Cyclic redundancy check optimization

Abstract

Embodiments of the present disclosure relate to devices, methods, apparatuses, and computer-readable storage media for cyclic redundancy check (CRC) optimization. The method includes generating a set of decoded sequences of an encoded signal received from a second device; determining an activation status of a cyclic redundancy check based on actual distribution characteristics associated with the set of decoded sequences and reference distribution characteristics associated with a set of decoded sequences; and determining a likelihood of successful decoding based on at least the activation status. In this way, a false alarm rate (FAR) can be reduced, and a desired joint FAR and block error rate (BLER) optimization can be achieved.

Description

Technical Field

[0001] Embodiments of this disclosure generally relate to the telecommunications field, and more particularly to apparatus, methods, devices, and computer-readable storage media optimized for Cyclic Redundancy Check (CRC). Background Technology

[0002] Polar codes are used as forward error correction (FEC) coding schemes to protect control signaling (downlink and uplink) in 5G New Radio (NR). The purpose of FEC codes is to facilitate the detection and correction of bit errors at the physical layer receiver. Therefore, good code should strive to minimize the bit / block error rate (BER / BLER).

[0003] In practical systems, the data payload is typically concatenated with a CRC code to verify whether the data has been correctly recovered after decoding the forward error correction code. However, it is well known that CRC codes are not 100% reliable in principle. For example, the recovered bit sequence may be correct, but it may fail the CRC check. It is also possible that the recovered bit sequence is actually incorrect, but it manages to pass the CRC check. The former is called a missed detection, and the latter is called a false alarm.

[0004] For critical 5G vertical applications, such as ultra-reliable low-latency communication (URLLC), smart factories, and remote surgery, both metrics need to be significantly reduced to achieve very positive performance, for example, BLER reduced to 10. -4 Up to 10 -5 At the same time, FAR will be kept at 1% or even lower. Summary of the Invention

[0005] Overall, the exemplary embodiments disclosed herein provide a solution for CRC optimization.

[0006] In a first aspect, a first device is provided. The first device includes at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code are configured, together with the at least one processor, to cause the first device to at least: generate a set of decoded sequences of encoded signals received from a second device; determine an activation state of a cyclic redundancy check based on actual distribution characteristics associated with the set of decoded sequences and reference distribution characteristics associated with the set of decoded sequences; and determine the probability of successful decoding based at least on the activation state.

[0007] In a second aspect, a method is provided. The method includes generating a set of decoded sequences of an encoded signal received from a second device; determining an activation state of a cyclic redundancy check (CRC) based on actual distribution characteristics associated with the set of decoded sequences and reference distribution characteristics associated with the set of decoded sequences; and determining the probability of successful decoding based at least on the activation state.

[0008] In a third aspect, an apparatus is provided, comprising: means for generating a set of decoded sequences of an encoded signal received from a second device; means for determining an activation state of a cyclic redundancy check based on actual distribution characteristics associated with the set of decoded sequences and reference distribution characteristics associated with the set of decoded sequences; and means for determining the probability of successful decoding based at least on the activation state.

[0009] In a fourth aspect, a computer-readable medium having a computer program stored thereon is provided, which, when executed by at least one processor of a device, causes the device to perform the method according to the second aspect.

[0010] Other features and advantages of embodiments of this disclosure will also become apparent from the following description of specific embodiments when read in conjunction with the accompanying drawings, which illustrate the principles of embodiments of this disclosure by way of example. Attached Figure Description

[0011] The embodiments disclosed herein are presented in an exemplary sense, and their advantages will be explained in more detail below with reference to the accompanying drawings, in which...

[0012] Figure 1 An example environment is shown that can implement example embodiments of this disclosure;

[0013] Figure 2 A flowchart is shown of an example method for enhanced decoding of polar codes according to some example embodiments of the present disclosure;

[0014] Figure 3 Example curves of theoretical thresholds according to some exemplary embodiments of this disclosure are shown;

[0015] Figure 4 Examples of combined FARs that can be implemented according to some example embodiments of this disclosure are shown;

[0016] Figures 5A-5D Simulation results for the first to fourth time points according to some exemplary embodiments of this disclosure are shown respectively;

[0017] Figures 6A-6B Exemplary simulation results according to some exemplary embodiments of this disclosure are shown;

[0018] Figure 7 A simplified block diagram of a device suitable for implementing example embodiments of the present disclosure is shown; and

[0019] Figure 8 A block diagram of an example computer-readable medium according to some embodiments of the present disclosure is shown.

[0020] Throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0021] The principles of this disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are presented for illustrative purposes only and to assist those skilled in the art in understanding and implementing this disclosure, and do not imply any limitation on the scope of this disclosure. The disclosure described herein can be implemented in various ways besides those described below.

