresponding to the smallest second sum;

updating the iteration speed value corresponding to each group of iteration variable combination values into a corresponding first update speed value, updating each group of iteration variable combination values into a corresponding first iteration variable combination value, and repeatedly executing the step of iterating the update speed values according to the individual optimal value and the group optimal value to obtain a corresponding first update speed value until the preset iteration times are met;

determining, as a target iterative variable combination value, an iterative variable combination value that minimizes a sum of the utilization rate change value and the coverage rate change value, including:

and determining the group optimal value meeting the preset iteration times as a target iteration variable combination value.

7. The method of claim 6, wherein calculating a first sum of the first utilization change value and the first coverage change value for each set of iterative variable combination values comprises:

determining sampling points needing to be transferred in the source cell according to a second iteration variable combination value, wherein the second iteration variable combination value is any one group of iteration variable combination values;

calculating a first average value of the total number of PRBs (resource blocks) occupied by the sampling points in the uplink shared channel and a second average value of the total number of RPBs (resource blocks) occupied by the sampling points in the downlink shared channel;

determining the maximum value of the first average value and the second average value as a utilization rate change value after the load balancing of the source cell;

determining the sum of the signal receiving power of each target cell after the sampling point is transferred to each target cell;

and calculating the ratio of the sum of the signal receiving powers of the sampling points in each target cell to the sum of the signal receiving powers of the corresponding target cells to obtain a coverage rate change value of the target cells after load balancing.

8. An apparatus for load balancing, the apparatus comprising:

the acquisition module is used for acquiring the characteristic data of the source cell and the adjacent cell thereof;

the analysis module is used for carrying out correlation analysis on the feature data of the source cell and any one of the adjacent cells and determining a target cell for load balancing in the adjacent cells according to an analysis result;

a first determining module, configured to determine a type of a load balancing handover event according to a frequency band of the source cell and a frequency band of the target cell;

a second determining module, configured to determine an iteration variable combination corresponding to each handover event category, where the iteration variable combination includes multiple target parameters, and the target parameters are used to perform load balancing on the source cell;

the iteration module is used for carrying out multiple iterations on the iteration variable combination, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration;

a third determining module, configured to determine, as a target iteration variable combination value, an iteration variable combination value that minimizes a sum of the utilization rate change value and the coverage rate change value;

and the load balancing module is used for carrying out load balancing on the source cell according to the target parameter value in the target iteration variable combination.

9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is configured to store at least one executable instruction that causes the processor to perform the operations of a method of load balancing according to any one of claims 1 to 7.

10. A computer-readable storage medium, having at least one executable instruction stored therein, which when executed on a computing device/apparatus, causes the computing device/apparatus to perform operations corresponding to the method of load balancing according to any one of claims 1 to 7.

Technical Field

The embodiment of the invention relates to the technical field of communication, in particular to a load balancing method, a load balancing device, computing equipment and a computer readable storage medium.

Background

With continuous preference of flow packages of operators, network flow is increased explosively, network cell traffic of some user hot spot areas is large, load is high, and user experience on the network is seriously influenced.

The traditional network distribution method includes that the number of users of a high-load cell and a co-station co-coverage pilot frequency adjacent cell and the RPB utilization rate are manually extracted for comparative analysis, and when the ratio of the number of users of the high-load cell or the difference value of the PRB utilization rates exceeds a certain range, the high-load cell and the co-coverage adjacent cell are considered to be unbalanced in load, and service distribution can be carried out. After determining that the service distribution can be carried out, repeatedly adjusting various parameters according to a certain step length on the basis of the original switching parameters, reselection parameters and MLB parameter setting values until the proportion of the number of users in the high-load cell or the difference value of the PRB utilization rate reaches a reasonable range.

The traditional network flow distribution method aims at load balancing, and causes poor network coverage on the premise of meeting network capacity.

Disclosure of Invention

In view of the foregoing problems, embodiments of the present invention provide a load balancing method, an apparatus, a computing device, and a computer-readable storage medium, which are used to solve the problem in the prior art that network coverage is poor on the premise of meeting network capacity.

According to an aspect of an embodiment of the present invention, there is provided a load balancing method, including:

acquiring feature data of a source cell and adjacent cells thereof;

performing correlation analysis on the feature data of the source cell and any one of the neighboring cells, and determining a target cell for load balancing in the neighboring cells according to an analysis result;

determining the switching event type of load balance according to the frequency band of the source cell and the frequency band of the target cell;

determining an iteration variable combination corresponding to each switching event category, wherein the iteration variable combination comprises a plurality of target parameters, and the target parameters are used for load balancing of the source cell;

performing multiple iterations on the iteration variable combination, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration;

determining an iteration variable combination value which minimizes the sum of the utilization rate change value and the coverage rate change value as a target iteration variable combination value;

and carrying out load balancing on the source cell according to each target parameter value in the target iteration variable combination value.

