Method for power allocation optimization of cell-free radio access network URLLC

Abstract

The application provides a power distribution optimization method for a cell-free radio access network URLLC, relates to a cell-free flexible duplex network architecture and a quantum intelligent optimization algorithm in the field of wireless communication, and the method comprises the following steps: a new type of cell-free radio access network (Cell-Free Radio Access Network-NAFD) suitable for a 6G application scene is proposed according to existing cell-free massive MIMO technology and network assisted full duplex (Network-Assisted Full-Duplex, NAFD) technology, an URLLC spectrum efficiency progressive expression method is given based on a ZF transceiver scheme, and the system performance under the constraints of transmission power and URLLC transmission rate is optimized in combination with a quantum backtracking search algorithm (Quantum-inspired backtracking search algorithm, QBSA). The application proposes a new type of user-centered cell-free flexible duplex system URLLC spectrum efficiency closed-form expression method, and the URLLC-QBSA intelligent optimization scheme based on the architecture has low calculation complexity, high convergence speed and ideal iteration performance.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically a power allocation optimization method for URLLC (Ultra-Limited Wireless Access Network). Background Technology

[0002] With the rapid development of electronic information technology and computer networks in the 21st century, the demand for mobile broadband services with faster transmission rates and wider coverage has prompted academia and industry to continuously explore new ways to break through the limitations of existing standards. Massive MIMO is one of the key technologies of 5G, with its large-scale antenna arrays providing high beamforming gain and spatial multiplexing gain. However, with the increasing demands of emerging application scenarios such as autonomous driving, the Industrial Internet, and telemedicine, 5G systems can no longer meet the needs of rapidly developing wireless communication applications. Therefore, high hopes are placed on the performance indicators and new application solutions for 6G wireless communication systems. The IMT-2023 (6G) Promotion Group of the Ministry of Industry and Information Technology proposed that 6G transmission technology indicators should reach peak rates in the Tbps range and system spectral efficiency in the Kbps / Hz range. This places high demands on the development of key technologies in the field of wireless communication and its deep integration with various industries.

[0003] Distributed massive MIMO (D-mMIMO) is one of the key technologies in wireless communication systems. When access points (APs) deployed in a system provide cooperative and consistent services to users, the cell boundary effect in traditional cellular networks is eliminated as the number of APs increases, forming a cellular-free mobile communication system. This significantly improves both network capacity and performance. Distributed transceiver schemes using cooperative transmission not only reduce the complexity of cellular-free MIMO systems but also enable the sustainable expansion of users and cooperating APs, forming Cell-Free Massive MIMO (CF-mMIMO) systems. CF-mMIMO improves spectral efficiency and enhances system robustness through a trade-off between diversity and multiplexing techniques, thus supporting Ultra-Reliable and Low-Latency Communications (URLLC) transmission. Implementation methods include fully centralized processing, dynamic cooperative processing, and fully distributed processing. Centralized processing can achieve optimal performance, but it is difficult to support large-scale scaling; dynamic collaborative processing can approach the system performance of centralized processing, but its requirements for signaling transmission and data link interaction are too stringent; fully distributed processing has low complexity and high scalability, but the number of access points required is far greater than the number of users, and the fronthaul overhead is large. Therefore, in the design and technical research of 6G cellular access network architecture, a trade-off between complexity and system performance needs to be considered. Summary of the Invention

[0004] Technical problem: Propose a non-cellular flexible duplex system architecture suitable for 6G access networks, and give an optimization method for power allocation in URLLC of non-cellular radio access networks.

[0005] Technical Solution: This invention provides a power allocation optimization method for URLLC (URLLC) in non-cellular wireless access networks, achieving a signal transmission scheme for scalable massive MIMO systems with low implementation complexity and good performance indicators. This invention is implemented as follows:

[0006] A closed-form representation method for URLLC spectral efficiency in non-cellular wireless access networks includes the following steps:

[0007] Step 1: Using a ZF receiver and precoding scheme, derive closed-form expressions for the signal-to-interference-plus-noise ratio (SIR) of the uplink and downlink of URLLC (Ultra-Reliable Low-Latency Transmission) based on the random matrix method:

[0008]

[0009]

[0010] The conventions for symbols in expressions are described below:

