阅读说明：本技术 一种窄带物联网高效上行无线资源管理方法 (Efficient uplink wireless resource management method for narrow-band Internet of things ) 是由 秦傲翔 伍沛然 夏明华 于 2020-06-05 设计创作，主要内容包括：为了在保证用户服务质量的同时,最大化资源利用率和用户接入数。本发明公开了一种窄带物联网高效上行无线资源管理方法,包括以下步骤：基站进行调度时,计算用户的优先级,并选择优先级高的用户；对基站的可行链路的参数进行选择,得到可行链路的参数集；结合可行链路的参数集,对基站的可行资源的分配参数进行选择,得到可行资源的参数集；综合可行链路的参数集和可行资源的参数集,进行于遗传算法的联合链路自适应与资源分配。本发明通过遗传算法求解最优配置,以较低的性能损失为代价大幅降低了算法的复杂度,从而更利于NB-IoT的实现,具有较高的实际工程价值,且对于具有低能耗和低复杂度要求的NB-IoT系统的实施部署有很好的指导意义。(The method aims to maximize the resource utilization rate and the user access number while ensuring the service quality of the user. The invention discloses a high-efficiency uplink wireless resource management method for a narrow-band Internet of things, which comprises the following steps: when the base station carries out scheduling, calculating the priority of the users, and selecting the users with high priority; selecting the parameters of the feasible links of the base station to obtain parameter sets of the feasible links; selecting the distribution parameters of the feasible resources of the base station by combining the parameter sets of the feasible links to obtain the parameter sets of the feasible resources; and integrating the parameter set of the feasible link and the parameter set of the feasible resource to perform joint link adaptation and resource allocation of the genetic algorithm. The optimal configuration is solved through the genetic algorithm, the complexity of the algorithm is greatly reduced at the cost of lower performance loss, so that the NB-IoT implementation is facilitated, the actual engineering value is higher, and the method has good guiding significance for implementation and deployment of the NB-IoT system with low energy consumption and low complexity requirements.)

1. A high-efficiency uplink wireless resource management method for a narrow-band Internet of things is characterized by comprising the following steps:

when the base station carries out scheduling, calculating the priority of the users, and selecting the users with high priority;

selecting the parameters of the feasible links of the base station to obtain parameter sets of the feasible links;

selecting the distribution parameters of the feasible resources of the base station by combining the parameter sets of the feasible links to obtain the parameter sets of the feasible resources;

and integrating the parameter set of the feasible link and the parameter set of the feasible resource to perform joint link adaptation and resource allocation of the genetic algorithm.

2. The narrowband internet of things efficient uplink radio resource management method according to claim 1, wherein the priority of the user is expressed by the following formula:

wherein, the SNR is the signal-to-noise ratio of the user, and t is_delayAccumulating time delay for the user; said t_delay＝t_delay+T。

3. The method for efficient uplink radio resource management of the narrowband internet of things of claim 1 or 2, wherein the step of selecting the parameters of the feasible links of the base station to obtain the parameter set of the feasible links comprises the following steps:

parameter set RU for defining feasible link_type，I_RU，I_TBS，N_repN of said_repIndicates the number of repeated transmissions, and N_rep∈{2⁰，2¹，…，2⁷}; the RU_typeRepresenting an RU type;

converting the code rate, wherein the conversion formula is as follows:

CR_dB＝f(SNR_req)＝-18.38×exp(-0.051×SNR_req)+11.52

under link adaptation, the signal-to-noise ratio of the user:

in the formula, the P_URFor the uplink reference signal received power, W is 180 kHz;

different RUs_typeThe equivalent SNR under the configuration is expressed as:

according to RU_typeCorresponding SNR_effCalculating to obtain each RU_typeSupportable maximum code rate CR_maxSaid CR_maxExpressed by the following formula:

for the RU_typeBy selecting { N }_RU，I_TBS，N_repSelecting combinations satisfying the following conditions

CR≤CR_max

The combination is defined as a set of parameters for the feasible links.

