@article{mehrotra_multi-dimensional_2025,

title = {Multi-Dimensional Sparse CSI Acquisition for Hybrid mmWave MIMO OTFS Systems},

author = {Anand Mehrotra and Jitendra Singh and Suraj Srivastava and Rahul Kumar Singh and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10918701},

doi = {10.1109/TCOMM.2025.3549501},

issn = {1558-0857},

year  = {2025},

date = {2025-09-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {9},

pages = {8330–8344},

abstract = {Multi-dimensional sparse channel state information (CSI) acquisition is conceived for Orthogonal time frequency space (OTFS) modulation-based millimetre wave (mmWave) multiple input and multiple output (MIMO) systems. A comprehensive end-to-end relationship is derived in the delay-Doppler (DDA) domain by additionally considering the angular parameters and a hybrid beamforming (HB) architecture. A time-domain pilot model tailored for CSI estimation (CE) in the DDA-domain is proposed, which exploits the inherent multi-dimensional (4D) sparsity that emerges in the DDA-domain during the CE process. An efficient low-complexity Bayesian learning (LC-BL) technique is conceived to fulfil the objective of CSI estimation in such systems. Subsequently, a comprehensive examination of the complexity of the algorithm under consideration is also provided. It is worth noting that the complexity of the BL scheme designed is similar to that of popular orthogonal matching pursuit (OMP), but significantly lower than that of the traditional expectation-maximization (EM) based BL technique. Moreover, a single-stage transmit precoder (TPC) and receiver combiner (RC) design is proposed. This procedure aims for maximizing the directional gain of the RF TPC/RC pair by optimizing their weights. Additionally, a series of comprehensive simulations are conducted which incorporate the use of a practical channel model and fractional Doppler shifts. In light of the inherent trade-offs between complexity and estimation algorithm performance, our proposed scheme, LC-BL, appears suitable, especially considering the substantial enhancement in the performance of CE compared to the existing benchmarks.},

keywords = {Bayes methods, Channel estimation, Complexity theory, delay-Doppler-angular domain, Estimation, high-mobility, hybrid precoding, Millimeter wave communication, MIMO, mmWave, Modulation, OFDM, OTFS, sparsity, Symbols, Training},

pubstate = {published},

tppubtype = {article}

}

@article{linfu_graph_2025,

title = {Graph Neural Network Aided Beamforming for Holographic Millimeter Wave MIMO Systems},

author = {Zou Linfu and Pan Zhiwen and Mohammed El-Hajjar},

url = {https://ieeexplore.ieee.org/document/10896848},

doi = {10.1109/TVT.2025.3544063},

issn = {1939-9359},

year  = {2025},

date = {2025-07-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

volume = {74},

number = {7},

pages = {10582–10595},

abstract = {Holographic multiple-input multiple-output (HMIMO) systems are considered as one of the potential techniques to meet the demands of next-generation communications by replacing costly and power-hungry devices with sub-half-wavelength antenna elements. However, optimizing the beamforming matrix in the base station (BS) for HMIMO systems is challenging, given the prohibitive overhead of directly estimating the channels between the BS and the user equipment. Instead of following the traditional method of channel estimation and beamforming optimization, in this paper we employ a deep-learning technique to optimize the beamformers at the BS based on a loss function. Specifically, in this paper we introduce a graph neural network (GNN) designed to map the received pilot signals to optimized beamforming matrices and to model interactions among user equipment within the network. The simulation results show that our deep-learning method effectively maximizes the sum-rate objective while using reduced number of pilots than traditional channel estimation and beamforming optimization techniques.},

keywords = {Accuracy, Array signal processing, Beamforming, Channel estimation, Downlink, Estimation, graph neural network, Graph neural networks, holographic MIMO, millimeter wave, Millimeter wave communication, OFDM, Optimization, Training},

pubstate = {published},

tppubtype = {article}

}

@article{smith_dimensional_2025,

title = {Dimensional Scaling Laws for Continuous Fluid Antenna Systems},

author = {Peter J. Smith and Amy S. Inwood and Michail Matthaiou and Rajitha Senanayake},

