在时分双工(TDD)毫米波大规模多输入多输出(MIMO)系统中,因为波束空间信道具有稀疏性,导致将低维测量数据重建为原始高维信道时会带来较高的复杂度。针对上行链路,在不考虑稀疏度的情况下,将传统优化算法和基于数据驱动的深度学习方法...在时分双工(TDD)毫米波大规模多输入多输出(MIMO)系统中,因为波束空间信道具有稀疏性,导致将低维测量数据重建为原始高维信道时会带来较高的复杂度。针对上行链路,在不考虑稀疏度的情况下,将传统优化算法和基于数据驱动的深度学习方法相结合,提出一种改进的基于深度学习的波束空间信道估计算法。从重建过程入手,通过交替建立梯度下降模块(GDM)和近端映射模块(PMM)来构建网络。首先根据SalehValenzuela信道模型进行理论公式推导并生成信道数据;其次构建一个由传统迭代收缩阈值算法(ISTA)的更新步骤所展开的多层网络,并将数据传输到该网络,每层对应于一次类似ISTA的迭代;最后对训练好的模型进行在线测试,恢复出待估计的信道。构建Py Torch环境,将该算法与正交匹配追踪(OMP)算法、近似消息传递(AMP)算法、可学习的近似消息传递(LAMP)算法、高斯混合LAMP(GM-LAMP)算法进行对比,结果表明:在估计精度方面,所提算法相对表现较好的深度学习算法LAMP、GM-LAMP分别提升约3.07和2.61 d B,较传统算法OMP、AMP分别提升约11.12和9.57 d B;在参数量方面,所提算法较LAMP、GM-LAMP分别减少约39%和69%。展开更多
Given that the concurrent L1-minimization(L1-min)problem is often required in some real applications,we investigate how to solve it in parallel on GPUs in this paper.First,we propose a novel self-adaptive warp impleme...Given that the concurrent L1-minimization(L1-min)problem is often required in some real applications,we investigate how to solve it in parallel on GPUs in this paper.First,we propose a novel self-adaptive warp implementation of the matrix-vector multiplication(Ax)and a novel self-adaptive thread implementation of the matrix-vector multiplication(ATx),respectively,on the GPU.The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size.Second,based on the above proposed kernels,the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from the perspective of the streams and the thread blocks on a GPU,and optimize their performance by using the new features of GPU such as the shuffle instruction and the read-only data cache.Finally,we design a concurrent L1-min solver on multiple GPUs.The experimental results have validated the high effectiveness and good performance of our proposed methods.展开更多
Two new versions of accelerated first-order methods for minimizing convex composite functions are proposed. In this paper, we first present an accelerated first-order method which chooses the step size 1/ Lk to be 1/ ...Two new versions of accelerated first-order methods for minimizing convex composite functions are proposed. In this paper, we first present an accelerated first-order method which chooses the step size 1/ Lk to be 1/ L0 at the beginning of each iteration and preserves the computational simplicity of the fast iterative shrinkage-thresholding algorithm. The first proposed algorithm is a non-monotone algorithm. To avoid this behavior, we present another accelerated monotone first-order method. The proposed two accelerated first-order methods are proved to have a better convergence rate for minimizing convex composite functions. Numerical results demonstrate the efficiency of the proposed two accelerated first-order methods.展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
文摘在时分双工(TDD)毫米波大规模多输入多输出(MIMO)系统中,因为波束空间信道具有稀疏性,导致将低维测量数据重建为原始高维信道时会带来较高的复杂度。针对上行链路,在不考虑稀疏度的情况下,将传统优化算法和基于数据驱动的深度学习方法相结合,提出一种改进的基于深度学习的波束空间信道估计算法。从重建过程入手,通过交替建立梯度下降模块(GDM)和近端映射模块(PMM)来构建网络。首先根据SalehValenzuela信道模型进行理论公式推导并生成信道数据;其次构建一个由传统迭代收缩阈值算法(ISTA)的更新步骤所展开的多层网络,并将数据传输到该网络,每层对应于一次类似ISTA的迭代;最后对训练好的模型进行在线测试,恢复出待估计的信道。构建Py Torch环境,将该算法与正交匹配追踪(OMP)算法、近似消息传递(AMP)算法、可学习的近似消息传递(LAMP)算法、高斯混合LAMP(GM-LAMP)算法进行对比,结果表明:在估计精度方面,所提算法相对表现较好的深度学习算法LAMP、GM-LAMP分别提升约3.07和2.61 d B,较传统算法OMP、AMP分别提升约11.12和9.57 d B;在参数量方面,所提算法较LAMP、GM-LAMP分别减少约39%和69%。
基金The research has been supported by the Natural Science Foundation of China under great number 61872422the Natural Science Foundation of Zhejiang Province,China under great number LY19F020028.
文摘Given that the concurrent L1-minimization(L1-min)problem is often required in some real applications,we investigate how to solve it in parallel on GPUs in this paper.First,we propose a novel self-adaptive warp implementation of the matrix-vector multiplication(Ax)and a novel self-adaptive thread implementation of the matrix-vector multiplication(ATx),respectively,on the GPU.The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size.Second,based on the above proposed kernels,the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from the perspective of the streams and the thread blocks on a GPU,and optimize their performance by using the new features of GPU such as the shuffle instruction and the read-only data cache.Finally,we design a concurrent L1-min solver on multiple GPUs.The experimental results have validated the high effectiveness and good performance of our proposed methods.
基金Sponsored by the National Natural Science Foundation of China(Grant No.11461021)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2017JM1014)
文摘Two new versions of accelerated first-order methods for minimizing convex composite functions are proposed. In this paper, we first present an accelerated first-order method which chooses the step size 1/ Lk to be 1/ L0 at the beginning of each iteration and preserves the computational simplicity of the fast iterative shrinkage-thresholding algorithm. The first proposed algorithm is a non-monotone algorithm. To avoid this behavior, we present another accelerated monotone first-order method. The proposed two accelerated first-order methods are proved to have a better convergence rate for minimizing convex composite functions. Numerical results demonstrate the efficiency of the proposed two accelerated first-order methods.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
