Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Apr. 15-20, 2018, Calgary, Alberta, Canada.

Antenna Selection for Large-Scale MIMO Systems with Low-Resolution ADCs

Jinseok Choi (1), Junmo Sung (1), Brian L. Evans (1) and Alan Gatherer (2)

(1) Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA
jinseokchoi89@gmail.com - junmo.sung@utexas.edu - bevans@ece.utexas.edu

(2) Huawei Technologies, Plano, Texas USA

Paper Draft - Software Release

Related journal paper:
"Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications" (2018)

Related conference papers:
"ADC Bit Optimization for Spectrum- and Energy-Efficient Millimeter Wave Communications" (2007)
"Space-Time Fronthaul Compression of Complex Baseband Uplink LTE Signals" (2016)

Related poster: "ADC Bit Allocation under a Power Constraint for MmWave Massive MIMO Communication Receivers"

Multiantenna Communications Project

Abstract

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

Questions & Answers

These are questions from the reviewers of the paper and our answers to them.

Q1) Provide the upper bound of the capacity corresponding to each K
A1) Since capacity is used as a selection measure and is already plotted in the figures, I think that this question asks to find the capacity with optimal subset of antennas. In this case, exhaustive search is required to find out the optimal subset of antennas at each realization of channels, which may not be feasible due to the large number of antennas.

Q2) How does the work go beyond this prior work in terms of incorporating sparsity aspects of the channel?
A2) As we did not consider any analog combining in this paper, the proposed antenna selection does not exploit the channel sparsity. For a potential journal version, this can be a possible research direction along with interference environment. I will be searching for papers that exploit the channel sparsity without analog combining.

Q3) Another aspect that needs consideration is the use of single antennas at the Rx end. This is not realistic.
A3) If I understand correctly, the reviewer wanted to know what will happen with single antenna selection as using many antennas is not realistic. In this case, there must be a single user and antenna selection reduces to a very simple problem. For example, we can find the optimal antenna by computing a performance measure once for each antenna.

Here are the questions that arose during the presentation of the paper: Q4) Do LNAs turn on and off when choosing different antennas?
A4) It depends. Assuming that a low-noise amplifier (LNA) is part of an RF chain after an analog combiner stage, it must turn on all the time.

Q5) Have you considered non-perfect CSI such as estimated channel realizations?
A5) No, the work is based on the perfect CSI. Junmo Sung is working on channel estimation techniques, and there might be a chance to put the research efforts together.


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Last Updated 04/25/18.