4. The narrowband internet of things efficient uplink radio resource management method of claim 3, wherein the "combining the parameter set of the feasible link to select the distribution parameter of the feasible resource of the base station to obtain the parameter set of the feasible resource" includes:

parameter set { ζ ] defining feasible resources_opt，k₀，f_strart}；

F is_startExpressed by the following formula:

in the formula (II)Is the NB-IoT uplink configurable number of subcarriers, when af is 15kHz,

k is as described₀Indicates scheduling delay, and k₀∈{8，16，32，64}；

Zeta of_optIndicating the optimal user number.

5. The method for efficient uplink radio resource management of the narrowband internet of things according to claim 4, wherein the step of combining the parameter set of the feasible links and the parameter set of the feasible resources to perform the joint link adaptation and resource allocation in the genetic algorithm comprises the following steps:

the chromosome gene contains all the parameters to be configured of the scheduling user, including candidate configuration ζ and { I_MCS，RU_type，k₀，f_srart}；

At initialization, configuration I is based on the following formula_MCSAnd RU_typeThereby speeding up the algorithm convergence:

CR≤CR_max

through an adaptive genetic algorithm, the cross and variation probability is determined according to the chromosome fitness F and the population maximum fitness F_maxAnd the average fitness F_avgVarying adaptively, p ═ k (F)_max-F)/(F_max-F_avg) P is less than or equal to 1, wherein k is a weighting coefficient;

the chromosome is passed to determine whether the following constraints are satisfied:

t_delay+t_end≤T_thres.

[O]_c，s∈{0，1}.

if not, the fitness of the chromosome is set to zero; if all satisfied, the fitness is calculated by the following formula:

the chromosome with the highest fitness is saved for the next iteration to ensure that the highest fitness of the population is non-decreasing.

6. The narrowband internet of things efficient uplink radio resource management method according to claim 5, wherein D is_ratioExpressed by the following formula:

d is the size of the data packet, and TBS is the size of the transmission block.

7. The narrowband internet of things efficient uplink radio resource management method according to claim 6, wherein D is_ratioThe following conditions need to be satisfied:

0.7≤D_ratio≤1。

8. the narrowband internet of things efficient uplink radio resource management method according to any one of claims 5 to 7, wherein the "k is a weighting coefficient" includes the following contents:

the crossed weighting coefficient is 0.5; the weighting factor for the variation is taken to be 0.1.

9. The narrowband internet of things efficient uplink radio resource management method according to any one of claims 5 to 7, wherein the number of iterations of the genetic algorithm is 1000, and the population number of the genetic algorithm is 500.

10.The method for managing high-efficiency uplink radio resources of the narrow-band internet of things as claimed in claim 9, wherein when the iteration number of the genetic algorithm is 1000, the complexity of the genetic algorithm is

Technical Field

The invention relates to the field of wireless communication, in particular to a high-efficiency uplink wireless resource management method for a narrowband Internet of things.

Background

In order to meet the requirements of 5G large-scale machine type communication scenes, NB-IoT is evolved by 3GPP aiming at the requirements of the Internet of things on the basis of LTE, and the NB-IoT has the characteristics of large connection, low power consumption, strong coverage and low time delay sensitivity. In order to ensure low cost and poor terminal performance, the NB-IoT cannot perform real-time decoding and uplink/downlink conversion, so that scheduling delay is introduced, and the selectable value is fixed to specify the transmission start time.

The scheduling Period of the Narrowband Physical Uplink Shared Channel (NPUSCH) is one NPDCCH Period (NPDCCH Period, NP), and scheduling is no longer performed in each subframe. In addition, the data volume of single communication of the NPUSCH is small, and the requirement on the communication rate is not high, so that the scheduling criterion of the NPUSCH is changed into that the resource utilization rate and the user access number are maximized while the Quality of Service (QoS) of the user is ensured, and the conventional LTE scheduling algorithm is not applicable any more. In scheduling, since different RU types and the number of repeated transmissions may result in different equivalent signal-to-noise ratios and different user data formats, the impact of the link adaptation algorithm on them also needs to be considered.

