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Author

A. Nallanathan

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Open access 2026

Fast Online Channel Estimation in Massive MIMO: A Zero-Shot Self-Supervised Approach

With the growing number of antennas in massive multiple-input multiple-output (MIMO) systems, robust and fast channel estimation becomes increasingly critical yet remains highly challenging. In this work, we propose a lightweight zero-shot self-supervised (ZS-SS) learning framework. It leverages non-local self-similari...

Zijun Gao, Wenqiang Yi, Fatma Benkhelifa et al. · 0 citations
2026

Hybrid Learning Framework for Throughput Enhancement in IoT Networks With Semi-Grant-Free NOMA

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user,...

M. Fayaz, Sohail Abbas, Abdullah Alajmi et al. · 0 citations
2026

PASS-Enabled Multi-UAV Integrated Sensing and Communications (ISAC): A Genetic Algorithm

A pinching-antenna system (PASS)-enabled multi-UAV integrated sensing and communication (ISAC) framework is proposed for adaptive downlink communications and UAV sensing. By jointly optimizing the pinching antenna (PA) activation, waveguide-level baseband precoding, and PA-level radiation power, the weighted sum of com...

Yang-Lin Hu, Tian-Kui Zhang, Xiao-Xia Xu et al. · 1 citation
#edge computing Open access Aug 2026

Distributed Trajectory Planning and Resource Allocation for Dynamic Multi-UAV Collaborative Computing

A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner and outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.

Tian-Kui Zhang, Wenlong Xu, Tian-Yi Shi et al. · 0 citations
2026

Distribution Deviation-Aware Split Federated Learning in Resource-Limited Wireless Networks

The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burden...

Chunfeng Xie, Zhixiong Chen, Wenqiang Yi et al. · 1 citation

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