Channel coding method and device and first user equipment

Abstract

Provided are a channel coding method and apparatus, and a first user equipment, the method comprising: a first user equipment acquiring K information bits, and setting an initial state of a system convolutional code, a non-system convolutional code, or a feed-forward system convolutional code; the K information bits are encoded using a systematic convolutional code, a non-systematic convolutional code, or a feedforward systematic convolutional code. According to the embodiment of the invention, under the condition that multiple user equipment multiplexes the same time-frequency or space-time-frequency resource for transmission, first user equipment presets that a shift register uses a system convolutional code, a non-system convolutional code or a feed-forward system convolutional code in an initial state; according to conditions such as a channel, the number of activated users and the like, a proper channel code is selected to code K pieces of information and determine whether to truncate the coded tail bit, so that non-orthogonal multiplexing of more users or random access of more users can be supported by using the coding scheme, and the system transmission or random access efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a channel coding method, apparatus and first user equipment. Background Technology

[0002] Channel coding is a crucial component of communication systems. By adding redundant bits to the transmitted information bits, it can effectively combat fading, interference, and noise. Commonly used channel coding methods include convolutional codes, low-density parity-check codes (LDPC codes), and polar codes. Among these, convolutional codes include systematic convolutional codes (SC), non-systematic convolutional codes (NSC), and recursive systematic convolutional codes (RSC).

[0003] Channel coding is widely used in multi-user systems. For example, in non-orthogonal multiple access (NOA) and passive multiple access (PMI), multiple user signals are multiplexed together after channel coding, bit repetition, or symbol spreading. Iterative decoding can be used in receivers to achieve better performance.

[0004] However, channel coding in single-user systems requires a low bit error rate (BER) at high signal-to-noise ratios (SNRs) and does not prioritize BER performance at low SNRs. However, in non-orthogonal multiple access (NOR) and passive multiple access (PMI) systems, inter-user interference is generally significant when the number of users is large, resulting in a low signal-to-interference-plus-noise ratio (SNR). Therefore, multi-user channel coding requires a low BER at low SNRs. Consequently, channel coding schemes with good single-user performance are unlikely to achieve optimal performance when applied to multi-user systems. Summary of the Invention

[0005] The purpose of this application is to provide a channel coding method, apparatus, and first user equipment that can solve the problem that existing single-user channel coding schemes cannot be applied to multi-user channel coding schemes.

[0006] To achieve the above objectives, this application provides a channel coding method, executed by a first user equipment in a user equipment group, wherein the user equipment group includes N user equipments, where N is an integer greater than 2; the method includes:

[0007] Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1;

[0008] The K information bits are encoded using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code.

[0009] The first user equipment is one of the N user equipments.

[0010] In some embodiments of this application, the method further includes:

[0011] The encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission.

[0012] In some embodiments of this application, the method further includes:

[0013] The encoded bits are modulated and spread, then mapped onto time-frequency or space-time-frequency resources for transmission.

[0014] In some embodiments of this application, the method further includes:

[0015] The encoded bits are sparsely modulated and then mapped onto time-frequency or space-time-frequency resources for transmission.

[0016] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros.

[0017] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register of the convolutional code.

[0018] In some embodiments of this application, the method further includes:

[0019] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

[0020] In some embodiments of this application, the method further includes:

[0021] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

[0022] In some embodiments of this application, the method further includes:

[0023] Based on the number of active user equipment in the user equipment group, determine at least one of the following from simulation results in an additive white Gaussian noise channel:

[0024] The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code;

[0025] The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0026] The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0027] Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

[0028] In some embodiments of this application, when the number of active user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is small;

[0029] When the number of activated user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is large.

[0030] In some embodiments of this application, the user equipment group includes the following user equipment:

[0031] User equipment in non-orthogonal multiple access schemes;

[0032] Alternatively, user equipment in a passive multiple access random access scheme.

[0033] This application embodiment also provides a channel coding apparatus applied to a first user equipment in a user equipment group, the user equipment group including N user equipments, the first user equipment being one of the N user equipments, where N is an integer greater than 2; wherein, the channel coding apparatus includes: a processor and a transceiver, the transceiver receiving and transmitting data under the control of the processor, the processor being configured to perform the following operations:

[0034] Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1;

[0035] The K information bits are encoded using systematic convolutional codes, non-systematic convolutional codes, or feedforward systematic convolutional codes.

