Adopting one-bit analog-to-digital convertors (ADCs) for massive multiple-input multiple-output (MIMO) implementations has great potential in reducing the hardware cost and power consumption. However, distortions caused by quantization raise great challenges. In MIMO orthogonal frequency-division modulation (OFDM) detection, coarse quantization renders the orthogonal separation among subcarriers inapplicable, forcing us to deal with a problem that has a very large problem size. In this paper we study the expectation-maximization (EM) approach for one-bit MIMO-OFDM detection. The idea is to iteratively decouple the MIMO-OFDM detection problem among subcarriers. Using the perspective of block coordinate descent, we describe inexact variants of the classical EM method for providing more flexible and computationally efficient designs. Simulation results are provided to illustrate the potential of the divide-and-conquer strategy enabled by EM.
翻译:采用一比位模拟数字转换器(ADCs)进行大规模多投入多输出(MIMO)执行,在降低硬件成本和电力消耗方面有很大的潜力,然而,由于量化造成的扭曲带来了巨大的挑战。在MOIMO 或远方频率变化调节(OFDM)检测中,粗微量化使得子容器之间的正方位分离无法适用,迫使我们处理一个问题大的问题。在本文中,我们研究了一比MIMO-OFDM探测的预期-最大化(EM)方法。其想法是将MIMO-ODM探测问题反复地在子容器中分离。我们从块坐标下移的角度描述了传统的EM方法的精确变体,以提供更灵活和计算效率高的设计。我们提供了模拟结果,以说明EMM所促成的鸿沟和平衡战略的潜力。