[0022] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0023] In this disclosure, references to "an embodiment," "an embodiment," "an exemplary embodiment," etc., indicate that the described embodiment may include specific features, structures, or characteristics, but not every embodiment necessarily includes specific features, structures, or characteristics. Furthermore, such phrases do not necessarily refer to the same embodiment. Additionally, when a specific feature, structure, or characteristic is described in conjunction with an exemplary embodiment, whether explicitly described or not, it is considered that incorporating other embodiments to affect such feature, structure, or characteristic is within the knowledge of those skilled in the art.

[0024] It should be understood that although the terms “first” and “second” may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish the functionality of various elements. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that, when used herein, the terms “comprising,” “having,” and / or “including” specify the said features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0026] As used in this application, the term "circuit system" may refer to one or more of the following:

[0027] (a) Pure hardware circuit implementation (such as implementations only in analog and / or digital circuit systems), and

[0028] (b) A combination of hardware circuitry and software, such as (if applicable):

[0029] (i) A combination of analog and / or digital hardware circuitry and software / firmware, and

[0030] (ii) Any part of one or more hardware processors having software (including one or more digital signal processors), software, and one or more memories, which work together to enable a device such as a mobile phone or server to perform various functions, and

[0031] (c) One or more hardware circuits and / or one or more processors, such as one or more microprocessors or a portion thereof, that require software (e.g., firmware) to operate, but the software may not be present when operation does not require it.

[0032] This definition of circuit system applies to all uses of the term in this application, including all uses in any claim. As a further example, as used herein, the term circuit system also covers only hardware circuitry or a processor (or processors) or a portion thereof and its accompanying software and / or firmware implementation. For example, and where applicable to specific claim elements, the term circuit system also covers baseband integrated circuits or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

[0033] As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard, such as fifth-generation (5G) systems, Long Term Evolution (LTE), LTE-A Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. Furthermore, communication between terminal devices and network devices in a communication network can be performed according to any suitable generation communication protocol, including but not limited to first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, future fifth-generation (5G) New Radio (NR) communication protocols, and / or any other protocols currently known or to be developed in the future. Embodiments of this disclosure can be applied to various communication systems. Given the rapid development of communications, there will naturally be communication technologies and systems that embody future types of this disclosure. The scope of this disclosure should not be construed as limited to the systems described above.

[0034] As used herein, the term "network device" refers to a node in a communication network through which terminal devices access the network and receive services. A network device can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), an NR next-generation Node B (gNB), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), a relay, or a low-power node (such as a femto, pico, etc.), depending on the terminology and technology applied. The RAN split architecture includes a gNB-CU (centralized unit, hosting RRC, SDAP, and PDCP) that controls multiple gNB-DUs (distributed units, hosting RLC, MAC, and PHY). A relay node may correspond to the DU portion of an IAB node.

[0035] The term "terminal device" refers to any terminal device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices can include, but are not limited to, mobile phones, cellular phones, smartphones, Voice over IP (VoIP) phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices (such as digital cameras), gaming terminal devices, music storage and playback devices, in-vehicle wireless terminal devices, wireless terminals, mobile stations, laptop embedded devices (LEE), laptop mounted devices (LME), USB dongles, smart devices, wireless customer premises equipment (CPE), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (such as remote surgery), industrial devices and applications (such as robots and / or other wireless devices operating in industrial and / or automated processing chain environments), consumer electronics devices, devices operating in commercial and / or industrial wireless networks, etc. Terminal equipment can also correspond to the mobile terminal (MT) portion of an integrated access and backhaul (IAB) node (also known as a relay node). In the following description, the terms "terminal equipment," "communication equipment," "terminal," "user equipment," and "UE" are used interchangeably.

[0036] While the functionality described herein can be performed in fixed and / or wireless network nodes in various example embodiments, in other example embodiments, the functionality can be implemented in a user equipment device (such as a mobile phone, tablet, laptop, desktop computer, mobile IoT device, or fixed IoT device). This user equipment device may, for example, be equipped with appropriate corresponding capabilities as described in combination with one or more fixed and / or wireless network nodes. The user equipment device may be a user equipment and / or a control device such as a chipset or processor, configured to control the user equipment when installed in it. Examples of such functionality include boot server functionality and / or a home subscriber server, which can be implemented in the user equipment device by providing software configured to cause the user equipment device to perform from the perspective of these functions / nodes.

