@article{li_stacked_2025,

title = {Stacked Intelligent Metasurface-Based Transceiver Design for Near-Field Wideband Systems},

author = {Qingchao Li and Mohammed El-Hajjar and Chao Xu and Jiancheng An and Chau Yuen and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10900449},

doi = {10.1109/TCOMM.2025.3544929},

issn = {1558-0857},

year  = {2025},

date = {2025-09-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {9},

pages = {8125–8139},

abstract = {Intelligent metasurfaces may be harnessed for realizing efficient holographic multiple-input and multiple-output (MIMO) systems, at a low hardware-cost and high energy-efficiency. As part of this family, we propose a hybrid beamforming design for stacked intelligent metasurfaces (SIM) aided wideband wireless systems relying on the near-field channel model. Specifically, the holographic beamformer is designed based on configuring the phase shifts in each layer of the SIM for maximizing the sum of the baseband eigen-channel gains of all users. To optimize the SIM phase shifts, we propose a layer-by-layer iterative algorithm for optimizing the phase shifts in each layer alternately. Then, the minimum mean square error (MMSE) transmit precoding method is employed for the digital beamformer to support multi-user access. Furthermore, the mitigation of the SIM phase tuning error is also taken into account in the digital beamformer by exploiting its statistics. The power sharing ratio of each user is designed based on the iterative waterfilling power allocation algorithm. Additionally, our analytical results indicate that the spectral efficiency attained saturates in the high signal-to-noise ratio (SNR) region due to the phase tuning error resulting from the imperfect SIM hardware quality. The simulation results show that the SIM-aided holographic MIMO outperforms the state-of-the-art (SoA) single-layer holographic MIMO in terms of its achievable rate. We further demonstrate that the near-field channel model allows the SIM-based transceiver design to support multiple users, since the spatial resources represented both by the angle domain and the distance domain can be exploited.},

keywords = {Array signal processing, Channel models, Hardware, holographic beamforming architecture, Low earth orbit satellites, Metamaterials, Metasurfaces, near-field channel model, phase tuning error, Stacked intelligent metasurface, Transceivers, Tuning, Vectors, Wideband, wideband system},

pubstate = {published},

tppubtype = {article}

}

@article{jafri_robust_2025,

title = {Robust Hybrid Beamforming in Cooperative Cell-Free mmWave MIMO Networks Relying on Imperfect CSI},

author = {Meesam Jafri and Pankaj Kumar and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10945645},

doi = {10.1109/TVT.2025.3555484},

issn = {1939-9359},

year  = {2025},

date = {2025-08-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

volume = {74},

number = {8},

pages = {12590–12602},

abstract = {A low-complexity robust cooperative hybrid beamformer is designed for both the downlink and uplink of cell-free millimeter wave (mmWave) multiple-input-multiple-output (MIMO) networks, while considering realistic imperfect channel state information (CSI). To begin with, a second-order cone program (SOCP)-based robust fully-digital beamformer (FDBF) is designed for minimizing the worst-case interference for the downlink of multiple-input-single-output (MISO) systems. Subsequently, we develop a Bayesian learning (BL) framework for determining both the analog and digital components of the hybrid transmit precoder (TPC) from the FDBF. The above designs are subsequently extended to employing eigenvector perturbation theory for constructing the robust TPC for the cell-free mmWave MIMO downlink, where the users have multiple receive antennas (RAs). Furthermore, the multi-dimensional covariance fitting (MCF) framework is harnessed for designing the robust TPC of the corresponding uplink. Finally, the efficiency of the proposed TPC designs is evaluated by simulation results both in terms of their ability to mitigate the multi-user interference (MUI), and improving the spectral efficiency achieved. Additionally, the proposed designs are shown to be computationally efficient and equivalent to a minimum variance hybrid beamformer.},

