阅读说明：本技术 数据传输中的不一致数据的检测 (Detection of inconsistent data in data transmission ) 是由 A.沙平 M.孙德贝格 G.维克斯特龙 K.基蒂乔克柴 N.安德加特 于 2018-03-23 设计创作，主要内容包括：本公开提供了用于通过检测接收到的信号的调制或其它信号特性中的不一致性来检测数据传输何时对数据传输已抢占或打孔的方法和设备。如果检测到不一致性,则可以采取措施减轻由不一致数据促使的潜在问题。在一个示例中,接收装置(30)可以识别已损坏的软缓冲器中的软位,并从所述软缓冲器中转储清除被损坏的部分。(The present disclosure provides methods and apparatus for detecting when a data transmission has preempted or punctured the data transmission by detecting inconsistencies in the modulation or other signal characteristics of the received signal. If inconsistencies are detected, measures may be taken to mitigate potential problems caused by inconsistent data. In one example, the receiving device (30) may identify soft bits in a corrupted soft buffer and flush the corrupted portion from the soft buffer.)

1. A method of detecting inconsistent data in a data transmission subject to data insertion or interference, the method comprising:

a data signal is received from a transmitting device,

demodulating the data signal to generate soft bit values;

computing detection metrics for two or more subsets of the soft bit values; and

detecting inconsistent data in at least one subset of the soft bit values based on the detection metric corresponding to the at least one subset.

2. The method of claim 1, wherein computing detection metrics for two or more subsets of the soft bit values comprises: calculating the detection metric based on statistical properties of the soft bit values within each subset.

3. The method of claim 1 or 2, wherein detecting inconsistent data in the at least a subset of the soft bit values based on the detection metric comprises: comparing the detection metric to a threshold.

4. The method of claim 3, further comprising: dynamically determining the threshold for each subset based on the detection metrics in the subset.

5. The method of claim 4, wherein dynamically determining the threshold for each subset based on the detection metrics in the subset further comprises:

calculating one or more thresholds for each subset of soft bit values; and

selecting one of the determined thresholds for detecting an inconsistency in the subset of soft bit values.

6. The method of claim 1, wherein computing detection metrics for two or more subsets of the soft bit values comprises: the variance of the soft bit values within each subset is calculated.

7. The method of claim 6, wherein detecting the inconsistent data in the at least one subset of the soft bit values based on the detection metrics corresponding to the subset comprises: comparing the variance of the soft bit values in the subset to a threshold.

8. The method of claim 7, further comprising: dynamically determining the threshold for each subset based on the detection metrics in the subset.

9. The method of claim 8, wherein dynamically determining the threshold value for each subset of soft bit values based on the detection metrics in the subset comprises:

determining a mean of the variances; and

calculating the threshold value from the mean of the variance.

10. The method of claim 9, wherein dynamically determining the threshold value for each subset of soft bit values based on the detection metrics in the subset further comprises:

calculating one or more thresholds for each subset of soft bit values from the mean of the variances; and

selecting one of the determined thresholds for detecting an inconsistency in the subset of soft bit values.

11. The method of claim 2, wherein detecting inconsistent data in the at least a subset of the soft bit values comprises: comparing the detection metric to an expected value for a known modulation scheme for the data signal.

12. The method of claim 2, wherein detecting inconsistent data in the at least a subset of the soft bit values comprises: comparing the detection metric to known sequences in the inconsistent data.

13. The method of claim 12, wherein the known sequence comprises: one of a known tag or a known reference signal sequence in the inconsistent data.

14. The method of claim 2, wherein detecting inconsistent data in the at least a subset of the soft bit values comprises: comparing the detection metric to a known modulation for the inconsistent data.

15. The method of any of claims 1 to 14, wherein calculating detection metrics for two or more subsets of the soft bit values comprises:

processing the data signal in a sliding window detector; and

for each of two or more different sliding window positions, a detection metric is calculated for the soft bit values contained within the sliding window.

16. The method of any of claims 1 to 17, further comprising:

in response to detection of inconsistent data, identifying one or more soft bit values corresponding to the inconsistent data; and

modifying the soft bit values corresponding to the inconsistent data to reduce decoding errors.

17. The method of claim 16, wherein modifying the soft bit values corresponding to the inconsistent data to reduce decoding errors comprises: setting the soft bit value to zero.

18. A receiving device (30) configured to detect inconsistent data in a data transmission subject to data insertion or interference, the receiving device (30) comprising:

a receiving circuit (30), the receiving circuit (30) configured to receive a data signal from a transmitting device,

a demodulator (60), the demodulator (60) configured to demodulate the data signal to generate soft bit values; and

a detection circuit (70), the detection circuit (70) configured to:

computing detection metrics for two or more subsets of the soft bit values; and

detecting inconsistent data in at least one subset of the soft bit values based on the detection metrics corresponding to the at least one subset.