[0037] Figure 1 An example communication network 100 in which embodiments of the present disclosure can be implemented is shown. Figure 1 As shown, the communication network 100 includes a receiving device 110 (hereinafter also referred to as the first device 110 or network device 110) and a transmitting device 120 (hereinafter also referred to as the second device 120 or terminal device 120). The transmitting device 120 can communicate with the receiving device 110. It is understood that... Figure 1 The number of network devices and terminal devices shown is given for illustrative purposes and is not intended to suggest any limitation. Communication network 100 may include any suitable number of network devices and terminal devices. Furthermore, it should be understood that a receiving device may also be referred to as terminal device 120, and a transmitting device may also be referred to as network device 110.

[0038] Depending on the communication technology, network 100 can be a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an Orthogonal Frequency Division Multiple Access (OFDMA) network, a Single Carrier Frequency Division Multiple Access (SC-FDMA) network, or any other network. The communications discussed in network 100 can conform to any suitable standard, including but not limited to New Radio Access (NR), Long Term Evolution (LTE), LTE Evolution, LTE-A Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), cdma2000, and Global System for Mobile Communications (GSM). Furthermore, communications can be performed according to any generation of communication protocols currently known or to be developed in the future. Examples of communication protocols include, but are not limited to, first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, and fifth-generation (5G) communication protocols. The technologies described herein can be used in the aforementioned wireless networks and radio technologies, as well as other wireless networks and wireless technologies. For clarity, certain aspects of the technology are described below in relation to LTE, and the term LTE is used in most of the following description.

[0039] As mentioned above, polar codes are used as forward error correction (FEC) coding schemes to protect the control signaling (downlink and uplink) of 5G NR. The purpose of FEC codes is to facilitate the detection and correction of bit errors at the physical layer receiver. Therefore, good code should strive to minimize the bit / block error rate (BER / BLER).

[0040] In practical systems, the data payload is typically concatenated with a CRC code to verify whether the data has been correctly recovered after decoding the forward error correction code. However, it is well known that CRC codes are not 100% reliable in principle. For example, the recovered bit sequence may actually be correct, but it may fail the CRC check. Conversely, the recovered bit sequence may actually be incorrect, but it may still pass the CRC check. The former is called a missed detection, and the latter is called a false alarm.

[0041] This could be caused by various factors, such as impaired channel estimation or rate matching mismatch. In summary, for a good forward error control coding scheme, the key is not only to minimize BLER, but also to reduce the false alarm rate (FAR) and false negative rate as much as possible.

[0042] Furthermore, based on the discussions regarding polar code standardization during version 15, optimizing BLER and FAR is particularly challenging. Attempting to reduce one metric leads to an increase in the other, making it seem impossible to minimize both metrics simultaneously.

[0043] For critical 5G vertical applications, such as URLLC, smart factories, and remote surgery, both metrics need to be significantly reduced to achieve very positive performance, for example, BLER reduced to 10. -4 Up to 10 -5 At the same time, FAR will be kept at 1% or even lower.

[0044] In discussions related to FAR improvements, it has been proposed to meet FAR requirements by extending the number of CRC bits to 24 bits (the reliability of CRC check is proportional to the CRC length). However, as the ratio of data payload to CRC bits decreases, this reduces the efficiency of radio transmission. Furthermore, implementing a 24-bit CRC is more complex and difficult than using shorter CRC codes. Therefore, further extending the CRC length makes it difficult to meet the stringent BLER and FAR requirements of 5G supercritical vertical applications.

[0045] Furthermore, the standardized construction method for uplink polar codes is called CA-Polar. Decoding CA-Polar consumes some CRC bits. In other words, one of the six CRC bits will be used for decoding the Polar code, rather than for integrity verification. The number depends on the list size used by the polar decoder. It is typically greater than 2, meaning that the remaining CRC bits used for integrity verification will not exceed three. A three-bit CRC can only guarantee 12.5% ​​FAR, hence the difference is more than tenfold.

[0046] Several schemes have been proposed to reduce FAR or improve decoding BLER performance, but neither approach can perform joint optimization. For example, a lower BLER can be achieved by increasing the decoder list size, where a key parameter of the 8 = 2^3 consecutive elimination list decoder used on the receiver side is often used as the optimal point between performance and complexity. However, a side effect is that increasing the list size to 16 is equivalent to increasing the FAR to 25%, while the target FAR is 1%. Therefore, this method is not applicable.

[0047] Furthermore, some discussions suggest using path metrics (PM) to reduce FAR. However, this would have a significant impact on decoding performance and could lead to missed detections. The reason is that if only path metrics are used, very accurate estimations are required because the margin for error in making the correct decision is extremely small. In practice, however, it is impossible to be accurate based on an extremely limited number of path metric values.

[0048] Another approach suggests eliminating the FAR problem by abandoning CRC, which makes CRC-assisted polar decoding impossible. Polar codes themselves, especially with shorter block sizes (e.g., the size of a typical UCI payload), exhibit very poor BLER performance before CRC is integrated into polar decoding. Therefore, CRC plays a crucial role, and omitting it is not feasible. Another problem is the inability to perform integrity checks. Consequently, it becomes uncertain whether the recovered bit sequence is identical to the transmitted bit sequence. Clearly, this makes omitting CRC infeasible.

