Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs
Junil Choi, Jianhua Mo, and Robert W. Heath, Jr.
Submitted to IEEE Trans. Commun., 2015. Available at ArXiv
In massive multiple-input multiple-output (MIMO) systems that operate with high bandwidths, 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. To achieve low complexity in the proposed nML detector, a strict constraint on the possible transmitted symbols 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. After obtaining the ML estimate by solving the convex problem, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. The minimum required number of receive antennas for detectors using one-bit ADCs to work is also discussed. Numerical results show that the proposed nML detector is efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detector makes use of the channel as part of the decoding, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detector and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.