19. The receiving device (30) of claim 18, wherein the detection circuit (70) is configured to calculate the detection metric based on a statistical property of the soft bit values within each subset.

20. The receiving device (30) of claim 18 or 19, wherein the detection circuit (70) is further configured to compare the detection metric to a threshold.

21. The receiving device (30) of claim 20, wherein the detection circuit (70) is further configured to dynamically determine the threshold value for each subset of soft bit values based on the detection metrics in the subset.

22. The receiving device (30) of claim 21, wherein the detection circuit (70) is further configured to dynamically determine the threshold value for each subset of soft bit values based on the detection metrics in the subset by:

calculating one or more thresholds for each subset of soft bit values; and

selecting one of the determined thresholds for detecting an inconsistency in the subset of soft bit values.

23. The receiving device (30) of claim 18, wherein the detection circuit (70) is further configured to compute a detection metric as a variance of the soft bit values within each subset.

24. The receiving device (30) of claim 23, wherein the detection circuit (70) is further configured to compare the variance of the soft bit values in the subset to a threshold.

25. The receiving device (30) of claim 24, wherein the detection circuit (70) is further configured to dynamically determine the threshold value for each subset of soft bit values based on the detection metrics in the subset.

26. The receiving device (30) of claim 25, wherein the detection circuit (70) is further configured to dynamically determine the threshold value for each subset of soft bit values by:

determining a mean of the variances for the subset of soft bit values; and

calculating the threshold value from the mean of the variance.

27. The receiving device (30) of claim 26, wherein the detection circuit (70) is further configured to dynamically determine the threshold value for each subset of soft bit values based on the detection metrics in the subset by:

calculating one or more thresholds for each subset of soft bit values from the mean of the variances; and

selecting one of the determined thresholds for detecting an inconsistency in the subset of soft bit values.

28. The receiving device (30) of claim 19, wherein the detection circuit (70) is further configured to compare the detection metric to an expected value for a known modulation scheme of the data signal.

29. The receiving device (30) of claim 19, wherein the detection circuit (70) is further configured to compare the detection metric to a known sequence in the disparity data.

30. The receiving device (30) of claim 29, wherein the known sequence comprises one of a known tag or a known reference signal sequence in the inconsistent data.

31. The receiving device (30) of claim 19, wherein the detection circuit (70) is further configured to compare the detection metric to a known modulation for the inconsistent data.

32. The receiving device (30) of any of claims 18-31, wherein the detection circuit (70) is further configured to compute detection metrics for two or more subsets of the soft bit values by:

processing the data signal in a sliding window detector; and

for each of two or more different sliding window positions, a detection metric is calculated for the soft bit values contained within the sliding window.

33. The receiving device (30) of any of claims 18-32, wherein the detection circuit (70) is further configured to:

in response to detection of inconsistent data, identifying one or more soft bit values corresponding to the inconsistent data; and

modifying the soft bit values corresponding to the inconsistent data to reduce decoding errors.

34. The receiving device (30) of claim 33, wherein the detection circuit (70) is further configured to modify the soft bit value corresponding to the inconsistent data by setting the soft bit value to zero.

35. A receiving device (30) configured to detect inconsistent data in a data transmission subject to data insertion or interference, the receiving device (30) configured to:

a data signal is received from a transmitting device,

demodulating the data signal to generate soft bit values; and

computing detection metrics for two or more subsets of the soft bit values; and

detecting inconsistent data in at least one subset of the soft bit values based on the detection metrics corresponding to the at least one subset.

36. The receiving device (30) of embodiment 35, configured to perform the method of any of embodiments 2-17.

37. A computer program (85) comprising executable instructions which, when executed by processing circuitry (50) in a receiving apparatus (30), cause the receiving apparatus (30) to perform any of the methods of embodiments 1-17.

38. A carrier containing the computer program of embodiment 37, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

39. A non-transitory computer readable storage medium (80) containing a computer program (85), the computer program (85) comprising executable instructions that, when executed by processing circuitry (50) in a receiving device (30), cause the receiving device (30) to perform any of the methods of embodiments 1-17.

Technical Field

The present invention relates generally to puncturing of data transmissions to enable data insertion, and more particularly to a method implemented by a receiving device for detecting when a data transmission has been punctured.

Background

In many wireless communication systems, HARQ (hybrid automatic repeat request) retransmission is a method for mitigating unpredictable interference and channel variations. When a wireless device attempts to decode a data message received on a downlink, the wireless device transmits an indicator to a transmitter indicating whether the decoding was successful. When the transmitter receives an indicator indicating unsuccessful decoding, the transmitter typically performs a retransmission of the data message, which the receiver typically combines with the originally received transmission. This combination is called soft combining, where chase (chase) combining and incremental redundancy combining are two well-known variants. Chase combining means that the same redundancy versions (i.e. the same set of soft values) are sent in the initial transmission and in the retransmission. Incremental redundancy means that different sets of soft values are sent in retransmissions. This set may partially overlap with the previous set, or be fully complementary. Soft combining manipulates the estimated bit quality. Typically, the bit quality is expressed as a log-likelihood ratio (LLR) that indicates how reliable each bit is. The combining increases the likelihood of successful decoding.

