Short packet communication-oriented optimal rate segmentation method and system for improving reliability

Abstract

The invention provides an optimal rate segmentation method for improving reliability for short packet communication, which comprises the following steps of: S1, establishing a communication model based on rate segmentation multiple access, comprising a plurality of rate segmentation strategies, and representing the error probability of decoding a single stream by a user by utilizing a finite code length domain theory; s2, based on the decoding error probability of the user side, constructing an optimization problem of minimizing a maximum user weighted decoding error rate by taking power distribution and code length distribution as optimization variables; s3, introducing slack variables, code length integer field slack and Taylor approximation methods, converting non-convex constraints in the optimization problem into convex constraints, and constructing a convex constraint optimization problem; and S4, solving the convex constraint optimization problem by adopting an iterative algorithm based on continuous convex approximation to obtain an optimal power allocation scheme, a code length allocation scheme and a rate segmentation strategy. The method aims at minimizing the maximum decoding error probability of the user side, so that the reliability of a multi-user short packet communication scene is improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and in particular relates to a rate segmentation method and system for improving the reliability of short packet communication. Background Technology

[0002] With the further development of wireless communication, research on next-generation mobile communication technologies has received widespread attention. In the IMT-2030 (Global 6G Vision) recommendation completed by the International Telecommunication Union (ITU), the application scenarios of 6G focus on Massive Communication and Hyper-Reliable and Low-Latency Communication.

[0003] Consider a typical 6G application scenario: the Industrial Internet of Things (IIoT). On one hand, traditional multiple access methods struggle to support massive access demands due to the requirements of large-scale communication. Rate-splitting multiple access (RSMA), by segmenting the rate at the message transmitter, bridges various interference management strategies such as orthogonalization, treating interference as noise, and decoding interference, improving spectrum and energy efficiency and flexibly responding to different user loads and interference levels. On the other hand, for ultra-reliable low-latency communication, the data packets in IIoT are generally small, and available code length and time domain resources are limited. Traditional Shannon domain analysis achieves error-free transmission with infinite code lengths, but this cannot accurately model the instantaneous reliability of wireless communication and is insufficient to meet low-latency requirements. Therefore, short-packet communication is used for analysis within a finite code length domain.

[0004] Thanks to the research on finite code length field theory by Polyanskiy et al., the decoding error probability in short packet communication can be modeled relatively well. Shortening the code length can reduce communication latency, but it also increases the probability of transmission errors. Meanwhile, increasing the transmit power can improve the signal-to-noise ratio, thus reducing the probability of transmission errors, but increasing the power of a particular stream can also increase the interference level of that stream on other streams. Therefore, how to balance code length and transmit power to select the optimal rate segmentation strategy in short packet communication is of research significance. Summary of the Invention

[0005] For downlink communication scenarios where a single base station serves two users, and addressing the lack of highly reliable low-latency transmission solutions between the base station and the user end, this invention patent designs an optimal rate segmentation method and system for improving weighted reliability in multi-user short packet communication. The aim is to minimize the maximum user end decoding error probability, thereby improving the reliability of multi-user short packet communication scenarios.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: An optimal rate segmentation method for improving reliability in short packet communication is characterized by the following steps: Step S1: For the downlink communication scenario of a single base station serving two users, establish a communication model based on rate segmentation multiple access. The communication model includes multiple rate segmentation strategies and uses the finite code length field theory to characterize the error probability of a user decoding a single stream. Step S2: Based on the decoding error probability of the user terminal, construct an optimization problem to minimize the maximum user-weighted decoding error rate, with power allocation and code length allocation as optimization variables; Step S3: Introduce slack variables, code length integer field relaxation, and Taylor approximation method to transform the non-convex constraints in the optimization problem into convex constraints, and construct a convex constraint optimization problem; Step S4: Solve the convex constraint optimization problem using an iterative algorithm based on continuous convex approximation to obtain the optimal power allocation scheme, code length allocation scheme, and rate segmentation strategy.