Currently, research on NB-IoT (narrowband internet of things) uplink RRM algorithms is not yet complete. The reference CN109922532A discloses a method and apparatus for resource scheduling in a communication system. However, the above-mentioned comparison documents do not jointly regulate and control the link and the resource through a genetic algorithm.

Disclosure of Invention

In order to overcome the defects of the prior art and the method, the invention provides a high-efficiency uplink wireless resource management method for a narrow-band Internet of things. The invention considers the requirement of low complexity of NB-IoT, solves the optimal configuration through the genetic algorithm, and greatly reduces the complexity of the algorithm at the cost of lower performance loss, thereby being more beneficial to the realization of NB-IoT, having higher actual engineering value, and having good guiding significance for the implementation and deployment of the NB-IoT system with low energy consumption and low complexity requirement.

In order to solve the technical problems, the technical scheme of the invention is as follows:

a high-efficiency uplink wireless resource management method for a narrow-band Internet of things comprises the following steps:

when the base station carries out scheduling, calculating the priority of the users, and selecting the users with high priority;

selecting the parameters of the feasible links of the base station to obtain parameter sets of the feasible links;

selecting the distribution parameters of the feasible resources of the base station by combining the parameter sets of the feasible links to obtain the parameter sets of the feasible resources;

and integrating the parameter set of the feasible link and the parameter set of the feasible resource to perform joint link adaptation and resource allocation of the genetic algorithm.

The invention considers the requirement of low complexity of NB-IoT, solves the optimal configuration through the genetic algorithm, and greatly reduces the complexity of the algorithm at the cost of lower performance loss, thereby being more beneficial to the realization of NB-IoT, having higher actual engineering value, and having good guiding significance for the implementation and deployment of the NB-IoT system with low energy consumption and low complexity requirement.

In a preferred embodiment, the priority of the user is expressed by the following formula:

wherein, the SNR is the signal-to-noise ratio of the user, and t is_delayAccumulating time delay for the user; said t_delay＝t_delay+T。

In a preferred embodiment, the "selecting the parameters of the feasible links of the base station to obtain the parameter set of the feasible links" includes the following contents:

parameter set RU for defining feasible link_type，I_RU，I_TBS，N_repN of said_repIndicates the number of repeated transmissions, and N_rep∈{2⁰，2¹，…，2⁷}; the RU_typeRepresenting an RU type;

converting the code rate, wherein the conversion formula is as follows:

CR_dB＝f(SNR_req)＝-18.38×exp(-0.051×SNR_req)+11.52

under link adaptation, the signal-to-noise ratio of the user:

in the formula, the P_URFor the uplink reference signal received power, W is 180 kHz;

different RUs_typeThe equivalent SNR under the configuration is expressed as:

according to RU_typeCorresponding SNR_effCalculating to obtain each RU_typeSupportable maximum code rate CR_maxWhat is, what is

The above CR_maxExpressed by the following formula:

for the RU_typeBy selecting { N }_RU，I_TBS，N_repSelecting combinations satisfying the following conditions

CR≤CR_max

The combination is defined as a set of parameters for the feasible links.

In a preferred embodiment, the "selecting the allocation parameters of the feasible resources of the base station in combination with the parameter set of the feasible links to obtain the parameter set of the feasible resources" includes the following contents:

parameter set { ζ ] defining feasible resources_opt，k₀，f_start}；

F is_startExpressed by the following formula:

in the formula (II)Is the NB-IoT uplink configurable number of subcarriers, when af is 15kHz,

k is as described₀Indicates scheduling delay, and k₀∈{8，16，32，64}；

Zeta of_optIndicating the optimal user number.

