iBEAMS: A Unified Framework for Secure and Energy-Efficient ISAC-MIMO Systems leveraging Bayesian Enhanced learning, and Adaptive Game-Theoretic Multi-Layer Strategies
Mehzabien Iqbal, Ahmad Y. Javaid

TL;DR
iBEAMS is a hierarchical framework that enhances secure, energy-efficient ISAC-MIMO systems by integrating Bayesian learning and game-theoretic strategies to counter eavesdroppers in mmWave and THz bands.
Contribution
The paper introduces a novel unified hierarchical framework combining Stackelberg, GNE, and Bayesian methods for secure and energy-efficient ISAC with distributed hybrid nodes.
Findings
Achieves 4.4--4.7 bps/Hz average secrecy rate.
Doubles Secrecy Energy Efficiency compared to baseline.
Maintains zero outage at 28 GHz.
Abstract
Next generation ISAC networks operating in the mmWave and THz bands must provide physical layer secrecy against potential eavesdroppers (mobile and static) while coordinating distributed hybrid edge nodes under stringent power and QoS constraints. However, these requirements are rarely addressed in a unified manner in existing ISAC physical layer security designs. This paper proposes iBEAMS, a hierarchical Stackelberg--GNE--Bayesian framework for secure and energy efficient ISAC with distributed hybrid nodes. The proposed architecture integrates: (i) a Stackelberg leader at the ISAC base station that jointly optimizes total transmit power, power splitting among confidential data, artificial noise, and sensing, and broadcasts incentive prices to shape follower utilities; (ii) a Generalized Nash Equilibrium Game in which hybrid nodes select transmit powers and transmission versus jamming…
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