keywords = {Antenna arrays, Array signal processing, cell-free networks, Channel estimation, cooperative beamforming, CSI uncertainty, Downlink, Millimeter wave communication, MIMO, mmWave, Radio frequency, robust beamforming, Uncertainty, Uplink, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{liu_otfs-based_2025,

title = {OTFS-Based CV-QKD Systems for Doubly Selective THz Channels},

author = {Xin Liu and Chao Xu and Soon Xin Ng and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10857395},

doi = {10.1109/TCOMM.2025.3535889},

issn = {1558-0857},

year  = {2025},

date = {2025-08-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {8},

pages = {6274–6289},

abstract = {The feasibility of continuous variable quantum key distribution (CV-QKD) is considered in the Terahertz (THz) band, experiencing time-varying and frequency-selective fading. Advanced multi-carrier modulation is required for improving the secret key rate (SKR). However, the hostile quantum channel requires powerful classical channel coding schemes for maintaining an adequate reconciliation performance. Against this background, for the first time in the open literature, we propose a multi-carrier quantum transmission regime that incorporates both orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) transmission over doubly selective fading THz channels. Furthermore, we propose a modified multi-dimensional reconciliation algorithm for CV-QKD, facilitating the integration of OFDM/OTFS quantum transmission with low-density parity check (LDPC) coded key reconciliation. Moreover, we harness multiple-input multiple-output (MIMO) beamforming for mitigating the severe THz path loss. Our SKR analysis results demonstrate that the proposed OTFS-based and LDPC-assisted CV-QKD system is capable of outperforming its OFDM counterpart in mobile wireless scenarios. Moreover, we also demonstrate that increasing the MIMO dimension reduces the transmission power required for achieving the secure transmission distance target.},

keywords = {Array signal processing, continuous variable quantum key distribution (CV-QKD), Faces, Fading channels, low-density parity check (LDPC), multiple-input multiple-out (MIMO), OFDM, Orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), Parity check codes, Random variables, secret key rate (SKR), Symbols, Terahertz (THz), Terahertz communications, Time-frequency analysis, Vectors},

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{rai_spectral_2025,

title = {The Spectral Versus Energy Efficiency Trade-Off in Dynamic User Clustering Aided mmWave NOMA Networks},

author = {Sudhakar Rai and Ekant Sharma and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10769478},

doi = {10.1109/TCOMM.2024.3506920},

issn = {1558-0857},

year  = {2025},

date = {2025-06-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {6},

pages = {4503–4519},

abstract = {The spectral efficiency (SE) and global energy efficiency (GEE) trade-off encountered in the design of millimeter-wave (mmWave)-based massive multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated with a particular focus on user clustering. By exploiting the similarity among user channels a pair of spectral and energy-efficient user clustering algorithms are proposed for dynamically selecting both the number of clusters and the number of users in each cluster. Subsequently, a joint analog precoder/combiner and user clustering technique is developed, followed by a multi-objective optimization (MOO) framework for flexibly balancing the GEE and SE objectives in a mmWave NOMA network subject to specific constraints. The MOO objective is initially transformed to a weighted sum rate maximization problem, followed by a quadratic-transform (QT)-based approach conceived for maximizing the non-convex objective by approximating it as a concave-convex function. Our simulation results demonstrate that the user clustering techniques designed attain a 85% performance gain over random clustering technique and demonstrating the benefits of the algorithm designed for mmWave NOMA networks.},

keywords = {Array signal processing, Clustering algorithms, Energy Efficiency, fractional programming, Heuristic algorithms, Hybrid power systems, hybrid precoding, Interference cancellation, Millimeter wave communication, MIMO, mmWave, NOMA, Optimization, Resource management, Spectral efficiency, user clustering},

pubstate = {published},

tppubtype = {article}

}

@article{singh_pareto-optimal_2025,

title = {Pareto-Optimal Hybrid Beamforming for Finite-Blocklength Millimeter Wave Systems},

author = {Jitendra Singh and Banda Naveen and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10854905},

