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Joint Spatial Registration and Resource Allocation for Transmissive RIS Enabled Cooperative ISCC Networks

Ziwei Liu, Wen Chen, Zhendong Li, Qiong Wu

TL;DR

This work tackles energy-efficient, TRIS-enabled cooperative ISCC networks by formulating a joint optimization that links sensing, offloading, and edge computing. A block-coordinate-descent framework decouples the nonconvex problem into sensing-beam scheduling, beamforming, and data/time allocation subproblems, while a spatial-registration mechanism via adjustable beamwidth aligns multi-node sensing regions. A combination of SDP, SCA, and iterative rank minimization is used to handle rank constraints and nonconvex rate terms, with convergence guaranteed through a nonincreasing objective sequence. The results show substantial energy savings and enhanced offloading performance compared with traditional transceivers and non-cooperative baselines, highlighting the practical value of TRIS and spatial registration for future ISCC networks.

Abstract

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-driven cooperative integrated sensing, computing, and communication (ISCC) network to meet the requirement for a diverse network with low energy consumption. The cooperative base stations (BSs) are equipped with TRIS transceivers to accomplish sensing data acquisition, communication offloading, and computation in a time slot. In order to obtain higher cooperation gain, we utilize a signal-level spatial registration algorithm, which is realized by adjusting the beamwidth. Meanwhile, for more efficient offloading of the computational task, multistream communication is considered, and rank-$N$ constraints are introduced, which are handled using an iterative rank minimization (IRM) scheme. We construct an optimization problem with the objective function of minimizing the total energy consumption of the network to jointly optimize the beamforming matrix, time slot allocation, sensing data allocation and sensing beam scheduling variables. Due to the coupling of the variables, the proposed problem is a non-convex optimization problem, which we decouple and solve using a block coordinate descent (BCD) scheme. Finally, numerical simulation results confirm the superiority of the proposed scheme in improving the overall network performance and reducing the total energy consumption of the network.

Joint Spatial Registration and Resource Allocation for Transmissive RIS Enabled Cooperative ISCC Networks

TL;DR

This work tackles energy-efficient, TRIS-enabled cooperative ISCC networks by formulating a joint optimization that links sensing, offloading, and edge computing. A block-coordinate-descent framework decouples the nonconvex problem into sensing-beam scheduling, beamforming, and data/time allocation subproblems, while a spatial-registration mechanism via adjustable beamwidth aligns multi-node sensing regions. A combination of SDP, SCA, and iterative rank minimization is used to handle rank constraints and nonconvex rate terms, with convergence guaranteed through a nonincreasing objective sequence. The results show substantial energy savings and enhanced offloading performance compared with traditional transceivers and non-cooperative baselines, highlighting the practical value of TRIS and spatial registration for future ISCC networks.

Abstract

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-driven cooperative integrated sensing, computing, and communication (ISCC) network to meet the requirement for a diverse network with low energy consumption. The cooperative base stations (BSs) are equipped with TRIS transceivers to accomplish sensing data acquisition, communication offloading, and computation in a time slot. In order to obtain higher cooperation gain, we utilize a signal-level spatial registration algorithm, which is realized by adjusting the beamwidth. Meanwhile, for more efficient offloading of the computational task, multistream communication is considered, and rank- constraints are introduced, which are handled using an iterative rank minimization (IRM) scheme. We construct an optimization problem with the objective function of minimizing the total energy consumption of the network to jointly optimize the beamforming matrix, time slot allocation, sensing data allocation and sensing beam scheduling variables. Due to the coupling of the variables, the proposed problem is a non-convex optimization problem, which we decouple and solve using a block coordinate descent (BCD) scheme. Finally, numerical simulation results confirm the superiority of the proposed scheme in improving the overall network performance and reducing the total energy consumption of the network.

Paper Structure

This paper contains 24 sections, 2 theorems, 57 equations, 12 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. The characteristics and necessity of TRIS
  4. Network Model and Mechanisms of ISCC
  5. Channel Model
  6. Radar Channel
  7. Communication Channel
  8. Signal Model
  9. Computation Model
  10. Data Acquisition Model
  11. ISCC Devices Local Computation Model
  12. MEC Remote Computation Model
  13. Multi-source Information Fusion Mechanisms
  14. Problem Formulation
  15. Joint Optimization Algorithm For the ISCC Network
  16. ...and 9 more sections

Key Result

Proposition 1

The $(g+1)$th largest eigenvalue $\lambda_{n-g}$ of matrix ${\bf U} \in \mathbb{S}_+^n$ is no greater than $e$ if and only if $e{\bf I}_{n-g} - {\bf V}^T{\bf U}{\bf V} \succeq {\bf 0}$, where ${\bf I}_{n-g}$ is the identity matrix with a dimension of $n-g$, ${\bf V} \in \mathbb{R}^{n \times (n-g)}$

Figures (12)

  • Figure 1: The principles and compositions of TRIS transceiver.
  • Figure 2: ISCC networks.
  • Figure 3: Mechanisms of ISCC.
  • Figure 4: Schematic diagram of beam coverage and beamwidth adjustment.
  • Figure 5: Convergence process: Total energy consumption versus number of iterations under different TRIS elements ($P_t = 1 {\rm W}, T=1{\rm s}$).
  • ...and 7 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Lemma 1