Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

Ashkan Sheikhi, Juan Vidal Alegría, Ove Edfors

TL;DR

This work addresses hardware distortion in LIS RX chains by first deriving SNDR under a memory-less polynomial distortion model and Bussgang-based decomposition, then introducing a double-parameter exponential distortion model to greatly improve analytical tractability. It applies this model to panel selection in panel-based LIS, showing that the SNDR optimization can be approximated in closed form with two sub-optimal solutions for the input power level $\rho$, yielding near-optimal performance compared with high-complexity heuristics. Numerical results confirm that the exponential model closely matches the polynomial model and that the proposed closed-form rho-opt solutions achieve SNDR nearly equal to the global optimum, with significant reductions in computation. Overall, the proposed distortion model enables tractable, distortion-aware design and optimization for LIS/MIMO systems, facilitating scalable deployment of panel-based LIS architectures.

Abstract

Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in MIMO and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.

Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

TL;DR

This work addresses hardware distortion in LIS RX chains by first deriving SNDR under a memory-less polynomial distortion model and Bussgang-based decomposition, then introducing a double-parameter exponential distortion model to greatly improve analytical tractability. It applies this model to panel selection in panel-based LIS, showing that the SNDR optimization can be approximated in closed form with two sub-optimal solutions for the input power level , yielding near-optimal performance compared with high-complexity heuristics. Numerical results confirm that the exponential model closely matches the polynomial model and that the proposed closed-form rho-opt solutions achieve SNDR nearly equal to the global optimum, with significant reductions in computation. Overall, the proposed distortion model enables tractable, distortion-aware design and optimization for LIS/MIMO systems, facilitating scalable deployment of panel-based LIS architectures.

Abstract

Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in MIMO and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.
Paper Structure (9 sections, 1 theorem, 15 equations, 4 figures)

This paper contains 9 sections, 1 theorem, 15 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. RX-chain non-linearity
  4. SNDR for MRC
  5. Exponential Model for Distortion Power
  6. Panel Selection in LIS
  7. Numerical Results
  8. Conclusion
  9. Proof of Approximations

Key Result

Lemma 3.1

The closed-form solution to the optimization problem SISOOpt can be approximated by either of the following optimal values for $\rho_{\text{opt}}$ with negligible error.

Figures (4)

  • Figure 1: LIS configuration and Panel Selection. Each panel, represented by a square, has the same number of antennas and green squares indicate the active panels. Each antenna is equipped with a non-linear analogue front end (AFE).
  • Figure 2: MLP and EXP Distortion Models.
  • Figure 3: Mean square error (MSE) for optimum $\rho$ approximations from lemma \ref{['ExpApprox12']}.
  • Figure 4: SE lower bound vs number of panels. The UE is at distance $d=50\lambda$ from the center of LIS transmitting with power $P$ such that SNR$=10dB$ at the center of LIS. Each panel is equipped with $M=16$ antenna elements with $\lambda/2$ spacing, $N_\text{max} = \lceil0.1N\rceil$, $b_\text{off}=7dB$, and there is a distance of $\delta_p=5\lambda$ between the center of adjacent panels.

Theorems & Definitions (1)

  • Lemma 3.1