This article proposes a low complexity hybrid precoding algorithm for the switch network‐based dynamic partially connected (SPC) structure, that is, named as HP‐SPC algorithm. First, via a new defined effective optimal precoding matrix, the analog switch precoding matrix optimization problem is transformed into a sparse representation problem. Thus, invoking that the key characteristic of only one nonzero entry in each row of the analog dynamic switch precoding matrix, the analog dynamic switch precoding matrix can be accurately and effectively solved. Second, the digital precoding matrix optimization problem is modeled as a dictionary update problem by the defined effective optimal precoding matrix and the combining matrix. Further, the digital precoding matrix is easily optimized based on the defined effective optimal precoding matrix, since the measurement vectors are sparsely represented by a single dictionary atom. Third, the analog phase shifter precoding matrix is given by the phase rotation method. Finally, the precoding matrices are alternant updated until convergence, a near optimal solution of the precoding matrix is obtained. Compared with the previous works, the proposed HP‐SPC algorithm provides better hybrid precoding performance, for examples: (1) it avoids complexity computation such as matrix inversion and singular value decomposition, so that it presents a low computational complexity; (2) it has a favorable property of convergence since a stable point can be reached with about 10 loop iterations. Based on the simulation results, the effectiveness of HP‐SPC algorithm is further demonstrated.
Existing linear algorithms based on subarray connection (SC) structures may cause noise enhancement and performance degradation. To solve this problem, this paper presents a nonlinear hybrid Tomlinson‐Harashima precoding (THP) algorithm based on SC structure, named as THP‐SC algorithm. In the proposed algorithm, the sum‐rate optimization problem can be transformed into two equivalent optimization subproblems including the transmitter optimization subproblem and the receiver optimization subproblem. These two subproblems are easy to solve. Furthermore, the analog precoding and combining matrices of the optimization subproblem are solved by a new improved successive interference cancelation (SIC) algorithm based on the singular value threshold shrinkage (SVS), which can effectively reduce the computational complexity while eliminating the interference. In the end, the digital precoding and combining matrices are obtained by TH method. Through the joint optimization of the transmitter and receiver matrices in the system, the optimal solution is obtained by alternate iteration of the matrices. In this paper, a more realistic line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) channel system is also considered. Simulations verify that the proposed algorithm can improve the spectral efficiency and energy efficiency to some extent with a comparable complexity compared to the previous works.
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