At the physical layer, each transmission includes a sequence of modulation symbols. Each modulation symbol is a signal having defined characteristics such as phase and amplitude. At the transmitter, a set of information bits is mapped to modulation symbols. The reverse process of mapping modulation symbols to estimated bit quality (LLR) at the receiver is called demapping or demodulation. Currently, in different standards, the following mapping/demapping or modulation/demodulation schemes are used: binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (QAM). The QAM may include 16 QAM, 64 QAM, and 256 QAM. Other modulation schemes may also be used.

URLLC (ultra-reliable low latency communication) is a data service with extremely stringent error and latency requirements. Error probability as 10^-5Such low or lower and 1 ms end-to-end delay or lower are expected requirements.

The third generation partnership project (3 GGP) is currently developing standards for fifth generation (5G) networks targeting a wide variety of data services, including mobile broadband (MBB) and URLLC. To enable optimized services, the length of the Transmission Time Interval (TTI) is expected to be different for different services, where URLLC may have a shorter TTI length compared to MBB to minimize latency. It is possible to transmit MBB blocks at the time when the URLLC data packet arrives at the transmitter. Therefore, it may be desirable to blank (interrupt) MBB transmissions in certain time-frequency resources and perform URLLC transmissions on these resources. A drawback with this approach is that a wireless device receiving a partial MBB will, with a high probability, not be able to properly decode the partial MBB, because the receiver will not be able to detect that URLLC data does not belong to an MBB data transmission, and therefore it will corrupt the decoding. This problem can be solved by performing HARQ retransmissions, but due to the partial corruption of the soft buffer (soft buffer) (for resources where the first transmission is blanked), a large number of HARQ retransmissions may be required to correctly decode the interrupted signal. The inserted bits may be discarded in the decoding process if the wireless device is aware of the resources that are blanked by the transmitter (since the inserted bits are known not to belong to the MBB transmission).

In Long Term Evolution (LTE), there have been mechanisms available to solve the decoding problem. In LTE, a new transmission is indicated to the wireless receiver device by toggling (toggle) a 1-bit New Data Indicator (NDI) flag. That is, the value of the NDI flag changes each time new data is transmitted. The change in the NDI flag indicates to the wireless receiver that the wireless receiver should reset or flush (flush) the soft buffer so that the wireless receiver does not attempt to combine old data with new data.

For example, during MBB data transmission, URLLC data may arrive and need to be transferred immediately. Thus, the MBB signal may be punctured (processed) and URLLC data may be inserted into the MBB data transmission. Puncturing (or another term, preemption) helps to transmit URLLC data along with MBB data without additional delay, and puncturing allows URLLC data to replace parts of scheduled MBB data.

The outlined problem of MBB transmissions being partially empty (which would partially corrupt the soft buffer) can be solved by means of a retransmission of data with an indication that the transmission is a new transmission. The wireless device will then flush the corrupted portions of the wireless device's soft buffer and, thus, will not combine the corrupted data. The drawback is that data that has not been punctured in the first transmission can no longer be combined with retransmitted data.

Another solution is to explicitly indicate which parts of the first transmission are blanked. For example, if an MBB transmission is divided in time into X slots and in frequency into Y slots, the MBB transmission may be divided into X Y code blocks, where each code block is protected by a Cyclic Redundancy Check (CRC). The wireless device may then divide the soft buffer into X Y soft buffers for each of the code blocks. If a URLLC transmission is punctured in one of the slots, the transmitter may indicate in the assignment of retransmissions which of the soft buffers need to be flushed. This approach requires several bits to indicate which of the soft buffers is affected by puncturing and promotes additional control signaling overhead. Further, this approach does not guarantee puncture detection with an acceptable probability.

Disclosure of Invention

The present disclosure provides methods and apparatus for detecting when a data transmission has been preempted or punctured by detecting an inconsistency in the modulation or other characteristics of a received signal. If inconsistencies are detected, measures may be taken to mitigate potential problems caused by inconsistent data, such as combining erroneous data. In one example, the receiving device may identify soft bits (soft bits) in a soft buffer that may have been corrupted and flush the corrupted portion from the soft buffer.