In an optional manner, the performing correlation analysis on the feature data of the source cell and any one of the neighboring cells, and determining a target cell for load balancing in the neighboring cells according to an analysis result includes:

determining a first switching frequency of the source cell to each adjacent cell within a preset time period, and a second switching frequency of each adjacent cell to the source cell within the preset time period;

calculating a switch-out correlation coefficient and a switch-in correlation coefficient of the source cell and each neighboring cell according to the first switching times and the second switching times;

determining the adjacent cell with the switching-out correlation coefficient larger than a preset first threshold value as a pre-flow cell;

determining the adjacent cell of which the switching-in correlation coefficient is larger than a preset second threshold value as a pre-throttling cell;

and performing coverage correlation analysis on the pre-flow cell and the pre-flow cell, and determining a target cell with balanced load according to the result of the coverage correlation analysis.

In an optional manner, the performing coverage correlation analysis on the pre-throttling cell and the pre-throttling cell, and determining a target cell with balanced load according to a result of the coverage correlation analysis includes:

counting a first sampling point number of the level intensity of the first cell higher than the level intensity of the source cell in the preset time period, and a second sampling point number of the level intensity of the first cell lower than the level intensity of the source cell in the preset time period; the first cell is any one of all pre-throttling cells or the pre-throttling cell;

calculating a first ratio of the number of the first sampling points to the number of the second sampling points, and a second ratio of the number of the second sampling points to the number of the first sampling points;

determining the pre-distribution cell with the first ratio larger than a preset third threshold value as a distribution cell;

determining the pre-throttling cell with the second ratio being greater than a preset fourth threshold value as a throttling cell;

and determining the set of the shunting cell and the throttling cell as a target cell with balanced load.

In an optional manner, the determining a first number of handovers, in which the source cell is handed over to each neighboring cell within a preset time period, and a second number of handovers, in which each neighboring cell is handed over to the source cell within the preset time period, includes:

determining the busy time interval of the source cell every day in a preset time period, wherein the busy time interval is the time interval of the maximum uplink PRB utilization rate or the maximum downlink PRB utilization rate of the source cell;

taking the sum of the switching times of the source cell to each adjacent cell in the busy period in the preset time period as the first switching time;

and taking the sum of the switching times of each adjacent cell to the source cell in the busy period in the preset time period as the second switching time.

In an optional manner, the determining a handover event category for load balancing according to the frequency band of the source cell and the frequency band of the target cell includes:

and if the frequency band of the source cell and the frequency band of the target cell belong to the same level of preset priority, determining that the type of the switching event is a first switching event, otherwise, determining that the type of the switching event is a second switching event, wherein the first switching event and the second switching event are different switching event types.

In an optional manner, the iterating the iteration variable combination for multiple times, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration includes:

randomly generating a plurality of groups of iteration variable combination values and update speed values corresponding to the iteration variable combination values, wherein each group of iteration variable combination values is used as an individual optimal value;

calculating a first sum of a first utilization rate change value and a first coverage rate change value corresponding to each group of iteration variable combination values;

determining a set of iterative variable combination values that minimizes the first sum as a population optimal value;

iterating the updating speed values according to the individual optimal values and the group optimal values to obtain corresponding first updating speed values;

iterating the corresponding iteration variable combination value according to the first updating speed value to obtain a corresponding first iteration variable combination value;

calculating a second sum of a second utilization rate change value and a second coverage rate change value corresponding to each group of first iteration variable combination values;

if any one of the second sums is smaller than the corresponding first sum, updating the corresponding individual optimal value to a corresponding first iterative variable combination value;

if the smallest second sum of all the second sums is smaller than the first sum corresponding to the group optimal value, updating the group optimal value to the first iteration variable combination value corresponding to the smallest second sum;

updating the iteration speed value corresponding to each group of iteration variable combination values into a corresponding first update speed value, updating each group of iteration variable combination values into a corresponding first iteration variable combination value, and repeatedly executing the step of iterating the update speed values according to the individual optimal value and the group optimal value to obtain a corresponding first update speed value until the preset iteration times are met;

determining, as a target iterative variable combination value, an iterative variable combination value that minimizes a sum of the utilization rate change value and the coverage rate change value, including:

and determining the group optimal value meeting the preset iteration times as a target iteration variable combination value.

In an alternative manner, the calculating a first sum of the first utilization rate change value and the first coverage rate change value corresponding to each set of iteration variable combination values includes:

determining sampling points needing to be transferred in the source cell according to a second iteration variable combination value, wherein the second iteration variable combination value is any one group of iteration variable combination values;

calculating a first average value of the total number of PRBs (resource blocks) occupied by the sampling points in the uplink shared channel and a second average value of the total number of RPBs (resource blocks) occupied by the sampling points in the downlink shared channel;

determining the maximum value of the first average value and the second average value as a utilization rate change value after the load balancing of the source cell;

determining the sum of the signal receiving power of each target cell after the sampling point is transferred to each target cell;

and calculating the ratio of the sum of the signal receiving powers of the sampling points in each target cell to the sum of the signal receiving powers of the corresponding target cells to obtain a coverage rate change value of the target cells after load balancing.