doi = {10.1109/TVT.2025.3534021},

issn = {1939-9359},

year  = {2025},

date = {2025-06-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

volume = {74},

number = {6},

pages = {9910–9915},

abstract = {Short-packet communication (SPC) is essentially synonymous with ultra-reliable low-latency communication (uRLLC), which must meet stringent latency and reliability requirements. However, achieving efficient hybrid beamforming (HBF) in SPC-based millimeter wave (mmWave) systems is challenging due to the constraints of finite block lengths, limited number of radio frequency chains (RFCs), and owing to the complex optimization of transmit precoders (TPCs). In this work, we investigate the achievable rate region of an SPC-based mmWave downlink system. We harness the HBF for finite block lengths low-latency communication, relying on a low number of RFCs. We formulate a Pareto optimization problem for characterizing the achievable rate region, while considering the transmit power, mmWave hardware, and block length constraints. To solve this highly non-convex problem, we propose a bisection search-based block coordinate descent (Bi-BCD) algorithm, in which we optimize the RF TPC, the baseband (BB) TPC, and the block length. Specifically, we jointly optimize the RF and BB TPCs for a fixed block length, which involves both Remanian conjugate gradient (RCG) and second-order cone programming (SOCP) techniques, and then we optimize the block length by the mixed integer programming method. Subsequently, we update the achievable rate via the bisection search method. Finally, we present simulation results and quantify the efficiency of the proposed scheme.},

keywords = {Array signal processing, Error probability, hybrid beamforming, millimeter wave, Millimeter wave communication, Millimeter wave technology, Optimization, Pareto boundary, Radio frequency, Short packet communication, Signal to noise ratio, Symbols, Ultra reliable low latency communication, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{singh_pareto_2025,

title = {Pareto Optimal Hybrid Beamforming for Short-Packet Millimeter-Wave Integrated Sensing and Communication},

author = {Jitendra Singh and Banda Naveen and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10778580},

doi = {10.1109/TCOMM.2024.3511704},

issn = {1558-0857},

year  = {2025},

date = {2025-06-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {6},

pages = {4570–4585},

abstract = {Pareto optimal solutions are conceived for radar beamforming error (RBE) and sum rate maximization in short-packet (SP) millimeter-wave (mmWave) integrated sensing and communication (ISAC). Our ultimate goal is to realize ultra-reliable low-latency communication (uRLLC) and real-time sensing capabilities for 6G applications. The ISAC base station (BS) transmits short packets in the downlink (DL) to serve multiple communication users (CUs) and detect multiple radar targets (RTs). We investigate the performance trade-off between the sensing and communication capabilities by optimizing both the radio frequency (RF) and the baseband (BB) transmit precoder (TPC), together with the block lengths. The optimization problem considers the minimum rate requirements of the CUs, the maximum tolerable radar beamforming error (RBE) for the RTs, the unit modulus (UM) elements of the RF TPC, and the finite transmit power as the constraints for SP transmission. The resultant problem is highly non-convex due to the intractable rate expression of the SP regime coupled with the non-convex rate and UM constraints. To solve this problem, we propose an innovative two-layer bisection search (TLBS) algorithm, wherein the RF and BB TPCs are optimized in the inner layer, followed by the block length in the outer layer. Furthermore, a pair of novel methods, namely a bisection search-based majorizer and minimizer (BMM) as well as exact penalty-based manifold optimization (EPMO) are harnessed for optimizing the RF TPC in the inner layer. Subsequently, the BB TPC and the block length are derived via second-order cone programming (SOCP) and mixed integer programming methods, respectively. Finally, our exhaustive simulation results reveal the effect of system parameters for various settings on the RBE-rate region of the SP mmWave ISAC system and demonstrate a significantly enhanced performance compared to the benchmarks.},

