Transmissive RIS Enabled Transceiver Systems:Architecture, Design Issues and Opportunities

TL;DR

This paper introduces a transmissive RIS-enabled transceiver architecture that offers a cost-effective, energy-efficient alternative to traditional multi-antenna systems, with detailed design, channel modeling, and optimization strategies for future wireless networks.

Contribution

It proposes a novel transmissive RIS-enabled transceiver architecture, including transmission schemes, channel models, and estimation methods, advancing beyond reflective RIS and traditional multi-antenna systems.

Findings

TRTC shows advantages over traditional multi-antenna systems.

The proposed channel estimation scheme is effective for TRTC.

Numerical simulations confirm the superiority of the optimization algorithm.

Abstract

Reconfigurable intelligent surface (RIS) is anticipated to augment the performance of beyond fifth-generation (B5G) and sixth-generation (6G) networks by intelligently manipulating the state of its components. Rather than employing reflective RIS for aided communications, this paper proposes an innovative transmissive RIS-enabled transceiver (TRTC) architecture that can accomplish the functions of traditional multi-antenna systems in a cost-effective and energy-efficient manner. First, the proposed network architecture and its corresponding transmission scheme are elaborated from the perspectives of downlink (DL) and uplink (UL) transmissions. Then, we illustrate several significant advantages and differences of TRTC compared to other multiantenna systems. Furthermore, the downlink modulation and extraction principle based on time-modulation array (TMA) is introduced in detail to tackle the multi-stream communications. Moreover, a near-far field channel model appropriate for this architecture is proposed. Based on the channel model, we summarize some state-of-the-art channel estimation schemes, and the channel estimation scheme of TRTC is also provided. Considering the optimization for DL and UL communications, we present numerical simulations that confirm the superiority of the proposed optimization algorithm. Lastly, numerous prospective research avenues for TRTC systems are delineated to inspire further exploration.