[0007] Furthermore, the rate segmentation strategy in step S1 includes: Orthogonal Rate Segmentation Multiple Access Strategy (OMA-RS): User messages are segmented into public and private streams, and each stream uses orthogonal resource transmission. Multiple access is achieved by allocating different code lengths. Non-orthogonal rate segmentation multiple access strategy (NOMA-RS): Each stream shares code length resources, non-orthogonal transmission is achieved by allocating different power levels, and continuous interference cancellation decoding is used; Hybrid-RS (Hybrid Rate Segmentation Multiple Access) strategy: Public flows are allocated code length and power independently, while private flows share code length and are allocated power. Furthermore, the error probability of the user decoding a single stream in step S1 is:

[0008] in, The error probability of decoding a single stream for the user. It is the tail distribution function of the standard normal distribution, and its standard definition is... , The signal-to-interference-plus-noise ratio (SINR) or SNR of the data stream. The length of the encoded block used for the data stream, The coding rate corresponding to the data stream. Channel dispersion depends on the signal-to-noise ratio. This refers to the channel transmission capacity.

[0009] Furthermore, the optimization problem in step S2, which minimizes the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables, is as follows:

[0010] in, It is a set of three rate-division multiple access strategies. It is one of the strategies; It is a set of optimization variables for three strategies. It is an optimization variable for one of the strategies; Represents a set of users. It is the index of the users; A set representing a stream, It is the index of the stream; Indicates user The weights, including the weight of user 1. Weight of User 2 ; Indicates a certain strategy Next user The decoding error rate, including the user 1 decoding error rate under the OMA-RS strategy. Decoding error rate of User 2 Decoding error rate of User 1 under NOMA-RS strategy Decoding error rate of User 2 Decoding error rate of 1 user under Hybrid-RS strategy Decoding error rate of User 2 ; Indicates a certain strategy Next user Decoded stream Error rate, including user 1 decoding stream under OMA-RS strategy error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream error rate Including the user 1 decoding stream under the NOMA-RS strategy. error rate Decoding stream with user 2 error rate User 1 decoding stream error rate Decoding stream with user 2 error rate User 2 decoding stream error rate ; Including user 1 decoding stream under Hybrid-RS strategy error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream error rate ; This represents the threshold error probability.

[0011] Further, step S3 includes: Step S3.1 The optimization problem of minimizing the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables is broken down into three sub-problems; Step S3.2 introduces slack variables to transform the min max problem into a min problem; Step S3.3 Based on the Taylor approximation method, the non-convex constraints in the optimization problem are transformed into convex constraints, and a convex constraint optimization problem is constructed.

[0012] Furthermore, the three sub-problems are as follows:

[0013]

[0014]

[0015] in, It is the flow allocated under the OMA-RS strategy The length of the code, Represents their set, It is the maximum available code length; It is allocated to the flow under the NOMA-RS strategy. power, Represents their set, This is the maximum transmission power of the base station; It is the allocation of streams under the Hybrid-RS strategy The length of the code, Represents their set, It is the Hybrid-RS strategy that allocates to the upper layer flow and lower layer flow power, This represents their set.

[0016] Furthermore, by introducing slack variables, the min max problem is transformed into a min problem:

[0017]

[0018]

[0019] in, The introduced slack variables transform the problem into a min problem; simultaneously, the code length variables in (ORS-P1) and (HRS-P1) are... Relax from the integer field to the real number field.

[0020] Furthermore, the convex constraint optimization problem is:

[0021]

[0022]

[0023] in, It is a Taylor approximation of the error rate; for The result of relaxing from the integer field to the real field is obtained by comparing adjacent integer solutions. :

[0024] in, The real code length solutions to problems (ORS-P2) and (HRS-P2) are... It is the lower bound integer of the corresponding real number solution. It is the upper bound of the corresponding real number solution. include and , which are the error rate slack variables for problems (ORS-P2) and (HRS-P2) when the code length is an integer, respectively.

