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Performance Analysis of Active Large Intelligent Surfaces (LISs): Uplink Spectral Efficiency and Pilot Training

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arxiv 1904.00453 v3 pith:SOV2HH52 submitted 2019-03-31 cs.IT math.IT

classification cs.ITmath.IT
keywords pilotnumbersystemcontaminationderivedlargeperformancesurfaces
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Large intelligent surfaces (LISs) constitute a new and promising wireless communication paradigm that relies on the integration of a massive number of antenna elements over the entire surfaces of man-made structures. The LIS concept provides many advantages, such as the capability to provide reliable and space-intensive communications by effectively establishing line-of-sight (LOS) channels. In this paper, the system spectral efficiency (SSE) of an uplink LIS system is asymptotically analyzed under a practical LIS environment with a well-defined uplink frame structure. In order to verify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Given this performance bound, an optimal pilot training length for multi-LIS systems subjected to pilot contamination is characterized and, subsequently, the performance-maximizing number of devices that the LIS system must service is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas, and the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the LOS path, even if the LIS is equipped with an infinite number of antennas. Additionally, the SSE obtained with the proposed pilot training length and number of scheduled devices is shown to reach the one obtained via a brute-force search for the optimal solution.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

    cs.IT 2019-08 conditional novelty 4.0 of 10

    The paper extends IRS beamforming optimization to broadcast, multicast, max-min fairness, and multi-IRS/multi-antenna settings using standard SDR alternating optimization, without numerical validation.

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