In a preferred embodiment, the "combining the parameter set of the feasible link and the parameter set of the feasible resource to perform the joint link adaptation and resource allocation in the genetic algorithm" includes the following steps:

the chromosome gene contains all the parameters to be configured of the scheduling user, including candidate configuration ζ and { I_MCS，RU_type，k₀，f_start}；

At initialization, configuration I is based on the following formula_MCSAnd RU_typeThereby speeding up the algorithm convergence:

CR≤CR_max

through an adaptive genetic algorithm, the cross and variation probability is determined according to the chromosome fitness F and the population maximum fitness F_maxAnd the average fitness F_avgVarying adaptively, p ═ k (F)_max-F)/(F_max-F_avg) P is less than or equal to 1, wherein k is a weighting coefficient;

the chromosome is passed to determine whether the following constraints are satisfied:

t_delay+t_end≤T_thres.

[O]_c，s∈{0，1}

if not, the fitness of the chromosome is set to zero; if all satisfied, the fitness is calculated by the following formula:

the chromosome with the highest fitness is saved for the next iteration to ensure that the highest fitness of the population is non-decreasing.

In a preferred embodiment, D is_ratioExpressed by the following formula:

d is the size of the data packet, and TBS is the size of the transmission block.

In a preferred embodiment, D is_ratioThe following conditions need to be satisfied:

0.7≤D_ratio≤1。

in the preferred scheme, the lower bound 0.7 is used to reduce resource waste, and the upper bound 1 is used to prevent extra signaling overhead caused by codeword blocking.

In a preferred embodiment, the "k is a weighting coefficient" includes the following:

the crossed weighting coefficient is 0.5; the weighting factor for the variation is taken to be 0.1.

In a preferred embodiment, the number of iterations of the genetic algorithm is 1000, and the population number of the genetic algorithm is 500.

In a preferred embodiment, when the number of iterations of the genetic algorithm is 1000,the complexity of the genetic algorithm is

Compared with the prior art, the technical scheme of the invention has the beneficial effects that:

compared with the spectrum access scheme and the energy collection strategy (random access mode and short-term strategy) of the traditional energy collection cognitive radio system, the method focuses on the effect in a short term and is not sufficient to focus on the defects of historical information and long-term effect. The invention can carry out intelligent spectrum access and energy collection selection from effectively utilizing historical information, thereby improving the transmission performance in a long-term sense under the condition of no prior information, and the result has higher spectrum efficiency and energy efficiency.

Drawings

FIG. 1 is a flow chart of an embodiment.

Detailed Description

The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;

it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.

Examples

As shown in fig. 1, a method for managing high-efficiency uplink radio resources of a narrowband internet of things includes the following steps:

when the base station carries out scheduling, calculating the priority of the users, and selecting the users with high priority;

selecting the parameters of the feasible links of the base station to obtain parameter sets of the feasible links;

selecting the distribution parameters of the feasible resources of the base station by combining the parameter sets of the feasible links to obtain the parameter sets of the feasible resources;

and integrating the parameter set of the feasible link and the parameter set of the feasible resource to perform joint link adaptation and resource allocation of the genetic algorithm.

The embodiment considers the requirement of low complexity of NB-IoT, solves the optimal configuration through the genetic algorithm, and greatly reduces the complexity of the algorithm at the cost of lower performance loss, thereby being more beneficial to the realization of NB-IoT, having higher actual engineering value, and having good guiding significance for the implementation and deployment of the NB-IoT system with the requirements of low energy consumption and low complexity.

