1.
Liu, Yuanwei; Xu, Jiaqi; Wang, Zhaolin; Mu, Xidong; Hanzo, Lajos
Near-field Communications: What Will Be Different? Journal Article
In: IEEE Wireless Communications, vol. 32, no. 2, pp. 262–270, 2025, ISSN: 1558-0687.
Abstract | Links | BibTeX | Tags: Antennas, Array signal processing, Channel models, Green's function methods, Next generation networking, Performance analysis, Physical layer security, Sensors
@article{liu_near-field_2025,
title = {Near-field Communications: What Will Be Different?},
author = {Yuanwei Liu and Jiaqi Xu and Zhaolin Wang and Xidong Mu and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/10944643},
doi = {10.1109/MWC.001.2300588},
issn = {1558-0687},
year = {2025},
date = {2025-04-01},
urldate = {2025-10-08},
journal = {IEEE Wireless Communications},
volume = {32},
number = {2},
pages = {262–270},
abstract = {The design dilemma of “What will be different between near-field communications (NFC) and far-field communications (FFC)?” is addressed from four perspectives. First, from the channel modelling perspective, the differences between near-field and far-field channel models are discussed. A novel Green's function-based channel model is proposed for continuous-aperture antennas, which is contrasted to conventional channel models tailored for spatially-discrete antennas. Second, from the performance analysis per-spective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided for both spatially-discrete and continuous-aperture antennas. Third, from the beamforming perspective, far-field beamforming is analogous to a “flashlight” that enables beamsteering, while near-field beamforming can be likened to a “spotlight” that facilitates beamfocusing. As a further advance, a couple of new beamforming structures are proposed for exploiting the new characteristics of NFC. Fourth, from the application perspective, new designs are discussed in the context of promising next-generation technologies in NFC, where our preliminary numerical results demonstrate that distance-aware target sensing and enhanced physical layer security can be realized in NFC. Finally, several future research directions of NFC are discussed.},
keywords = {Antennas, Array signal processing, Channel models, Green's function methods, Next generation networking, Performance analysis, Physical layer security, Sensors},
pubstate = {published},
tppubtype = {article}
}
The design dilemma of “What will be different between near-field communications (NFC) and far-field communications (FFC)?” is addressed from four perspectives. First, from the channel modelling perspective, the differences between near-field and far-field channel models are discussed. A novel Green's function-based channel model is proposed for continuous-aperture antennas, which is contrasted to conventional channel models tailored for spatially-discrete antennas. Second, from the performance analysis per-spective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided for both spatially-discrete and continuous-aperture antennas. Third, from the beamforming perspective, far-field beamforming is analogous to a “flashlight” that enables beamsteering, while near-field beamforming can be likened to a “spotlight” that facilitates beamfocusing. As a further advance, a couple of new beamforming structures are proposed for exploiting the new characteristics of NFC. Fourth, from the application perspective, new designs are discussed in the context of promising next-generation technologies in NFC, where our preliminary numerical results demonstrate that distance-aware target sensing and enhanced physical layer security can be realized in NFC. Finally, several future research directions of NFC are discussed.
2.
Hanzo, Lajos; Babar, Zunaira; Cai, Zhenyu; Chandra, Daryus; Djordjevic, Ivan B.; Koczor, Balint; Ng, Soon Xin; Razavi, Mohsen; Simeone, Osvaldo
Quantum Information Processing, Sensing, and Communications: Their Myths, Realities, and Futures Journal Article
In: Proceedings of the IEEE, pp. 1–51, 2025, ISSN: 1558-2256.