url = {https://ieeexplore.ieee.org/document/10965723},

doi = {10.1109/LWC.2025.3560861},

issn = {2162-2345},

year  = {2025},

date = {2025-07-01},

urldate = {2025-10-08},

journal = {IEEE Wireless Communications Letters},

volume = {14},

number = {7},

pages = {2004–2008},

abstract = {Consider the signal-to-noise ratio (SNR) of a continuous fluid antenna system (CFAS) operating over a Rayleigh fading channel. In this letter, we extend traditional system assumptions and consider spatially coherent isotropic correlation, continuous positioning of the antenna rather than discrete, and the use of multi-dimensional space (1D, 2D and 3D). By focusing on the upper tail of the received SNR distribution (the high SNR probability (HSP)), we are able to derive asymptotically exact closed-form formulas for the HSP. Finally, these results lead to scaling laws which describe the increase in the HSP as we employ more dimensions and the optimal CFAS dimensions.},

keywords = {3D antenna geometries, Antennas, Correlation, Fluid antenna systems, Fluids, high SNR probability, random fields, Rayleigh channels, Rayleigh fading, Shape, Signal to noise ratio, Tail, Three-dimensional displays, Training, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{wang_3d_2025,

title = {A 3D Spatial Information Compression Based Deep Reinforcement Learning Technique for UAV Path Planning in Cluttered Environments},

author = {Zhipeng Wang and Soon Xin Ng and Mohammed El-Hajjar},

url = {https://ieeexplore.ieee.org/document/10878448},

doi = {10.1109/OJVT.2025.3540174},

issn = {2644-1330},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Open Journal of Vehicular Technology},

volume = {6},

pages = {647–661},

abstract = {Unmanned aerial vehicles (UAVs) can be considered in many applications, such as wireless communication, logistics transportation, agriculture and disaster prevention. The flexible maneuverability of UAVs also means that the UAV often operates in complex 3D environments, which requires efficient and reliable path planning system support. However, as a limited resource platform, the UAV systems cannot support highly complex path planning algorithms in lots of scenarios. In this paper, we propose a 3D spatial information compression (3DSIC) based deep reinforcement learning (DRL) algorithm for UAV path planning in cluttered 3D environments. Specifically, the proposed algorithm compresses the 3D spatial information to 2D through 3DSIC, and then combines the compressed 2D environment information with the current UAV layer spatial information to train UAV agents for path planning using neural networks. Additionally, the proposed 3DSIC is a plug and use module that can be combined with various DRL frameworks such as Deep Q-Network (DQN) and deep deterministic policy gradient (DDPG). Our simulation results show that the training efficiency of 3DSIC-DQN is 4.028 times higher than that directly implementing DQN in a 100 textbackslashtimes 100 textbackslashtimes 50 3D cluttered environment. Furthermore, the training efficiency of 3DSIC-DDPG is 3.9 times higher than the traditional DDPG in the same environment. Moreover, 3DSIC combined with fast recurrent stochastic value gradient (FRSVG), which can be considered as the most state-of-the-art DRL algorithm for UAV path planning, exhibits 2.35 times faster training speed compared with the original FRSVG algorithm.},

keywords = {3D path planning, 3D spatial information compression, Autonomous aerial vehicles, Classification algorithms, Convergence, deep reinforcement learning, Navigation, Path planning, Principal component analysis, Search problems, Solid modeling, Three-dimensional displays, Training, training efficiency, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}

@article{chien_improved_2025,

title = {Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO},

author = {Trinh Van Chien and Nguyen Hoang Viet and Symeon Chatzinotas and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11080325},

doi = {10.1109/TVT.2025.3589240},

issn = {1939-9359},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

pages = {1–6},

abstract = {Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.},

keywords = {Cell-free massive MIMO, Channel estimation, Closed-form solutions, Contamination, Correlation, differential evolution, Massive MIMO, Optimization, Rayleigh channels, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces, Training, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{nguyen_hybrid_2025,

title = {Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems},

author = {Doan Hieu Nguyen and Xuan Tung Nguyen and Seon-Geun Jeong and Trinh Van Chien and Lajos Hanzo and Won-Joo Hwang},

url = {https://ieeexplore.ieee.org/document/11091511},

doi = {10.1109/TVT.2025.3588212},

issn = {1939-9359},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

pages = {1–6},

abstract = {A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.},

keywords = {Cell-free massive MIMO, Convergence, Convolutional neural networks, Integrated circuit modeling, Massive MIMO, Pilot Allocation, Quantum circuit, Quantum Machine Learning, Quantum state, Qubit, Throughput, Training, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{zheng_over–air_2025,