In an embodiment, the following extensions may also be made: the priority of the user is expressed by the following formula:

wherein, the SNR is the signal-to-noise ratio of the user, and t is_delayAccumulating time delay for the user; said t_delay＝t_delay+T。

In the embodiment and the above improved embodiment, the following extension can be made: the "selecting the parameter of the feasible link of the base station to obtain the parameter set of the feasible link" includes the following contents:

parameter set RU for defining feasible link_type，I_RU，I_TBS，N_repN of said_repIndicates the number of repeated transmissions, and N_rep∈{2⁰，2¹，…，2⁷}; the RU_typeRepresenting an RU type;

converting the code rate, wherein the conversion formula is as follows:

CR_dB＝f(SNR_req)＝-18.38×exp(-0.051×SNR_req)+11.52

under link adaptation, the signal-to-noise ratio of the user:

in the formula, the P_URFor the uplink reference signal received power, W is 180 kHz;

different RUs_typeThe equivalent SNR under the configuration is expressed as:

according to RU_typeCorresponding SNR_effCalculating to obtain each RU_typeSupportable maximum code rate CR_maxSaid CR_maxExpressed by the following formula:

for the RU_typeBy selecting { N }_RU，I_TBS，N_repSelecting combinations satisfying the following conditions

CR≤CR_max

The combination is defined as a set of parameters for the feasible links.

In the embodiment and the above improved embodiment, the following extension can be made: the "combining the parameter set of the feasible link to select the distribution parameter of the feasible resource of the base station to obtain the parameter set of the feasible resource" includes the following contents:

parameter set { ζ ] defining feasible resources_opt，k₀，f_start}；

F is_startExpressed by the following formula:

in the formula (II)Is the NB-IoT uplink configurable number of subcarriers, when af is 15kHz,

k is as described₀Indicates scheduling delay, and k₀∈{8，16，32，64}；

Zeta of_optIndicating the optimal user number.

In the embodiment and the above improved embodiment, the following extension can be made: the method for combining the parameter set of the feasible link and the parameter set of the feasible resource to perform the joint link adaptation and the resource allocation of the genetic algorithm comprises the following steps of:

the chromosome gene contains all the parameters to be configured of the scheduling user, including candidate configuration ζ and { I_MCS，RU_type，k₀，f_start}；

At initialization, configuration I is based on the following formula_MCSAnd RU_typeThereby speeding up the algorithm convergence:

CR≤CR_max

through an adaptive genetic algorithm, the cross and variation probability is determined according to the chromosome fitness F and the population maximum fitness F_maxAnd the average fitness F_avgVarying adaptively, p ═ k (F)_max-F)/(F_max-F_avg) P is less than or equal to 1, wherein k is a weighting coefficient;

the chromosome is passed to determine whether the following constraints are satisfied:

t_delay+t_end≤T_thres.

[O]_c，s∈{0，1}

if not, the fitness of the chromosome is set to zero; if all satisfied, the fitness is calculated by the following formula:

the chromosome with the highest fitness is saved for the next iteration to ensure that the highest fitness of the population is non-decreasing.

In the embodiment and the above improved embodiment, the following extension can be made: said D_ratioExpressed by the following formula:

d is the size of the data packet, and TBS is the size of the transmission block.

In the embodiment and the above improved embodiment, the following extension can be made: said D_ratioThe following conditions need to be satisfied:

0.7≤D_ratio≤1。

in the improved embodiment, the lower bound 0.7 is used to reduce resource waste, and the upper bound 1 is used to prevent extra signaling overhead caused by codeword blocking.

In the embodiment and the above improved embodiment, the following extension can be made: the "k is a weighting coefficient" includes the following:

the crossed weighting coefficient is 0.5; the weighting factor for the variation is taken to be 0.1.

In the embodiment and the above improved embodiment, the following extension can be made: the iteration times of the genetic algorithm are 1000 times, and the population number of the genetic algorithm is 500.

In the embodiment and the above improved embodiment, the following extension can be made: when the iteration times of the genetic algorithm are 1000 times, the complexity of the genetic algorithm is

In the detailed description of the embodiments, various technical features may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The same or similar reference numerals correspond to the same or similar parts;

the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent; for example, the calculation formula of the ion conductivity in the embodiment is not limited to the formula illustrated in the embodiment, and the calculation formula of the ion conductivity is different for different species. The foregoing is a definition of the embodiments and is not to be construed as limiting the present patent.

It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.