[0036] In some embodiments of this application, the processor is also configured to perform the following operations:

[0037] The encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission.

[0038] In some embodiments of this application, the processor is also configured to perform the following operations:

[0039] The encoded bits are modulated and spread, then mapped onto time-frequency or space-time-frequency resources for transmission.

[0040] In some embodiments of this application, the processor is also configured to perform the following operations:

[0041] The encoded bits are sparsely modulated and then mapped onto time-frequency or space-time-frequency resources for transmission.

[0042] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros.

[0043] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register of the convolutional code.

[0044] In some embodiments of this application, the processor is also configured to perform the following operations:

[0045] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

[0046] In some embodiments of this application, the processor is also configured to perform the following operations:

[0047] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

[0048] In some embodiments of this application, the processor is also configured to perform the following operations:

[0049] Based on the number of active user equipment in the user equipment group, determine at least one of the following from simulation results in an additive white Gaussian noise channel:

[0050] The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code;

[0051] The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0052] The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0053] Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

[0054] In some embodiments of this application, when the number of active user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is small;

[0055] When the number of activated user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is large.

[0056] In some embodiments of this application, the user equipment group includes the following user equipment:

[0057] User equipment in non-orthogonal multiple access schemes;

[0058] Alternatively, user equipment in a passive multiple access random access scheme.

[0059] This application also provides a first user equipment, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the channel coding method described above.

[0060] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the channel coding method described above.

[0061] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the channel coding method described above.

[0062] The above-mentioned technical solution of this application has at least the following beneficial effects:

[0063] In the channel coding method, apparatus, and first user equipment of this application embodiment, when multiple user equipments reuse the same time-frequency or space-time-frequency resources for transmission, the first user equipment uses systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code to encode K information bits. Using this coding scheme can support more users' non-orthogonal multiplexing or random access, thereby improving system transmission efficiency. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating the channel coding method provided in an embodiment of this application;

[0065] Figure 2 An example diagram illustrating the channel coding method provided in this application embodiment;

[0066] Figure 3 This diagram illustrates one application example of the channel coding method provided in the embodiments of this application.

[0067] Figure 4 Figure 2 illustrates an application example of the channel coding method provided in this application embodiment;

[0068] Figure 5Figure 3 illustrates an application example of the channel coding method provided in this application.

[0069] Figure 6 Figure 4 illustrates an application example of the channel coding method provided in this application embodiment;

[0070] Figure 7 Example diagram representing a systematic convolutional code;

[0071] Figure 8 Example diagram representing the convolutional code of a recursive system;

[0072] Figure 9 Example diagram representing non-systematic convolutional codes;

[0073] Figure 10 The diagram illustrates the performance of channel coding using three different convolutional codes when the constraint length is 3.

[0074] Figure 11 The figure shows an example of the performance of channel coding using three different convolutional codes when the constraint length is 7.

[0075] Figure 12 This diagram illustrates the random access performance using SC codes and NSC / RSC codes for channel coding, respectively.

[0076] Figure 13 This is a schematic diagram showing the structure of the information encoding device provided in the embodiments of this application. Detailed Implementation

[0077] To make the technical problems, technical solutions and advantages of this application clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0078] like Figure 1 As shown, this application provides a channel coding method, executed by a first user equipment in a user equipment group, wherein the user equipment group includes N user equipments, where N is an integer greater than 2; the method includes:

[0079] Step 101: Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1.

[0080] Step 102: Encode the K information bits using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code.

[0081] The first user equipment is one of the N user equipments.

[0082] In this embodiment of the application, when multiple user equipments reuse time-frequency or space-time-frequency resources, the interference between user equipments is relatively large, and the signal-to-noise ratio (SNR) of the users is low. Therefore, this embodiment of the application proposes to use a channel coding method with a low bit error rate when the SNR is low for the above-mentioned user equipment group. Specifically, the channel coding method with a low bit error rate when the SNR is low includes: using systematic convolutional codes, non-systematic convolutional codes, or feedforward systematic convolutional codes to encode the K information bits to be encoded.

[0083] For example, such as Figure 2 The image shows an example of a channel coding method, which includes:

[0084] Step 201: Set the number of active users and determine the parameters of the random access or multiple access scheme, such as the spread spectrum sequence length, the number of bit repetitions, the number of zeros, and at least one of the modulation schemes.

