In massive multiple input and multiple output （MIMO） systems the challenge is the detection of the individual signals from the composite signal with a large system limit. The optimal detector becomes prohibitively c...In massive multiple input and multiple output （MIMO） systems the challenge is the detection of the individual signals from the composite signal with a large system limit. The optimal detector becomes prohibitively complex. The approximate message passing （AMP） algorithm, designed for compressed sensing, has attracted researchers to counter this problem due to its reduced complexity with a large system limit. For this reason the AMP algorithm has been used for detection in massive MIMO systems. In this paper, we focus on implementing this algorithm in a fixed-point format. To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm. These estimation functions are obtained using the log-sum approximation, then taking the exponent of the result. The log-sum approximation is obtained by the Jacobian logarithm with a correction function. We also provide a modification of the correction function for the approximations that best suits our case. We then transform the algorithm with the approximated functions to fixed-point and provide a BER performance for the algorithm with the variables set to 16-bit word lengths using the hybrid ＂ScaledDouble＂ data types.展开更多
为降低低密度奇偶校验(LDPC:Low-Density Parity-Cheek)码译码算法的复杂度,同时提高通信系统性能,在研究对数似然比置信传播(LLR-BP:Log-Likelihood Ratio Belief Propagation)算法及其简化的译码算法基础上,提出了一种基于归一化最小...为降低低密度奇偶校验(LDPC:Low-Density Parity-Cheek)码译码算法的复杂度,同时提高通信系统性能,在研究对数似然比置信传播(LLR-BP:Log-Likelihood Ratio Belief Propagation)算法及其简化的译码算法基础上,提出了一种基于归一化最小和(NMS:Normalized Min Sum)算法的改进算法。深入研究了Gamma-Gamma分布模型,并将Gamma-Gamma分布作为仿真的信道模型,应用Matlab仿真比较了LLR-BP及其简化算法在不同湍流强度信道下的性能。仿真结果表明,无论在AWGN(Additive White Gaussian Noise)信道还是不同湍流强度的Gamma-Gamma信道下,改进算法在几种算法中都有较好的译码性能,尤其是在中湍流和强湍流信道下改进算法的性能优势更为明显。通过码长对BER(Bit Error Rate)影响的仿真结果表明,适当增加码长可有效改善算法的性能。展开更多
文摘In massive multiple input and multiple output （MIMO） systems the challenge is the detection of the individual signals from the composite signal with a large system limit. The optimal detector becomes prohibitively complex. The approximate message passing （AMP） algorithm, designed for compressed sensing, has attracted researchers to counter this problem due to its reduced complexity with a large system limit. For this reason the AMP algorithm has been used for detection in massive MIMO systems. In this paper, we focus on implementing this algorithm in a fixed-point format. To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm. These estimation functions are obtained using the log-sum approximation, then taking the exponent of the result. The log-sum approximation is obtained by the Jacobian logarithm with a correction function. We also provide a modification of the correction function for the approximations that best suits our case. We then transform the algorithm with the approximated functions to fixed-point and provide a BER performance for the algorithm with the variables set to 16-bit word lengths using the hybrid ＂ScaledDouble＂ data types.
文摘为降低低密度奇偶校验(LDPC:Low-Density Parity-Cheek)码译码算法的复杂度,同时提高通信系统性能,在研究对数似然比置信传播(LLR-BP:Log-Likelihood Ratio Belief Propagation)算法及其简化的译码算法基础上,提出了一种基于归一化最小和(NMS:Normalized Min Sum)算法的改进算法。深入研究了Gamma-Gamma分布模型,并将Gamma-Gamma分布作为仿真的信道模型,应用Matlab仿真比较了LLR-BP及其简化算法在不同湍流强度信道下的性能。仿真结果表明,无论在AWGN(Additive White Gaussian Noise)信道还是不同湍流强度的Gamma-Gamma信道下,改进算法在几种算法中都有较好的译码性能,尤其是在中湍流和强湍流信道下改进算法的性能优势更为明显。通过码长对BER(Bit Error Rate)影响的仿真结果表明,适当增加码长可有效改善算法的性能。