[0011] Tr indicates finding the trace of the matrix, where (l,i) and (m,q) represent the pilot number and user number of the uplink UE and downlink UE, respectively, where UE stands for "User Equipment"; E UL and E DL These represent the number of uplink EDUs and downlink EDUs, respectively. u and r represent the indices of the EDUs; here, EDU stands for "Edge Distributed Processing Unit". K UL and K DL These represent the number of uplink and downlink users, respectively. M represents the number of antennas in each AP. Here, AP is an abbreviation for "wireless access point". p represents the transmit power of the uplink UE(l,i). DL,m,q,r P represents the AP power control factor of the downlink UE(m,q) in the r-th downlink EDU. DL,r This is the power control factor matrix for the downlink AP. Let M represent the noise variance of the downlink data channel, and λ represent the number of antennas. IUI This represents the large-scale fading factor among UEs; Represents the unit selection vector for the uplink UE(l,i);

[0012] Based on the above symbolic definitions, the matrices appearing in the expressions are defined as follows:

[0013] express The closed-form derivation results, This represents a complex matrix, where the superscript indicates the dimension. This represents the channel estimation matrix calculated at the u-th uplink EDU. This indicates its conjugate transpose form;

[0014] express The closed-form derivation result, where cov represents the covariance matrix operator. This represents the channel estimation error matrix calculated at the u-th uplink EDU. W represents the DL-UL interference matrix between the r-th downlink EDU and the u-th uplink EDU. r Let x represent the precoding matrix calculated for the r-th downlink EDU. DL,r Let x represent the downlink transmission symbol vector of the r-th downlink EDU. UL Indicates the uplink transmitted symbol vector;

[0015] Tr(P DL,r Φ DL,m,q,r ) item represents The closed-form derivation results, in which Let represent the channel estimation error vector between the r-th downlink EDU and the downlink UE(l,i). This indicates its conjugate transpose.

[0016] Step 2: Based on the closed-form expression for signal-to-interference-plus-noise ratio proposed in Step 1, a closed-form expression for the rate of URLLC ultra-reliable low-latency transmission is given, and based on this, a closed-form expression for the total spectral efficiency of the CF-RAN-NAFD cellular wireless access network system is given:

[0017]

[0018]

[0019]

[0020] Among them, SE URLLC URLLC stands for Ultra-Reliable Low-Latency Spectrum Efficiency. UL,l,i and R DL,m,q These represent the corresponding URLLC (Ultra-Reliable Low Latency Communication) uplink and downlink transmission rates, respectively. UL,l,i and γ DL,m,q Indicates its SINR value, a UL and a DLQ represents the bandwidth and rate weighting factor, respectively, and B and T represent the bandwidth and duration of URLLC (Ultra-Reliable Low-Latency Transmission). -1 (·) denotes the inverse function of the Gaussian function, ε UL,l,i Let ε be the decoding error probability of the uplink UE(l,i). DL,m,q Let be the decoding error probability of the downlink UE(m,q).

[0021] The power allocation optimization method for URLLC (Ultra-Limited Radio Access Network) includes the following steps:

[0022] Step A: Based on the closed-form expression of the total spectral efficiency of the CF-RAN-NAFD cellular wireless access network system, propose the power allocation optimization problem for URLLC ultra-reliable low-latency transmission under power allocation and rate control constraints:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] in, Represents any object. This represents the transmit power of user (l,i). Indicates the maximum power. and w represents the minimum URLLC (Ultra-Reliable Low Latency Communication) transmission rate. m,q,n,r Let represent the precoding vector at the nth AP in the rth downlink EDU, with user index (m, q);

[0029] Step B: Construct the fitness function for each quantum individual in the QBSA intelligent optimization algorithm, where Representing the measurement state of any quantum entity:

[0030]

[0031] Among them, a UL,l,i and a DL,m,q These represent the rate weighting factors for uplink UE(l,i) and downlink UE(m,q), respectively.

[0032] Step C: Input the parameters of the CF-RAN-NAFD cellular wireless access network system, initialize the system parameters and QBSA history memory set, and set the initial iteration number; in the first iteration, randomly generate quantum individuals, calculate the fitness value of each quantum individual according to the fitness function, and obtain the globally optimal measurement state.