[0085] Step 202: Set multiple convolutional code encoding methods, such as initial state setting, traversing systematic convolutional codes, non-systematic convolutional codes, or feedforward systematic convolutional codes, using multiple generator polynomials to generate the required convolutional codes (such as generator polynomials with constraint lengths from 2 to 10), and traversing the encoded tail bits without truncating or truncating them.

[0086] Step 203: Simulate the average bit error rate or block error rate for multiple users in the AWGN channel;

[0087] Step 204: Obtain the convolutional code parameters that minimize the average bit error rate or block error rate for the corresponding multi-user operation.

[0088] Optionally, the generator polynomial of the convolutional code is determined through simulation under an AWGN channel. The average multi-user bit error rate (BER) or block error rate (BOR) is simulated, and the generator polynomial is found based on the principle of minimizing the BER or BOR. It is then determined whether to truncate the tail bits of the encoded code. This generator polynomial can be used in other channels, such as multipath fading channels. In single-user scenarios, convolutional codes with larger constraint lengths are generally used to achieve lower BERs. However, in multi-user scenarios, we found that when the number of active users is large, using convolutional codes with smaller constraint lengths for non-systematic or feedforward systematic convolutional codes can achieve better performance. In this case, using non-systematic or feedforward systematic convolutional codes with larger constraint lengths for iterative detection fails to converge.

[0089] In one implementation, such as Figure 3 As shown, for interleaved multiple access scenarios, the encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission.

[0090] Among them, time and frequency refer to time and frequency, while space time and frequency refer to space, time and frequency. Space generally refers to the antenna; no specific limitation is made here.

[0091] In another implementation, such as Figure 4 As shown, for scenarios of Multi-User Shared Access (MUSA) or Pattern Defined Multiple Access (PDMA), the encoded bits are modulated and spread before being mapped to time-frequency or space-time-frequency resources for transmission.

[0092] In another implementation, such as Figure 5 As described above, in the Sparse Code Multiple Access (SCMA) scenario, the encoded bits are sparsely modulated and then mapped to time-frequency or space-time-frequency resources for transmission. Sparse modulation involves making part of the modulated signal zero to reduce interference. Simultaneously, to improve performance, two different bit-to-symbol mappings are used in the modulation.

[0093] In another implementation, such as Figure 6 As shown, for the sparse interleaved multiple access scenario, the K information bits are preambled and the encoded bits are then mapped to time-frequency or space-time-frequency resources for transmission after bit repetition, zero-filling, interleaving, and modulation.

[0094] In at least one embodiment of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros. For example, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is assigned using zero-padding.

[0095] Optionally, the zero-filling method requires additional bits for initial state assignment, but since the initial state is known, its performance against multi-user interference is better than the tail-biting method under certain conditions.

[0096] In at least one embodiment of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register of the convolutional code.

[0097] Alternatively, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code may be information bits defined in other ways.

[0098] As an optional embodiment, the method further includes:

[0099] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

[0100] As another optional embodiment, the method further includes:

[0101] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

[0102] It should be noted that in single-user scenarios, tail-biting convolutional codes improve transmission efficiency and achieve better performance, such as lower bit error rate, due to truncating the last few bits of the code. However, in multi-user scenarios, tail-biting convolutional codes may not offer the same advantage. Using all-zero initial state non-systematic convolutional codes or feedforward systematic convolutional codes, without truncating the convolutional code, can achieve better performance than tail-biting convolutional codes when the number of users is large. Therefore, the choice of channel coding differs significantly between multi-user and single-user scenarios. When the number of users is small, because tail-biting convolutional codes truncate the last L bits, saving L bits, it is possible that the system performance in multi-user scenarios will be better than that of untruncated convolutional codes. The appropriate channel coding can be determined through simulation. Even if the channel is a multipath channel, the appropriate channel coding can be determined by simulating the average multi-user bit error rate and block error rate in the AWGN channel. This includes determining which type of coding it is: a systematic convolutional code, an unsystematic convolutional code, or a feedforward systematic convolutional code; what the generator polynomial of the convolutional code is; whether the initial state is all zeros or the first L bits or the last L bits of the information bits; and whether the encoded result should be truncated. The conclusions drawn from the above questions in the AWGN channel can be applied to multipath fading channels.