According to another aspect of the embodiments of the present invention, there is provided a load balancing apparatus, including:

the acquisition module is used for acquiring the characteristic data of the source cell and the adjacent cell thereof;

the analysis module is used for carrying out correlation analysis on the feature data of the source cell and any one of the adjacent cells and determining a target cell for load balancing in the adjacent cells according to an analysis result;

a first determining module, configured to determine a type of a load balancing handover event according to a frequency band of the source cell and a frequency band of the target cell;

a second determining module, configured to determine an iteration variable combination corresponding to each handover event category, where the iteration variable combination includes multiple target parameters, and the target parameters are used to perform load balancing on the source cell;

the iteration module is used for carrying out multiple iterations on the iteration variable combination, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration;

a third determining module, configured to determine, as a target iteration variable combination value, an iteration variable combination value that minimizes a sum of the utilization rate change value and the coverage rate change value;

and the load balancing module is used for carrying out load balancing on the source cell according to the target parameter value in the target iteration variable combination.

According to another aspect of embodiments of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation of the load balancing method.

According to another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having at least one executable instruction stored therein, the executable instruction causing a computing device/apparatus to perform the operations of one of the load balancing methods described above.

According to the embodiment of the invention, the target cell with balanced load is obtained by performing correlation analysis on the source cell and the adjacent cell, so that the obtained target cell is more accurate; determining the switching event type with balanced load according to the frequency bands of the source cell and the target cell, and determining corresponding iteration variable combinations for different switching event types; and carrying out multiple iterations on the iteration variable combination to obtain a target iteration variable combination value which enables the sum of the utilization rate change value of the source cell and the coverage rate change value of the target cell to be minimum, and carrying out load balancing on the source cell according to the target iteration variable combination value. The embodiment of the invention comprehensively considers the utilization rate change of the source cell and the coverage rate change of the target cell during load balancing, ensures that the network load of the source cell is effectively reduced, has better coverage of the adjacent cell, and avoids the problem of poor network coverage caused by meeting the network capacity.

The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.

Drawings

The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a schematic flowchart illustrating a load balancing method according to an embodiment of the present invention;

fig. 2 is a schematic flowchart illustrating an iteration flow of an iterative variable combination in a load balancing method according to an embodiment of the present invention;

fig. 3 is a flowchart illustrating a calculation of a utilization rate change value and a coverage rate change value in a load balancing method according to an embodiment of the present invention;

fig. 4 shows a functional block diagram of an apparatus for load balancing according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein.

The application scenario of the embodiment of the invention is load balancing of adjacent cells. The embodiment of the invention is suitable for any current communication network, such as a 4G communication network, a 5G communication network and the like. The embodiment of the invention comprehensively considers the utilization rate change of the high-load source cell and the coverage rate change of the target neighbor cell after load balancing when performing load balancing, and avoids the problem of poor network coverage of the target neighbor cell after load balancing. The following describes embodiments of the present invention.

Fig. 1 shows a flowchart of a load balancing method according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:

step 110: and acquiring the characteristic data of the source cell and the adjacent cell thereof.

In this step, the source cell is any one of the high-load cells, and the neighboring cell is a cell in which the load utilization rate is lower than the threshold value among all the different-frequency neighboring cells of the high-load cell. The characteristic data comprises working parameter data, network management performance data, MRO data and the like. The engineering parameter data is basic engineering parameter data and is data collected during base station construction. The working parameters include Common Gateway Interface (CGI), cell coverage scenario, and cell location. The cell coverage scene comprises colleges and universities, business circles, high-speed rail stations and the like. The network management performance data comes from the Operation and Maintence Center (OMC). The network management performance data includes the number of Physical Resource Blocks (PRBs) in uplink, the number of PRBs in downlink, and the like. MRO data is data formed by Measurement Reports (MRs) reported by user terminals. The MRO data includes: reference signal received power mr.ltecscrrsrp of the TD-LTE serving cell, reference signal received power mr.ltecescrsrp of a cell with a defined neighbor relation and an undefined neighbor relation of the TD-LTE, carrier number mr.ltecearfcn of the source cell, physical cell identification mr.ltecscpci of the source cell, neighbor carrier number mr.ltecearfcn of a cell with a defined neighbor relation and an undefined neighbor relation of the TD-LTE, physical cell identification mr.ltecpci of a cell with a defined neighbor relation and an undefined neighbor relation of the TD-LTE, PRB number mr.ltecschprbm of an uplink shared channel (pursc) of the source cell, PRB number mr.ltecpusch number of a downlink shared channel (PDLSC), and the like.

Step 120: and performing correlation analysis on the characteristic data of the source cell and any one of the adjacent cells, and determining a target cell for load balancing in the adjacent cells according to the analysis result.

