Channel sensing for holographic interference surfaces based on the principle of interferometry

TL;DR

This paper introduces a novel holographic interference-based channel sensing architecture for wireless communication that reduces hardware complexity and resource usage by leveraging interference principles and advanced algorithms.

Contribution

It extends optical holography principles to communication holography and proposes a new channel estimation method that avoids traditional pilot-based approaches.

Findings

The proposed architecture reduces hardware complexity.

The PSIS method effectively suppresses self-interference.

The PMCS algorithm accurately estimates CSI in multi-user scenarios.

Abstract

The Holographic Interference Surface (HIS) provides a new paradigm for building a more cost-effective wireless communication architecture. In this paper, we derive the principles of holographic interference theory for electromagnetic wave reception and transmission, whereby the optical holography is extended to communication holography and a channel sensing architecture for holographic interference surfaces is established. Unlike the traditional pilot-based channel estimation approaches, the proposed architecture circumvents the complicated processes like filtering, analog to digital conversion (ADC), down conversion. Instead, it relies on interfering the object waves with a pre-designed reference wave, and therefore reduces the hardware complexity and requires less time-frequency resources for channel estimation. To address the self-interference problem in the holographic recording process, we propose a phase shifting-based interference suppression (PSIS) method according to the structural characteristics of communication hologram and interference composition. We then propose a Prony-based multi-user channel segmentation (PMCS) algorithm to acquire the channel state information (CSI). Our theoretical analysis shows that the estimation error of the PMCS algorithm converges to zero when the number of HIS units is large enough. Simulation results show that under the holographic architecture, our proposed algorithm can accurately estimate the CSI in multi-user scenarios.