Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 180-192· 0 citations· 18 references
TL;DR
Experimental findings indicate that TFL achieves quicker convergence and delivers a superior weighted ISAC utility for users, alongside improved communication and sensing performance, and a more potent blend of communication-sensing advantages compared to per-cell learning, standard federated learning, privacy-compromising federated learning, and mobility-aware federated learning approaches.
Abstract
The development of next-generation wireless technologies heavily relies on Integrated sensing and communication (ISAC) for its ability to handle both data transfer and environmental awareness using a singular infrastructure, leveraging shared spectrum or hardware. However, multi-cell ISAC systems face issues with inter-cell interference, heterogeneous statistics from cell to cell, and user mobility on the time variance of the channel itself. Coordination of beamforming has reached a deadlock as federated learning (FL) and personalized-FL techniques only apply to the instantaneous channel state or snapshot of the channel. As a result of this process, FL techniques fail to account for what has been experienced through the channel over a long period of time. Due to this, FL and personalized FL techniques are unable to be applied to high-mobility regimes. In this paper, we propose a mobility aware Beamforming Temporal Federated Learning (TFL) framework for a Integrated Sensing and Communication (ISAC) multi-cell system. We introduce a novel method to leverage temporal sequence of prior communication/sensing channels as input to the federated learning framework allowing for adaptive and robust beamforming to the user's mobility and movement of sensing targets and Doppler effect. Experimental findings indicate that TFL achieves quicker convergence and delivers a superior weighted ISAC utility for users, alongside improved communication and sensing performance, and a more potent blend of communication-sensing advantages compared to per-cell learning, standard federated learning, privacy-compromising federated learning, and mobility-aware federated learning approaches.
This study proposes a novel FL framework where the CF-mMIMO participants are APs, and proposes a client selection strategy that prioritizes APs based on their average received signal power, showing competitive performance compared to baseline methods, while also addressing the scalability and privacy requirements of mMTC systems.
Ali Elkeshawy, W. Jaafar, Haifa Fares et al.· IEEE Transactions on Machine...· 0 citations
Distinct from conventional integrated sensing and communication (ISAC) techniques, breakthroughs in LoRa-aided ISAC achieve hardware-unified sensing and communication capabilities for low-power devices. By combining such a novel technology with wireless power transfer (WPT), it yields wireless powered sensing and communication networks (WPSCNs). Information fusion, a widely adopted technique in such networks, relies heavily on the fresh fused information for effective system decision-making. However, age of information (AoI) is ineffective for measuring freshness of fused information. To tackle this dilemma, a novel metric, age of sensing (AoS), is introduced. Specifically, we study timeliness of a WPSCN, where a fusion center (FC) wirelessly powers sensing nodes (SNs) to collect sensing information from the SNs for generating fused information. Moreover, the impact of multi-cycle sensing on the AoS is first explored in the WPSCNs. We also adopt the adaptive transmission strategy for flexibly reducing the transmission duration. After obtaining a closed-form of the average AoS, it is then minimised by optimising WPT duration, multi-cycle sensing strategy and the SN locations. Ultimately, the numerical results validate the accuracy of our theoretical analysis. The effect of multi-cycle sensing and the superiority of adaptive transmission strategy are also demonstrated. Our findings offer valuable insights for analysing and improving the fusion system timeliness, and provide a theoretical foundation for the practical deployment of the WPSCNs.
Yali Zheng, Shuai Shen, Ziye Xiang et al.· IEEE Transactions on Communi...· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Z. Behdad, Özlem Tuğfe Demir, Ki Won Sung et al.· 0 citations
In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data rates, efficiency, and security. This requirement has motivated the extensive adoption of machine learning methods to develop intelligent and real-time network management frameworks, enabling the system to continuously monitor and react to channel variations and user behavior while maintaining efficient information delivery. In this context, the integration of machine learning with beamforming enables adaptive and data-driven beam direction selection, improving both the efficiency and security of wireless links. In this work, a 3GPP-based system model is first implemented under a no-attacker scenario, and an exhaustive search is employed as a reference to identify the best beamforming configurations. The proposed framework is then evaluated in the presence of an attacker and under different network scalability conditions. We demonstrate that the reinforcement learning-based approaches, namely Q-learning and SARSA (State-Action-Reward-State-Action), consistently outperform random selection in terms of total channel capacity, attacker detection accuracy, and performance stability. Among the evaluated reinforcement learning methods, Q-learning achieves the best overall trade-off between detection accuracy and computational efficiency. Our results indicate that the proposed framework provides a stable, scalable, and effective solution for joint beamforming and security-aware decision-making in dynamic and adversarial wireless environments.
Integrated sensing and communication (ISAC) extends mobile networks from information transfer toward perception of passive objects and environments. By exploiting propagation delay, Doppler, angle, and temporal variation, a mobile network can support detection and tracking while reusing licensed spectrum, infrastructure, and edge computing at network scale and under operator control. Unlike conventional radar, ISAC must additionally address multi-application access, heterogeneous sensing entities, uncertainty, privacy, trust, and integration with communication services. This article reviews the evolution of ISAC in 3GPP from 5G-Advanced to 6G. It explains Release-19 ISAC service requirements and channel models, the Release-20 sensing function architecture and reporting abstractions, and the 5G monostatic baseline for drone detection and tracking. It also examines 6G-native sensing, including passive object sensing, communication assistance, multiple sensing modes, and multi-source data integration. By connecting the evolving 3GPP architecture and radio studies across 5G-Advanced and 6G, this article provides a unified technical perspective on current standardization choices, their implementation tradeoffs, and the open challenges shaping network-grade sensing.