[0049] Therefore, embodiments of the present invention propose a solution to achieve the goal of joint FAR and BLER optimization. In this solution, when a decoding process is performed on an encoded signal and a set of decoded sequences has been generated, the receiving device can compare the actual distribution characteristics associated with the set of decoded sequences with the reference distribution characteristics associated with the set of decoded sequences, and determine whether CRC should be activated based on this comparison. The probability of successful decoding can be determined based on the activation state of CRC. In this way, FAR can be reduced, and the desired joint FAR and BLER optimization can be achieved.

[0050] The following will refer to Figure 2 The principles and implementation of this disclosure are described in detail. Figure 2 A flowchart of an example method 200 for enhanced decoding according to some example embodiments of the present disclosure is shown. Method 200 can be implemented as follows: Figure 1 The receiving device 110 shown is implemented here. For the purposes of discussion, reference will be made to... Figure 1 Description method 200.

[0051] In the transmission of a signal sequence from the transmitting device 120 to the receiving device 110, the signal sequence should be encoded based on a specific encoding mode before transmission to avoid security risks during transmission. When the receiving device 110 receives the encoded signal sequence, the receiving device 110 should decode the encoded signal sequence to obtain the original signal sequence.

[0052] like Figure 2 As shown, at 210, receiving device 110 generates a set of decoded sequences of the encoded signals received from transmitting device 120. For example, CRC-assisted consecutive cancellist decoding (SCL-CRC) can be used in the decoding process.

[0053] For example, the received coded signal sequence input can first be demodulated by a soft demodulator, and the output is a log-likelihood ratio (LLR) sequence represented as y.

[0054] y = {y0, y1, y2, ..., y n-2 ,y n-1 ,} (1)

[0055] In an LLR sequence y, for the corresponding bit of the original signal sequence, each element can be referred to as the likelihood of a binary value of 0 or a binary value of 1.

[0056] From here on, processing will be performed in the logarithmic field in the form of LLR.

[0057]

[0058] Equation (2) is the logarithmic ratio of two conditional probabilities conditioned on the received y, where y is the bit sequence received at receiving device 110 and is the i-th bit transmitted by transmitting device 120. It should be understood that LLR can also be defined by exchanging the numerator and denominator.

[0059] The basic principle of CRC-assisted SCL decoding is to trace multiple decoding paths throughout the decoding process until the decoding of the last coded bit. Several metrics are typically used to evaluate the correctness of taking a particular path relative to all possible paths in terms of likelihood or probability; one of these is the so-called path metric (PM), calculated during the decoding process. Multiple traces of the path are stored and maintained in a list. The size of the list can be constant by removing old paths with the lowest likelihood and adding new paths with higher likelihood.

[0060] For example, the path metric is taken using the assumed bit of the l-th path, i.e. It can be defined as:

[0061]

[0062] in

[0063] In some example embodiments, to generate a set of decoded sequences, receiving device 110 may construct a binary tree for decoding the encoded signal. For example, as described above, each element of the likelihood sequence may represent the likelihood of the binary value 1 or 0 for each bit of the original signal sequence. The binary tree can be constructed by sequentially processing each element of the likelihood sequence.

[0064] By traversing the binary tree, the receiving device 110 can determine multiple decoding paths, i.e., path metrics. For example, a path metric is calculated for each possible path. A single path metric can represent the probability of a specific path from one node in the binary tree to another.

[0065] During tree traversal, new path metrics are continuously generated. For example, a maximum of L path metrics can be stored, where L is the list size. Therefore, the path metrics are sorted in ascending / descending order and compared with each other. New path metrics are added to the top of this list, while path metrics at the bottom of the list may be eliminated.

[0066] This process is repeated continuously until the last element. Therefore, a list containing L most promising path metrics can be obtained, which can be viewed as a set of decoded sequences. That is, the receiving device 110 can determine the set of decoded sequences based on multiple decoding paths.

[0067] Return to reference Figure 2 At 220, receiving device 120 determines the activation state of cyclic redundancy check (CRC) based on the actual distribution characteristics associated with a set of decoded sequences and the reference distribution characteristics associated with a set of decoded sequences. To this end, receiving device 120 can determine the actual distribution characteristics associated with a set of decoded sequences.

[0068] The determination of the actual distribution characteristics associated with a set of decoded sequences can depend on values ​​that characterize the precision of the decoding path for each decoded sequence in the set. These values ​​will also be referred to as PM values ​​below.

