arXiv subjects

Hehe Ban

Publications and source records attributed to Hehe Ban.

3 recordsLinked to original sources

arXiv2026

Blind Interference Suppression in IRS-Aided Wireless Systems: A Statistical Channel Ratio Estimation Approach

This paper addresses the problem of suppressing non-cooperative interference in intelligent reflecting surface (IRS)-aided wireless links without any channel state information (CSI) or cooperation from the interferer. We propose a fully blind framework that relies solely on received signal power measurements. A key insight is that nulling the aggregate interference channel requires only the complex ratios between the IRS-reflected paths and the direct interference link, rather than absolute CSI. We develop a novel estimation algorithm that obtains unbiased estimates of these channel ratios using only power samples collected under random IRS configurations. Theoretically, we prove that unbiased estimation is feasible when the number of discrete phase levels $K\geq 3$, and establish the Cramer-Rao lower bounds (CRLBs) for both the phase offset and amplitude ratio estimates, thus providing design guidance. Based on the estimated ratios, we propose two low-complexity IRS phase optimization algorithms: a one-shot greedy method and an iterative variant that mitigates error propagation from weakly reflecting elements. Simulations demonstrate that the proposed schemes can suppress strong interference to within a few dB of the interference-free upper bound, offering a practical, CSI-free solution for robust wireless communications in contested spectral environments.

eess.SP↗

arXiv2026

Blind Interference Suppression for IRS-Aided Robust Wireless Communications

The application of intelligent reflecting surfaces (IRSs) to suppress interference in wireless communication systems has recently attracted significant research attention. Most existing approaches rely on complete or partial channel state information (CSI) to configure the IRS. However, acquiring accurate CSI in IRS-assisted systems involves considerable pilot overhead and introduces non-negligible delays. This issue is further exacerbated under strong interference conditions, where interfering sources are typically non-cooperative, making CSI acquisition even more challenging. As a result, existing CSI-dependent interference suppression methods become difficult to deploy in practice. To address these limitations, we propose a novel blind interference suppression strategy that combines a proportional phase-inversion (PPI) algorithm with the conditional sample mean (CSM) method. The proposed approach determines the IRS configuration using only the received signal power, without requiring any prior CSI. We conduct a comprehensive performance evaluation by deriving the theoretical performance of the proposed scheme, which is subsequently verified through numerical simulations. Furthermore, simulation results across various parameter settings demonstrate that the proposed blind interference suppression scheme reduces the interference power to the level of noise, thereby achieving a marked signal-to-interference-plus-noise ratio (SINR) improvement and outperforms existing benchmark schemes.

eess.SP↗

arXiv2026

Blind IRS Beamforming Using Only Received Signal Power Measurements

This paper proposes a novel blind beamforming strategy for signal enhancement aided by an intelligent reflecting surface (IRS), using only samples of the received signal power. Unlike existing conditional sampling mean (CSM) based approaches, which rely solely on comparing the CSM values, the proposed algorithm fully exploits the information in the sampled data by constructing a least squares problem. This enables the estimation of the phase difference between the IRS-reflected channel and the direct channel, ultimately yielding the optimal IRS phase configuration. The simulation results verify that, by fully leveraging the collected data, the proposed scheme outperforms the CSM-based method, especially when the number of IRS phase quantization levels is larger than 2, while incurring the same level of computational complexity.

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