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

TL;DR

This work analyzes Large Intelligent Surfaces (LIS) built from low-cost RX chains by modeling hardware distortion with a memory-less polynomial and using Bussgang decomposition to derive closed-form SNDR expressions under maximum ratio combining. It studies how AGC and back-off interact with non-ideal RX chains and develops distortion-aware antenna and panel selection schemes to sustain performance while improving energy efficiency. A key result is a tractable SNDR approximation for large LIS via a disk (RIemann) integral, plus a closed-form solution for 3rd-order panel selection. The findings highlight that hardware quality fundamentally limits LIS gains as the array scales, but carefully designed antenna/panel selection can significantly mitigate distortion effects, guiding practical, energy-efficient LIS deployments.

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.

Figures (12)

• Figure 1: LIS and UE configuration. The LIS is centered around origin and the UE is on the bore-sight of the LIS.
• Figure 2: Panels configuration, the panel-based LIS is centered around origin with a fixed distance $\delta_p$ between adjacent panels.
• Figure 3: Achievable data rate vs LIS radius in terms of $\lambda$. The UE is at distance $d=25\lambda$ from the center of LIS.
• Figure 4: Numerical and Closed-form achievable data rate with perfect AGC vs LIS radius in terms of $\lambda$. The UE is at distance $d=25\lambda$ from the center of LIS.
• Figure 5: Achievable data rate with perfect AGC vs LIS radius in terms of $\lambda$ with fixed back-off for optimal antenna selection solved from \ref{['generalOpt1']} and dominant antenna selection where the antennas with highest received power are selected.

Theorems & Definitions (2)

• Lemma 2.1
• Lemma 3.1