[0025] Further, step S4 includes: For each strategy, the required parameters are first initialized, and then iterative solutions are performed until the convergence tolerance is reached to obtain real solutions of the same code length. The final integer solution is obtained by comparing adjacent integers.

[0026] On the other hand, the present invention provides an optimal rate segmentation system for improving reliability in short packet communication, comprising: Communication model construction module: It is used to establish a communication model based on rate segmentation multiple access for downlink communication scenarios where a single base station serves two users. The communication model includes multiple rate segmentation strategies and uses the finite code length field theory to characterize the error probability of a user decoding a single stream. Optimization Problem Construction Module: It is used to construct an optimization problem based on the decoding error probability of the user terminal, which minimizes the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables; Convex constraint optimization module: It is used to introduce relaxation variables, code length integer field relaxation and Taylor approximation method to transform the non-convex constraints in the optimization problem into convex constraints and construct a convex constraint optimization problem; The solution module is used to solve the convex constraint optimization problem using an iterative algorithm based on continuous convex approximation, and obtain the optimal power allocation scheme, code length allocation scheme and rate segmentation strategy.

[0027] Compared with the prior art, the present invention has the following beneficial effects: This invention provides an optimal rate segmentation method and system for improving weighted reliability in multi-user short packet communication, which satisfies the requirements of high reliability and low latency in communication. It also proposes an efficient method to reduce computational complexity and has extremely high scalability and a wide range of practical application scenarios. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This invention proposes a downlink short packet communication scenario for serving two users with a single antenna. Figure 2 The orientation proposed in this invention Figure 1 A schematic diagram of different rate segmentation multiple access strategies in a communication scenario; Figure 3 A flowchart of the efficient solution method for optimal power and code length allocation proposed; Figure 4 To verify the convergence of the proposed algorithm; Figure 5 This diagram illustrates the optimal RSMA strategy under different channel conditions. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] Example 1 The invention will now be further described with reference to the accompanying drawings.

[0032] Figure 1 This invention proposes an optimal rate segmentation method and system for improving weighted reliability in short packet communication. The proposed downlink communication scenario includes one single-antenna base station and two single-antenna users, with the base station sending messages to both users. It is a public message that both users need. These are the private messages needed by each of the two users. The base station's transmit power is limited to 4.0W, and the maximum usable coded block length is 500 bits.

[0033] Figure 2 This invention is aimed at Figure 1 Three RSMA strategies are proposed for this scenario. The base station splits the messages between two users into a public message stream. and private message stream Transmission is then performed. The first strategy is based on Orthogonal Multiple Access RSMA (OMA-RS), where all three streams use the maximum transmit power. Use code length respectively Transmission. The second strategy is based on non-orthogonal multiple access RSMA (NOMA-RS), where all three streams use the maximum code length. At different power levels Uplink transmission. The third strategy is based on hybrid orthogonal-nonorthogonal RSMA (Hybrid-RS). Power used in the upper layer and maximum code length transmission , Power is used in the lower layer. They share the same code length for transmission.

[0034] Figure 3 This is the algorithm flow for solving the efficient code length and power allocation proposed in this invention. First, regarding... Figure 2Three RSMA strategies are modeled, and weighted reliability is characterized based on finite code length field theory. Then, the optimal RSMA strategy selection problem with power / code length variables is constructed with the goal of improving weighted reliability. Subsequently, by splitting the problem into subproblems, introducing relaxation variables to transform the min-max problem into a min problem, relaxing the code length from the integer field to the real field, and using Taylor approximation, it is finally transformed into three convex subproblems, which are solved using an algorithm based on continuous convex approximation.

[0035] Figure 4 The figure shows the convergence performance of the proposed algorithm. As can be seen, the algorithms under the three RSMA strategies converge to a result close to the global search result within 10 iterations. This verifies the convergence and effectiveness of the proposed algorithm.

