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

TL;DR

This paper introduces a hybrid resource trading framework for ISAC in wireless networks, combining offline and online mechanisms to enable stable, flexible, and efficient resource allocation for both users and base stations.

Contribution

It proposes the Future Resource Bank framework with two novel mechanisms, offRFW$^2$M and onEBW$^2$M, ensuring stability, rationality, and optimality in resource trading.

Findings

Improves social welfare, latency, and energy efficiency in simulations.

Ensures stability, individual rationality, and Pareto optimality of resource contracts.

Effectively reallocates unmet demand and surplus supply dynamically.

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.