[0069] As mentioned above, multiple decoding paths can be generated during the decoding process by traversing the tree structure. There is only one correct decoding path, but the decoder can search virtually all paths. With tree pruning enabled, some paths will be pruned, keeping the batch size relatively small. The PM value for each decoding path can be used to evaluate whether a path needs to be kept or discarded. Typically, the PM value is accumulated by negative values / penalties, meaning that the PM value will remain unchanged or decrease after each decoding step. In other words, the value characterizing the precision of a decoding path can be associated with each bit in each decoded sequence in a set of decoded sequences.

[0070] It should be understood that, apart from the correct path, most paths will have negative PM values ​​with very large absolute values. For each new decoding step, the added penalty will be much smaller than the current PM value, meaning the difference between one step and another won't change significantly. Since each new PM value will be compared with other values ​​during decoding, it will be pruned if its PM is not part of the top-level PM values. Therefore, this implies a trend where all paths, except the correct path, will be similar or convergent. Thus, two inferences about the PM value can be derived:

[0071]

[0072]

[0073] Based on the PM value, the receiving device 110 can determine the actual distribution characteristics associated with a set of decoded sequences. For example, the actual distribution characteristics associated with a set of decoded sequences can be determined by performing a normal distribution operation on the PM value. The actual distribution characteristics associated with a set of decoded sequences can also be referred to as instantaneous moments.

[0074] Furthermore, determining the reference distribution property associated with a set of decoded sequences can depend on a threshold probability of the difference between the reference distribution property and the actual distribution property.

[0075] A threshold probability of the difference can be determined during the offline process. During offline processing, the number of bits used to verify a set of decoded sequences can be determined. For example, the bits may include at least one of PC bits and CRC bits. The bit configuration can depend on the BLER. Different BLERs may have different configurations. The remaining PC bits and CRC bits are then used for tree pruning to minimize residual errors. In some cases (very high signal-to-noise ratio (SNR), very good reception quality), PC verification may even be unnecessary, i.e., 0 PC bits.

[0076] It should be understood that the segmentation of PC bits and CRC bits for different purposes is based on empirical methods, and the accurate ratio depends on fine-tuning during system implementation and the total number of available bits.

[0077] During offline processing, it is also important to find a threshold probability of the difference between the reference distribution characteristics and the actual distribution characteristics. This difference can be called the theoretical threshold γ, which describes the joint correlation between BLER, the number of PC bits and CRC bits, and the target FAR. The threshold is used to determine whether a CRC check should be performed, and it can be calculated as follows.

[0078]

[0079] Combined FAR It is the target portfolio / average / total FAR, for example, 1%;

[0080] P refers to the number of PC bits;

[0081] C is the effective number of CRC bits used for check. Note that the effective number of CRC bits means the remaining CRC bits used for check, because in list decoding with size L, log2(L) CRC bits are equivalently consumed, which is the total number of CRC bits excluding log2(L). Note that P and C depend on the estimated BLER;

[0082] BLER is the currently estimated BLER, ranging from 0 to 1.

[0083] The motivation for setting this threshold is that when BLER is high, the risk of FAR is also high, thus requiring strict control over verification. When BLER is low, the risk of FAR is also low, thus saving on verification. This means deriving the threshold for whether to perform CRC check based on BLER.

[0084] The BLER, theoretical threshold γ, number of PC and CRC bits, and target FAR can be summarized as follows. For example, assuming the polarity decoder uses a list size of 8 and a maximum of 2 PC bits are available, the relationship between BLER, FAR, and the number of check bits (PC+CRC) to be used is as follows, according to equation (1). In this case, the list size is 8 = 2^3, which means that decoding will consume 3 CRC bits, so the available CRC bits are 6 - 3 = 3. The total number 6 will be defined and determined by the specification.

[0085] Table 1: PC and CRC decision thresholds with a maximum of 2 PC bits

[0086]

[0087] The main advantage is that the solution of this invention does not require a very precise threshold, which means there is a large margin to tolerate imperfect estimates of the path metric, making the solution implementation-friendly. The table can be interpreted as follows: for example, as shown in row 1, when BLER is 1, it is not necessary to perform a CRC check to determine if each block is correct; instead, it is reasonable to consider it an unsuccessful decoding without a CRC check. As shown in Table 1, γ = 0.32, which means there is a 1 - 0.32 = 68% probability that we have made the correct decision without relying on CRC, which is good enough.

[0088] Figure 3 Example curves for theoretical thresholds according to some exemplary embodiments of this disclosure are shown. Figure 3 As shown, in most regions, the threshold curve 310 is above the theoretical non-validation region boundary, i.e., the 1-BLER curve, so it does not affect BLER performance.