[0033] Step D: Update the historical memory set, generate new quantum individuals through evolution and crossover strategies, calculate the corresponding measurement state, calculate the fitness value of the newly generated quantum individuals, iteratively update the quantum individuals and corresponding measurement states according to the greedy mechanism, obtain the global optimal measurement state, and update the iteration count; if the maximum number of iterations has not been reached, continue to execute Step D, otherwise execute Step E;

[0034] Step E: Reaching the maximum number of iterations or algorithm convergence, i.e., obtaining the globally optimal measurement state, is the result of URLLC ultra-reliable low-latency transmission power control and rate constraint optimization. The output is:

[0035]

[0036]

[0037] in, SE represents the transmit power control result for the nth URLLC uplink user. max This is for the optimized URLLC spectral efficiency.

[0038] Beneficial effects: The novel CF-RAN-NAFD noncellular wireless access network system architecture proposed in this invention combines the technical advantages of noncellular access networks and flexible duplex, achieving a balance between system complexity and performance; the URLLC spectral efficiency closed-form expression method proposed in this invention is highly close to the performance of Monte Carlo simulation, and the quantum backtracking search intelligent optimization scheme proposed based on this has good convergence speed and performance. Attached Figure Description

[0039] To more clearly illustrate the technical solutions mentioned in the embodiments of the present invention, the accompanying drawings required in the description of this embodiment are introduced below. These drawings are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0040] Figure 1 This is the CF-RAN-NAFD non-cellular wireless access network system architecture proposed in this embodiment of the invention;

[0041] Figure 2 and Figure 3These are the theoretical derivation of the asymptotic expression for the URLLC spectral efficiency in this embodiment of the invention and the simulation results of Monte Carlo simulation.

[0042] Figure 4 These are simulation results of the URLLC-QBSA intelligent optimization scheme in the embodiments of the present invention;

[0043] Figure 5 This is the URLLC performance optimization process based on the QBSA intelligent algorithm in this embodiment of the invention. Detailed Implementation

[0044] According to the claims, the technical solutions in the embodiments of the present invention will be described in full and clearly below. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are protected by the present invention.

[0045] Example 1:

[0046] This embodiment combines noncellular access network and NAFD technology to propose a novel CF-RAN-NAFD noncellular radio access network system architecture, and provides a closed-form expression method for URLLC spectral efficiency under this architecture. (Appendix) Figure 1 For system architecture.

[0047] CF-RAN-NAFD system model

[0048] Appendix Figure 1 A schematic diagram of the CF-RAN-NAFD cellular wireless access network system architecture is presented. In this architecture, uplink and downlink operate simultaneously on the same frequency, and the UCDU is responsible for determining the slot structure for each AP. When DL-to-UL interference exists, the UCDU and EDU share interference information and utilize channel state information (CSI) between APs for cancellation within the EDU. UL-to-DL interference can be reduced through multi-user scheduling of the UCDU. With the help of NAFD technology, co-frequency co-time full-duplexing (CCFD), traditional time-division duplexing, and frequency-division duplexing modes can all be configured within the system.

[0049] URLLC transmission rate closed-form representation method

[0050] In the CF-RAN-NAFD cellular wireless access network system, the AP is responsible for signal reception / transmission and analog-to-digital conversion, the EDU is responsible for channel estimation and multi-user detection / multi-user joint precoding in baseband signal processing, and the UCDU is responsible for combining signals from the EDU. (l,i) and (m,q) are defined as the pilot sequence number and user sequence number of the uplink UE and downlink UE, respectively. The URLLC transmission rates of uplink UE (l,i) and uplink UE (m,q) are represented by SINR as follows:

[0051]

[0052]

[0053] This gives the expression for the total spectral efficiency of URLLC:

[0054]

[0055] R UL,l,i and R DL,m,q These represent the corresponding URLLC uplink and downlink transmission rates, γ UL,l,i and γ DL,m,q Indicates its SINR, a UL and a DL They represent the rate weighting factor, B and T respectively, and the URLLC transmission bandwidth and duration, respectively. -1 (·) denotes the inverse function of the Gaussian function, ε UL,l,i Let ε be the decoding error probability of the uplink UE(l,i). DL,m,q Let be the decoding error probability of the downlink UE(m,q). The closed-form expressions for SINR of the uplink UE(l,i) and downlink UE(m,q) are as follows:

[0056]

[0057]

[0058] Where E UL and E DL K represents the number of uplink EDUs and downlink EDUs, respectively. UL and K DL These represent the number of uplink and downlink users, respectively, and M represents the number of antennas in each AP. p represents the transmit power of the uplink UE(l,i). DL,m,q,r P represents the AP power control factor of the downlink UE(m,q) in the r-th downlink EDU. DL,r This is the power control factor matrix for the downlink AP. Let M represent the noise variance of the downlink data channel, and λ represent the number of antennas. IUIThis represents the large-scale fading factor among UEs; This represents the unit selection vector for the uplink UE(l,i). This represents a matrix related to large-scale uplink fading. This represents a matrix related to uplink channel estimation error, DL-UL interference channel, and uplink data channel noise. This represents a matrix related to downlink channel estimation error.