In this step, the correlation analysis includes a handover correlation analysis and a coverage correlation analysis. The switching correlation analysis is to analyze the times of mutual switching between the source cell and the adjacent cell. The coverage correlation analysis is to analyze the level strengths of the source cell and the target cell. The coverage correlation analysis is based on the results of the handover correlation analysis. Specifically, when performing handover correlation analysis, a first handover frequency for a source cell to handover to each neighboring cell within a preset time period and a second handover frequency for each neighboring cell to handover to the source cell within the preset time period are determined. And calculating a switching-out correlation coefficient and a switching-in correlation coefficient of the source cell and each adjacent cell according to the first switching times and the second switching times. Taking a preset time period as 7 consecutive days as an example, within 7 days, the first switching frequency of switching from a source cell to a certain adjacent cell j is H_ojThe second switching times of the adjacent cell j to the source cell is H_ijIf the total number of the neighboring cells is T, the handover correlation coefficient between the source cell and the neighboring cell j is:the switch-in correlation coefficient of the source cell and the neighboring cell j is:wherein H_otFor the first number of handovers of the t-th neighbouring cell, H_itAnd the second switching times of the t-th adjacent cell. Comparing the switching-out correlation coefficient of the source cell and each adjacent cell with a preset first threshold value, and switching out the adjacent cell of which the correlation coefficient is larger than the preset first threshold value as a pre-flow cell; and comparing the switching-in correlation coefficient of the source cell and each adjacent cell with a preset second threshold value, wherein the adjacent cell with the switching-in correlation coefficient larger than the preset second threshold value is a pre-throttling cell. The preset first threshold and the preset second threshold may be the same or different. The pre-flow cell is a cell for receiving the load of the source cell, and the pre-throttling cell is a cell for switching the load to the source cell.

In some embodiments, the first number of handovers to each neighboring cell within a preset time period, and the second number of handovers to the source cell within the preset time period of each neighboring cell are determined by: determining the busy time interval of each day of a source cell in a preset time period, wherein the busy time interval is the time interval of the maximum uplink PRB utilization rate or the maximum downlink PRB utilization rate of the source cell; taking the sum of the switching times of switching the source cell to each adjacent cell in the busy period of a preset time period as a first switching time; and taking the sum of the switching times of each adjacent cell to the source cell in the busy period in a preset time period as a second switching time. For example, the preset time period is 7 consecutive days, a busy time period is determined every day, the sum of the switching times of the source cell to one of the neighboring cells in the busy time period every day in the 7 consecutive days is used as the first switching time of the source cell to the neighboring cell, and the sum of the switching times of a certain neighboring cell to the source cell in the busy time period every day in the 7 consecutive days is used as the second switching time of the neighboring cell to the source cell. By the mode, the effective switching between the source cell and the adjacent cell is met when the busy time interval is satisfied.

And performing coverage correlation analysis on the pre-flow cell and the pre-throttling cell to determine a target cell with balanced load. Specifically, the number of first sampling points in which the level intensity of the first cell is higher than the level intensity of the source cell in a preset time period is counted, and the number of second sampling points in which the level intensity of the first cell is lower than the level intensity of the source cell in the preset time period is counted. The first cell is any one of all pre-throttle cells or pre-throttle cells, and is described herein as the first cell. Coverage correlation analysis is required for all pre-flow cells or pre-throttle cells. The sampling points are the sampling points of the measurement report MR data, and the reported information comes from the user equipment in the source cell. And the user equipment periodically reports MR information to the base station in the cell access process, wherein the MR information comprises an RSRP level value. The first sampling point number is the number of sampling points which satisfy the following condition: mr. lterncsrsrp-mr. ltescrsrp >3 db; LtencRSRP > -110 dBm. The number of the second sampling points is the number of the sampling points which satisfy the following condition: mr. lterncsrsrp-mr. ltescrsrp < -3 db; LtencRSRP > -110 dBm. The values of 3db and-110 dBm can be set to other values by those skilled in the art, and the embodiments of the present invention are not limited thereto.

And calculating a first ratio of the number of the first sampling points to the number of the second sampling points, and a second ratio of the number of the second sampling points to the number of the first sampling points. And determining the pre-flow cell with the first ratio larger than a preset third threshold value as a flow distribution cell, and determining the pre-throttling cell with the second ratio larger than a preset fourth threshold value as a throttling cell. And the set of the shunting cell and the throttling cell is a target cell with balanced load. The third threshold and the fourth threshold may be the same or different. In one embodiment, the third threshold and the fourth threshold are both set to 2 for an active 4G network.

Step 130: and determining the switching event type of load balance according to the frequency band of the source cell and the frequency band of the target cell.

In this step, the handover event type indicates the rule and manner of the load handover, and the handover parameters corresponding to different handover event types are different. And if the frequency band of the source cell and the frequency band of the target cell belong to the same level of preset priority, determining the type of the switching event as a first switching event, otherwise, determining the type of the switching event as a second switching event. In a 4G network, the first handover event is an A3 event and the second handover event is an a4 event. The priority of the D band is 6, the priority of the FDD1800 band is 6, the priorities of the F1 and F2 bands are 5, and the priority of the FDD900 band is 4.

Step 140: and determining an iteration variable combination corresponding to each switching event type.