[0089] Figure 4 Example achievable combined FARs according to some exemplary embodiments of this disclosure are shown. An achievable combined FAR can be considered as the overall effective FAR observed at the receiver. The combined FAR curve 410 peaks in the middle and rapidly declines to a very low level. Fortunately, the peak remains below the required 1% target. If a larger list size, such as 16, is used, the combined FAR will exceed 1%, so reducing the list size in this region or setting a conservative verification threshold that may affect BLER performance may be useful. It should be understood that... Figure 2The results shown are specific to certain code blocks and rate configurations, but others will have a similar curve shape with some left or right shift along the X-axis.

[0090] In another example, under similar principles, a maximum of 3 PC bits are available, with the threshold and parity bits configured as follows:

[0091] Table 2: PC and CRC decision thresholds with a maximum of 3 PC bits

[0092]

[0093] As described above, the difference threshold probability between the reference distribution characteristics and the actual distribution characteristics, i.e., the theoretical threshold γ, can be determined. Then, the reference distribution characteristics can be determined based on the threshold probability and the actual distribution characteristics.

[0094] First, a scheme based on the x-th moment can be chosen. For a specific SNR, at that specific SNR point, the probability that the instantaneous moment (calculated during the decoding run) is less than the threshold β should be equal to the calculated theoretical threshold γ. Mathematically, the relationship between the instantaneous moment and the reference distribution characteristic (threshold β) can be shown as follows:

[0095] Prob(instantaneous moment < β(SNR)) = γ(7)

[0096] As described above, the actual distribution characteristics associated with a set of decoded sequences, i.e., the instantaneous time, can be determined by performing a normal distribution operation on the PM values. Therefore, the reference distribution characteristics (threshold β) can be determined.

[0097] In some example embodiments, the threshold β can also be fixed and associated with the PM value. Assuming there are n PM values, the five possible statistical methods we investigate can be any of the following:

[0098] Range: Max(PM1,PM2,…,PM) n )-Min(PM1,PM2,…,PM n )

[0099] First moment (also known as mean): Mean(PM1, PM2, ..., PM) n )

[0100] Second moment (also known as standard deviation): Sqrt(var(PM1,PM2,…,PM)) n ))

[0101] The third moment, i.e., skewness, is calculated as: Skewness(PM1, PM2, ..., PM) n ) 1 / 3

[0102] Fourth moment, i.e., kurtosis: Kurtosis(PM1,PM2,…,PM) n ) 1 / 4

[0103] Regarding the statistical method "range", assuming all PM values ​​are reordered so that PM1 is the maximum value, then

[0104] PM i =PM i -PM1 (8)

[0105] The range method now becomes:

[0106] -Min(PM1,PM2,…,PM n (9)

[0107] Considering the absolute value, it becomes the maximum value. The expected value is:

[0108]

[0109] Where Φ is the CDF of the standard normal distribution.

[0110] For the first to fourth moments, we can find a series of expected values ​​of the order statistics moments that follow a log-normal distribution. Figures 5A-5D The simulation results for the first to fourth moments are shown respectively.

[0111] like Figures 5A-5D As shown, in the simulation, two block sizes and two SNRs are evaluated, and PM values ​​are processed to verify their distribution, where Cr. represents a correct block and In. represents an incorrect block. Preferably, the distribution of PM values ​​for blocks of the same type, whether correct or incorrect, overlaps as much as possible and is separated from blocks of other types as much as possible. One may find that the mean method is better than the range method, the standard deviation is better, and the fourth moment is best. It is very stable and insensitive to SNR and block size.

[0112] The activation state of cyclic redundancy check is determined when both the actual distribution characteristics associated with a set of decoded sequences and the reference distribution characteristics associated with a set of decoded sequences are determined.

[0113] Return to reference Figure 2 If the actual distribution characteristics are less than the reference distribution characteristics, the receiving device 120 can activate a cyclic redundancy check (CRC) on a set of decoded sequences. At 230, the receiving device 120 can determine the probability of successful decoding based on the CRC result.

[0114] If the actual distribution characteristics exceed the reference distribution characteristics, the decoding process can be considered a failure. That is, cyclic redundancy check (CRC) will no longer be activated, and the encoded signal will be decoded again.

[0115] Figure 6A and 6B Exemplary simulation results according to some exemplary embodiments of this disclosure are shown. Specifically, Figure 6A Simulation results of decoding performance are shown, while Figure 6B Simulation results for FAR comparison are shown. In this simulation, the data payload length with / o CRC (denoted as K) is 19, and in this case, the coded polar codeword sequence N is 336. All SNRs use a constant threshold β, β = -10dB. Performance can be further improved by using tighter thresholds and SNR-related thresholds. Furthermore, all CRC bits are used for checksum. As mentioned above, in high SNR regions, some or all of the CRC bits can be used for pruning. Figure 6A and 6B As can be seen, for all SNR conditions, the decoding performance (curve 601) is improved, and the FAR (curve 602) is effectively reduced to below 1%. This means that the proposed method is effective.

