This paper investigates joint design and optimization of both low density parity check(LDPC)codes and M-algorithm based detectors including iterative tree search(ITS)and soft-output M-algorithm(SOMA)in multiple-input ...This paper investigates joint design and optimization of both low density parity check(LDPC)codes and M-algorithm based detectors including iterative tree search(ITS)and soft-output M-algorithm(SOMA)in multiple-input multiple-output(MIMO)systems via the tool of extrinsic information transfer(EXIT)charts.First,we present EXIT analysis for ITS and SOMA.We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves,and the explanation for such a phenomenon is given,while for SOMA,the true EXIT curves can be computed,enabling the code design.Then,we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems.The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly,and can easily attain the desired code with the target rate.Also,it can transform the optimization problem into a linear one,which is computationally simple.The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.展开更多
针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快...针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快速获取光伏组件的功率,并在确定全局最大功率点后切换到维持阶段;在维持阶段,通过PID微调,使工作点稳定运行在最大功率点附近。该算法计算量小,便于在各类嵌入式平台中实现。Simulink仿真结果表明,S&M算法相较于扰动观察法(perturb and observe,P&O)能有效缓解组件本身失配下的多峰值问题,相较于粒子群优化(particle swarm optimization,PSO)算法能够抑制组件间失配时多组件并行MPPT所引起的相互干扰。其全局最大功率点跟踪成功率达到99%以上,收敛时间可缩短至0.01 s;通过实物验证进一步证明了该算法在实际系统中能够实现多组件协同的全局最大功率点跟踪。展开更多
基金Supported by the National Basic Research Program of China(Grant No.2009CB320406)the National Natural Science Foundation of China(Grant No.60872048)Specialized Major Science and Technology Project of China(Grant Nos.2008ZX03003-004,2009ZX03003-009)
摘要This paper investigates joint design and optimization of both low density parity check(LDPC)codes and M-algorithm based detectors including iterative tree search(ITS)and soft-output M-algorithm(SOMA)in multiple-input multiple-output(MIMO)systems via the tool of extrinsic information transfer(EXIT)charts.First,we present EXIT analysis for ITS and SOMA.We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves,and the explanation for such a phenomenon is given,while for SOMA,the true EXIT curves can be computed,enabling the code design.Then,we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems.The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly,and can easily attain the desired code with the target rate.Also,it can transform the optimization problem into a linear one,which is computationally simple.The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.
摘要针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快速获取光伏组件的功率,并在确定全局最大功率点后切换到维持阶段;在维持阶段,通过PID微调,使工作点稳定运行在最大功率点附近。该算法计算量小,便于在各类嵌入式平台中实现。Simulink仿真结果表明,S&M算法相较于扰动观察法(perturb and observe,P&O)能有效缓解组件本身失配下的多峰值问题,相较于粒子群优化(particle swarm optimization,PSO)算法能够抑制组件间失配时多组件并行MPPT所引起的相互干扰。其全局最大功率点跟踪成功率达到99%以上,收敛时间可缩短至0.01 s;通过实物验证进一步证明了该算法在实际系统中能够实现多组件协同的全局最大功率点跟踪。