title = {Over-the-Air Computation Enabled Semi-Asynchronous Wireless Federated Learning},

author = {Zijian Zheng and Yansha Deng and Wenqiang Yi and Hyundong Shin and Arumugam Nallanathan},

url = {https://ieeexplore.ieee.org/document/11048956},

doi = {10.1109/TCOMM.2025.3582727},

issn = {1558-0857},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

pages = {1–1},

abstract = {The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.},

keywords = {aggregation optimization, Atmospheric modeling, Computational modeling, Convergence, Federated learning, Noise, Optimization, over-the-air computation, Semi-asynchronous federated learning, Servers, Synchronization, Training, Wireless networks},

pubstate = {published},

tppubtype = {article}

}

@article{chen_federated_2024,

title = {Federated Learning Enabled Link Scheduling in D2D Wireless Networks},

author = {Tianrui Chen and Xinruo Zhang and Minglei You and Gan Zheng and Sangarapillai Lambotharan},

url = {https://ieeexplore.ieee.org/document/10268986},

doi = {10.1109/LWC.2023.3321500},

issn = {2162-2345},

year  = {2024},

date = {2024-01-01},

urldate = {2025-10-08},

journal = {IEEE Wireless Communications Letters},

volume = {13},

number = {1},

pages = {89–92},

abstract = {Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federated learning (FL) based method is proposed to solve the link scheduling problem, where a global model is distributedly trained at local devices, and a server is used for aggregating model parameters instead of training samples. Specially, a more realistic scenario with limited channel state information (CSI) is considered instead of full CSI. Despite a decentralized implementation, simulation results demonstrate that the proposed FL based approach with limited CSI performs close to the conventional optimization algorithm. In addition, the FL based solution achieves almost the same performance as that of the centralized training.},

keywords = {Computational modeling, device-to-device (D2D), Device-to-device communication, Federated learning, link scheduling, Scheduling, Servers, Training, Wireless networks},

pubstate = {published},

tppubtype = {article}

}

@article{aristodemou_bayesian_2023,

title = {Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning},

author = {Marios Aristodemou and Xiaolan Liu and Sangarapillai Lambotharan and Basil AsSadhan},

url = {https://ieeexplore.ieee.org/document/10214572},

doi = {10.1109/ACCESS.2023.3304541},

issn = {2169-3536},

year  = {2023},

date = {2023-01-01},

urldate = {2025-10-08},

journal = {IEEE Access},

volume = {11},

pages = {86214–86226},

abstract = {Metaverse is envisioned to be the next-generation human-centric Internet which can offer an immersive experience for users with a broad application in healthcare, education, entertainment, and industries. These applications require the analysis of massive data that contains private and sensitive information. A potential solution to preserving privacy is deploying distributed learning frameworks, including federated learning (FL) and split learning (SL), due to their ability to address privacy leakage and analyze personalised data without sharing raw data. However, it is known that FL and SL are still susceptible to adversarial poisoning attacks. In this paper, we analyse such critical issues for the privacy-preserving mechanism in Metaverse services. We develop a novel poisoning attack based on Bayesian optimisation to emulate the adversarial behaviour against FL (BO-FLPA) and SL (BO-SLPA) which is important for the development of effective defense algorithms in the future. Specifically, we develop a layer optimisation method using the intuition of black-box optimisation with assuming that there is a function between the prediction’s uncertainty and layer optimisation parameters. The result of this optimisation provides the optimal weight parameters for the hidden layer, such as the first or the second layer for FL, and the first layer for SL. Numerical results demonstrate that in both FL and SL, the poisoned hidden layers have the ability to increase the susceptibility of the model to adversarial attacks in terms of prediction with low confidence or having a larger deviation of the probability density function of the predictions.},

keywords = {Adversarial machine learning, Adversarial machine learning (AdvML), Data models, Distance learning, Distributed databases, Federated learning, federated learning (FL), Human factors, machine learning, Metaverse, Optimization, poisoning attacks, Servers, split learning (SL), Training},

pubstate = {published},

tppubtype = {article}

}