Configuration Tuning for ISAC: Cost-Efficient Adaptation via RACE-CMA

This paper studies a feedback driven configuration tuning framework for adaptive sensing feedback in Integrated Sensing and Communication (ISAC) systems. We propose a framework in which the User Equipment (UE) adapts sensing parameters under dynamic conditions while satisfying network defined constraints. The problem is formulated as a stochastic constrained optimization problem, to improve sensing reliability and latency. We consider a bistatic ISAC sensing feedback setup and instantiate the framework via threshold optimization as a representative case study, enabling benchmarking against baseline methods. To ensure efficiency under UE computational limits, we propose Ranking Aware, Constrained, and Efficient CMAES (RACE CMA), which integrates two stage racing, common random numbers, noise aware ranking, and feasible constraint handling. Results show that the proposed approach improves sensing reliability by about 35 percent while reducing computational cost by about 25 percent, yielding roughly a twofold gain in performance cost efficiency. This highlights that UE side configuration tuning is a promising mechanism for enhancing closed loop ISAC performance under practical system constraints.

802.11bf Multiband Passive Sensing: Reusing Wi-Fi Signaling for Sensing

This paper presents a novel multiband passive sensing system that leverages IEEE 802.11bf Wi-Fi signals for environmental sensing, focusing on both sub-7 GHz and millimeter-wave (mmWave) bands. By combining Channel State Information (CSI) from multiple bands, the system enhances accuracy and reliability in detecting human presence, movement, and activities in indoor environments. Utilizing a novel model, called MILAGRO, the system demonstrates robust performance across different scenarios, including monitoring human presence in workspaces and tracking movement in corridors. Experimental results show high accuracy (95-100%), with improved performance by integrating multiband data. The system also addresses key security concerns associated with passive sensing, proposing measures to mitigate potential risks. This work advances the use of Wi-Fi for passive sensing by reducing reliance on active sensing infrastructure and extending the capabilities of low-cost, non-intrusive environmental monitoring.

ISAC Channel Modelling -- Perspectives from ETSI

Integrated Sensing and Communications (ISAC) is defined as one of six usage scenarios in the ITU-R International Mobile Telecommunications (IMT) 2030 framework for 6G. ISAC is envisioned to introduce the sensing capability into the cellular network, where sensing may be obtained using the cellular radio frequency (RF) signals with or without additional auxiliary sensors. To enable ISAC, specification bodies such as European Telecommunications Standards Institute (ETSI) and Third Generation Partnership Project (3GPP) have already started to look into detailed ISAC use cases, their requirements, and the channel models and evaluation methodologies that are necessary to design and evaluate ISAC performance. With focus on the channel model, the current communication-centric channel models like those specified in 3GPP technical report (TR) 38.901 do not cover the RF signals interactions between the transmitter, target object, receiver and their surrounding environment. To bridge this gap, 3GPP has been looking into the basic changes that are necessary to make to their TR38.901 channel model with focus on selected use cases from the 3GPP SA1 5G-Advanced feasibility study. In parallel, ETSI ISAC Industry Specification Group (ISG) has been studying the more advanced ISAC channel modelling features that are needed to support the variety of ISAC use cases envisioned in 6G. In this paper, we present the baseline and advanced features developed thus far in 3GPP and ETSI ISAC ISG, respectively, towards a comprehensive view of the ISAC channel model in 6G.

Smart Interference Management xApp using Deep Reinforcement Learning

Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutions are essential for tackling interference, and thus achieving the desired performance levels particularly at the cell-edge. In future network architecture, RAN intelligent controller (RIC) running with near-real-time applications, called xApps, is considered as a potential component to enable RRM. In this paper, based on deep reinforcement learning (RL) xApp, a joint sub-band masking and power management is proposed for smart interference management. The sub-band resource masking problem is formulated as a Markov Decision Process (MDP) that can be solved employing deep RL to approximate the policy functions as well as to avoid extremely high computational and storage costs of conventional tabular-based approaches. The developed xApp is scalable in both storage and computation. Simulation results demonstrate advantages of the proposed approach over decentralized baselines in terms of the trade-off between cell-centre and cell-edge user rates, energy efficiency and computational efficiency.