In this step, the handover parameters corresponding to different handover event categories are different. The handover parameters include fixed parameters and iteration variables. The value of the fixed parameter is a fixed value and cannot be modified, so that the embodiment of the invention only considers the iteration variable. For the a3 event, the handover parameters include: a pilot frequency A1 RSRP triggering threshold InterFreqHoA1ThdRsrp based on A3, a pilot frequency A2 RSRP triggering threshold A3InterFreqHoA2ThdRsrp based on A3, pilot frequency A1A2 amplitude hysteresis InterFreqHoA1A2Hyst, same frequency switching amplitude hysteresis IntraFreqHoA3Hyst, pilot frequency A3Offset InterFreqHoA3Offset and connection state frequency Offset QOFFFSEREQCONN. Wherein, InterFreqHoA1A2Hyst, IntraFreqHoA3Hyst and InterFreqHoA3Offset are fixed parameters, and InterFreqHoA1ThdRsrp, A3InterFreqHoA2ThdRsrp and QOFFSSETFREQCONN are adjustable parameters. For the a4 event, the handover parameters include: the pilot frequency A1 RSRP triggers a threshold InterFreqHoA1ThdRsrp, the pilot frequency A2 RSRP triggers a threshold InterFreqHoA2ThdRsrp, the pilot frequency A1A2 amplitude hysteresis InterFreqHoA1A2Hyst, the same frequency switching amplitude hysteresis IntraFreqHoA4Hyst, the pilot frequency A4Offset InterFreqHoA4Offset and the connection state frequency Offset QOFFSSEREQCONN. Wherein, InterFreqHoA1A2Hyst, IntraFreqHoA4Hyst and InterFreqHoA4Offset are fixed parameters, and InterFreqHoA1ThdRsrp, InterFreqHoA2ThdRsrp, InterFreqHoA4ThdRsrp and QOFFSSEREQCONN are adjustable parameters. The iterative variable combination is a combination of a plurality of target parameters, and the target parameters are used for load balancing of the source cell. For the above-mentioned A3 event and a4 event, the target parameters are the corresponding tunable parameters. The set of iterative variables in each switching event category is the set of iterative variables for the corresponding switching event category.

Step 150: and carrying out multiple iterations on the iteration variable combination, and calculating a utilization rate change value of the source cell after load balancing and a coverage rate change value of the target cell after load balancing according to the iteration variable combination value obtained after each iteration.

In this step, the method of iterating the iterative variable combination determined by the A3 event and the iterative variable combination determined by the a4 event is the same, and the difference is only that the specific target parameters included in the iterative variable combinations are different. The embodiment of the present invention does not limit the specific method of iteration.

Step 160: and determining the iteration variable combination value which minimizes the sum of the utilization rate change value and the coverage rate change value as a target iteration variable combination value.

In this step, the group optimal value satisfying the preset iteration number is determined as a target iteration variable combination value.

Step 170: and carrying out load balancing on the source cell according to each target parameter value in the target iteration variable combination value.

According to the embodiment of the invention, the target cell with balanced load is obtained by performing correlation analysis on the source cell and the adjacent cell, so that the obtained target cell is more accurate; determining the switching event type with balanced load according to the frequency bands of the source cell and the target cell, and determining corresponding iteration variable combinations for different switching event types; and carrying out multiple iterations on the iteration variable combination to obtain a target iteration variable combination value which enables the sum of the utilization rate change value of the source cell and the coverage rate change value of the target cell to be minimum, and carrying out load balancing on the source cell according to the target iteration variable combination value. The embodiment of the invention comprehensively considers the utilization rate change of the source cell and the coverage rate change of the target cell during load balancing, ensures that the network load of the source cell is effectively reduced, has better coverage of the adjacent cell, and avoids the problem of poor network coverage caused by meeting the network capacity.

In some embodiments, applying a particle swarm algorithm to embodiments of the present invention, the iterative variable combinations are iterated to find a target iterative variable combination value that minimizes the sum of the utilization change value and the coverage change value. That is, the objective of the iteration is to minimize the sum of the utilization change value and the coverage change value. In the embodiment of the present invention, determining the target iterative variable combination value by the particle swarm algorithm includes the following steps as shown in fig. 2:

step 210: and randomly generating a plurality of groups of iteration variable combination values and update speed values corresponding to the iteration variable combination values, wherein each group of iteration variable combination values is used as an individual optimal value.

In this step, each of the generated sets of iterative variable combination values includes a target parameter value included in the set of iterative variable combination. For example, if the iterative variable combination includes 3 target parameters, a set of iterative variable combination values includes three elements. Each group of iteration variable combination values corresponds to a group of updating speed values, and the updating speed values are used for updating the iteration variable combination. The number of elements included in a set of update speed values is the same as the number of elements included in a set of iterative variable combinations. And each group of iteration variable combination makes the group of iteration variable combination values with the minimum sum of the utilization rate change value and the coverage rate change value in the whole iteration process be the individual optimal values of the group of iteration variable combination. Before the first iteration, each generated iteration variable combination value group is an initial individual optimal value, and the number of the initial individual optimal values is the same as that of the generated iteration variable combination value groups. In the iterative process, the individual optimal values are continuously updated.