keywords = {Array signal processing, Copper, hybrid beamforming, Integrated sensing and communication, Millimeter wave communication, Millimeter wave radar, Optimization, Pareto boundary, Programming, Radio frequency, Short packet communication, Signal to noise ratio, Ultra reliable low latency communication, Ultra-reliable low latency communication},

pubstate = {published},

tppubtype = {article}

}

@article{kumar_gadamsetty_sum-rate_2025,

title = {Sum-Rate Maximization of RIS-Aided Digital and Holographic Beamformers in MU-MISO Systems},

author = {Pavan Kumar Gadamsetty and K. V. S. Hari and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10737121},

doi = {10.1109/TCOMM.2024.3487305},

issn = {1558-0857},

year  = {2025},

date = {2025-05-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {5},

pages = {3106–3118},

abstract = {Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra-thin, lightweight antennas onto the transceiver, for creating sharp, high-gain directional beams. We formulate a user sum-rate maximization problem for our RHS-RIS-based hybrid beamformer. Explicitly, we jointly design the digital, holographic, and passive beamformers for maximizing the sum-rate of all user equipment (UE). To tackle the resultant nonconvex optimization problem, we propose an alternating maximization (AM) framework for decoupling and iteratively solving the subproblems involved. Specifically, we employ the zero-forcing criterion for the digital beamformer, leverage fractional programming to determine the radiation amplitudes of the RHS and utilize the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix of the passive beamformer. Our simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer outperforms its conventional counterpart operating without an RIS in multi-UE scenarios. The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs, which is significant.},

keywords = {alternating maximization (AM), Array signal processing, Arrays, Beamforming, Millimeter wave communication, MISO communication, Programming, Radio frequency, Reconfigurable holographic surfaces (RHS), reconfigurable intelligent surfaces (RIS), Signal to noise ratio, sum-rate, Transceivers, Vectors, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{singh_joint_2025,

title = {Joint Hybrid Transceiver and Reflection Matrix Design for RIS-Aided mmWave MIMO Cognitive Radio Systems},

author = {Jitendra Singh and Suraj Srivastava and Surya P. Yadav and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10561503},

doi = {10.1109/TCCN.2024.3415620},

issn = {2332-7731},

year  = {2025},

date = {2025-02-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Cognitive Communications and Networking},

volume = {11},

number = {1},

pages = {391–407},

abstract = {In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.},

keywords = {Array signal processing, cognitive radio, Downlink, hybrid beamforming, Interference, Millimeter wave communication, MIMO communication, mmWave, Quality of service, Radio frequency, Riemannian conjugate gradient, RIS},

pubstate = {published},

tppubtype = {article}

}

@article{sui_ris-assisted_2025,

title = {RIS-Assisted Cell-Free Massive MIMO Relying on Reflection Pattern Modulation},

author = {Zeping Sui and Hien Quoc Ngo and Trinh Van Chien and Michail Matthaiou and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10640072},

