Robust Communication Design in RIS-Assisted THz Channels

Abstract

Terahertz (THz) communication offers the necessary bandwidth to meet the high data rate demands of next-generation wireless systems. However, it faces significant challenges, including severe path loss, dynamic blockages, and beam misalignment, which jeopardize communication reliability. Given that many 6G use cases require both high data rates and strong reliability, robust transmission schemes that achieve high throughput under these challenging conditions are essential for the effective use of high-frequency bands. In this context, we propose a novel mixed-criticality superposition coding scheme for reconfigurable intelligent surface (RIS)-assisted THz systems. This scheme leverages both the strong but intermittent direct line-of-sight link and the more reliable, yet weaker, RIS path to ensure robust delivery of high-criticality data while maintaining high overall throughput. We model a mixed-criticality queuing system and optimize transmit power to meet reliability and queue stability constraints. Simulation results show that our approach significantly reduces queuing delays for critical data while sustaining high overall throughput, outperforming conventional time-sharing methods. Additionally, we examine the impact of blockage, beam misalignment, and beamwidth adaptation on system performance. These results demonstrate that our scheme effectively balances reliability and throughput under challenging conditions, while also underscoring the need for robust beamforming techniques to mitigate the impact of misalignment in RIS-assisted channels.

Figures (10)

• Figure 1: Example for data significance classification in a VR application, highlighting different QoS priorities for high- and low-criticality information.
• Figure 2: System model of a RIS-assisted BS-UE downlink channel. The direct BS-UE link as well as the RIS-UE channel are affected by dynamic blockage and beam misalignment. The arriving packets at the BS are classified according to their criticality and stored in a high- and a low-criticality (HC/LC) buffer. The BS applies superposition coding (SC) of the mixed-criticality data streams and the UE adopts a successive decoding (SD) approach.
• Figure 3: Illustration of beam misalignment on the direct BS-UE path, showing the transmission beam footprint and UE's effective area with a pointing error $\epsilon_d$.
• Figure 4: Illustration of mixed-criticality transmission scheme: (a) As long as the direct path is available, both messages are successively decoded at the UE. (b) If the direct path is blocked, only the HC message is decoded via the RIS-path.
• Figure 5: Feasibility region of the proposed MC-SC scheme in comparison to time sharing, showing the total achievable throughput versus the achievable HC throughput for $\alpha \in [0,1]$. The overall throughput is maximized at $\alpha_\mathrm{sum}^*$. For $\alpha < \alpha_\mathrm{sum}^*$, total throughput and the portion of reliable HC rate have a synergetic relation, whereas a tradeoff is exhibited for $\alpha > \alpha_\mathrm{sum}^*$. All points on the thick red line are Pareto optimal, and a tradeoff solution balancing throughput and reliability by solving \ref{['alpha_opt']} is achieved with $\alpha_T^*$.

Theorems & Definitions (1)

• Remark 1