In one embodiment, the detection is performed by identifying changes in signal characteristics of the received data, including, for example, changes in modulation order and/or allocated power. The receiving device, which may be a base station or User Equipment (UE), may have assigned by the network portions of these signal characteristics, such as modulation orders. In one embodiment, the detection process includes searching for deviations in known signal characteristics, such as modulation order. In another embodiment, relative changes in unknown signal characteristics, such as power levels, are used for detection, assuming the unknown signal characteristics do not change at least over a known subset of resource allocations for data transmission. For example, it may be assumed that the power is the same over the entire block or at least a known subset of the block. In one embodiment, a receiving device monitors the variance of log-likelihood ratio (LLR) moduli (modules) (i.e., absolute values of LLRs) within a sliding window during a detection process to detect inconsistent data. The sliding window size may be based on a smallest possible size of a damaged region in one Orthogonal Frequency Division Multiplexing (OFDM) symbol. The estimation of the LLRs may be performed by a bank of supported demapper/demodulators (bank). If a portion of the received signal is replaced by another signal having different characteristics, the variance of the LLR moduli within the sliding window will deviate from the expected value if the data had been replaced. Dynamic detection thresholds are introduced for enhanced detection algorithms. A technical effect of these embodiments is that data inconsistencies may be detected in a more efficient and specific manner. Depending on the type and degree of inconsistency, this provides the possibility to avoid explicit signaling (explicit signaling) and to enable the adaptive component to be applied. Whenever an inconsistency is detected, the extremum of the variance of the LLR moduli is used for locating the damaged region. The LLR of the detected corrupted bit may be set to the most neutral value (e.g., "0"), or may be flagged by any other technique for further decoding processes.

One aspect of the present disclosure includes a method implemented by a receiving apparatus (e.g., a User Equipment (UE) or a base station) in a wireless communication network of receiving downlink control information. In one embodiment, a receiving device receives a data signal from a transmitting device and demodulates the data signal to generate soft bit values. The receiving device further calculates detection metrics for two or more subsets of the soft bit values and detects inconsistent data in at least one of the subsets of the soft bit values based on the detection metrics corresponding to the at least one subset. If inconsistencies or data corruption are detected, problems such as combining erroneous data in the decoder, prompted by inconsistent data, can be avoided to improve decoding performance and reduce the number of retransmissions, which in turn will improve spectral efficiency and system capacity.

Another aspect of the present disclosure is to use a sliding window detector to search for known deviations from expected values in signal characteristics, such as modulation order or power level, to detect inconsistent data. The sliding window detector provides an efficient mechanism for detecting inconsistent data.

Another aspect of the method is the use of dynamic thresholds to enable detection of inconsistent data. The dynamic threshold may be calculated based on statistical properties of the received data signal, such as variance. An upper threshold and a lower threshold may be used to detect both a maximum and a minimum. The use of dynamic thresholds in both directions compensates for drift effects to allow more reliable detection of inconsistent data. Another aspect of the present disclosure includes a receiving device configured to detect insertion of inconsistent data in a received data signal. The receiving is configured to perform the method described in the preceding paragraph.

In one embodiment, the receiving apparatus includes: a receiving circuit configured to receive a data signal from a transmitting device; a demodulator configured to demodulate the data signal to generate soft bit values; and a detection circuit. The detection circuitry is configured to compute detection metrics for two or more subsets of the soft bit values, and to detect inconsistent data in at least one subset of the soft bit values based on the detection metrics corresponding to the at least one subset.

In another embodiment, the receiving apparatus includes: a receiving circuit configured to receive a data signal from a transmitting device; and a processing circuit. The processing circuit is configured as a demodulator configured to demodulate the data signal to generate soft bit values, calculate detection metrics for two or more subsets of the soft bit values, and detect inconsistent data in at least one subset of the soft bit values based on the detection metrics corresponding to the at least one subset.

Another aspect of the disclosure includes a computer program comprising executable instructions that, when executed by processing circuitry in a receiving apparatus, cause the receiving apparatus to perform any of the methods described above.

Another aspect of the disclosure includes a carrier containing a computer program as described in the preceding paragraph, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.

Another aspect of the disclosure includes a non-transitory computer-readable storage medium containing a computer program comprising executable instructions that, when executed by processing circuitry in a receiving device, cause the receiving device to perform any of the methods described above.

The methods and apparatus described herein enable a receiving device (e.g., a UE or a base station) to detect inconsistent or corrupted data. If inconsistencies or data corruption are detected, problems caused by inconsistent data, such as combining erroneous data in a decoder, may be mitigated by taking certain measures based on the detected inconsistencies. The method and apparatus will improve decoding performance and reduce the number of retransmissions, which in turn will improve spectral efficiency and system capacity.

Drawings

Fig. 1 shows MBB transmission with contiguous regions punctured to transmit URLLC data.

Fig. 2 shows MBB transmission with multiple punctured regions for transmitting URLLC data.

Fig. 3 illustrates an exemplary receiving apparatus (e.g., a base station or UE) according to an exemplary embodiment.

Fig. 4 shows soft bit values (LLRs) in which a 16 QAM signal is inserted into a QPSK sign (sign) and demapped by a QPSK demodulator.

Fig. 5 shows the variance of the LLR moduli within the sliding window for the signal in fig. 4.