doi = {10.1109/TCOMM.2024.3446589},

issn = {1558-0857},

year  = {2025},

date = {2025-02-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

volume = {73},

number = {2},

pages = {968–982},

abstract = {We propose reflection pattern modulation-aided reconfigurable intelligent surface (RPM-RIS)-assisted cell-free massive multiple-input-multiple-output (CF-mMIMO) schemes for green uplink transmission. In our RPM-RIS-assisted CF-mMIMO system, extra information is conveyed by the indices of the active RIS blocks, exploiting the joint benefits of both RIS-assisted CF-mMIMO transmission and RPM. Since only part of the RIS blocks are active, our proposed architecture strikes a flexible energy vs. spectral efficiency (SE) trade-off. We commence with introducing the system model by considering spatially correlated channels. Moreover, we conceive a channel estimation scheme subject to the linear minimum mean-square error (MMSE) constraint, yielding sufficient information for the subsequent signal processing steps. Then, upon exploiting a so-called large-scale fading decoding (LSFD) scheme, the uplink signal-to-interference-and-noise ratio (SINR) is derived based on the RIS ON/OFF statistics, where both maximum ratio (MR) and local minimum mean-square error (L-MMSE) combiners are considered. By invoking the MR combiner, the closed-form expression of the uplink SE is formulated based only on the channel statistics. Furthermore, we derive the total energy efficiency (EE) of our proposed RPM-RIS-assisted CF-mMIMO system. Additionally, we propose a chaotic sequence-based adaptive particle swarm optimization (CSA-PSO) algorithm to maximize the total EE by designing the RIS phase shifts. Specifically, the initial particle diversity is promoted by invoking chaotic sequences, and an adaptive time-varying inertia weight is developed to improve its particle search performance. Furthermore, the particle mutation and reset steps are appropriately selected to enable the algorithm to escape from local optima. Finally, our simulation results demonstrate that the proposed RPM-RIS-assisted CF-mMIMO architecture strikes an attractive SE vs. EE trade-off, while the CSA-PSO algorithm is capable of attaining a significant EE performance gain compared to conventional solutions.},

keywords = {Array signal processing, Cell-free massive MIMO, Channel estimation, Chaotic communication, Energy Efficiency, iterative optimization, Optimization, Reconfigurable Intelligent Surfaces, reflection pattern modulation, Spectral efficiency, Symbols, Technological innovation, Uplink},

pubstate = {published},

tppubtype = {article}

}

@article{li_xl-mimo_2025,

title = {XL-MIMO Based Hierarchical Receive Beamforming Subject to Hardware Impairments in the Uplink of Cell-Free Networks},

author = {Qingchao Li and Mohammed El-Hajjar and Chao Xu and Chao Zhang and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11091536},

doi = {10.1109/TVT.2025.3592149},

issn = {1939-9359},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

pages = {1–11},

abstract = {Extremely large-scale multiple-input and multiple-output (XL-MIMO) exhibit substantial spatial multiplexing capabilities owing to their high degree of freedom. As the number of antenna elements increases, it becomes more practically suitable to utilize cost-effective antennas equipped with low-resolution RF chains. However, hardware impairments (HWIs) associated with these cost-effective antennas lead to performance saturation in the high signal-to-noise ratio (SNR) region, which cannot be mitigated by merely increasing the transmit power. To address these challenges, we propose a hierarchical receive beamforming method for XL-MIMO based near-field cell-free networks with HWIs. Specifically, the antenna array of each access point (AP) is partitioned into multiple sub-arrays, with each sub-array independently harnessing the minimum mean-square error (MMSE) receive beamforming algorithm. The local data estimates at each AP are then optimized using the results from all sub-arrays, and the central processing unit (CPU) performs its final information recovery by integrating these local estimates. Our theoretical analysis shows that the proposed hierarchical receive beamforming method achieves a higher ergodic sum-rate than the state-of-the-art (SoA) scheme in XL-MIMO systems in the face of HWIs.},

keywords = {Antenna arrays, Antennas, Array signal processing, cell-free network, Central Processing Unit, Computer architecture, Estimation, Extremely large-scale multiple-input and multiple-output (XL-MIMO), Hardware, hardware impairment (HWI), hierarchical detection, near-field, Signal processing algorithms, Signal to noise ratio, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{meng_network-level_2025,

title = {Network-level ISAC: An Analytical Study of Antenna Topologies Ranging from Massive to Cell-Free MIMO},

author = {Kaitao Meng and Kawon Han and Christos Masouros and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11030947},

doi = {10.1109/TWC.2025.3576432},

issn = {1558-2248},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Wireless Communications},

pages = {1–1},

abstract = {A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks having N ISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2 N scaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2 N). The AOA-based methods follow a ln N scaling law, while the hybrid methods scale as a ln2 N+b ln N, where a and b represent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.},

