Precoding Design and PMI Selection for BICM-MIMO Systems With 5G New Radio Type-I CSI

• Published in: IEEE transactions on communications Vol. 72; no. 9; pp. 5334 - 5348
• Main Authors: Maleki, Marjan, Jin, Juening, Wang, Hao, Haardt, Martin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; 5G new radio (NR); Algorithms; Bit-interleaved coded modulation (BICM); channel state information (CSI); Codes; Complexity; Constraints; Error analysis; Fourier transforms; Heuristic methods; Iterative algorithms; Likelihood ratio; Measurement; MIMO communication; Modulation; Mutual information; Nonlinear systems; Optimization; precoder matrix index (PMI); Precoding; Search methods; Symbols; type-I codebook
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2024.3383106

This paper proposes novel linear precoding algorithms for Multiple-Input Multiple-Output Bit-Interleaved Coded Modulation (MIMO-BICM) systems that maximize the achievable rate subject to power constraints. To overcome the nonlinear and nonconvex nature of the optimization problem, we rewrite the achievable rate in terms of the log-likelihood ratio (LLR) and introduce manifold-based gradient ascent (MGA) precoding and low-complexity non-iterative algorithms. Simulation results show significant gains in achievable rate and block error rate compared to existing techniques. Additionally, we extend our investigation to linear precoding with the constraint that the precoding matrix is selected from the codebook type-I adopted in Fifth-Generation New Radio (5G NR) networks. We propose heuristic algorithms that exploit the Kronecker and Discrete Fourier Transform (DFT) structure of the codebook and consider the singular vector decomposition (SVD) precoder as the optimal reference precoder. The traditional exhaustive search methods require a high complexity, especially for large codebook sizes. However, our proposed algorithms apply a combination of direct estimation and a low-dimensional search for deriving the indices, resulting in a reduced number of codebook precoder candidates. Simulation results show that our proposed low-complexity algorithms perform comparably to exhaustive search baselines.