Nonlinear Precoding Algorithm Research Articles

Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders.

One-Bit Downlink Precoding for Massive MIMO OFDM System

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.

List Encoding of Vector Perturbation Precoding

In this letter, we propose a new nonlinear precoding algorithm, List encoding of Vector Perturbation precoding (LVP) for Multiple Input Multiple Output (MIMO) system. Different from the traditional VP based on sphere search strategy to minimize the Frobeniuas norm of the precoded signal with a exponential search complexity, LVP adopts multiple encoders design, a list of encoder perform only one dimensional integer optimization through several iterations in parallel. During each iteration each encoder searches through several perturbation results and retain one of them to the next iteration. When the iterations ends, the historical optimal perturbation result with the least Frobenius norm is output as the precoding result. The search complexity of LVP is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(M)$</tex-math></inline-formula> which increases linearly with the antenna number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> . This complexity is much smaller than sphere search based strategy. Compared with other low dimensional search algorithm represented by D2VP, the search complexity drops from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(M^{2})$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(M)$</tex-math></inline-formula> , with a significant BER gain thanks to the multiple encoder design. To the best of our knowledge, with similar BER performance the LVP algorithm has the lowest complexity.

Efficient Nonlinear Precoding for Massive MIMO Downlink Systems With 1-Bit DACs

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS). Using 1-bit DACs can significantly reduce the power consumption. This paper addresses the precoding problem for the massive narrow-band MU-MIMO downlink system equipped with 1-bit DACs at each BS. In such a system, the precoding problem plays a central role as the precoded symbols are affected by extra distortion introduced by 1-bit DACs. In this paper, we develop a highly efficient nonlinear precoding algorithm based on the alternative direction method framework. Unlike the classic algorithms, such as the semidefinite relaxation (SDR) and squared-infinity norm Douglas-Rachford splitting (SQUID) algorithms, which solve convex relaxed versions of the original precoding problem, the new algorithm solves the original nonconvex problem directly. The new algorithm is guaranteed to globally converge under some mild conditions. A sufficient condition for its convergence has been derived. The experimental results in various conditions demonstrated that the new algorithm can achieve the state-of-the-art performance comparable with the SDR algorithm while being much more efficient (e.g., more than 300 times faster than the SDR algorithm).

Quantized Precoding for Massive MU-MIMO

Massive multiuser (MU) multiple-input multiple-output (MIMO) is foreseen to be one of the key technologies in fifth-generation wireless communication systems. In this paper, we investigate the problem of downlink precoding for a narrowband massive MU-MIMO system with low-resolution digital-to-analog converters (DACs) at the base station (BS). We analyze the performance of linear precoders, such as maximal-ratio transmission and zero-forcing, subject to coarse quantization. Using Bussgang’s theorem, we derive a closed-form approximation on the rate achievable under such coarse quantization. Our results reveal that the performance attainable with infinite-resolution DACs can be approached using DACs having only 3–4 bits of resolution, depending on the number of BS antennas and the number of user equipments (UEs). For the case of 1-bit DACs, we also propose novel nonlinear precoding algorithms that significantly outperform linear precoders at the cost of an increased computational complexity. Specifically, we show that nonlinear precoding incurs only a 3 dB penalty compared with the infinite-resolution case for an uncoded bit-error rate of 10−3, in a system with 128 BS antennas that uses 1-bit DACs and serves 16 single-antenna UEs. In contrast, the penalty for linear precoders is about 8 dB.

A Novel Nonlinear Precoding Algorithm for the Downlink of Multiple Antenna Multi-User Systems

By pre-equalizing inter-layer interference at the transmitter, Tomlinson-Harashima precoding (THP) algorithm provides a solution for the downlink of multiple antenna multi-user systems, in which the decentralized structure of the receivers makes the receiver-processing algorithms impossible. However, for the zero-forcing (ZF) THP algorithm developed in the literature there are significant performance differences between specific mobile stations. In this paper, a novel version of the THP algorithm is proposed. It greatly improves the worst mobile's performance and ensures balanced performance of all the mobiles. For the new THP algorithm, better performance can be obtained by suitably ordering the rows of the channel matrix. We show that the "best-first" ordering method achieves optimal order for BER performance in 2/spl times/2 systems and achieves near optimal order in systems of larger dimensions. Simulation is used to show the advantages of the new THP algorithm and the "best-first" ordering method.