The performance of uplink distributed massive multiple-input multiple-output(MIMO)systems with crosslayer design(CLD) is investigated over Rayleigh fading channel, which combines the discrete rate adaptive modulation ...The performance of uplink distributed massive multiple-input multiple-output(MIMO)systems with crosslayer design(CLD) is investigated over Rayleigh fading channel, which combines the discrete rate adaptive modulation with truncated automatic repeat request. By means of the performance analysis, the closed-form expressions of average packet error rate(APER)and overall average spectral efficiency(ASE)of distributed massive MIMO systems with CLD are derived based on the conditional probability density function of each user’s approximate effective signal-to-noise ratio(SNR)and the switching thresholds under the target packet loss rate(PLR)constraint.With these results,using the approximation of complementary error functions,the approximate APER and overall ASE are also deduced. Simulation results illustrate that the obtained theoretical ASE and APER can match the corresponding simulations well. Besides,the target PLR requirement is satisfied,and the distributed massive MIMO systems offer an obvious performance gain over the co-located massive MIMO systems.展开更多
In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed...In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.展开更多
基金supported in part by the National Natural Science Foundation of China (No. 61971220)the Fundamental Research Funds for the Central Universities of Nanjing University of Aeronautics and Astronautics(NUAA)(No.kfjj20200414)Natural Science Foundation of Jiangsu Province in China (No. BK20181289)。
文摘The performance of uplink distributed massive multiple-input multiple-output(MIMO)systems with crosslayer design(CLD) is investigated over Rayleigh fading channel, which combines the discrete rate adaptive modulation with truncated automatic repeat request. By means of the performance analysis, the closed-form expressions of average packet error rate(APER)and overall average spectral efficiency(ASE)of distributed massive MIMO systems with CLD are derived based on the conditional probability density function of each user’s approximate effective signal-to-noise ratio(SNR)and the switching thresholds under the target packet loss rate(PLR)constraint.With these results,using the approximation of complementary error functions,the approximate APER and overall ASE are also deduced. Simulation results illustrate that the obtained theoretical ASE and APER can match the corresponding simulations well. Besides,the target PLR requirement is satisfied,and the distributed massive MIMO systems offer an obvious performance gain over the co-located massive MIMO systems.
基金Foundation item： Projects（61102106, 61102105） supported by the National Natural Science Foundation of China Project（2013M530148） supported by China Postdoctoral Science Foundation Project（HEUCF120806） supported by the Fundamental Research Funds for the Central Universities of China
文摘In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.
