arXiv subjects

Muhammad Awais Jadoon

Publications and source records attributed to Muhammad Awais Jadoon.

4 recordsLinked to original sources

arXiv2026

Architectural Enhancements for Efficient Sensing Data Utilization in 6G ISAC

Current architecture proposals within standards development organizations such as ETSI and 3GPP enable sensing capabilities in mobile networks; however, they do not include a repository for storing sensing data. Such a repository can be used for AI model training and to complement ongoing sensing service provisioning by improving efficiency and accuracy. One way of realizing this is through the fusion of historical sensing data with live sensing data. In this paper, we study historical and live sensing data fusion for Integrated Sensing and Communication in future 6G systems and introduce a Sensing Data Storage Function to store historical sensing data and sensing results. We show how the Sensing Data Storage Function can be used with other network functions in a 6G architecture proposition for Integrated Sensing and Communication. We validate our proposal with a measurement model and show performance improvements in terms of detection probability and false-alarm rate. The network functionality to fuse and process sensing data combines live sensing measurements with previously sensed historical sensing data using a map-aware hard filter that rejects detections consistent with known static structures. Our simulation illustrates that, for a traffic junction scenario, map-aware hard filtering substantially reduces false alarms without degrading detection probability.

cs.NI↗

arXiv2023

Learning Random Access Schemes for Massive Machine-Type Communication with MARL

In this paper, we explore various multi-agent reinforcement learning (MARL) techniques to design grant-free random access (RA) schemes for low-complexity, low-power battery operated devices in massive machine-type communication (mMTC) wireless networks. We use value decomposition networks (VDN) and QMIX algorithms with parameter sharing (PS) with centralized training and decentralized execution (CTDE) while maintaining scalability. We then compare the policies learned by VDN, QMIX, and deep recurrent Q-network (DRQN) and explore the impact of including the agent identifiers in the observation vector. We show that the MARL-based RA schemes can achieve a better throughput-fairness trade-off between agents without having to condition on the agent identifiers. We also present a novel correlated traffic model, which is more descriptive of mMTC scenarios, and show that the proposed algorithm can easily adapt to traffic non-stationarities

cs.IT↗

arXiv2022

Collision Resolution with Deep Reinforcement Learning for Random Access in Machine-Type Communication

Grant-free random access (RA) techniques are suitable for machine-type communication (MTC) networks but they need to be adaptive to the MTC traffic, which is different from the human-type communication. Conventional RA protocols such as exponential backoff (EB) schemes for slotted-ALOHA suffer from a high number of collisions and they are not directly applicable to the MTC traffic models. In this work, we propose to use multi-agent deep Q-network (DQN) with parameter sharing to find a single policy applied to all machine-type devices (MTDs) in the network to resolve collisions. Moreover, we consider binary broadcast feedback common to all devices to reduce signalling overhead. We compare the performance of our proposed DQN-RA scheme with EB schemes for up to 500 MTDs and show that the proposed scheme outperforms EB policies and provides a better balance between throughput, delay and collision rate

cs.IT↗

arXiv2022

Deep Reinforcement Learning for Random Access in Machine-Type Communication

Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabilize the system to avoid low throughput and excessive delays. However, these backoff techniques show varying performance for different underlying assumptions and analytical models. Therefore, finding a better transmission policy for slotted ALOHA RA is still a challenge. In this paper, we show the potential of deep reinforcement learning (DRL) for RA. We learn a transmission policy that balances between throughput and fairness. The proposed algorithm learns transmission probabilities using previous action and binary feedback signal, and it is adaptive to different traffic arrival rates. Moreover, we propose average age of packet (AoP) as a metric to measure fairness among users. Our results show that the proposed policy outperforms the baseline EB transmission schemes in terms of throughput and fairness.

cs.IT↗