Fig. 6 shows soft bit values (LLRs) in which a 16 QAM signal is inserted into QPSK symbols and demapped by a QPSK demodulator.

Fig. 7 shows the variance of the LLR moduli within the sliding window for the signal in fig. 5.

Fig. 8 illustrates an exemplary method implemented by a receiving device to detect inconsistent data in a received data signal.

Fig. 9 illustrates additional aspects of a method of detecting inconsistent data in a received data signal.

Fig. 10 illustrates an exemplary receiving apparatus according to another embodiment.

Detailed Description

For a better understanding of the invention and related services, exemplary embodiments are described in the context of MBB and URLLC transmissions. However, it will be appreciated that the concepts described herein are more generally applicable to a first transmission over a set of physical resources spanning a limited time and frequency grid, which is preempted by a second transmission using a subset of the set of physical resources. When the first data transmission is preempted, a subset of the resources allocated to the first data transmission are punctured or blanked to allow insertion of the second data signal. That is, the second data signals preempt allocation of resources for the first data transmission.

Fig. 1 and 2 illustrate exemplary puncturing patterns for preempting a first data transmission by a second data transmission. In fig. 1, the MBB data transmission 10 is punctured by a single block 20 to transmit URLLC data. In fig. 2, the MBB data transmission 10 is punctured by a plurality of blocks 20 to convey URLLC data. When the MBB 10 is punctured to transmit URLLC data, the insertion of URLLC data introduces inconsistencies in the characteristics of the received data signal that can be detected by the receiver.

Fig. 3 illustrates an exemplary receiving device 30 configured to detect data insertion based on inconsistencies in the characteristics of the received signal. The receiving apparatus may include a base station (also referred to as an evolved node b (eNB) or 5gnodeb (gnnb)) for uplink transmission, or a User Equipment (UE) for downlink transmission. The receiving device 30 comprises a receiving circuit 40, a processing circuit 50 and a memory 80.

The receive circuitry 40 includes radio frequency circuitry coupled to one or more antennas 45 for receiving signals from a transmitting device (not shown). After analog-to-digital conversion, the processing circuit 50 processes the received signal as described below to detect inconsistencies in the characteristics of the received signal to indicate data insertion or puncturing.

The processing circuit 50 processes signals received by the receiving device 30 and controls the operation of the receiving device 30. The processing circuit 50 is configured to demodulate the data signal to generate soft bit values, calculate detection metrics for two or more subsets of the soft bit values, and detect inconsistent data in at least one of the soft bit values based on the detection metrics corresponding to the at least one subset. The processing circuit 50 may include one or more microprocessors, hardware, firmware, or a combination thereof. The technical effect is that data inconsistencies can be detected in a more efficient and specific manner. This provides the following advantages: this provides that explicit signaling can be avoided and adaptive components can be applied, depending on the type and degree of inconsistency.

The memory 80 includes a non-transitory computer readable medium that stores computer program code (85) and data required for operation by the processing circuit 50. The memory 80 includes both volatile and nonvolatile memory for storing computer program code and data required for operation by the processing circuit 50. The memory 80 may include any tangible, non-transitory computer readable storage medium for storing data, including electronic, magnetic, optical, electromagnetic, or semiconductor data storage. The memory 80 stores a computer program 85, the computer program 85 comprising executable instructions that configure the processing circuitry 50 to implement the methods and processes described herein (including the methods shown in fig. 8 and 9). Generally, computer program instructions and configuration information are stored in a non-volatile memory such as a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), or a flash memory. Temporary data generated during operation may be stored in volatile memory, such as Random Access Memory (RAM). In some embodiments, the computer program 85 for configuring the processing circuit 50 may be stored in a removable memory such as a portable compact disc, portable digital video disc, or other removable media. The computer program 85 may also be embodied in a carrier such as an electronic signal, optical signal, radio signal, or computer readable storage medium.

In one embodiment, processing circuitry 50 includes equalization and Orthogonal Frequency Division Multiplexing (OFDM) demodulation circuitry 55, demodulator 60, soft buffer 65, detection circuitry 70, and decoder 75. The receiving circuit 40 converts the received signal into a digital baseband signal to be input to the processing circuit 50. The equalization and OFDM demodulation circuit 55 equalizes the received signal and performs Inverse Fast Fourier Transform (IFFT) to recover the modulation symbols. Demodulator 60, which is part of a bank of demodulators 60, demaps/demodulates the modulated symbols to obtain bit qualities (e.g., LLRs). The receiving device 30 knows the assigned modulation scheme for the data transmission, which is established or provided by a signaling procedure prior to transmission. The demodulator 60 calculates a bit quality (LLR) for each bit of information, and inputs the bit quality (LLR) to the soft buffer 65 and the detection circuit 70. The detection circuit 70 analyzes the set of bit qualities (LLRs) corresponding to one transmission to detect inconsistencies in the statistical properties of the soft values. More advanced methods such as demodulation/demapping of the same signal through different (not just one) modulation schemes can be used for statistical inconsistency analysis. If inconsistencies are detected, the detection circuitry 70 locates data that may be corrupted and takes action to mitigate the potential effects of data corruption. In one embodiment, the detection circuitry 70 sends a flush command to the soft buffer using the corrupted bit and/or an index of bits that may be corrupted. The soft buffer 65 gets the flush command and sets the identified bit quality (LLR) value to the most neutral value (e.g., "0"). The resulting set of bit qualities (LLRs) is then passed on for further processing (e.g., by decoder 75). Flushing the decoded bits improves decoding performance and reduces the number of retransmissions.