Step 220: and calculating a first sum of the first utilization rate change value and the first coverage rate change value corresponding to each group of iteration variable combination values.

In this step, each set of iterative variable combination values is calculated to obtain a corresponding first utilization rate change value and a corresponding first coverage rate change value. And assuming that the randomly generated iteration variable combination values are n groups and are respectively represented by x [0] to x [ n-1], obtaining n first utilization rate change values and n first coverage rate change values. The calculation method of the utilization rate change value corresponding to each group of iteration variable combination values and the iteration variable combination values generated in the iteration process is the same, and the calculation method of the coverage rate change value is also the same. For a specific calculation method, refer to the detailed description of the next embodiment.

Step 230: a set of iterative variable combination values that minimizes the first sum is determined as a population optimal value.

In this step, a first sum of a first utilization rate change value and a first coverage rate change value corresponding to each set of iterative variable combination values is calculated, and a set of iterative variable combination values that minimizes the first sum is determined as a population optimal value. The group optimal value is a group of iteration variable combinations which enable the sum of the utilization rate change value and the coverage rate change value to be minimum in all the iteration variable combinations, and the group optimal value is continuously updated in the whole iteration process.

Step 240: and iterating the updating speed values according to the individual optimal values and the group optimal values to obtain corresponding first updating speed values.

In this step, the formula for iterating any group of update velocity values v [ i ] in any iteration process is as follows:

v[i+1]＝w*v[i]+c1*rand(0,1)*(pbest[i]-x[i])+c2*rand(0,1)*(gbest-x[i])；

w is an inertia weight, c1 and c2 respectively represent a first learning parameter and a second learning parameter, and w, c1 and c2 are all preset constants; v [ i ] represents an updating speed value corresponding to the ith group of iteration variable combination, and v [ i +1] represents a first updating speed value obtained after the ith group of iteration variable combination is iterated; rand (0,1) represents a random number between 0 and 1; pbest [ i ] represents the individual optimum of the ith set of iterative variable combinations; x [ i ] represents the ith set of iterative variable combinations; the gbest represents the population optimal value of all the iterative variable combinations, that is, a group of iterative variable combinations that minimizes the sum of the utilization rate change value and the coverage rate change value of all the iterative variable combinations in the whole iterative process.

In the first iteration, taking the iteration process of the update speed value v [0] corresponding to the first group of iteration variable combination values x [0] as an example, the updated first update speed value is:

v[1]＝w*v[0]+c1*rand(0,1)*(pbest[0]-x[0])+c2*rand(0,1)*(gbest-x[0])；

wherein pbest [0] is x [0] at the first iteration.

Step 250: and iterating the corresponding iteration variable combination value according to the first updating speed value to obtain the corresponding first iteration variable combination value.

In this step, the iterative formula for iterating any group of iterative variable combinations x [ i ] in any iteration process is: x [ i +1] ═ x [ i ] + v [ i +1 ]. Taking the initial value x 0 of the first group of iterative variable combination as an example, the first iterative variable combination value obtained after the first iteration is x 0 + v 1.

Step 260: and calculating a second sum of the second utilization rate change value and the second coverage rate change value corresponding to each group of the first iteration variable combination values.

In this step, the calculation methods of the second utilization rate change value and the second coverage rate change value are the same as those of the first utilization rate change value and the first coverage rate change value. Please refer to the specific description of the first utilization rate variation value and the first coverage rate variation value.

Step 270: if any of the second sums is smaller than the corresponding first sum, the corresponding individual optimum value is updated to the corresponding first iterative variable combination value.

Step 280: and if the minimum second sum of all the second sums is smaller than the first sum corresponding to the group optimal value, updating the group optimal value to the minimum second sum corresponding to the first iteration variable combination value.

Step 290: if the preset iteration times are not met, updating the iteration speed value corresponding to each group of iteration variable combination values to a corresponding first updating speed value, updating each group of iteration variable combination values to a corresponding first iteration variable combination value, and returning to the step 240 until the preset iteration times are met.

The embodiment of the invention determines the target iteration variable combination which minimizes the sum of the utilization rate change value and the coverage rate change value through the particle swarm optimization, determines the updating speed according to the individual optimal value and the group optimal value, and updates the iteration variable combination value according to the updating speed, namely, the updated iteration variable combination value is continuously updated towards the direction of the individual optimal value and the group optimal value, so that a better group optimal value is conveniently searched. In addition, through the particle swarm optimization, the individual optimal value and the group optimal value are subjected to iterative updating, namely the individual optimal value and the group optimal value are searched for the optimal value in parallel, so that the iterative speed is obviously improved, and the reliability of the searched group optimal value is ensured.

In some embodiments, the calculation of the utilization change value and the coverage change value for each set of iterative variable combination values includes the following steps as shown in fig. 3:

step 310: and determining the sampling point needing to be transferred in the source cell according to the second iteration variable combination value.

