A Novel Detection Method for Single-RF MIMO-OFDM Systems

2026-06-02

AI Summary

This work addresses the irreducible error floor in bit error rate (BER) that emerges at high signal-to-noise ratios (SNR) in single-RF MIMO-OFDM systems employing reconfigurable antennas, such as ESPAR antennas, due to modeling inaccuracies. To mitigate this issue, the study introduces Mahalanobis distance into the maximum likelihood detection framework for the first time and proposes a novel detection algorithm that effectively compensates for the modeling errors. Theoretical analysis and simulation results demonstrate that the proposed method significantly reduces BER at high SNR, successfully eliminates the error floor, and thereby substantially enhances overall system performance.

Abstract

A novel detection method based on maximum-likelihood (ML) detection leveraging Mahalanobis distance is proposed for single-radio-frequency (RF) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. It can enhance bit error rate (BER) performance and is based on the observation that when using reconfigurable antennas (such as electronically steerable parasitic array radiators (ESPARs) to create a single-RF MIMO system, an additional model error arising from the reconfigurable antennas is introduced. These modeling errors produce an irreducible BER (error floor) at high signal-to-noise ratios (SNRs). Simulation results, using ESPAR as an example, validate our error floor analysis and demonstrate that our proposed enhanced detection method can effectively address the error floor and reduce the BER at high transmit SNRs.

Problem

• single-RF MIMO
• OFDM
• error floor
• reconfigurable antennas
• BER

Innovation

• Mahalanobis distance
• single-RF MIMO
• error floor mitigation
• reconfigurable antennas
• ML detection