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Reconfigurable Intelligent Surface for Green Edge Inference

Sheng Hua, Yong Zhou, Kai Yang, Yuanming Shi

TL;DR

This article considers an RIS-aided green edge inference system, where the inference tasks generated from resource-constrained mobile devices are uploaded to and cooperatively performed at multiple resource-enhanced base stations (BSs).

Abstract

Reconfigurable intelligent surface (RIS) as an emerging cost-effective technology can enhance the spectrum- and energy-efficiency of wireless networks. In this paper, we consider an RIS-aided green edge inference system, where the inference tasks generated from resource-limited mobile devices (MDs) are uploaded to and cooperatively performed at multiple resource-enhanced base stations (BSs). Taking into account both the computation and uplink/downlink transmit power consumption, we formulate an overall network power consumption minimization problem, which calls for the joint design of the set of tasks performed by each BS, transmit and receive beamforming vectors of the BSs, transmit power of the MDs, and uplink/downlink phase-shift matrices at the RIS. Such a problem is a mixed combinatorial optimization problem with nonconvex constraints and is highly intractable. To address the challenge of the combinatorial objective, a group sparse reformulation is proposed by exploiting the group sparsity structure of the beamforming vectors, while a block-structured optimization (BSO) approach is proposed to decouple the optimization variables. Finally, we propose a BSO with mixed $\ell_{1,2}$-norm and difference-of-convex-functions (DC) based three-stage framework to solve the problem, where the mixed $\ell_{1,2}$-norm is adopted to induce the group sparsity of beamforming vectors and DC is adopted to effectively handle the nonconvex rank-one constraint after matrix lifting. Numerical results demonstrate the supreme gain of deploying an RIS and confirm the effectiveness of the proposed algorithm over the baseline algorithms.

Reconfigurable Intelligent Surface for Green Edge Inference

TL;DR

This article considers an RIS-aided green edge inference system, where the inference tasks generated from resource-constrained mobile devices are uploaded to and cooperatively performed at multiple resource-enhanced base stations (BSs).

Abstract

Reconfigurable intelligent surface (RIS) as an emerging cost-effective technology can enhance the spectrum- and energy-efficiency of wireless networks. In this paper, we consider an RIS-aided green edge inference system, where the inference tasks generated from resource-limited mobile devices (MDs) are uploaded to and cooperatively performed at multiple resource-enhanced base stations (BSs). Taking into account both the computation and uplink/downlink transmit power consumption, we formulate an overall network power consumption minimization problem, which calls for the joint design of the set of tasks performed by each BS, transmit and receive beamforming vectors of the BSs, transmit power of the MDs, and uplink/downlink phase-shift matrices at the RIS. Such a problem is a mixed combinatorial optimization problem with nonconvex constraints and is highly intractable. To address the challenge of the combinatorial objective, a group sparse reformulation is proposed by exploiting the group sparsity structure of the beamforming vectors, while a block-structured optimization (BSO) approach is proposed to decouple the optimization variables. Finally, we propose a BSO with mixed -norm and difference-of-convex-functions (DC) based three-stage framework to solve the problem, where the mixed -norm is adopted to induce the group sparsity of beamforming vectors and DC is adopted to effectively handle the nonconvex rank-one constraint after matrix lifting. Numerical results demonstrate the supreme gain of deploying an RIS and confirm the effectiveness of the proposed algorithm over the baseline algorithms.

Paper Structure

This paper contains 25 sections, 2 theorems, 57 equations, 4 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Contributions
  3. Organization and Notations
  4. System Model and Problem Formulation
  5. System Model
  6. Uplink Transmission
  7. Downlink Transmission
  8. Power Consumption Model
  9. Computation Power Consumption
  10. Communication Power Consumption
  11. Problem Formulation and Analysis
  12. Group Sparse Reformulation
  13. Block-Structured Optimization Approach
  14. Optimizing Variables text, and text
  15. Optimizing Variable text
  16. ...and 10 more sections

Key Result

Proposition 1

With the BSO approach, the objective value of $\mathscr{P}_\textrm{1}$ is non-increasing in the consecutive iterations.

Figures (4)

  • Figure 1: A reconfigurable intelligent surface (RIS)-aided edge inference system with $N$ base stations (BSs) collaboratively serving $K$ mobile devices (MDs). The RIS is deployed on the facade of a building.
  • Figure 2: The overall network power consumption versus target SINR $\gamma_{k}^{{\textrm{DL}}}$.
  • Figure 3: Convergence behaviors of both BSO-$\ell_{1,2}$-DC and BSO-$\ell_{1,2}$-SDR algorithms.
  • Figure 4: Different components of the overall network power consumption versus target SINR $\gamma_{k}^{{\textrm{DL}}}$.

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Proposition 2
  • proof