[0036] Figure 5 This diagram illustrates the optimal RSMA strategy selection under different channel conditions. As shown in the diagram, When approaching the target, the OMA-RS strategy performs optimally; When the gap widens, the NOMA-RS strategy performs best; while in other regions, the Hybrid-RS strategy outperforms both OMA-RS and NOMA-RS.

[0037] Preferably, the two user indices in step S1 are The messages needed by users 1 and 2 are respectively .Will Divide as shown in the diagram, where It's a public message and needs to be decoded by two users. and These are private messages, decoded separately by users 1 and 2. The segmented messages are then encoded into three data streams. . Specifically, Includes public messages , Contains user 1's private message , Contains user 2's private message Consider three data streams, as shown in the diagram, with different resource allocation methods. The first method, OMA-RS, uses an orthogonal approach, with the three data streams using different code lengths and transmitting at the same power level. The second method, NOMA-RS, uses a non-orthogonal approach, with the three data streams sharing a code length but transmitting at different power levels. The third method, Hybrid-RS, uses a hybrid orthogonal-non-orthogonal approach. Transmission at the upper power level, stream and They share the same code length for transmission at the lower power level. The maximum transmit power of the base station is... The maximum usable code length is Base stations and users The channel between them is , The mean is 0 and the variance is Additive white Gaussian noise. The set of three strategies is defined as follows: ,index .

[0038] Consider the first strategy, OMA-RS. The code lengths allocated to the three streams are as follows: , and Receiver user Received signal for:

[0039] user Simultaneously decode stream Signal-to-noise ratio (SNR) during decoding for:

[0040] According to the finite code length field theory, the error probability of a user decoding a single stream can be expressed as:

[0041] in, The error probability of decoding a single stream for the user. It is the tail distribution function of the standard normal distribution, and its standard definition is... , The signal-to-interference-plus-noise ratio (SINR) or SNR of the data stream. The length of the encoded block used for the data stream, The coding rate corresponding to the data stream. Channel dispersion depends on the signal-to-noise ratio. Let be the channel transmission capacity, and let be the capacity of the Shannon channel. .

[0042] Under the OMA-RS strategy, users All streams containing the user's message must be correctly decoded, so the user's decoding error rate can be expressed as:

[0043] in, , These are the decoding error rates of User 1 under the OMA-RS strategy. Decoding error rate for User 2; User Decoded stream Error rate, It is the user 1 decoding stream under the OMA-RS strategy. Error rate, It is the user 2 decoding stream under the OMA-RS strategy. Error rate, , , , These represent the decoding error rate of User 1 under the OMA-RS strategy, the decoding error rate of User 2 under the OMA-RS strategy, and the decoding stream of User 1 under the OMA-RS strategy. Error rate, User 2 decoding stream under OMA-RS strategy Error rate.

[0044] Consider the second strategy, NOMA-RS. The power allocated to the three streams is as follows: , and Receiver user The received signal is :

[0045] in, It is the base station and the user Channel coefficients between The mean is 0 and the variance is Additive white Gaussian noise, A set representing a stream, It is the index of the stream; The user uses Continuous Interference Cancellation (SIC) for decoding. Since SIC generally prioritizes decoding the stronger signal, the signal strength order of the three streams is assumed to be... Therefore, the SIC decoding order is as follows: .

[0046] First, users Will All are considered interference, decode SINR is:

[0047] decoding Then, the signal is removed from the received signal via SIC. Then Treat it as interference, decode The signal-to-interference-plus-noise ratio (SINR) of the data stream is:

[0048] decoding Afterwards, User 1 has obtained the necessary information. User 2 still needs to decode it. Removed from the remaining received signal via SIC. Finally, decode. The signal-to-interference-plus-noise ratio (SNR) is:

[0049] Under the NOMA-RS strategy, decoding errors can be categorized into three possibilities: (1) decoding stream errors. Error; (2) Decoding stream Correct, but the decoded stream Error; (3) Decoding stream Correct, but the decoded stream Error. Due to user 1's message encoding in the stream. In the middle, only decoding is required. User 2's message encoding in the stream In the middle, decoding is required. The user's decoding error rate can be expressed as:

[0050] in, , This represents the decoding error rate of user 1 and the decoding error rate of user 2 under the NOMA-RS strategy; User Decoded stream Error rate, User Decoded stream Error rate, It is a user 2 decoded stream Error rate. , This represents the user 1 decoding stream under the NOMA-RS strategy. Error rate and user 2 decoding stream Error rate; , This represents the user 1 decoding stream under the NOMA-RS strategy. Error rate and user 2 decoding stream decoding stream Error rate; This indicates the user 2 decoding stream under the NOMA-RS strategy. error rate Consider the third strategy, Hybrid-RS. Assign it to the upper layer flow. The power is Assigned to the lower layer flow and The power is Meanwhile, the lower laminar flow and Shared code length, the allocated code lengths are as follows: and Receiver user The received signal is:

[0051] The user uses SIC for decoding. Assume the signal strength order is as follows: Therefore, the SIC decoding order is as follows: .

[0052] First, users Will All are considered interference, decode The signal-to-interference-plus-noise ratio (SINR) of the data stream is:

[0053] decoding Then, the signal is removed from the received signal via SIC. Decode simultaneously The signal-to-interference-plus-noise ratio (SNR) of the data stream is:

[0054] Under the Hybrid-RS strategy, decoding errors can be categorized into two types: (1) decoding stream errors. Error; (2) Decoding stream Correct, but the decoded stream Error or decoded stream Error. Due to user 1's message encoding in the stream. In the stream, user 2's message encoding is... In this context, the user's decoding error rate can be expressed as:

[0055] in, User Decoded stream Error rate, It is the user 1 decoding stream Error rate, It is a user 2 decoded stream Error rate. , These are the decoding error rates for user 1 and user 2 under the Hybrid-RS strategy, respectively. , These are the decoding streams for User 1 under the Hybrid-RS strategy. Error rate and user 2 decoding stream Error rate; , These are the decoding streams for User 1 under the Hybrid-RS strategy. Error rate and user 2 decoding stream Error rate.

[0056] Preferably, step S2 aims to achieve the optimal weighted reliability under a finite code length domain, meeting the requirements of high reliability and low latency. Specifically, it selects the optimal RSMA strategy to minimize the overall system error probability. To reflect the different service quality requirements of various users, a non-negative reliability weighting factor is introduced. and ,satisfy Accordingly, the user's weighted decoding error rate is characterized as follows: The reliability of a system can be measured by the maximum user-weighted decoding error rate, i.e. For each , Includes corresponding optimization variables and :

[0057] express The feasible region. Then the corresponding optimization problem of finding the optimal strategy to minimize the maximum user-weighted decoding error rate of the system can be expressed as:

[0058] Among these, constraints guarantee the quality of service for each stream. It is the set error rate threshold. It is a set of three rate-division multiple access strategies. It is one of the strategies; It is a set of optimization variables for three strategies. It is an optimization variable for one of the strategies; Represents a set of users. It is the index of the users; A set representing a stream, It is the index of the stream; Indicates user The weights, including the weight of user 1. Weight of User 2 ; Indicates a certain strategy Next user The decoding error rate, including the user 1 decoding error rate under the OMA-RS strategy. Decoding error rate of User 2 Decoding error rate of User 1 under NOMA-RS strategy Decoding error rate of User 2 Decoding error rate of 1 user under Hybrid-RS strategy Decoding error rate of User 2 ; Indicates a certain strategy Next user Decoded stream Error rate, including user 1 decoding stream under OMA-RS strategy error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream error rate Including the user 1 decoding stream under the NOMA-RS strategy. error rate Decoding stream with user 2 error rate User 1 decoding stream error rate Decoding stream with user 2 error rate User 2 decoding stream error rate ; Including user 1 decoding stream under Hybrid-RS strategy error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream error rate ; This represents the threshold error probability.