[0116] In some example embodiments, the means capable of performing method 500 (e.g., implemented at network device 220) may include components for performing the various steps of method 500. This means may be implemented in any suitable form. For example, the means may be implemented in a circuit or software module.

[0117] In some example embodiments, the apparatus includes components for generating a set of decoded sequences of encoded signals received from a second device; components for determining an activation state of a cyclic redundancy check based on actual distribution characteristics associated with the set of decoded sequences and reference distribution characteristics associated with the set of decoded sequences; and components for determining the probability of successful decoding based at least on the activation state.

[0118] In the solution of this invention, FAR can be mitigated by reducing the number / frequency of CRC checks, thereby improving decoding BLER performance while reducing FAR. In this way, FAR can be reduced to 1% without increasing the number of CRC bits, and the desired joint FAR and BLER optimization can be achieved.

[0119] Figure 7 This is a simplified block diagram of a device 700 suitable for implementing embodiments of the present disclosure. The device 700 can be provided to implement a communication device, such as... Figure 1The receiving device 120 shown is illustrated. As shown, device 700 includes one or more processors 710, one or more memories 740 coupled to processors 710, and one or more communication modules 740 coupled to processors 710.

[0120] The communication module 740 is used for bidirectional communication. The communication module 740 has at least one antenna to facilitate communication. The communication interface can represent any interface required for communication with other network elements.

[0121] Processor 710 can be any type suitable for a local technology network and can include one or more of the following: as non-limiting examples, general-purpose computers, special-purpose computers, microprocessors, digital signal processors (DSPs), and processors based on multi-core processor architectures. Device 700 can have multiple processors, such as application-specific integrated circuit chips, which are time-subordinate to a clock synchronized with the main processor.

[0122] Memory 720 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 724, electrically programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), and other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 722 and other volatile memories that do not persist during power-off periods.

[0123] Computer program 730 includes computer-executable instructions that are executed by the associated processor 710. Program 730 may be stored in ROM 720. Processor 710 can perform any appropriate actions and processes by loading program 730 into RAM 720.

[0124] The embodiments of this disclosure can be implemented via program 730, enabling device 700 to execute as described in the reference. Figure 2 Any process discussed in this disclosure. The various embodiments of this disclosure can also be implemented in hardware or a combination of software and hardware.

[0125] In some embodiments, program 730 may be tangibly contained in a computer-readable medium, which may be contained in device 700 (e.g., in memory 720) or in other storage devices accessible to device 700. Device 700 may load program 730 from the computer-readable medium into RAM 722 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. Figure 7An example of a computer-readable medium 700 in the form of a CD or DVD is shown. A program 730 is stored on the computer-readable medium.

[0126] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while others may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the block diagrams, devices, systems, techniques, or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry, or logic, general-purpose hardware, controllers, or other computing devices, or some combination thereof.

[0127] This disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in a program module, which execute in a device on a target real or virtual processor to perform the above-referenced... Figure 2 The method 200 performs a specific task or implements a specific abstract data type. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. Machine-executable instructions for program modules can be executed locally or in a distributed device. In a distributed device, program modules can reside in both local and remote storage media.

[0128] Program code used to perform the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that, when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a stand-alone software package, partially on a computer, partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0130] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any suitable combination thereof. More specific examples of computer-readable storage media will include electrical connections having one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0131] Furthermore, although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order or sequence shown, or requiring all of the operations shown to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the foregoing discussion, these details should not be construed as limiting the scope of this disclosure, but rather as descriptions of features specific to particular embodiments. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0132] Although this disclosure is described in language specific to structural features and / or methodological behavior, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or behaviors described above. Rather, the specific features and actions described above are disclosed as exemplary forms for implementing the claims.

Claims

1. A first device for communication, comprising: At least one processor; and At least one memory, which includes computer program code; The at least one memory and the computer program code are configured, together with the at least one processor, to cause the first device to at least: Generate a decoded sequence of encoded signals received from the second device; The activation state of cyclic redundancy check is determined based on the actual distribution characteristics associated with the set of decoded sequences and the reference distribution characteristics associated with the set of decoded sequences. The probability of successful decoding is determined at least based on the activation state; Determine an estimate of the block error rate of the signal; Determine the number of bits used to verify the set of decoded sequences; Based on the target false alarm rate used to decode the signal, the estimated block error rate, and the number of bits, a threshold probability of the difference between the reference distribution characteristics and the actual distribution characteristics is determined; The actual distribution characteristics are determined based on the set of decoded sequences; as well as The reference distribution characteristics are determined based on the threshold probability and the actual distribution characteristics.