[0103] In at least one embodiment of this application, the method further includes:

[0104] Based on the simulation results of the number of active user equipment in the user equipment group in the additive white Gaussian noise channel, determine at least one of the following:

[0105] The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code;

[0106] The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0107] The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0108] Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

[0109] Within a certain bit error rate range, such as 0.15 to 0.05, non-systematic convolutional codes with shorter constraint lengths (e.g., constraint length of 3) or feedforward systematic convolutional codes have better bit error rates than those with longer constraint lengths (e.g., constraint length of 7). Optionally, when the number of active user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is smaller (e.g., constraint length of 3); when the number of active user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is larger (e.g., constraint length not less than 7).

[0110] In this embodiment, using polynomials with larger constraint lengths for non-systematic convolutional codes or feedforward systematic convolutional codes can achieve lower bit error rates (BERs) when the signal-to-noise ratio (SNR) is high. However, non-systematic convolutional codes or feedforward systematic convolutional codes with larger constraint lengths have lower BER performance when the SNR is low compared to those with smaller constraint lengths. Therefore, when there are many users (more users mean greater interference, resulting in a lower SNR, corresponding to a low SNR region in channel coding), using non-systematic convolutional codes or feedforward systematic convolutional codes with smaller constraint lengths can achieve better performance.

[0111] In at least one embodiment of this application, the user equipment group includes the following user equipment:

[0112] User equipment in non-orthogonal multiple access schemes;

[0113] Alternatively, user equipment in a passive multiple access random access scheme.

[0114] Alternatively, the non-orthogonal multiple access scheme can be IDMA (Interleaver division multiple access), MUSA (Multi-user shared access), PDMA (Pattern-division multiple access), or SCMA (Sparse code multiple access).

[0115] Optionally, the passive multiple access random access scheme can be a sparse interleaver-division multiple access (SIDMA) scheme, a sparse Kronecker product coding multiple access scheme, an on-off division multiple access (ODMA) scheme, or other passive multiple access random access schemes, without being specifically limited here.

[0116] In one example, a systematic convolutional code is shown below. Figure 7 As shown. An example of Recursive Systematic Convolutional Code (RSC) is shown below. Figure 8 As shown. An example of a non-systematic convolutional code (NSC) is shown below. Figure 9 As shown. Figure 10 The following shows the performance of the three convolutional codes mentioned above. The code rate is 1 / 2, the modulation method is BPSK (Binary Phase Shift Keying), AWGN (Additive White Gaussian Noise) channel, the information bit length is 87, all convolutional codes are initially all zeros, and the tail bits are not truncated after encoding.

[0117] In another example, in massive random access, assuming the number of active users within a user equipment group is 330, the code rate is 1 / 2, the data length is 28000, the modulation is BPSK, and the K information bits are 87 bits each, SC convolutional codes, NSC convolutional codes, and RSC convolutional codes are used to encode the K information bits respectively. The initial state is set to all zeros, and the tail bits are not truncated after encoding. After encoding, the bits are repeated twice, zero-filled, interleaved, and modulated before being transmitted on time-frequency resources; for example... Figure 11 and Figure 12 The figure shows the signal-to-noise ratios (SNRs) E for SC, NSC, and RSC convolutional codes, respectively. b / N0 and bit error rate (BER) diagram. Figure 11 and Figure 12 In this case, g represents the generator polynomial of the convolutional code, expressed in octal. Figure 11 In this context, g = [5, 7] is represented in binary as [1 0 1, 1 1 1], with a constraint length of 3. g contains two generator polynomials, 5 and 7, and the number of registers for a single generator polynomial 5 or 7 is L = 3. For example... Figure 12 In K, g = [133, 171] is represented in binary as [1 0 1 1 0 1 1, 1 1 1 100 1], with a constraint length of 7. g contains two generator polynomials, 133 and 171, and the number of registers for a single generator polynomial 133 or 177 is L = 7. a This indicates the number of active users.