[0059] Preferably, the optimization problem in step S2 is not convex. First, the overall problem (OP) is broken down into three sub-problems:

[0060]

[0061]

[0062] in, It is the flow allocated under the OMA-RS strategy The length of the code, Represents their set, It is the maximum available code length; It is allocated to the flow under the NOMA-RS strategy. power, Represents their set, This is the maximum transmission power of the base station; It is the allocation of streams under the Hybrid-RS strategy The length of the code, Represents their set, It is the Hybrid-RS strategy that allocates to the upper layer flow and lower layer flow power, This represents their set.

[0063] Introducing slack variables The min max problem is transformed into a min problem. Meanwhile, since the code length is an integer, it is relaxed to the real number domain:

[0064]

[0065]

[0066] Existing technologies and research have proven the following theorem: Theorem 1: In short code transmission, the error rate is jointly convex with respect to both code length and transmit power, i.e. right Convex, if signal-to-noise ratio .

[0067] Theorem 1 shows that the transmission error rate of a single stream is jointly convex with respect to code length and transmit power. Observe the constraints in the transformed subproblem. This item actually contains the error rate of a single data stream. The product of . Therefore, it is not convex. To address this, a first-order Taylor approximation is performed in the nth iteration:

[0068] thus, It can be represented as:

[0069]

[0070] Through these approximations, the non-convex terms in the constraints are transformed into convex ones. The final optimization problem is:

[0071]

[0072]

[0073] in, It is a Taylor approximation of the error rate, and the specific approximation is as follows:

[0074]

[0075] Therefore, the subproblem is already a convex problem, which can be solved using convex optimization tools.

[0076] Corollary 1: Problems (ORS-P2), (NRS-P2), and (HRS-P2) are all convex.

[0077] Proof: Taking problem (ORS-P2) as an example, according to Taylor approximation,

[0078] Among them, if but ,like but According to Theorem 1, It is convex. Due to the weights , It is convex, meaning the objective function is convex. It's easy to further deduce that all constraints are also convex. Therefore, problem (ORS-P2) is convex. The analysis of problems (NRS-P2) and (HRS-P2) is similar to that of (ORS-P2), and convexity can also be derived.

[0079] It can be deduced that the three subproblems constructed in the end are all convex and can be solved using standard convex optimization tools. For each strategy, the required parameters are first initialized, and then iterative solutions are performed. Taking problem (ORS-P2) as an example, in the nth iteration, the variable results of the (n-1)th iteration are used for solving until convergence tolerance is reached. Simultaneously, the code length variable in problems (ORS-P2) and (HRS-P2) is relaxed from integers to the real number domain. After obtaining the real number solution for the code length, the final integer solution is obtained by comparing adjacent integers.

[0080] in, The real code length solutions to problems (ORS-P2) and (HRS-P2) are... It is the lower bound integer of the corresponding real number solution. It is the upper bound of the corresponding real number solution. include and , which are the error rate slack variables for problems (ORS-P2) and (HRS-P2) when the code length is an integer, respectively.

[0081] The specific algorithm is as follows: Step 1: Traverse all strategies: For each predefined resource allocation strategy (such as ORS / NRS, etc.), execute the following optimization process.

[0082] Step 2 Initialize parameters: Initialize the number of iterations, slack variables, code length, power, and convergence tolerance: Set the initial state: Step 3: Begin the iterative loop and repeat the following sub-steps until convergence: Solving the ORS-P2 subproblem: using Solve problem ORS-P2 to obtain Update the slack variables and power based on the solution of ORS-P2.

[0083] Solving the NRS-P2 subproblem: using Solve problem NRS-P2 to obtain .

[0084] use Solving the optimization problem (HRS-P2), we obtain... ; renew ; like If the iteration ends, then repeat Step 3-9.