In this step, the second iterative variable combination value is an arbitrary set of iterative variable combination values.

For the a3 event, the sample points that need to be shifted satisfy the following condition:

Ms<InterFreqHoA2ThdRsrp；

Ms-Mfn>-QOFFSETERQCONN；

for the a4 event, the sample points that need to be shifted satisfy the following condition:

Ms<InterFreqHoA2ThdRsrp；

Ms-Mfn>InterFreqHoA4ThdRsrp-QOFFSETERQCONN+1；

wherein Ms is MR.LtescRSRP, M of sampling point S to be transferred in source cell_fnLteoncrrp in a target cell of frequency f for the nth sample point to be transferred.

Step 320: and calculating a first average value of the total number of PRBs of the uplink shared channel occupied by the sampling points to be transferred and a second average value of the total number of PRBs of the downlink shared channel occupied by the sampling points to be transferred.

In this step, the first average value of the total number of PRBs of the uplink shared channel isThe first average value of the PRB total number of the downlink shared channel isWhere K represents the number of sample points S that need to be shifted.

Step 330: and determining the maximum value of the first average value and the second average value as the utilization rate change value of the source cell after load balancing.

In this step, the utilization change value In some embodiments, S is_prbDivided by 100 to give a percentage representation of the utilization change value.

Step 340: and determining the sum of the signal receiving power of each target cell after the sampling point needing to be transferred is transferred to each target cell.

In this step, the sum of the received powers of the signals of one target cell is the sum of the received power sum of the original sampling point in the target cell and the received power sum of the sampling point transferred to the target cell.

Step 350: and calculating the ratio of the sum of the signal receiving power of the sampling points to be transferred in each target cell to the sum of the signal receiving power of the corresponding target cell to obtain the coverage rate change value of the target cell after load balancing.

In this step, the coverage change value of the target cellWherein, sigma M_nRepresenting the sum of the received powers of the original sampling points in the target cell.

According to the embodiment of the invention, the utilization rate change value of the source cell and the coverage rate change value of the target cell are obtained through calculation, so that the iteration variable combination which enables the sum of the utilization rate change value of the source cell and the coverage rate change value of the target cell to be minimum can be conveniently determined according to the result.

Fig. 4 shows a functional block diagram of a load balancing apparatus according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes: an acquisition module 410, an analysis module 420, a first determination module 430, a second determination module 440, an iteration module 450, a third determination module 460, and a load balancing module 470. The obtaining module 410 is configured to obtain feature data of a source cell and a neighboring cell thereof. The analysis module 420 is configured to perform correlation analysis on the feature data of the source cell and any one of the neighboring cells, and determine a target cell for load balancing in the neighboring cells according to an analysis result. The first determining module 430 is configured to determine a handover event category of load balancing according to the frequency band of the source cell and the frequency band of the target cell. The second determining module 440 is configured to determine an iterative variable combination corresponding to each handover event category, where the iterative variable combination includes a plurality of target parameters, and the target parameters are used to perform load balancing on the source cell. The iteration module 450 is configured to perform multiple iterations on the iteration variable combination, and calculate a utilization rate change value after load balancing of the source cell and a coverage rate change value after load balancing of the target cell according to the iteration variable combination value obtained after each iteration. The third determining module 460 is configured to determine an iteration variable combination value that minimizes the sum of the utilization rate change value and the coverage rate change value as a target iteration variable combination value. The load balancing module 470 is configured to perform load balancing on the source cell according to the target parameter value in the target iteration variable combination.

In an alternative manner, the analysis module 420 is further configured to:

determining a first switching frequency of the source cell to each adjacent cell within a preset time period, and a second switching frequency of each adjacent cell to the source cell within the preset time period;

calculating a switch-out correlation coefficient and a switch-in correlation coefficient of the source cell and each neighboring cell according to the first switching times and the second switching times;

determining the adjacent cell with the switching-out correlation coefficient larger than a preset first threshold value as a pre-flow cell;

determining the adjacent cell of which the switching-in correlation coefficient is larger than a preset second threshold value as a pre-throttling cell;

and performing coverage correlation analysis on the pre-flow cell and the pre-flow cell, and determining a target cell with balanced load according to the result of the coverage correlation analysis.

In an alternative manner, the analysis module 420 is further configured to:

counting a first sampling point number of the level intensity of the first cell higher than the level intensity of the source cell in the preset time period, and a second sampling point number of the level intensity of the first cell lower than the level intensity of the source cell in the preset time period; the first cell is any one of all pre-throttling cells or the pre-throttling cell;

calculating a first ratio of the number of the first sampling points to the number of the second sampling points, and a second ratio of the number of the second sampling points to the number of the first sampling points;

determining the pre-distribution cell with the first ratio larger than a preset third threshold value as a distribution cell;

determining the pre-throttling cell with the second ratio being greater than a preset fourth threshold value as a throttling cell;

and determining the set of the shunting cell and the throttling cell as a target cell with balanced load.