[0059] Appendix Figure 2 and 3 The closed-form expression for the total spectral efficiency of URLLC in the CF-RAN-NAFD cellular wireless access network system and the simulation results of Monte Carlo simulation are presented, with the independent variables being the number of antennas and the number of access points, respectively.

[0060] Example 2:

[0061] This embodiment is based on the QSBA intelligent algorithm and uses the closed-form expression of URLLC spectral efficiency in Embodiment 1 as the objective function to provide a performance optimization scheme.

[0062] QBSA-URLLC Performance Optimization Solution

[0063] Step 1: Propose a URLLC performance optimization scheme under power allocation and rate constraints for the CF-RAN-NAFD non-cellular radio access network system, where the constraints represent URLLC uplink transmission rate constraints, URLLC downlink transmission rate constraints, user transmit power control, and AP transmit power control, respectively.

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Based on the QBSA intelligent algorithm, a solution method is presented for the above URLLC optimization problem:

[0070] Step 2: Input the CF-RAN-NAFD cellular radio access network system parameters, initialize the system parameters and QBSA historical memory set, set the initial iteration number, and randomly generate quantum individuals in the first iteration.

[0071] Step 3: Calculate the fitness value of each quantum individual based on the fitness function, and obtain the globally optimal measurement state. The fitness function for each quantum individual is expressed as:

[0072]

[0073] Step 4: Update the historical memory set, generate new quantum individuals through evolution and crossover strategies, calculate the corresponding measurement state, and calculate the fitness value of the newly generated quantum individuals;

[0074] Step 5: Update the quantum individual and its corresponding measurement state according to the greedy mechanism to obtain the globally optimal measurement state and update the iteration count; if the maximum number of iterations has not been reached, return to step 2.3; otherwise, obtain the globally optimal measurement state, i.e., the power control and rate constraint optimization result.

[0075] Step 6: Output:

[0076]

[0077]

[0078] Appendix Figure 4 and 5 Simulation results and specific methods for performance optimization of the CF-RAN-NAFD non-cellular wireless access network system QBSA-URLLC are presented respectively.

[0079] This specification describes embodiments of the present invention with reference to the accompanying drawings. However, the present invention is not limited to the above-described embodiments; that is, the specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art, guided by the teachings of this invention, can make many modifications without departing from the spirit and scope of the claims, and all such modifications fall within the protection scope of this invention.

Claims

1. A power allocation optimization method for URLLC (Ultra-Limited Radio Access Network), characterized in that, Includes the following steps: Step 1: Under the ZF receiver and precoding scheme, give the closed-form expressions for the signal-to-interference-plus-noise ratio (SINR) of the uplink and downlink of URLLC ultra-reliable low-latency transmission based on the random matrix method; Step 2: Based on the closed-form expression for signal-to-interference-plus-noise ratio proposed in Step 1, a closed-form expression for the rate of URLLC ultra-reliable low-latency transmission is given, and based on this, a closed-form expression for the total spectral efficiency of the CF-RAN-NAFD non-cellular radio access network system is given. Step 3: Based on the closed-form expression of the total spectral efficiency of the CF-RAN-NAFD cellular wireless access network system, propose the optimization problem of power allocation for URLLC ultra-reliable low-latency transmission under power allocation and rate control constraints. Step 4: Construct the fitness function for each quantum individual in the QBSA intelligent optimization algorithm, where Representing the measurement state of any quantum entity: where a UL,l,i and a DL,m,q represent the rate weighting factors for the uplink UE (l,i) and the downlink UE (m,q), respectively; Step 5: Input the parameters of the CF-RAN-NAFD cellular wireless access network system, initialize the system parameters and QBSA history memory set, and set the initial iteration number; in the first iteration, randomly generate quantum individuals, calculate the fitness value of each quantum individual according to the fitness function, and obtain the globally optimal measurement state. Step 6: Update the historical memory set, generate new quantum individuals through evolution and crossover strategies, calculate the corresponding measurement state, calculate the fitness value of the newly generated quantum individuals, iteratively update the quantum individuals and corresponding measurement states according to the greedy mechanism, obtain the global optimal measurement state, and update the iteration count; if the maximum number of iterations has not been reached, continue to execute Step 6, otherwise execute Step 7; Step 7: Reach the maximum number of iterations or algorithm convergence, i.e., obtain the globally optimal measurement state, which is the optimization result of URLLC ultra-reliable low-latency transmission power control and rate constraint. The output is: in, SE represents the transmit power control result for the nth URLLC uplink user. max This is for the optimized URLLC spectral efficiency.