2. The first device according to claim 1, wherein the first device is configured to generate the set of decoded sequences by: Construct a binary tree for decoding the encoded signal; Multiple decoding paths are determined by performing a traversal of the binary tree; and The set of decoded sequences is generated based on the multiple decoding paths.

3. The first device according to claim 1, wherein the bits used for the verification include at least one of the following: The first set of bits used to perform cyclic redundancy check, and The second set of bits is used to perform parity checking.

4. The first device according to claim 1 or 2, wherein the first device is further configured to: Based on the bits in each decoded sequence, determine a value representing the precision of the decoding path for each decoded sequence in the set of decoded sequences; and The actual distribution characteristics are determined based on the value.

5. The first device of claim 1, wherein the first device is configured to determine the activation by: Compare the reference distribution characteristics with the actual distribution characteristics; and In response to determining that the actual distribution characteristic is less than the reference distribution characteristic, the cyclic redundancy check is activated for the set of decoded sequences.

6. The first device according to claim 1, wherein the first device is configured to determine the probability of successful decoding by: In response to determining that the activation state indicates that the cyclic redundancy check is not activated, the decoding failure is determined.

7. The first device of claim 1, wherein the first device is configured to determine the probability of successful decoding by: In response to determining that the activation state indicates the cyclic redundancy check will be activated, the cyclic redundancy check is performed on the set of decoded sequences; and The probability of successful decoding is determined based on the result of the cyclic redundancy check.

8. The first device according to any one of claims 1-7, wherein the first device includes a receiving device and the second device includes a transmitting device.

9. A method for communication, comprising: Generate a decoded sequence of encoded signals received from the second device; The activation state of cyclic redundancy check is determined based on the actual distribution characteristics associated with the set of decoded sequences and the reference distribution characteristics associated with the set of decoded sequences. The probability of successful decoding is determined at least based on the activation state; Determine an estimate of the block error rate of the signal; Determine the number of bits used to verify the set of decoded sequences; Based on the target false alarm rate used to decode the signal, the estimated block error rate, and the number of bits, a threshold probability of the difference between the reference distribution characteristics and the actual distribution characteristics is determined; The actual distribution characteristics are determined based on the set of decoded sequences; as well as The reference distribution characteristics are determined based on the threshold probability and the actual distribution characteristics.

10. The method of claim 9, wherein generating the set of decoded sequences comprises: Construct a binary tree for decoding the encoded signal; Multiple decoding paths are determined by performing the traversal process of the binary tree; as well as The set of decoded sequences is generated based on the multiple decoding paths.

11. The method according to claim 9, The bits used for the verification include at least one of the following: The first set of bits used to perform cyclic redundancy check, and The second set of bits is used to perform parity checking.

12. The method according to claim 9 or 10, further comprising: Based on the bits in each decoded sequence, determine a value representing the precision of the decoding path for each decoded sequence in the set of decoded sequences; as well as The actual distribution characteristics are determined based on the value.

13. The method of claim 9, wherein determining the activation comprises: Compare the reference distribution characteristics with the actual distribution characteristics; as well as In response to determining that the actual distribution characteristic is less than the reference distribution characteristic, the cyclic redundancy check is activated for the set of decoded sequences.

14. The method of claim 9, wherein determining the probability of successful decoding comprises: In response to determining that the activation state indicates that the cyclic redundancy check is not activated, the decoding failure is determined.

15. The method of claim 9, wherein determining the probability of successful decoding comprises: In response to determining that the activation state indicates that the cyclic redundancy check will be activated, the cyclic redundancy check is performed on the set of decoded sequences; as well as The probability of successful decoding is determined based on the result of the cyclic redundancy check.

16. The method according to any one of claims 9-15, wherein the method is performed at a first device, the first device comprising a receiving device and the second device comprising a transmitting device.

17. An apparatus for communication, comprising: A component for generating a set of decoded sequences of encoded signals received from a second device; A component for determining the activation state of cyclic redundancy check based on the actual distribution characteristics associated with the set of decoded sequences and the reference distribution characteristics associated with the set of decoded sequences; Components used to determine the probability of successful decoding based at least on the activation state; A component used to determine an estimate of the block error rate of the signal; A component for determining the number of bits used to verify the set of decoded sequences; A component for determining a threshold probability of the difference between the reference distribution characteristics and the actual distribution characteristics based on the target false alarm rate for decoding the signal, the estimated block error rate, and the number of bits; A component for determining the actual distribution characteristics based on the set of decoded sequences; as well as A component used to determine the reference distribution characteristics based on the threshold probability and the actual distribution characteristics.

18. A non-transient computer-readable medium comprising program instructions for causing a device to perform at least the method according to any one of claims 9-16.

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