In an alternative manner, the analysis module 420 is further configured to:

determining the busy time interval of the source cell every day in a preset time period, wherein the busy time interval is the time interval of the maximum uplink PRB utilization rate or the maximum downlink PRB utilization rate of the source cell;

taking the sum of the switching times of the source cell to each adjacent cell in the busy period in the preset time period as the first switching time;

and taking the sum of the switching times of each adjacent cell to the source cell in the busy period in the preset time period as the second switching time.

In an alternative manner, the first determining module 430 is further configured to:

and if the frequency band of the source cell and the frequency band of the target cell belong to the same level of preset priority, determining that the type of the switching event is a first switching event, otherwise, determining that the type of the switching event is a second switching event, wherein the first switching event and the second switching event are different switching event types.

In an alternative approach, the iteration module 450 is further configured to:

randomly generating a plurality of groups of iteration variable combination values and update speed values corresponding to the iteration variable combination values, wherein each group of iteration variable combination values is used as an individual optimal value;

calculating a first sum of a first utilization rate change value and a first coverage rate change value corresponding to each group of iteration variable combination values;

determining a set of iterative variable combination values that minimizes the first sum as a population optimal value;

iterating the updating speed values according to the individual optimal values and the group optimal values to obtain corresponding first updating speed values;

iterating the corresponding iteration variable combination value according to the first updating speed value to obtain a corresponding first iteration variable combination value;

calculating a second sum of a second utilization rate change value and a second coverage rate change value corresponding to each group of first iteration variable combination values;

if any one of the second sums is smaller than the corresponding first sum, updating the corresponding individual optimal value to a corresponding first iterative variable combination value;

if the smallest second sum of all the second sums is smaller than the first sum corresponding to the group optimal value, updating the group optimal value to the first iteration variable combination value corresponding to the smallest second sum;

updating the iteration speed value corresponding to each group of iteration variable combination values into a corresponding first update speed value, updating each group of iteration variable combination values into a corresponding first iteration variable combination value, and repeatedly executing the step of iterating the update speed values according to the individual optimal value and the group optimal value to obtain a corresponding first update speed value until the preset iteration times are met;

determining, as a target iterative variable combination value, an iterative variable combination value that minimizes a sum of the utilization rate change value and the coverage rate change value, including:

and determining the group optimal value meeting the preset iteration times as a target iteration variable combination value.

In an alternative approach, the iteration module 450 is further configured to: :

determining sampling points needing to be transferred in the source cell according to a second iteration variable combination value, wherein the second iteration variable combination value is any one group of iteration variable combination values;

calculating a first average value of the total number of PRBs (resource blocks) occupied by the sampling points in the uplink shared channel and a second average value of the total number of RPBs (resource blocks) occupied by the sampling points in the downlink shared channel;

determining the maximum value of the first average value and the second average value as a utilization rate change value after the load balancing of the source cell;

determining the sum of the signal receiving power of each target cell after the sampling point is transferred to each target cell;

and calculating the ratio of the sum of the signal receiving powers of the sampling points in each target cell to the sum of the signal receiving powers of the corresponding target cells to obtain a coverage rate change value of the target cells after load balancing.

According to the embodiment of the invention, the target cell with balanced load is obtained by performing correlation analysis on the source cell and the adjacent cell, so that the obtained target cell is more accurate; determining the switching event type with balanced load according to the frequency bands of the source cell and the target cell, and determining corresponding iteration variable combinations for different switching event types; and carrying out multiple iterations on the iteration variable combination to obtain a target iteration variable combination value which enables the sum of the utilization rate change value of the source cell and the coverage rate change value of the target cell to be minimum, and carrying out load balancing on the source cell according to the target iteration variable combination value. The embodiment of the invention comprehensively considers the utilization rate change of the source cell and the coverage rate change of the target cell during load balancing, ensures that the network load of the source cell is effectively reduced, has better coverage of the adjacent cell, and avoids the problem of poor network coverage caused by meeting the network capacity.

Fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.

As shown in fig. 5, the computing device may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508.

Wherein: the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508. A communication interface 504 for communicating with network elements of other devices, such as clients or other servers. The processor 502, configured to execute the program 510, may specifically perform the relevant steps described above for an embodiment of the load balancing method.

In particular, program 510 may include program code comprising computer-executable instructions.

The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.

And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

Specifically, program 510 may be invoked by processor 502 to cause a computing device to perform steps 110 to 170 in fig. 1, steps 210 to 290 in fig. 2, steps 310 to 350 in fig. 3, and to implement the functions of modules 410 to 470 in fig. 4.

An embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores at least one executable instruction, and when the executable instruction is executed on a computing device/apparatus, the computing device/apparatus is caused to execute a method for load balancing in any of the above method embodiments.

Embodiments of the present invention provide a computer program that can be invoked by a processor to cause a computing device to perform a method of load balancing in any of the above method embodiments.

Embodiments of the present invention provide a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions that, when run on a computer, cause the computer to perform the method of load balancing in any of the above-described method embodiments.

The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.