On the Downlink Average Energy Efficiency of Non-Stationary XL-MIMO

TL;DR

This paper investigates the energy efficiency of non-stationary XL-MIMO systems considering visibility regions, proposing estimation methods, deriving approximate expressions for energy efficiency, and optimizing system parameters to enhance performance.

Contribution

It introduces a novel approach to estimate visibility regions in non-stationary XL-MIMO and derives deterministic formulas for ergodic energy efficiency, along with an optimization algorithm.

Findings

VR estimation errors impact energy efficiency.

Proposed algorithm improves energy efficiency.

Approximate formulas match simulation results.

Abstract

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic {average energy efficiency (EE)} with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average {EE} is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average {EE} and demonstrate the effectiveness of our proposed algorithm.