[0118] In summary, in the channel coding of multi-user multiplexing time-frequency or space-time-frequency resources in this application embodiment, when the constraint length is 7, a systematic convolutional code is used. Compared with non-systematic convolutional codes or feedforward systematic convolutional codes using the same constraint length, this can support more user access. When the convolutional code is a non-systematic convolutional code or a feedforward systematic convolutional code, compared with the corresponding convolutional code with a constraint length of 7, a constraint length of 3 can support more user access. Using the channel coding method provided in this application embodiment can support more user access, or require a lower signal-to-noise ratio for the same number of users. It can meet the requirements of massive random access in the future Internet of Things, support more user data transmission rates, or meet the requirements of lower latency and higher reliability transmission.

[0119] like Figure 13 As shown in the illustration, this application provides an information encoding device applied to a first user equipment in a user equipment group, wherein the user equipment group includes N user equipments, and the first user equipment is one of the N user equipments, where N is an integer greater than 2; wherein the first user equipment includes a processor 300 and a transceiver 310, the transceiver 310 receiving and transmitting data under the control of the processor 300, and the processor 300 performing the following operations:

[0120] Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1;

[0121] The K information bits are encoded using systematic convolutional codes, non-systematic convolutional codes, or feedforward systematic convolutional codes.

[0122] In some embodiments of this application, the processor is also configured to perform the following operations:

[0123] The encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission. Time-frequency refers to both time and frequency, while space-time-frequency refers to space, time, and frequency; space generally refers to the antenna.

[0124] In some embodiments of this application, the processor is also configured to perform the following operations:

[0125] The encoded bits are modulated and spread, then mapped onto time-frequency or space-time-frequency resources for transmission. Time-frequency refers to both time and frequency, while space-time-frequency refers to space, time, and frequency; space generally refers to the antenna.

[0126] In some embodiments of this application, the processor is also configured to perform the following operations:

[0127] The encoded bits are sparsely modulated and then mapped onto time-frequency or space-time-frequency resources for transmission. Time-frequency refers to both time and frequency, while space-time-frequency refers to space, time, and frequency; space generally refers to the antenna.

[0128] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros.

[0129] In some embodiments of this application, the initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register of the convolutional code.

[0130] In some embodiments of this application, the processor is also configured to perform the following operations:

[0131] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

[0132] In some embodiments of this application, the processor is also configured to perform the following operations:

[0133] After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

[0134] In some embodiments of this application, the processor is also configured to perform the following operations:

[0135] Based on the simulation results of the number of active user equipment in the user equipment group in the additive white Gaussian noise channel, determine at least one of the following:

[0136] The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code;

[0137] The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0138] The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code;

[0139] Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

[0140] In some embodiments of this application, when the number of active user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is small;

[0141] When the number of activated user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is large.

[0142] In some embodiments of this application, the user equipment group includes the following user equipment:

[0143] User equipment in non-orthogonal multiple access schemes;

[0144] Alternatively, user equipment in a passive multiple access random access scheme.

[0145] In this embodiment of the application, when multiple user equipments reuse the same time-frequency or space-time-frequency resources for transmission, the first user equipment encodes the K information bits using a systematic convolutional code, a non-systematic convolutional code, or a feedforward systematic convolutional code to obtain an encoded sequence, and then transmits the obtained encoded sequence on the corresponding time-frequency or space-time-frequency resources. Using this encoding scheme can support more users' non-orthogonal multiplexing or random access, thereby improving the system transmission efficiency.

[0146] It should be noted that the first user equipment provided in the embodiments of this application is a user equipment capable of executing the above-described channel coding method. Therefore, all embodiments of the above-described channel coding method are applicable to this user equipment and can achieve the same or similar beneficial effects, which will not be repeated here.

[0147] This application also provides a first user equipment, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the various processes in the channel coding method embodiments described above and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0148] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the various processes described above in the channel coding method embodiments, achieving the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] This application also provides a computer program product, including computer instructions. When executed by a processor, the computer instructions implement the various processes of the channel coding method embodiments described above and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0150] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0151] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 A device for one or more processes and / or the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce a paper article including an instruction means, the instruction means being implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment, causing the computer or other programmable equipment to perform a series of operational steps to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0154] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A channel coding method, characterized in that, The method is executed by the first user equipment in a user equipment group, wherein the user equipment group comprises N user equipments, where N is an integer greater than 2; the method includes: Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1; The K information bits are encoded using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code; The first user equipment is one of the N user equipments.

2. The method according to claim 1, characterized in that, The method further includes: The encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission.