2. The power allocation optimization method for URLLC (Ultra-Limited Radio Access Network) as described in claim 1, characterized in that, In step 1, the closed-form expression for the signal-to-interference-plus-noise ratio (SINR) is: The conventions for symbols in expressions are described below: Tr indicates finding the trace of the matrix, where (l,i) and (m,q) represent the pilot number and user number of the uplink UE and downlink UE, respectively, where UE stands for "User Equipment"; E UL and E DL These represent the number of uplink EDUs and downlink EDUs, respectively. u and r represent the indices of the EDUs; here, EDU stands for "Edge Distributed Processing Unit". K UL and K DL These represent the number of uplink and downlink users, respectively. M represents the number of antennas in each AP. Here, AP is an abbreviation for "wireless access point". p represents the transmit power of the uplink UE(l,i). DL,m,q,r P represents the AP power control factor of the downlink UE(m,q) in the r-th downlink EDU. DL,r This is the power control factor matrix for the downlink AP. Let M represent the noise variance of the downlink data channel, and λ represent the number of antennas. IUI This represents the large-scale fading factor among UEs; Represents the unit selection vector for the uplink UE(l,i); Based on the above symbolic definitions, the matrices appearing in the expressions are defined as follows: express The closed-form derivation results, This represents a complex matrix, where the superscript indicates the dimension. This represents the channel estimation matrix calculated at the u-th uplink EDU. This indicates its conjugate transpose form; express The closed-form derivation result, where cov represents the covariance matrix operator. This represents the channel estimation error matrix calculated at the u-th uplink EDU. W represents the DL-UL interference matrix between the r-th downlink EDU and the u-th uplink EDU. r Let x represent the precoding matrix calculated for the r-th downlink EDU. DL,r Let x represent the downlink transmission symbol vector of the r-th downlink EDU. UL Indicates the uplink transmitted symbol vector; Tr(P DL,r Φ DL,m,q,r ) item represents The closed-form derivation results, in which Let represent the channel estimation error vector between the r-th downlink EDU and the downlink UE (l,i). This indicates its conjugate transpose.

3. The power allocation optimization method for URLLC in a non-cellular wireless access network as described in claim 2, characterized in that, In step 2, the closed-form expression for the total spectral efficiency is: Among them, SE URLLC URLLC stands for Ultra-Reliable Low-Latency Transmission Spectral Efficiency. UL,l,i and R DL,m,q These represent the corresponding URLLC (Ultra-Reliable Low Latency Communication) uplink and downlink transmission rates, respectively. UL,l,i and γ DL,m,q Indicates its SINR value, a UL and a DL Q represents the bandwidth and rate weighting factor, respectively, and B and T represent the bandwidth and duration of URLLC (Ultra-Reliable Low-Latency Transmission). -1 (·) denotes the inverse function of the Gaussian function, ε UL,l,i Let ε be the decoding error probability of the uplink UE(l,i). DL,m,q Let be the decoding error probability of the downlink UE(m,q).

4. The power allocation optimization method for URLLC in a non-cellular wireless access network as described in claim 3, characterized in that, In step 3, the power allocation optimization problem for URLLC ultra-reliable low-latency transmission under power allocation and rate control constraints is expressed as: in, Represents any object. This represents the transmit power of user (l,i). Indicates the maximum power. and w represents the minimum URLLC (Ultra-Reliable Low Latency Communication) transmission rate. m,q,n,r Let represent the precoding vector at the nth AP in the r-th downlink EDU, with user indices (m, q).

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