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Future Resource Bank for ISAC: Achieving Fast and Stable Win-Win Matching for Both Individuals and Coalitions

Houyi Qi, Minghui Liwang, Seyyedali Hosseinalipour, Liqun Fu, Sai Zou, Wei Ni

TL;DR

The paper tackles the challenge of resource allocation in dynamic ISAC networks by introducing FRBank, a hybrid offline-online market that jointly serves communications and sensing. It advances the state of the art with two matching mechanisms (offRFW$^2$M for offline contracts and onEBW$^2$M as an online backup) and overbooking to absorb demand fluctuations, complemented by sensing coalitions to reduce redundancy. The authors prove key properties (stability, individual rationality, and weak Pareto optimality) and demonstrate performance gains in social welfare, latency, and energy efficiency through extensive synthetic and real-world evaluations. The work offers a practical, scalable framework for fast, stable, and mutually beneficial resource trading in ISAC networks, with clear directions for adaptive overbooking, coalition economics, and volunteer incentives.

Abstract

Future wireless networks must support emerging applications where environmental awareness is as critical as data transmission. Integrated Sensing and Communication (ISAC) enables this vision by allowing base stations (BSs) to allocate bandwidth and power to mobile users (MUs) for communications and cooperative sensing. However, this resource allocation is highly challenging due to: (i) dynamic resource demands from MUs and resource supply from BSs, and (ii) the selfishness of MUs and BSs. To address these challenges, existing solutions rely on either real-time (online) resource trading, which incurs high overhead and failures, or static long-term (offline) resource contracts, which lack flexibility. To overcome these limitations, we propose the Future Resource Bank for ISAC, a hybrid trading framework that integrates offline and online resource allocation through a level-wise client model, where MUs and their coalitions negotiate with BSs. We introduce two mechanisms: (i) Role-Friendly Win-Win Matching (offRFW$^2$M), leveraging overbooking to establish risk-aware, stable contracts, and (ii) Effective Backup Win-Win Matching (onEBW$^2$M), which dynamically reallocates unmet demand and surplus supply. We theoretically prove stability, individual rationality, and weak Pareto optimality of these mechanisms. Through simulations, we show that our framework improves social welfare, latency, and energy efficiency compared to existing methods.

Future Resource Bank for ISAC: Achieving Fast and Stable Win-Win Matching for Both Individuals and Coalitions

TL;DR

The paper tackles the challenge of resource allocation in dynamic ISAC networks by introducing FRBank, a hybrid offline-online market that jointly serves communications and sensing. It advances the state of the art with two matching mechanisms (offRFWM for offline contracts and onEBWM as an online backup) and overbooking to absorb demand fluctuations, complemented by sensing coalitions to reduce redundancy. The authors prove key properties (stability, individual rationality, and weak Pareto optimality) and demonstrate performance gains in social welfare, latency, and energy efficiency through extensive synthetic and real-world evaluations. The work offers a practical, scalable framework for fast, stable, and mutually beneficial resource trading in ISAC networks, with clear directions for adaptive overbooking, coalition economics, and volunteer incentives.

Abstract

Future wireless networks must support emerging applications where environmental awareness is as critical as data transmission. Integrated Sensing and Communication (ISAC) enables this vision by allowing base stations (BSs) to allocate bandwidth and power to mobile users (MUs) for communications and cooperative sensing. However, this resource allocation is highly challenging due to: (i) dynamic resource demands from MUs and resource supply from BSs, and (ii) the selfishness of MUs and BSs. To address these challenges, existing solutions rely on either real-time (online) resource trading, which incurs high overhead and failures, or static long-term (offline) resource contracts, which lack flexibility. To overcome these limitations, we propose the Future Resource Bank for ISAC, a hybrid trading framework that integrates offline and online resource allocation through a level-wise client model, where MUs and their coalitions negotiate with BSs. We introduce two mechanisms: (i) Role-Friendly Win-Win Matching (offRFWM), leveraging overbooking to establish risk-aware, stable contracts, and (ii) Effective Backup Win-Win Matching (onEBWM), which dynamically reallocates unmet demand and surplus supply. We theoretically prove stability, individual rationality, and weak Pareto optimality of these mechanisms. Through simulations, we show that our framework improves social welfare, latency, and energy efficiency compared to existing methods.

Paper Structure

This paper contains 46 sections, 26 theorems, 90 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Challenges and Motivations
  3. Related Works
  4. Resource Allocation in Conventional Wireless Communication and Sensor Networks
  5. Resource Allocation in ISAC Networks
  6. Matching-Based Resource Provisioning
  7. Overview and Summary of Contributions
  8. Overview and Key System Components
  9. Overview of Our Methodology
  10. Modeling of MUs and BSs
  11. Modeling of MUs
  12. Modeling of BSs
  13. Modeling of ISAC
  14. Communication Model
  15. Sensing Model
  16. ...and 31 more sections

Key Result

Proposition 1

(Individual Rationality of offRFW$^2$M) The proposed offRFW$^2$M mechanism ensures individual rationality for BSs, individual MUs, and sensing coalitions as follows: $\bullet$ For each BS: (i) the bandwidth and power resources of BS $s_j$ booked to matched clients $\varphi^\mathsf{(c)}\left(s_j\righ

Figures (6)

  • Figure 1: Schematic of our future resource bank for ISAC networks, illustrating two types of services (communication and sensing) and two levels of clients (individual MUs and sensing coalitions).
  • Figure 2: Performance comparisons in terms of social welfare, utility of BSs, and utility of MUs. We consider 5 BSs in (a)-(c), and 7 BSs in (d)-(f).
  • Figure 3: Performance comparisons in terms of RT, NI, DIBC, and ECIBC metrics.
  • Figure 4: Demonstration of individual rationality of MUs and BSs.
  • Figure 5: Performance comparisons in terms of RDSLC and DRLC.
  • ...and 1 more figures

Theorems & Definitions (50)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Proposition 1
  • Proposition 2
  • Proposition 3
  • ...and 40 more