@article{gong_rydberg_2025,

title = {Rydberg Atomic Quantum Receivers for Classical Wireless Communication and Sensing},

author = {Tierui Gong and Aveek Chandra and Chau Yuen and Yong Liang Guan and Rainer Dumke and Chong Meng Samson See and Mérouane Debbah and Lajos Hanzo},

url = {https://ieeexplore.ieee.org/document/10972179},

doi = {10.1109/MWC.015.2400381},

issn = {1558-0687},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-08},

journal = {IEEE Wireless Communications},

volume = {32},

number = {5},

pages = {90–100},

abstract = {Rydberg atomic quantum receivers (RAQRs) are emerging quantum precision sensing platforms designed for receiving radio frequency (RF) signals. It relies on the creation of Rydberg atoms from normal atoms by exciting one or more electrons to a very high energy level, thereby making the atom sensitive to RF signals. RAQRs realize RF-to-optical conversions based on atom-light interactions relying on the so-called electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS) so that the desired RF signal can be read out optically. The large dipole moments of Rydberg atoms associated with rich choices of Rydberg states facilitate an ultra-high sensitivity (textbackslashsimtextbackslashtextnv/textbackslashtextcm/textbackslashsqrttextbackslashtextHz) and an ultra-broadband tunability (direct-current to Terahertz). RAQRs also exhibit compelling scalability and lend themselves to the construction of innovative, compact receivers. Initial experimental studies have demonstrated their capabilities in classical wireless communications and sensing. To fully harness their potential in a wide variety of applications, we commence by outlining the underlying fundamentals of Rydberg atoms, followed by the principles and schemes of RAQRs. Then, we overview the state-of-the-art studies from both physics and communication societies. Furthermore, we conceive Rydberg atomic quantum single-input single-output (RAQ-SISO) and multiple-input multiple-output (RAQ-MIMO) schemes to facilitate the integration of RAQRs with classical wireless systems. Finally, we conclude with a set of potent research directions.},

keywords = {Atomic measurements, Electric fields, Electrons, Energy states, Laser beams, Quantum communication, Radio frequency, Receivers, RF signals, Rydberg atoms},

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{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{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{singh_spectral_2025,

title = {Spectral Efficiency Maximization for Mmwave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints},

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

url = {https://ieeexplore.ieee.org/document/11027785},

doi = {10.1109/TVT.2025.3577955},

issn = {1939-9359},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

pages = {1–16},

abstract = {A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal performance, we formulate an optimization problem relying on sum spectral efficiency (SE) maximization of the communication users (CUs), while satisfying certain radar beampattern similarity (RBPS), total transmit power, and constant modulus constraints, where the latter are attributed to the hybrid mmWave MIMO architecture. Since the aforementioned problem is non-convex and intractable, a sequential approach is proposed wherein the TPCs are designed first, followed by the RCs. To deal with the non-convex MSE and constant modulus constraints in the TPC design problem, we propose a majorization and minimization (MM) based Riemannian conjugate gradient (RCG) method, which restricts the tolerable MSE of the beampattern to within a predefined limit. Moreover, the least squares and the zero-forcing methods are adopted for maximizing the sum-SE and for mitigating the multiuser interference (MUI), respectively. Furthermore, to design the RC at each CU, we propose a linear MM-based blind combiner (LMBC) scheme that does not rely on the knowledge of the TPC at the CUs and has a low complexity. To achieve user fairness, we further extend the proposed sequential approach for maximizing the geometric mean (GM) of the CU's rate. Simulation results are presented, which show the superior performance of the proposed hybrid TPC and RC in comparison to the state-of-the-art designs in the mmWave MIMO ISAC systems under consideration.},

keywords = {Copper, Hybrid power systems, Integrated sensing and communication, Interference, millimeter wave, Millimeter wave communication, Optimization, Radar, radar beampattern similarity, Radio frequency, Signal to noise ratio, Spectral efficiency},

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{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{singh_multi-beam_2025,

title = {Multi-Beam Object-Localization for Millimeter-Wave ISAC-Aided Connected Autonomous Vehicles},

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

url = {https://ieeexplore.ieee.org/document/10677488},

doi = {10.1109/TVT.2024.3451480},

issn = {1939-9359},

year  = {2025},

date = {2025-01-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Vehicular Technology},

volume = {74},

number = {1},

pages = {1725–1729},

abstract = {Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems capable of integrated sensing and communication (ISAC) constitute a key technology for connected autonomous vehicles (CAVs). In this context, we propose a multi-beam object-localization (MBOL) model for enhancing the sensing beampattern (SBP) gain of adjacent objects in CAV scenarios. Given the ultra-narrow beams of mmWave MIMO systems, a single pencil beam is unsuitable for closely located objects, which tend to require multiple beams. Hence, we formulate the SBP gain maximization problem, considering also the constraints on the signal-to-interference and noise ratio (SINR) of the communication users (CUs), on the transmit power, and the constant modulus of the phase-shifters in the mmWave hybrid transceiver. To solve this non-convex problem, we propose a penalty-based triple alternating optimization algorithm to design the hybrid beamformer. Finally, simulation results are provided for demonstrating the efficacy of the proposed model.},

keywords = {connected autonomous vehicles (CAVs), Copper, Integrated sensing and communication, Integrated sensing and communication (ISAC), Interference, millimeter wave, Millimeter wave communication, Optimization, Radio frequency, sensing beampattern (SBP) gain, Signal to noise ratio},

pubstate = {published},

tppubtype = {article}

}

@article{lu_near-field_2024,

title = {Near-Field Localization and Channel Reconstruction for ELAA Systems},

author = {Zhizheng Lu and Yu Han and Shi Jin and Michail Matthaiou},

url = {https://ieeexplore.ieee.org/document/10345492},

doi = {10.1109/TWC.2023.3336328},

issn = {1558-2248},

year  = {2024},

date = {2024-07-01},

urldate = {2025-10-08},

journal = {IEEE Transactions on Wireless Communications},

volume = {23},

number = {7},

pages = {6938–6953},

abstract = {In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.},

keywords = {Antenna arrays, Channel estimation, Channel reconstruction, localization, Location awareness, Mobile antennas, near-field, PEB, Precoding, Radio frequency, Receiving antennas, subarray hybrid ELAA systems},

pubstate = {published},

tppubtype = {article}

}