@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{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{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_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{maity_variational_2025,

title = {Variational Bayesian Learning for 3-D Localization of Extended Targets in mmWave MIMO OFDM ISAC Systems},

author = {Priyanka Maity and Deepika Harish and Suraj Srivastava and Aditya K. Jagannatham and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10990143},

doi = {10.1109/OJCOMS.2025.3567429},

issn = {2644-125X},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

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

volume = {6},

pages = {4421–4436},

abstract = {Variational Bayesian learning (VBL)-aided extended target localization is conceived for orthogonal frequency division multiplexing (OFDM) based-mmWave MIMO systems using the OFDM integrated sensing and communication (ISAC) waveform. The proposed framework also considers the intercarrier interference (ICI) effects encountered in mobile scenarios and the clutter present in the environment. The proposed algorithm is based on a hybrid mmWave MIMO architecture, where the number of radio frequency (RF) chains is significantly lower than the number of antennas. A range, Doppler and angular (RDA)-domain representation of the target in three-dimensional (3D) space is conceived for accurate target parameter estimation. The proposed algorithm exploits the four-dimensional (4D) sparsity arising in the RDA domain of the scattering scene and employs the powerful VBL framework for the estimation of target parameters, such as elevation angle, azimuth angle, range and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid VBL is developed for recursively updating the parameter grid, thereby progressively improving the estimates. We also determine the Cramér-Rao bound (CRB) and Bayesian CRB for the estimation of the target parameters in order to bound the estimation performance. Our simulation results validate the superior performance of the proposed approach in comparison to the existing algorithms.},

keywords = {azimuth angle, Bayes methods, Bayesian learning, Clutter, Direction-of-arrival estimation, Doppler effect, elevation angle, Estimation, extended targets, Integrated sensing and communication, Location awareness, Millimeter wave communication, MIMO, mmWave, OFDM, Radar, sparsity, Three-dimensional displays},

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}

}