The detection circuit 70 processes the received data signal to blindly detect that transmission is being preempted. The methods described herein may be used in conjunction with other known methods to detect a preemptive transmission. For example, the receiving device 30 may receive a notification (e.g., a 1-bit NDI flag) from the network indicating when a first data transmission is preempted by a second data transmission. In this case, the method as described herein may be performed only when a notification is received. This approach avoids the need to process data signals all the time to detect inconsistent data and thus reduces the processing load, while still providing the advantage that explicit signaling, e.g. indicating in which time-frequency resources preemption occurred, can be avoided. The notification may provide information to the receiving device to locate the data insertion to further reduce the amount of processing required to detect the data insertion.

In one embodiment, the process for detecting inconsistencies in a data signal is as follows

1. Parameters defining the algorithm: sliding window size, empirical "coefficient" value

2. The bit quality is received from the demodulator/demapper. (see, e.g., FIGS. 4 and 6)

3. For each position of the sliding window

Calculating variance of bit quality (LLR) within a sliding window

Wherein

Is the number of possible positions of the sliding window. The result of this step is lengthVector of (2)

4. By passing

Finding a target vector

Mean value of

5. The value of the dynamic threshold is calculated. The coefficient value must be in the interval 0,1]In

a. Upper threshold = μ · coefficient

b. Lower threshold = μ · (2-factor)

6. To determine which threshold to use for detection, an expression is used

(lower threshold-min (x)) < (max (x)) -upper threshold)

a. If the expression is satisfied, an upper threshold is used for detection:

if: max (x)_i）>If the threshold is higher, then an inconsistency is detected

b. Otherwise, a lower threshold is used for detection:

if: min (x)_i）<If the threshold is lower, then an inconsistency is detected

7. If an inconsistency is detected at 6a or 6 b:

a. finding an extremum above the detection threshold (6 a case, e.g., FIG. 5) or below the detection threshold (6 b case, e.g., FIG. 7)

b. Finding the corresponding extremum x_{Start of}And x_{End up}Position of the sliding window

c. Recalculating sliding window positions to obtain bit quality (LLR) indices that should be flushed taking into account the size of the sliding window

d. Sending flush commands to soft buffers using corrupted bits and/or indices of bits that may be corrupted

e. If there is another damaged area, steps 7a-7e are repeated

f. If all damaged areas are cleared, return to step 2 and receiving device 30 waits for the next transmission

8. If no inconsistency is detected by step 6a or 6b, the process returns to step 2 and the receiving device 30 waits for the next transmission.

Fig. 8 illustrates an exemplary method 100 implemented by the receiving device 30 according to one embodiment. The method 100 begins when the receiving device 30 receives a data signal from a transmitting device (block 110). The receiving device 30 demodulates the data signal to generate soft bit values (block 120). The receiving device 30 then calculates detection metrics for two or more subsets of soft bit values (block 130), and uses the detection metrics to detect inconsistent data in at least one subset of the data signals (block 140). In one embodiment, receiving device 30 may use a sliding window to calculate a detection metric and perform the detection, as described above.

The detection technique may be applicable, for example, to downlink transmissions from the gNB to the UE (in which case the UE performs detection), and the detection technique may be applicable to uplink transmissions from the UE to the gNB (in which case the gNB performs detection).

Sliding window

The size of the sliding window is a parameter that can be optimized. Any size may be used, but the detection process will achieve better performance when the size is equal to the smallest possible damage area size. In each implementation, the size of the sliding window is a value for optimization.

The computational complexity of the proposed algorithm can be reduced by specifying a step size for the sliding window movement. Thus, it is possible to move the window with a step size larger than 1 bit, thus reducing the number of sliding window positions and reducing the amount of computation at step 3 of the detection process. In one embodiment, the step size may be equal to the window size such that the subsets of soft bits processed by detection circuit 70 do not overlap.

To enable detection of puncturing of a transmission that spans more than one symbol in time, the sliding window may have a two-dimensional extent in both time and frequency. This two-dimensional sliding window may preferably be combined by the above step sizes to search for predefined puncture sizes in the time and frequency directions.