[0085] like ,but

[0086] The obtained real number Based on the integer optimization problem, the final integer solution is obtained. and the integer solutions corresponding to ; To obtain the optimal strategy

[0087] Example 2 This embodiment provides an optimal rate segmentation system for improving weighted reliability in short packet communication, characterized by comprising: Communication model construction module: It is used to establish a communication model based on rate segmentation multiple access for downlink communication scenarios where a single base station serves two users. The communication model includes multiple rate segmentation strategies and uses the finite code length field theory to characterize the error probability of a user decoding a single stream. Optimization Problem Construction Module: It is used to construct an optimization problem based on the decoding error probability of the user terminal, which minimizes the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables; Convex constraint optimization module: It is used to introduce relaxation variables, code length integer field relaxation and Taylor approximation method to transform the non-convex constraints in the optimization problem into convex constraints and construct a convex constraint optimization problem; The solution module is used to solve the convex constraint optimization problem using an iterative algorithm based on continuous convex approximation, and obtain the optimal power allocation scheme, code length allocation scheme and rate segmentation strategy.

[0088] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0089] It should be understood that the above description of the preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. It is neither necessary nor possible to exhaustively describe all possible implementations. Those skilled in the art, guided by this invention, can make substitutions or modifications without departing from the scope of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. An optimal rate segmentation method for improving reliability in short packet communication, characterized in that, Includes the following steps: Step S1: For the downlink communication scenario of a single base station serving two users, establish a communication model based on rate segmentation multiple access. The communication model includes multiple rate segmentation strategies and uses the finite code length field theory to characterize the error probability of a user decoding a single stream. Step S2: Based on the decoding error probability of the user terminal, construct an optimization problem to minimize the maximum user-weighted decoding error rate, with power allocation and code length allocation as optimization variables; Step S3: Introduce slack variables, code length integer field relaxation, and Taylor approximation method to transform the non-convex constraints in the optimization problem into convex constraints, and construct a convex constraint optimization problem; Step S4: Solve the convex constraint optimization problem using an iterative algorithm based on continuous convex approximation to obtain the optimal power allocation scheme, code length allocation scheme, and rate segmentation strategy.

2. The optimal rate segmentation method for improving reliability in short packet communication according to claim 1, characterized in that, The rate segmentation strategy in step S1 includes: Orthogonal Rate Segmentation Multiple Access Strategy (OMA-RS): User messages are segmented into public and private streams, and each stream uses orthogonal resource transmission. Multiple access is achieved by allocating different code lengths. Non-orthogonal rate segmentation multiple access strategy (NOMA-RS): Each stream shares code length resources, non-orthogonal transmission is achieved by allocating different power levels, and continuous interference cancellation decoding is used; Hybrid-RS (Hybrid Rate Segmentation Multiple Access) strategy: Public flows are allocated code length and power independently, while private flows share code length and are allocated power.

3. The optimal rate segmentation method for improving reliability in short packet communication according to claim 2, characterized in that, The error probability of the user decoding a single stream in step S1 is: in, The error probability of decoding a single stream for the user. It is the tail distribution function of the standard normal distribution, and its standard definition is... , The signal-to-interference-plus-noise ratio (SINR) or SNR of the data stream. The length of the encoded block used for the data stream, The coding rate corresponding to the data stream. Channel dispersion depends on the signal-to-noise ratio. This refers to the channel transmission capacity.

4. The optimal rate segmentation method for improving reliability in short packet communication according to claim 3, characterized in that, The optimization problem in step S2, which uses power allocation and code length allocation as optimization variables to minimize the maximum user-weighted decoding error rate, is as follows: in, It is a set of three rate-division multiple access strategies. It is one of the strategies; It is a set of optimization variables for three strategies. It is an optimization variable for one of the strategies; Represents a set of users. It is the index of the users; A set representing a stream, It is the index of the stream; Indicates user The weights, including the weight of user 1. Weight of User 2 ; Indicates a certain strategy Next user The decoding error rate, including the user 1 decoding error rate under the OMA-RS strategy. Decoding error rate of User 2 Decoding error rate of User 1 under NOMA-RS strategy Decoding error rate of User 2 Decoding error rate of 1 user under Hybrid-RS strategy Decoding error rate of User 2 ; Indicates a certain strategy Next user Decoded stream Error rate, including user 1 decoding stream under OMA-RS strategy error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream error rate Including the user 1 decoding stream under the NOMA-RS strategy. error rate Decoding stream with user 2 error rate User 1 decoding stream Error rate Decoding stream with user 2 error rate User 2 decoding stream Error rate ; Including user 1 decoding stream under Hybrid-RS strategy Error rate Decoding stream with user 2 error rate User 1 decoding stream error rate User 2 decoding stream Error rate ; This represents the threshold error probability.