keywords = {antenna allocation, Antenna arrays, Antenna measurements, Antennas, Array signal processing, cooperative sensing and communication, Geometry, Integrated sensing and communication, Location awareness, MIMO, multi-cell networks, network performance analysis, Resource management, stochastic geometry, Transmitting antennas},

pubstate = {published},

tppubtype = {article}

}

@article{liu_hybrid_2025,

title = {Hybrid Beamforming Assisted OTFS-Based CV-QKD Systems for Doubly Selective THz Channels},

author = {Xin Liu and Chao Xu and Stephen Wang and Soon Xin Ng and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11069272},

doi = {10.1109/TCOMM.2025.3585501},

issn = {1558-0857},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

pages = {1–1},

abstract = {Continuous-variable quantum key distribution (CV-QKD) maps information onto the quadrature components of electromagnetic waves, so that off-the-shelf wireless transceivers can be utilized. This motivates the move from optical to Terahertz (THz) bands. However, wireless THz channels suffer from severe path loss, while the mobility of wireless users imposes doubly selective fading. Against this background, we propose a new CV-QKD regime that relies on hybrid beamforming (HBF) assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) system, where the channel’s transmissivity and robustness against double selectivity are overcome by HBF and OTFS, respectively. Secondly, in order to provide channel state information (CSI) for both the transmitter (CSI-T) and receiver (CSI-R), practical channel estimation methods are conceived. They operate in the time-frequency domain for OFDM and in the delay-Doppler domain for OTFS. Thirdly, soft-decision detection is devised for our MIMO OFDM/OTFS aided multi-dimensional reconciliation (MDR) scheme. Low-density parity-check (LDPC) coding is invoked for further improving secure CV-QKD transmission distance in the THz band. Our simulation results demonstrate that the proposed HBF MIMO OTFS-based CV-QKD system relying on realistic estimated CSI is capable of achieving an adequate secret key rate (SKR) and secure transmission distance in hostile doubly selective THz channels.},

keywords = {Array signal processing, Channel estimation, continuous variable quantum key distribution (CV-QKD), Decoding, Fading channels, hybrid beamforming (HBF), low-density parity check (LDPC), multiple-input multiple-out (MIMO), OFDM, Orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), Parity check codes, secret key rate (SKR), Terahertz (THz), Terahertz communications, Time-frequency analysis, Vectors, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_bayesian_2025,

title = {Bayesian Learning Aided Parameter Estimation and Joint Beamformer Design in mmWave MIMO-OFDM ISAC Systems},

author = {Awadhesh Gupta and Jitendra Singh and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11030617},

doi = {10.1109/TCOMM.2025.3578813},

issn = {1558-0857},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Communications},

pages = {1–1},

abstract = {A three-dimensional (3D) sparse signal recovery problem formulation is conceived for delay, Doppler, and angular (DDA) domain target parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems relying on a hybrid beamforming architecture. Subsequently, a 3D-sparse Bayesian learning (3D-BL) algorithm is proposed to jointly estimate the angular, range, velocity, and radar cross-section (RCS) parameters of the targets. Furthermore, an uplink beamformer is designed for the user equipment (UE) to alleviate the complexity of uplink parameter estimation at the dual-functional radar-communication (DFRC) base station (BS) by eliminating the need for angle of departure (AoD) estimation. Additionally, a Bayesian alternating minimization (BAT-MIN) algorithm is constructed for the designing of a DFRC waveform, enabling the simultaneous generation of beams toward both the radar targets and the UE. Furthermore, the sparse Bayesian learning lower bound (SBL-LB) and the Bayesian Cramér-Rao lower bound (BCRLB) are derived to serve as benchmarks for estimation performance. Finally, simulation results are presented to showcase the enhanced performance of the proposed methodologies in terms of multiple performance metrics when contrasted both to the existing sparse recovery techniques and to conventional non-sparse parameter estimation algorithms. The simulation outcomes unequivocally demonstrate the commendable performance of the proposed 3D-BL estimation methodology, approaching closely to the SBL-LB. Notably, this approach exhibits a substantial gain of at least 5 dB when compared to alternative techniques. Additionally, the introduced BAT-MIN beamformer emerges as a highly competitive solution, closely approximating the capabilities of a fully digital beamformer while maintaining a noteworthy minimum advantage over its contemporaries. These findings underscore the significance and efficacy of the proposed techniques in the context of advanced signal processing and beamforming.},

