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Exploring Hybrid Active-Passive RIS-Aided MEC Systems: From the Mode-Switching Perspective

Hao Xie, Dong Li, Bowen Gu

TL;DR

This work proposes a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes, and develops an alternating optimization-based iterative algorithm that can achieve flexible mode switching and significantly outperforms existing algorithms.

Abstract

Mobile edge computing (MEC) has been regarded as a promising technique to support latencysensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy- efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost. In light of the intractability of the problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition (SVD) to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

Exploring Hybrid Active-Passive RIS-Aided MEC Systems: From the Mode-Switching Perspective

TL;DR

This work proposes a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes, and develops an alternating optimization-based iterative algorithm that can achieve flexible mode switching and significantly outperforms existing algorithms.

Abstract

Mobile edge computing (MEC) has been regarded as a promising technique to support latencysensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy- efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost. In light of the intractability of the problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition (SVD) to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.
Paper Structure (31 sections, 83 equations, 14 figures, 1 table, 3 algorithms)

This paper contains 31 sections, 83 equations, 14 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Backgroud
  3. Motivation and Contributions
  4. Organization
  5. System Model and Problem Formulation
  6. Local Computing
  7. Task Offloading
  8. Problem Formulation
  9. The Mode-switching Algorithm For The Hybrid RIS
  10. Optimizing $p_k$, $t_k$, and $\beta_k$
  11. Optimizing $\boldsymbol{\rm \Theta}_k$, $\alpha_{k,n}$, $\boldsymbol{\rm \Lambda}_k$, and $f_k^{\textrm{local}}$
  12. The Transformation of $C_5$
  13. The Transformations of $C_2$ and $E_{\rm total}$
  14. The Transformation of $C_4$
  15. Convergence Analysis
  16. ...and 16 more sections

Figures (14)

  • Figure 1: A hybrid RIS-aided MEC system.
  • Figure 2: The transmission time slot structure.
  • Figure 3: The simulated scenario of the considered hybrid RIS-aided system.
  • Figure 4: Tradeoff between energy consumption and the latency under different numbers of users.
  • Figure 5: Convergence analysis for the proposed algorithm.
  • ...and 9 more figures

Theorems & Definitions (5)

  • proof
  • proof
  • proof
  • proof
  • proof