Empirical coefficient

Empirical coefficients, expressed as coefficients in the above description, are used to tune the detection circuit 70 for different radio conditions, where the variance of the bit mass modulus can vary from a value very close to zero to a value greater than 10. This is one reason why an absolute threshold should not be used for detection. To compensate for drift effects, a dynamic threshold may be used. The use of both the upper and lower thresholds enables the receiving device to achieve the detection of different modulation inconsistency patterns (compare fig. 5 and 7). The empirical coefficients are always used to offset the mean square error values according to the expression at step 5 of the detection process. In other words, the empirical coefficients define how far the variance within the sliding window should be from the average expected variance value to treat it as inconsistent. Empirical coefficients may be empirically established based on simulations to provide optimal performance.

Detection based on components of an inserted signal

The puncture transmission is expected to contain at least three distinct components:

1. a control part, for example, a Physical Downlink Control Channel (PDCCH). The modulation is fixed (QPSK). Expected in the first symbol of a punctured transmission

2. A reference signal portion, e.g., a demodulation reference signal (DMRS). The modulation is fixed (QPSK) and the DMRS sequence is known. Should configure and know the location-dependent puncture transmission start

3. A data portion, e.g., a Physical Downlink Shared Channel (PDSCH) or a Physical Uplink Shared Channel (PUSCH). Different modulations are expected.

As a reference, the detection circuit 70 checks the data portion of the inserted signal to detect a difference. Other components of the punctured transmission may be useful in cases where the modulation is the same in both the punctured and punctured transmissions.

In one embodiment, detection circuit 70 searches for the presence of one OFDM symbol long QPSK transmission to mark the beginning of the punctured transmission. With knowledge of the configurable small gap (mini-slot) duration, punctured symbols can be marked if the MBB modulations are different.

In another embodiment, the detection circuit 70 searches for a known QPSK sequence (as one example, a Zadoff-Chu sequence) corresponding to the DMRS. The punctured symbols may be marked if the position of the DMRS in the small gap and the small gap duration are known.

In one embodiment, the QPSK sequences to be used for puncturing the transmission are predefined, allowing any MBB UE to easily distinguish the range of punctured resources. Typically, the sequence is UE-specific, which may make it difficult for an MBB UE to descramble (descramble) the MBB UE. The QPSK sequences used for puncturing the transmission DMRS may also be specified in a non-constant manner, depending on cell identity, subframe number, etc., but are preferably not UE-specific.

Alternative solution

The method for detecting modulation inconsistencies may be based on different states of the received data. For example, the detection may be based on a set of received symbols or soft values after demodulation. Based on the sliding window approach described above, alternative detection methods may be used, including, for example, using the average euclidean distance between symbols in the received data to detect modulation inconsistencies, or comparing soft values to expected values for a known base modulation scheme. Different methods may perform well under different radio conditions. The proposed method based on the variance of the LLR moduli provides good performance with reasonable complexity.

Applications of

The detection techniques may be applied to detection of punctured regions in Long Term Evolution (LTE) or new air interface (NR) communication systems. In one embodiment, puncturing as described herein means that eNb/gNb replaces some of the resources previously scheduled for the UE with URLLC resources targeted to the same UE or another UE.

Also, the detection technique may be used on transmissions that experience time/frequency limited interference (i.e., interference occurs over a limited area in time and frequency). By detecting where interference has occurred, the interference can be removed from the transmission in the same manner as for puncturing.

The portion of the original transmission that may still be present in the reception (albeit with a lower signal to interference plus noise ratio (SINR)) when subject to interference or any other form of interference, which may be from the same eNb/gNb or a different eNb/gNb or another communication system. By estimating the SINR level, some portions of the soft values may be retained rather than completely removed. That is, the soft values may be modified by scaling down (down-scale) the soft values, rather than discarding or removing the soft values, which would give the soft values less importance in soft combining.

Fig. 8 illustrates an exemplary method 100 implemented by the receiving device 30 in accordance with an aspect of the disclosure. The method 100 begins when the receiving device 30 receives a data signal from a transmitting device (block 110). The receiving device 30 demodulates the data signal to generate soft bit values (block 120). The receiving device 30 then calculates detection metrics for two or more subsets of soft bit values (block 130), and uses the detection metrics to detect inconsistent data in at least one subset of the data signals (block 140). In one embodiment, receiving device 30 may use a sliding window to calculate a detection metric and perform the detection, as described above.

Fig. 9 illustrates an exemplary method of detecting inconsistent data in a data signal, which may be performed at block 140 in fig. 8. The receiving device 30 determines one or more thresholds for each subset (block 160). In some embodiments, one or more thresholds for each subset are calculated separately. In some embodiments, the same threshold or set of thresholds is used for all subsets or groups of subsets. To detect inconsistent data, the receiving device 30 compares the detection metric for each subset to one or more thresholds determined in block 160 (block 180). In embodiments where multiple thresholds are used (e.g., an upper threshold and a lower threshold), receiving device 30 may optionally select a threshold to apply to the detection of inconsistent data (block 170).