Abstract | Links | BibTeX | Tags: Codes, Encoding, Error correction codes, Europe, Information processing, Next generation networking, Prevention and mitigation, Quantum communications, Quantum computing, quantum error correction coding, quantum error mitigation, quantum key distribution (QKD), Quantum Machine Learning, quantum sensing, quantum-secured direct communications (QSDC), Qubit, Wireless communication
@article{hanzo_quantum_2025,
title = {Quantum Information Processing, Sensing, and Communications: Their Myths, Realities, and Futures},
author = {Lajos Hanzo and Zunaira Babar and Zhenyu Cai and Daryus Chandra and Ivan B. Djordjevic and Balint Koczor and Soon Xin Ng and Mohsen Razavi and Osvaldo Simeone},
url = {https://ieeexplore.ieee.org/document/10828532},
doi = {10.1109/JPROC.2024.3510394},
issn = {1558-2256},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {Proceedings of the IEEE},
pages = {1–51},
abstract = {The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.},
keywords = {Codes, Encoding, Error correction codes, Europe, Information processing, Next generation networking, Prevention and mitigation, Quantum communications, Quantum computing, quantum error correction coding, quantum error mitigation, quantum key distribution (QKD), Quantum Machine Learning, quantum sensing, quantum-secured direct communications (QSDC), Qubit, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.
3.
Feng, Xinyu; El-Hajjar, Mohammed; Xu, Chao; Hanzo, Lajos
Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink Journal Article
In: IEEE Open Journal of Vehicular Technology, vol. 6, pp. 1593–1612, 2025, ISSN: 2644-1330.
Abstract | Links | BibTeX | Tags: Artificial neural networks, Detectors, graph factor, graph neural network (GNN), Graph neural networks, Index modulation (IM), Indexes, machine learning, Message passing, message passing (MP), multi-user, Next generation networking, Peak to average power ratio, Symbols, Uplink, Vectors
@article{feng_graph_2025,
title = {Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink},
author = {Xinyu Feng and Mohammed El-Hajjar and Chao Xu and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11017516},
doi = {10.1109/OJVT.2025.3574934},
issn = {2644-1330},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Open Journal of Vehicular Technology},
volume = {6},
pages = {1593–1612},
abstract = {The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Serving a large number of users in an MU-MIMO-UL system leads to substantial Multi-User Interference (MUI). Hence, we design the Space-Frequency (SF) domain matrix as a joint factor graph, where the Approximate Message Passing (AMP) and Expectation Propagation (EP) based MU detectors can be utilized. In the LS-MU-MIMO-UL scenario considered, the proposed system uses optimal Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detectors as benchmarks for comparison with the proposed MP-based detectors. These MP-based detectors significantly reduce the detection complexity compared to ML detection, making the design eminently suitable for LS-MU scenarios. To further reduce the detection complexity and improve the detection performance, we propose a pair of Graph Neural Network (GNN) based detectors, which rely on the orthogonal AMP (OAMP) and on the EP algorithm, which we refer to as the GNN-AMP and GEPNet detectors, respectively. The GEPNet detector maximizes the detection performance, while the GNN-AMP detector strikes a performance versus complexity trade-off. The GNN is trained for a single system configuration and yet it can be used for any number of users in the system. The simulation results show that the GNN-based detector approaches the ML performance in various configurations.},
keywords = {Artificial neural networks, Detectors, graph factor, graph neural network (GNN), Graph neural networks, Index modulation (IM), Indexes, machine learning, Message passing, message passing (MP), multi-user, Next generation networking, Peak to average power ratio, Symbols, Uplink, Vectors},
pubstate = {published},
tppubtype = {article}
}
The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Serving a large number of users in an MU-MIMO-UL system leads to substantial Multi-User Interference (MUI). Hence, we design the Space-Frequency (SF) domain matrix as a joint factor graph, where the Approximate Message Passing (AMP) and Expectation Propagation (EP) based MU detectors can be utilized. In the LS-MU-MIMO-UL scenario considered, the proposed system uses optimal Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detectors as benchmarks for comparison with the proposed MP-based detectors. These MP-based detectors significantly reduce the detection complexity compared to ML detection, making the design eminently suitable for LS-MU scenarios. To further reduce the detection complexity and improve the detection performance, we propose a pair of Graph Neural Network (GNN) based detectors, which rely on the orthogonal AMP (OAMP) and on the EP algorithm, which we refer to as the GNN-AMP and GEPNet detectors, respectively. The GEPNet detector maximizes the detection performance, while the GNN-AMP detector strikes a performance versus complexity trade-off. The GNN is trained for a single system configuration and yet it can be used for any number of users in the system. The simulation results show that the GNN-based detector approaches the ML performance in various configurations.