5. The optimal rate segmentation method for improving reliability in short packet communication according to claim 4, characterized in that, Step S3 includes: Step S3.1 The optimization problem of minimizing the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables is broken down into three sub-problems; Step S3.2 introduces slack variables to transform the min max problem into a min problem; Step S3.3 Based on the Taylor approximation method, the non-convex constraints in the optimization problem are transformed into convex constraints, and a convex constraint optimization problem is constructed.

6. The optimal rate segmentation method for improving reliability in short packet communication according to claim 5, characterized in that, The three sub-problems are as follows: in, It is the flow allocated under the OMA-RS strategy The length of the code, Represents their set, It is the maximum available code length; It is allocated to the flow under the NOMA-RS strategy. power, Represents their set, This is the maximum transmission power of the base station; It is the allocation of streams under the Hybrid-RS strategy The length of the code, Represents their set, It is the Hybrid-RS strategy that allocates to the upper layer flow and lower layer flow power, This represents their set.

7. The optimal rate segmentation method for improving reliability in short packet communication according to claim 6, characterized in that, Introducing slack variables transforms the min max problem into a min problem: in, The introduced slack variables transform the problem into a min problem; simultaneously, the code length variables in (ORS-P1) and (HRS-P1) are... Relax from the integer field to the real number field.

8. The optimal rate segmentation method for improving reliability in short packet communication according to claim 6, characterized in that, The convex constraint optimization problem is: in, It is a Taylor approximation of the error rate; for The result of relaxing from the integer field to the real field is obtained by comparing adjacent integer solutions. : The real code length solutions to problems (ORS-P2) and (HRS-P2) are... It is the lower bound integer of the corresponding real number solution. It is the upper bound of the corresponding real number solution. include and , which are the error rate slack variables for problems (ORS-P2) and (HRS-P2) when the code length is an integer, respectively.

9. The optimal rate segmentation method for improving reliability in short packet communication according to claim 6, characterized in that, Step S4 includes: For each strategy, the required parameters are first initialized, and then iterative solutions are performed until the convergence tolerance is reached to obtain real solutions of the same code length. The final integer solution is obtained by comparing adjacent integers.

10. An optimal rate segmentation system for improving reliability in short packet communication, characterized in that, include: Communication model construction module: It is used to establish a communication model based on rate segmentation multiple access for downlink communication scenarios where a single base station serves two users. The communication model includes multiple rate segmentation strategies and uses the finite code length field theory to characterize the error probability of a user decoding a single stream. Optimization Problem Construction Module: It is used to construct an optimization problem based on the decoding error probability of the user terminal, which minimizes the maximum user-weighted decoding error rate with power allocation and code length allocation as optimization variables; Convex constraint optimization module: It is used to introduce relaxation variables, code length integer field relaxation and Taylor approximation method to transform the non-convex constraints in the optimization problem into convex constraints and construct a convex constraint optimization problem; The solution module is used to solve the convex constraint optimization problem using an iterative algorithm based on continuous convex approximation, and obtain the optimal power allocation scheme, code length allocation scheme and rate segmentation strategy. The optimal rate segmentation system for improving reliability in short packet communication is used to perform the steps in the optimal rate segmentation method for improving reliability in short packet communication as described in any one of claims 1-9.

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