In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming...In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.展开更多
The closed-form formula derivation of the power domain cooperative non-orthogonal multiple access(NOMA)system is of great significance for further improving the performance of the system.However,the system performance...The closed-form formula derivation of the power domain cooperative non-orthogonal multiple access(NOMA)system is of great significance for further improving the performance of the system.However,the system performance formulas of the channel capacity and the paired bit error rate pairwise error probability(PEP)are too complicated,which have increased the difficulty in system performance optimization.Therefore,based on the amplify forward(AF)relay cooperative NOMA model,the signal interference noise ratio(SINR)formulas of the two user nodes are constructed.Through the assumption of that,the symbol error rate(SER)of each user is fair,the simplification condition of moment generating function(MGF)with the harmonic mean form is satisfied.Combined with the SER calculation formula of MGF,the system SER asymptotically tight approximation formula with simple structure is derived at high signal-to-noise ratio(SNR).The Monte Carlo simulation results show that,the formula can accurately describe the SER performance of the power domain cooperative NOMA system with the non-ideal successive interference cancellation(SIC)system when SNR is high.Under the condition of certain total power,the optimal power allocation factor is solved in order to minimize the total system SER.展开更多
Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ...Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.展开更多
In the uplink grant-free non-orthogonal multiple access(NOMA)scenario,since the active user at the sender has a structured sparsity transmission characteristic,the compressive sensing recovery algorithm is initially a...In the uplink grant-free non-orthogonal multiple access(NOMA)scenario,since the active user at the sender has a structured sparsity transmission characteristic,the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data.However,the existing compressed sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio(SNR)as the priori conditions,which greatly reduces the algorithm adaptability in multi-user detection.Therefore,an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit(CVA-SSAOMP)is proposed to realize multi-user detection in dynamic change communication scenario of channel state information(CSI).The proposed algorithm transforms the structured sparsity model into a block sparse model,and without the priori conditions above,the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation.The simulation results show that,compared with the traditional orthogonal matching pursuit(OMP)algorithm,subspace pursuit(SP)algorithm and cross validation aided block sparsity adaptive subspace pursuit(CVA-BSASP)algorithm,the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio(BER),and has the advantage of low-complexity.展开更多
基金Supported by the National Natural Science Foundation of China(No.62001001).
文摘In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.
基金the National Natural Science Foundation of China(No.62001001)。
文摘The closed-form formula derivation of the power domain cooperative non-orthogonal multiple access(NOMA)system is of great significance for further improving the performance of the system.However,the system performance formulas of the channel capacity and the paired bit error rate pairwise error probability(PEP)are too complicated,which have increased the difficulty in system performance optimization.Therefore,based on the amplify forward(AF)relay cooperative NOMA model,the signal interference noise ratio(SINR)formulas of the two user nodes are constructed.Through the assumption of that,the symbol error rate(SER)of each user is fair,the simplification condition of moment generating function(MGF)with the harmonic mean form is satisfied.Combined with the SER calculation formula of MGF,the system SER asymptotically tight approximation formula with simple structure is derived at high signal-to-noise ratio(SNR).The Monte Carlo simulation results show that,the formula can accurately describe the SER performance of the power domain cooperative NOMA system with the non-ideal successive interference cancellation(SIC)system when SNR is high.Under the condition of certain total power,the optimal power allocation factor is solved in order to minimize the total system SER.
基金the National Natural Science Foundation of China(No.62001001).
文摘Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.
基金Supported by the National Natural Science Foundation of China(No.62001001)。
文摘In the uplink grant-free non-orthogonal multiple access(NOMA)scenario,since the active user at the sender has a structured sparsity transmission characteristic,the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data.However,the existing compressed sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio(SNR)as the priori conditions,which greatly reduces the algorithm adaptability in multi-user detection.Therefore,an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit(CVA-SSAOMP)is proposed to realize multi-user detection in dynamic change communication scenario of channel state information(CSI).The proposed algorithm transforms the structured sparsity model into a block sparse model,and without the priori conditions above,the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation.The simulation results show that,compared with the traditional orthogonal matching pursuit(OMP)algorithm,subspace pursuit(SP)algorithm and cross validation aided block sparsity adaptive subspace pursuit(CVA-BSASP)algorithm,the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio(BER),and has the advantage of low-complexity.
