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Reconfigurable intelligent surface (RIS) as an emerging cost-effectivetechnology can enhance the spectrum- and energy-efficiency of wirelessnetworks. 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 basestations (BSs). Taking into account both the computation and uplink/downlinktransmit power consumption, we formulate an overall network power consumptionminimization problem, which calls for the joint design of the set of tasksperformed 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 nonconvexconstraints and is highly intractable. To address the challenge of thecombinatorial objective, a group sparse reformulation is proposed by exploitingthe group sparsity structure of the beamforming vectors, while ablock-structured optimization (BSO) approach is proposed to decouple theoptimization variables. Finally, we propose a BSO with mixed $\ell_{1,2}$-normand difference-of-convex-functions (DC) based three-stage framework to solvethe problem, where the mixed $\ell_{1,2}$-norm is adopted to induce the groupsparsity of beamforming vectors and DC is adopted to effectively handle thenonconvex rank-one constraint after matrix lifting. Numerical resultsdemonstrate the supreme gain of deploying an RIS and confirm the effectivenessof the proposed algorithm over the baseline algorithms.

Reconfigurable Intelligent Surface for Green Edge Inference