3. The method according to claim 1, characterized in that, The method further includes: The encoded bits are modulated and spread, then mapped onto time-frequency or space-time-frequency resources for transmission.

4. The method according to claim 1, characterized in that, The method further includes: The encoded bits are sparsely modulated and then mapped onto time-frequency or space-time-frequency resources for transmission.

5. The method according to any one of claims 1-4, characterized in that, The initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros.

6. The method according to any one of claims 1-4, characterized in that, The initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register in the convolutional code.

7. The method according to any one of claims 1-4, characterized in that, The method further includes: After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

8. The method according to any one of claims 1-4, characterized in that, The method further includes: After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

9. The method according to claim 5, characterized in that, The method further includes: Based on the simulation results of the number of active user equipment in the user equipment group in the additive white Gaussian noise channel, determine at least one of the following: The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code; The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code; Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

10. The method according to claim 9, characterized in that, When the number of activated user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is small. When the number of activated user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is large.

11. The method according to claim 1, characterized in that, The user equipment group includes the following user equipment: User equipment in non-orthogonal multiple access schemes; Alternatively, user equipment in a passive multiple access random access scheme.

12. A channel coding apparatus, applied to a first user equipment in a user equipment group, characterized in that, The user equipment group comprises N user equipments, where the first user equipment is one of the N user equipments, and N is an integer greater than 2; wherein, the channel coding device comprises a processor and a transceiver, the transceiver receiving and transmitting data under the control of the processor, and the processor performing the following operations: Obtain K information bits, and set the initial state of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; K is an integer greater than 1; The K information bits are encoded using systematic convolutional codes, non-systematic convolutional codes, or feedforward systematic convolutional codes.

13. The apparatus according to claim 12, characterized in that, The processor is also used to perform the following operations: The encoded bits are repeated, interleaved, modulated, and then mapped onto time-frequency or space-time-frequency resources for transmission.

14. The apparatus according to claim 12, characterized in that, The processor is also used to perform the following operations: The encoded bits are modulated and spread, then mapped onto time-frequency or space-time-frequency resources for transmission.

15. The apparatus according to claim 12, characterized in that, The processor is also used to perform the following operations: The encoded bits are sparsely modulated and then mapped onto time-frequency or space-time-frequency resources for transmission.

16. The apparatus according to any one of claims 12-15, characterized in that, The initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is all zeros.

17. The apparatus according to any one of claims 12-15, characterized in that, The initial state of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code is the first L bits or the last L bits of the information bits, where L is the number of a single generator polynomial register in the convolutional code.

18. The apparatus according to any one of claims 12-15, characterized in that, The processor is also used to perform the following operations: After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are truncated to obtain a tail-bitten systematic convolutional code, a tail-bitten non-systematic convolutional code, or a tail-bitten feedforward systematic convolutional code; where L is the number of single generator polynomial registers in the convolutional code.

19. The apparatus according to any one of claims 12-15, characterized in that, The processor is also used to perform the following operations: After encoding using the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code, the last L bits are not truncated.

20. The apparatus according to claim 16, characterized in that, The processor is also used to perform the following operations: Based on the simulation results of the number of active user equipment in the user equipment group in the additive white Gaussian noise channel, determine at least one of the following: The constraint length of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code; The generator polynomial of the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code; The initial state settings for the systematic convolutional code, the non-systematic convolutional code, or the feedforward systematic convolutional code; Whether the tail bits of the systematic convolutional code, non-systematic convolutional code, or feedforward systematic convolutional code are truncated.

21. The apparatus according to claim 20, characterized in that, When the number of activated user equipment in the user equipment group is large, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is small. When the number of activated user equipment in the user equipment group is small, the constraint length of the non-systematic convolutional code or feedforward systematic convolutional code is large.

22. The apparatus according to claim 12, characterized in that, The user equipment group includes the following user equipment: User equipment in non-orthogonal multiple access schemes; Alternatively, user equipment in a passive multiple access random access scheme.

23. A first user equipment, comprising a memory, a processor, and a program stored in the memory and executable on the processor; characterized in that, When the processor executes the program, it implements the channel coding method as described in any one of claims 1-11.

24. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the channel coding method as described in any one of claims 1-11.

25. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the channel coding method as described in any one of claims 1-11.

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