keywords = {Array signal processing, Bayes methods, dual-functional radar-communication (DFRC), Estimation, hybrid analog-digital (HAD) beamforming, Integrated sensing and communication (ISAC), millimeter wave (mmWave), Millimeter wave communication, MIMO, OFDM, Parameter estimation, Radar, Radar cross-sections, Radio frequency, sparse Bayesian learning (SBL)},

pubstate = {published},

tppubtype = {article}

}

@article{li_indoor_2025,

title = {Indoor Localization and Tracking in Reconfigurable Intelligent Surface Aided mmWave Systems},

author = {Kunlun Li and Mohammed El-Hajjar and Chao Xu and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11050944},

doi = {10.1109/OJVT.2025.3582885},

issn = {2644-1330},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Open Journal of Vehicular Technology},

volume = {6},

pages = {1815–1831},

abstract = {Millimeter wave (mmWave) carriers have a high available bandwidth, which can be beneficial for high-resolution localization in both the angular and temporal domains. However, the limited coverage due to severe path loss and line-of-sight (LoS) blockage are considered to be major challenges in mmWave. A promising solution is to employ reconfigurable intelligent surfaces (RIS) to circumvent the lack of line-of-sight paths, which can assist in localization. Furthermore, radio localization and tracking are capable of accurate real-time monitoring of the UE's locations and trajectories. In this paper, we propose a three-stage indoor tracking scheme. In the first stage, channel sounding is harnessed in support of the transmitter beamforming and receiver combining design. Based on the estimation in the first stage, a simplified received signal model is obtained, while using a discrete Fourier transform (DFT) matrix for the configuration of the RIS phase shifter for each time block. Based on the simplified received signal model, tracking initialization is carried out. Finally, in the third stage, Kalman filtering is employed for tracking. Our results demonstrate that the proposed scheme is capable of improving both the accuracy and robustness of tracking compared to single-shot successive localization. Additionally, we derive the position error bounds (PEB) of single-shot localization.},

keywords = {Array signal processing, Channel estimation, Covariance matrices, Estimation, Kalman filters, localization/positioning, Location awareness, Millimeter wave communication, mmWave, OFDM, Radar tracking, Reconfigurable Intelligent Surfaces, sparse Bayesian learning, tracking},

pubstate = {published},

tppubtype = {article}

}

@article{singh_geometric_2025,

title = {Geometric Mean Rate Maximization in RIS-Aided mmWave ISAC Systems Relying on a Non-Diagonal Phase Shift Matrix},

author = {Jitendra Singh and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/11012749/similar},

doi = {10.1109/OJCOMS.2025.3573196},

issn = {2644-125X},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Open Journal of the Communications Society},

