Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs
Junil Choi, Jianhua Mo, Robert W. Heath Jr

TL;DR
This paper introduces a near maximum likelihood detector and channel estimator for uplink massive MIMO systems with one-bit ADCs, enabling efficient detection and channel estimation with low power consumption.
Contribution
It proposes a convex optimization-based near ML detector and a two-stage detector for one-bit ADC massive MIMO systems, improving detection performance and supporting higher-order modulations.
Findings
Efficient detection of multiuser signals with one-bit ADCs.
Supports higher-order constellations like 16-QAM.
Provides a low power, complete uplink MIMO solution.
Abstract
In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a high-resolution analog-to-digital converter (ADC) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Advanced Wireless Communication Techniques · Cooperative Communication and Network Coding
