Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

TL;DR

This paper proposes a novel multi-tier computing network architecture using transmissive RMS transceivers, formulating a complex energy minimization problem and solving it with advanced optimization techniques, demonstrating significant energy savings.

Contribution

Introduces a new transmissive RMS transceiver-enabled network architecture and develops an effective optimization algorithm for energy-efficient resource allocation.

Findings

Proposed algorithm reduces total energy consumption significantly.

Transmissive RMS transceivers enhance multi-tier network performance.

Optimization approach outperforms benchmark methods.

Abstract

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.