volume = {6},

pages = {4756–4771},

abstract = {The joint optimization of the hybrid transmit precoders (HTPCs) and reflective elements of a millimeter wave (mmWave) integrated sensing and communication (ISAC) system is considered. The system also incorporates a reconfigurable intelligent surface (RIS) relying on a non-diagonal RIS (NDRIS) phase shift matrix. Specifically, we consider a hybrid architecture at the ISAC base station (BS) that serves multiple downlink communication users (CUs) via the reflected links from the RIS, while concurrently detecting multiple radar targets (RTs). We formulate an optimization problem that aims for maximizing the geometric mean (GM) rate of the CUs, subject to the sensing requirement for each RT. Additional specifications related to the limited transmit power and unit modulus (UM) constraints for both the HTPCs and the reflective elements of the NDRIS phase shift matrix make the problem challenging. To solve this problem, we first transform the intractable GM rate expression to a tractable weighted sum rate objective and next split the transformed problem into sub-problems. Consequently, we propose an iterative alternating optimization approach that leverages the majorization-minimization (MM) framework and block coordinate descent (BCD) method to solve each sub-problem. Furthermore, to tackle the UM constraints in the sub-problem of the HTPC design, we propose a penalty-based Riemannian manifold optimization (PRMO) algorithm, which optimizes the HTPCs on the Riemannian manifold. Similarly, the phases of the reflective elements of the NDRIS are optimized by employing the Riemannian manifold, and the locations of the non-zero entries of the NDRIS phase shift matrix are obtained by the maximal ratio combining (MRC) criterion. Finally, we present our simulation results, which show that deploying an NDRIS achieves additional gains for the CUs over a conventional RIS, further enhancing both the communication efficiency and sensing reliability. Furthermore, we compare the results to the pertinent benchmarks, which validate the effectiveness of our proposed algorithms.},

keywords = {and geometric mean rate, Array signal processing, Base stations, Copper, Costs, Hardware, Integrated sensing and communication, millimeter wave, Millimeter wave communication, Optimization, Radio frequency, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces},

pubstate = {published},

tppubtype = {article}

}

@article{li_holographic_2025,

title = {Holographic Metasurface-Based Beamforming for Multi-Altitude LEO Satellite Networks},

author = {Qingchao Li and Mohammed El-Hajjar and Kaijun Cao and Chao Xu and Harald Haas and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/abstract/document/10844052/?casa_token=5kb4rgy_qqAAAAAA:Qy4zV9IQ3FSfC6Cy7it5EvcxjQM2a675RSsbRiRKNPsADHjWFXZ0VHem5zJ_dVf5IBDhE6R2sg},

doi = {10.1109/TWC.2025.3527962},

issn = {1558-2248},

year  = {2025},

date = {2025-01-01},

urldate = {2025-02-24},

journal = {IEEE Transactions on Wireless Communications},

pages = {1–1},

publisher = {arXiv},

abstract = {Low Earth Orbit (LEO) satellite networks are capable of improving the global Internet service coverage. In this context, we propose a hybrid beamforming design for holographic metasurface based terrestrial users in multi-altitude LEO satellite networks. Firstly, the holographic beamformer is optimized by maximizing the downlink channel gain from the serving satellite to the terrestrial user. Then, the digital beamformer is designed by conceiving a minimum mean square error (MMSE) based detection algorithm for mitigating the interference arriving from other satellites. To dispense with excessive overhead of full channel state information (CSI) acquisition of all satellites, we propose a low-complexity MMSE beamforming algorithm that only relies on the distribution of the LEO satellite constellation harnessing stochastic geometry, which can achieve comparable throughput to that of the algorithm based on the full CSI in the case of a dense LEO satellite deployment. Furthermore, it outperforms the maximum ratio combining (MRC) algorithm, thanks to its inter-satellite interference mitigation capacity. The simulation results show that our proposed holographic metasurface based hybrid beamforming architecture is capable of outperforming the state-of-the-art antenna array architecture in terms of its throughput, given the same physical size of the transceivers. Moreover, we demonstrate that the beamforming performance attained can be substantially improved by taking into account the mutual coupling effect, imposed by the dense placement of the holographic metasurface elements.},

note = {arXiv:2501.04164 [cs]},

keywords = {Array signal processing, Computer architecture, Downlink, holographic metasurface, hybrid beamforming, inter-satellite interference, Low Earth Orbit (LEO) satellite communication, Low earth orbit satellites, Metasurfaces, Precoding, Satellite broadcasting, Satellite communications, Satellites, stochastic geometry, Throughput},

pubstate = {published},

tppubtype = {article}

}