In some embodiments of the method 100, calculating detection metrics for two or more subsets of soft bit values comprises: a detection metric is calculated based on statistical properties of the soft bit values within each subset.

In some cases, the receiving device dynamically determines the threshold for each subset based on the detection metrics in the subset.

In some embodiments of the method 100, detecting inconsistent data in at least a subset of the soft bit values comprises: the detection metric is compared to an expected value for a known modulation scheme for the data signal.

In some embodiments of the method 100, detecting inconsistent data in at least a subset of the soft bit values comprises: the detection metric is compared to known sequences in the inconsistent data.

In some embodiments of the method 100, the known sequence comprises one of a known tag in the inconsistent data or a known reference signal sequence.

In some embodiments of the method 100, detecting inconsistent data in at least a subset of the soft bit values comprises: the detection metric is compared to the known modulation for the inconsistent data.

In some embodiments of the method 100, detecting inconsistent data in at least a subset of the soft bit values based on the detection metric comprises: the detection metric is compared to a threshold.

Some embodiments of the method 100 further comprise: a threshold value for each subset is dynamically determined based on the detection metrics in the subset.

In some embodiments of the method 100, dynamically determining the threshold for each subset based on the detection metrics in the subset further comprises: one or more thresholds are calculated for each subset of soft bit values, and one of the determined thresholds is selected for detecting an inconsistency in the subset of soft bit values.

In some embodiments of the method 100, calculating detection metrics for two or more subsets of soft bit values comprises: the variance of the soft bit values within each subset is calculated.

Some embodiments of the method 100 further comprise detecting inconsistent data in at least one subset of the soft bit values based on the detection metrics corresponding to the subset comprises: the variance of the soft bit values in the subset is compared to a threshold.

In some embodiments of the method 100, further comprising: a threshold value for each subset is dynamically determined based on the calculated variance in the subset.

In some embodiments of the method 100, dynamically determining the threshold value for each subset of soft bit values based on the detection metrics in the subset comprises: the mean of the variances is determined, and a threshold is calculated according to the mean of the variances.

In some embodiments of the method 100, dynamically determining the threshold value for each subset of soft bit values based on the detection metrics in the subset further comprises: one or more thresholds for each subset of soft bit values are calculated as a mean of the variances, and one of the determined thresholds is selected for detecting an inconsistency in the subset of soft bit values.

Some embodiments of the method 100 further comprise: in response to detection of inconsistent data, one or more soft bit values corresponding to the inconsistent data are identified, and the soft bit values corresponding to the inconsistent data are modified to reduce decoding errors.

In some embodiments of method 100, modifying soft bit values corresponding to inconsistent data to reduce decoding errors comprises: the soft bit value is set to zero.

The various aspects of the method 100 described above may be used in combination where the aspects are not inconsistent or incompatible in different situations.

Fig. 10 illustrates another exemplary receiving device 200 configured to perform the methods illustrated in fig. 8 and 9. The receiving apparatus 200 comprises a receiving unit/module 210, a demodulation unit/module 220 and a detection unit/module 230. The various units/modules may be implemented by hardware circuitry, processing circuitry and/or software. The receiving unit/module 210 is configured to receive data signals from a transmitting device. The demodulation unit/module 220 is configured to demodulate the data signal to generate soft bit values. The detection circuit 70 is configured to calculate detection metrics for two or more subsets of soft bit values and to detect inconsistent data in at least one subset of soft bit values based on the detection metrics corresponding to the at least one subset.

It is noted that the above-described apparatus may perform the methods herein and any other processes by implementing any functional components, modules, units or circuitry. For example, in one embodiment, an apparatus includes corresponding circuitry or circuitry configured to perform the steps shown in the method diagrams. In this regard, the circuitry or circuitry may comprise circuitry dedicated to performing certain functional processes and/or one or more microprocessors in conjunction with a memory. For example, the circuitry may include one or more microprocessors or microcontrollers, as well as other digital hardware, which may include Digital Signal Processors (DSPs), dedicated digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), random access memory, cache memory, flash memory devices, optical storage devices, and the like. In several embodiments, the program code stored in the memory may include program instructions for performing one or more telecommunications and/or data communications protocols, as well as instructions for performing one or more of the techniques described herein. In embodiments employing memory, the memory stores program code that, when executed by the one or more processors, performs the techniques described herein.

The methods and apparatus described herein enable a receiving device (e.g., a UE or base station) to enable detection of inconsistent or corrupted data. If inconsistencies or data corruption are detected, measures may be taken to mitigate potential problems caused by inconsistent data. For example, problems such as combining erroneous data in the decoder can be avoided. The method and apparatus will improve decoding performance and reduce the number of retransmissions, which in turn will improve spectral efficiency and system capacity.