Autonomous RISs and Oblivious Base Stations: The Observer Effect and its Mitigation

TL;DR

This paper examines the robustness of an HRIS-assisted massive multiple-input multipleoutput (mMIMO) system by considering its critical components and stringent conditions and proposes a physical-layer orchestration framework that aligns HRIS and mMIMO operations.

Abstract

Autonomous reconfigurable intelligent surfaces (RISs) offer the potential to simplify deployment by reducing the need for real-time remote control between a base station (BS) and an RIS. However, we highlight two major challenges posed by autonomy. The first is implementation complexity, as autonomy requires hybrid RISs (HRISs) equipped with additional onboard hardware to monitor the propagation environment and perform local channel estimation (CHEST), a process known as probing. The second challenge, termed probe distortion, reflects a form of the observer effect: during probing, an HRIS can inadvertently alter the propagation environment, potentially disrupting the operations of other communicating devices sharing the environment. Although implementation complexity has been extensively studied, probe distortion remains largely unexplored. To further assess the potential of autonomous RIS, this paper comprehensively and pragmatically studies the fundamental trade-offs posed by these challenges collectively. In particular, we examine the robustness of an HRIS-assisted massive multiple-input multiple-output (mMIMO) system by considering its critical components and stringent conditions. The latter include: (a) two extremes of implementation complexity, represented by minimalist operation designs of two distinct HRIS hardware architectures, and (b) an oblivious BS that fully embraces probe distortion. To make our analysis possible, we propose a physical-layer orchestration framework that aligns HRIS and mMIMO operations. We present empirical evidence that autonomous RISs remain promising under stringent conditions and outline research directions to deepen probe distortion understanding.

Figures (9)

• Figure 1: Open system models of autonomous RISs allow multiple HRISs to enhance communication performance between BSs and UEs without dedicated and explicit control.
• Figure 2: Geometric representation of the -assisted system, illustrating the , , and , with channel notation defined for the direction.
• Figure 3: Temporal evolution of the proposed -layer orchestration framework within a coherence block. The system alternates between two operation phases: $1$) chest and $2$) comm; while the autonomously alternates between two operation modes: $i$) probe and $ii$) reflection.
• Figure 4: Example of the evolution of the equivalent - channel gain, $\mathbf{h}_k[s]$ in \ref{['eq:equivalent-channel-over-samples']}, over a coherence block of $32$ subblocks with the changing its configuration every subblock. During the probe mode, the channel state of the probing equivalent channels can vary significantly whereas the reflecting equivalent channel remains stable during the reflection mode. The oblivious attempts to estimate the reflecting equivalent channel while the is probing; as a result, probe distortion can degrade the quality of the at the .
• Figure 5: Sample-based organization of a coherence block.

Theorems & Definitions (16)

• Definition 1: Subblock
• Example 1
• Definition 2: Duration of the modes
• Definition 3: Relative duration of the modes within the CHEST phase
• Corollary 1: PD-enabled probing performance
• proof
• Corollary 2: DSP-enabled probing performance
• proof
• Corollary 3: CHEST at the BS
• proof