2018/04/27 by Daniel Plabst, Plabst, Daniel, Jawad Munir +5
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Advanced Power Amplifier Design #Advanced Wireless Communication Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1804.10695
Accepted in ISWCS 2018, 15th International Symposium on Wireless Communication Systems, Lisbon, Portugal
openalex publication_date 2018/04/27 · arxiv created 2018/06/28 · arxiv updated 2018/06/29 · openalex created_date 2022/09/18 · openalex updated_date 2026/07/28
This paper addresses the problem of data detection for a massive Multiple-Input-Multiple-Output (MIMO) base station which utilizes 1-bit Analog-to-Digital Converters (ADCs) for quantizing the uplink signal. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission over frequency-selective channels. In this paper, we propose a computationally efficient block processing equalizer based on the Expectation Maximization (EM) algorithm in CP-free transmission for 1-bit quantized systems. We investigate the optimal block length and overlapping factor in relation to the Channel Impulse Response (CIR) length based on the Bit Error-Rate (BER) performance metric. As EM is a non-linear algorithm, the optimal estimate is found iteratively depending on the initial starting point of the algorithm. Through numerical simulations we show that initializing the EM-algorithm with a Wiener-Filter (WF) estimate, which takes the underlying quantization into account, achieves superior BER-performance compared to initialization with other starting points.