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Rotatable Antenna-Enabled Mobile Edge Computing

Qiyao Wang, Beixiong Zheng, Xue Xiong, Weidong Mei, Changsheng You, Qingqing Wu, Jie Tang

Abstract

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

Rotatable Antenna-Enabled Mobile Edge Computing

Abstract

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.
Paper Structure (13 sections, 34 equations, 4 figures, 1 algorithm)

This paper contains 13 sections, 34 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model And Problem Formulation
  3. Communication Model
  4. Computing Model
  5. Problem Formulation
  6. Proposed Algorithm For Problem (P1)
  7. Joint Optimization of Offloaded Data Size and Edge Computing Resource Allocation
  8. Joint Optimization of Receive Beamforming and Pointing Matrix
  9. Overall Algorithm
  10. Simulation Results
  11. Conclusion
  12. Derivations of $\nabla u_{k,j}(\vec{\mathbf{f}}_n)$ and $\nabla^2 u_{k,j}(\vec{\mathbf{f}}_n)$
  13. Construction of $\delta_{k,j,n}$

Figures (4)

  • Figure 1: An RA-enabled MEC system.
  • Figure 2: The maximum computation latency versus offloading power.
  • Figure 3: The maximum computation latency versus the maximum computing capacity.
  • Figure 4: The maximum computation latency versus number of devices.