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Massive MIMO-OFDM Systems with Low Resolution ADCs: Cramér-Rao Bound, Sparse Channel Estimation, and Soft Symbol Decoding | IEEE Journals & Magazine | IEEE Xplore

Massive MIMO-OFDM Systems with Low Resolution ADCs: Cramér-Rao Bound, Sparse Channel Estimation, and Soft Symbol Decoding


Abstract:

We consider the delay-domain sparse channel estimation and data detection/decoding problems in a massive multiple-input-multiple-output (MIMO) orthogonal frequency divisi...Show More

Abstract:

We consider the delay-domain sparse channel estimation and data detection/decoding problems in a massive multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication system with low-resolution analog-to-digital converters (ADCs). The non-linear distortion due to coarse quantization leads to severe performance degradation in conventional OFDM receivers, which necessitates novel receiver techniques. First, we derive Bayesian Cramér-Rao-lower-bounds (CRLB) on the mean squared error (MSE) in recovering jointly compressible vectors from quantized noisy underdetermined measurements. Second, we formulate the pilot-assisted channel estimation as a multiple measurement vector (MMV) sparse recovery problem, and develop a variational Bayes (VB) algorithm to infer the posterior distribution of the channel. We benchmark the MSE performance of our algorithm with that of the CRLB, and numerically show that the VB algorithm meets the CRLB. Third, we present a soft symbol decoding algorithm that infers the posterior distributions of the data symbols given the quantized observations. We utilize the posterior statistics of the detected data symbols as virtual pilots, and propose an iterative soft symbol decoding and data-aided channel estimation procedure. Finally, we present a variant of the iterative algorithm that utilizes the output bit log-likelihood ratios of the channel decoder to adapt the data prior to further improve the performance. We provide interesting insights into the impact of the various system parameters on the MSE and bit error rate of the proposed algorithms, and benchmark them against the state-of-the-art.
Published in: IEEE Transactions on Signal Processing ( Volume: 70)
Page(s): 4835 - 4850
Date of Publication: 22 March 2022

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I. Introduction

Recent research in wireless communications has investigated the use of a massive number of antennas at the base station (BS) to increase the network capacity and data rates [2]. The benefits of massive multiple input multiple output (MIMO) communications are now very well understood. However, they come at the expense of high power consumption and hardware cost, which needs to be addressed to make massive MIMO commercially viable. One potential solution is to employ low-resolution analog-to-digital converters (ADCs) in the receivers [3]–[5]. The power consumption of an ADC increases exponentially with its bit-width. Hence, in massive MIMO systems with one RF chain per antenna, employing low-resolution ADCs can result in dramatic power savings [6], [7]. Low resolution ADCs also relax the stringent linearity range requirements on the RF circuitry, reducing the hardware cost [8]. However, they bring new challenges receiver design, the large quantization noise introduced by them needs to be countered. This paper develops novel receiver architectures in the context of multiuser massive MIMO orthogonal frequency division multiplexing (OFDM) communications.

References

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