Large Intelligent Surfaces with Low-End Receivers: From Scaling to Antenna and Panel Selection

TL;DR

This paper investigates the impact of hardware distortion in large intelligent surface systems with low-cost receivers, deriving analytical models and proposing antenna and panel selection schemes to enhance scalability and energy efficiency.

Contribution

It provides analytical expressions for signal quality under hardware impairments and introduces practical selection schemes to improve LIS performance and energy efficiency.

Findings

Hardware distortion limits LIS scalability.

Antenna and panel selection schemes improve energy efficiency.

Analytical models enable performance evaluation with non-ideal hardware.

Abstract

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.