4.
Xiao, Yun; Wang, Enhao; Chen, Yunfei
Integrated Sensing and Communications With Multiple Targets and Multiple Users in Mixed Field Proceedings Article
In: 2024 IEEE 24th International Conference on Communication Technology (ICCT), pp. 1288–1292, 2024, ISSN: 2576-7828, (ISSN: 2576-7828).
Abstract | Links | BibTeX | Tags: Antenna arrays, Array signal processing, Beamforming, far-filed, Integrated sensing and communication, integrated sensing and communications, Interference, mixed field, model mismatch, multiple-target, near-field, Next generation networking, Numerical models, Optimization, Propagation losses, Signal to noise ratio, Wireless communication
@inproceedings{xiao_integrated_2024,
title = {Integrated Sensing and Communications With Multiple Targets and Multiple Users in Mixed Field},
author = {Yun Xiao and Enhao Wang and Yunfei Chen},
url = {https://ieeexplore.ieee.org/document/10946468},
doi = {10.1109/ICCT62411.2024.10946468},
issn = {2576-7828},
year = {2024},
date = {2024-10-01},
urldate = {2025-10-08},
booktitle = {2024 IEEE 24th International Conference on Communication Technology (ICCT)},
pages = {1288–1292},
abstract = {Integrated sensing and communications (ISAC) plays a crucial role in the next-generation wireless systems. Owing to the deployment of high carrier frequencies and/or large-scale antenna arrays, targets and communications users in the ISAC systems may follow different propagation models. However, most existing works assume the same propagation model for both communications and sensing. This work considers a practical scenario where multiple targets and communications users are in different fields. Beamforming design is proposed to optimize the sensing signal-to-clutter-plus-noise ratio (SCNR) of each target. Specifically, a sensing performance fairness profile optimization (FPO) problem is formulated, and a Dinkelbach-type algorithm is proposed to solve the problem. Numerical results show the tradeoff between mixed-field communications and sensing, the effects of antenna size and model mismatch between near field and far field on the sensing performance of the mixed-field ISAC.},
note = {ISSN: 2576-7828},
keywords = {Antenna arrays, Array signal processing, Beamforming, far-filed, Integrated sensing and communication, integrated sensing and communications, Interference, mixed field, model mismatch, multiple-target, near-field, Next generation networking, Numerical models, Optimization, Propagation losses, Signal to noise ratio, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Integrated sensing and communications (ISAC) plays a crucial role in the next-generation wireless systems. Owing to the deployment of high carrier frequencies and/or large-scale antenna arrays, targets and communications users in the ISAC systems may follow different propagation models. However, most existing works assume the same propagation model for both communications and sensing. This work considers a practical scenario where multiple targets and communications users are in different fields. Beamforming design is proposed to optimize the sensing signal-to-clutter-plus-noise ratio (SCNR) of each target. Specifically, a sensing performance fairness profile optimization (FPO) problem is formulated, and a Dinkelbach-type algorithm is proposed to solve the problem. Numerical results show the tradeoff between mixed-field communications and sensing, the effects of antenna size and model mismatch between near field and far field on the sensing performance of the mixed-field ISAC.
5.
Mohammadi, Mohammadali; Mobini, Zahra; Ngo, Hien Quoc; Matthaiou, Michail
Next-Generation Multiple Access With Cell-Free Massive MIMO Journal Article
In: Proceedings of the IEEE, vol. 112, no. 9, pp. 1372–1420, 2024, ISSN: 1558-2256.
