Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Joint Beamforming and Offloading Design for Integrated Sensing, Communication and Computation System

Peng Liu, Zesong Fei, Xinyi Wang, Yiqing Zhou, Yan Zhang, Fan Liu

TL;DR

The paper addresses latency-minimization in a three-tier ISCC system with a cloud, multiple MEC servers, and multiple UTs by jointly designing transmit beamforming and sensing-data offloading under SINR and power constraints. It develops an alternating optimization framework that decouples the problem into beamforming and offloading subproblems; beamforming is solved via multidimensional fractional programming with a quadratic transform and SCA, while offloading decisions are obtained from a relaxed linear program and then binarized. The proposed THCO-BO method demonstrates faster convergence and lower task latency than two-tier MEC-based approaches and other three-tier baselines, particularly under limited MEC resources and varying bandwidth/power budgets. The approach enables efficient ISAC task processing in dense networks by exploiting both edge and cloud computing resources, offering scalable latency improvements for sensing and computation workloads.

Abstract

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework.

Joint Beamforming and Offloading Design for Integrated Sensing, Communication and Computation System

TL;DR

The paper addresses latency-minimization in a three-tier ISCC system with a cloud, multiple MEC servers, and multiple UTs by jointly designing transmit beamforming and sensing-data offloading under SINR and power constraints. It develops an alternating optimization framework that decouples the problem into beamforming and offloading subproblems; beamforming is solved via multidimensional fractional programming with a quadratic transform and SCA, while offloading decisions are obtained from a relaxed linear program and then binarized. The proposed THCO-BO method demonstrates faster convergence and lower task latency than two-tier MEC-based approaches and other three-tier baselines, particularly under limited MEC resources and varying bandwidth/power budgets. The approach enables efficient ISAC task processing in dense networks by exploiting both edge and cloud computing resources, offering scalable latency improvements for sensing and computation workloads.

Abstract

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework.
Paper Structure (9 sections, 26 equations, 4 figures, 1 algorithm)

This paper contains 9 sections, 26 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Algorithm Design
  5. Beamforming Matrix Optimization
  6. Offloading Decision Optimization
  7. Overall Algorithm
  8. Simulation Results
  9. Conclusion

Figures (4)

  • Figure 1: An illustration of the three-tier ISCC system.
  • Figure 2: Average Sensing Task Execution Time v.s. Iteration Number.
  • Figure 3: Average Sensing Task Execution Time v.s. ISAC Signal Bandwidth.
  • Figure 4: Average Sensing Task Execution Time v.s. Transmit Power Budget.