Abstract | Links | BibTeX | Tags: 5G mobile communication, 6G mobile communication, Cell-free massive multiple-input multiple-output (CF-mMIMO), Channel estimation, Energy Efficiency, energy efficiency (EE), Massive MIMO, Millimeter wave communication, Next generation networking, Signal to noise ratio, sixth-generation (6G) wireless, Spectral efficiency, spectral efficiency (SE), Surveys, Telecommunication traffic, Wireless networks
@article{mohammadi_next-generation_2024,
title = {Next-Generation Multiple Access With Cell-Free Massive MIMO},
author = {Mohammadali Mohammadi and Zahra Mobini and Hien Quoc Ngo and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/10684238},
doi = {10.1109/JPROC.2024.3451372},
issn = {1558-2256},
year = {2024},
date = {2024-09-01},
urldate = {2025-10-08},
journal = {Proceedings of the IEEE},
volume = {112},
number = {9},
pages = {1372–1420},
abstract = {To meet the unprecedented mobile traffic demands of future wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems is imperative. Cell-free massive multiple-input multiple-output (CF-mMIMO) represents a practical and scalable embodiment of distributed/network MIMO systems. It inherits not only the key benefits of co-located massive MIMO systems but also the macro-diversity gains from distributed systems. This innovative architecture has demonstrated significant potential in enhancing network performance from various perspectives, outperforming co-located mMIMO and conventional small-cell systems. Moreover, CF-mMIMO offers flexibility in integration with emerging wireless technologies such as full-duplex (FD), nonorthogonal transmission schemes, millimeter-wave (mmWave) communications, ultrareliable low-latency communication (URLLC), unmanned aerial vehicle (UAV)-aided communication, and reconfigurable intelligent surfaces (RISs). In this article, we provide an overview of current research efforts on CF-mMIMO systems and their promising future application scenarios. We then elaborate on new requirements for CF-mMIMO networks in the context of these technological breakthroughs. We also present several current open challenges and outline future research directions aimed at fully realizing the potential of CF-mMIMO systems in meeting the evolving demands of future wireless networks.},
keywords = {5G mobile communication, 6G mobile communication, Cell-free massive multiple-input multiple-output (CF-mMIMO), Channel estimation, Energy Efficiency, energy efficiency (EE), Massive MIMO, Millimeter wave communication, Next generation networking, Signal to noise ratio, sixth-generation (6G) wireless, Spectral efficiency, spectral efficiency (SE), Surveys, Telecommunication traffic, Wireless networks},
pubstate = {published},
tppubtype = {article}
}
To meet the unprecedented mobile traffic demands of future wireless networks, a paradigm shift from conventional cellular networks to distributed communication systems is imperative. Cell-free massive multiple-input multiple-output (CF-mMIMO) represents a practical and scalable embodiment of distributed/network MIMO systems. It inherits not only the key benefits of co-located massive MIMO systems but also the macro-diversity gains from distributed systems. This innovative architecture has demonstrated significant potential in enhancing network performance from various perspectives, outperforming co-located mMIMO and conventional small-cell systems. Moreover, CF-mMIMO offers flexibility in integration with emerging wireless technologies such as full-duplex (FD), nonorthogonal transmission schemes, millimeter-wave (mmWave) communications, ultrareliable low-latency communication (URLLC), unmanned aerial vehicle (UAV)-aided communication, and reconfigurable intelligent surfaces (RISs). In this article, we provide an overview of current research efforts on CF-mMIMO systems and their promising future application scenarios. We then elaborate on new requirements for CF-mMIMO networks in the context of these technological breakthroughs. We also present several current open challenges and outline future research directions aimed at fully realizing the potential of CF-mMIMO systems in meeting the evolving demands of future wireless networks.