1.
Li, Qingchao; El-Hajjar, Mohammed; Xu, Chao; An, Jiancheng; Yuen, Chau; Hanzo, Lajos
Stacked Intelligent Metasurface-Based Transceiver Design for Near-Field Wideband Systems Journal Article
In: IEEE Transactions on Communications, vol. 73, no. 9, pp. 8125–8139, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
2.
Li, Qingchao; El-Hajjar, Mohammed; Xu, Chao; Zhang, Chao; Hanzo, Lajos
XL-MIMO Based Hierarchical Receive Beamforming Subject to Hardware Impairments in the Uplink of Cell-Free Networks Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–11, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
3.
Singh, Jitendra; Jagannatham, Aditya K.; Hanzo, Lajos
Geometric Mean Rate Maximization in RIS-Aided mmWave ISAC Systems Relying on a Non-Diagonal Phase Shift Matrix Journal Article
In: IEEE Open Journal of the Communications Society, vol. 6, pp. 4756–4771, 2025, ISSN: 2644-125X.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
4.
Winter, Samuel; Zhang, Yangyishi; Zheng, Gan; Hanzo, Lajos
A Lattice-Reduction Aided Vector Perturbation Precoder Relying on Quantum Annealing Journal Article
In: IEEE Wireless Communications Letters, vol. 13, no. 5, pp. 1225–1229, 2024, ISSN: 2162-2345.
Abstract | Links | BibTeX | Tags: Annealing, downlink precoding, Hardware, Lattices, MIMO, Perturbation methods, Quantum annealing, Qubit, Symbols, vector perturbation
@article{winter_lattice-reduction_2024,
title = {A Lattice-Reduction Aided Vector Perturbation Precoder Relying on Quantum Annealing},
author = {Samuel Winter and Yangyishi Zhang and Gan Zheng and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/10436537},
doi = {10.1109/LWC.2024.3365874},
issn = {2162-2345},
year = {2024},
date = {2024-05-01},
urldate = {2025-10-08},
journal = {IEEE Wireless Communications Letters},
volume = {13},
number = {5},
pages = {1225–1229},
abstract = {Quantum annealing (QA) is proposed for vector perturbation precoding (VPP) in multiple input multiple output (MIMO) communications systems. The mathematical framework of VPP is presented, outlining the problem formulation and the benefits of lattice reduction algorithms. Lattice reduction aided quantum vector perturbation (LRAQVP) is designed by harnessing physical quantum hardware, and the optimization of hardware parameters is discussed. We observe a 5dB gain over lattice reduction zero forcing precoding (LRZFP), which behaves similarly to a quantum annealing algorithm operating without a lattice reduction stage. The proposed algorithm is also shown to approach the performance of a sphere encoder, which exhibits an exponentially escalating complexity.},
keywords = {Annealing, downlink precoding, Hardware, Lattices, MIMO, Perturbation methods, Quantum annealing, Qubit, Symbols, vector perturbation},
pubstate = {published},
tppubtype = {article}
}
Quantum annealing (QA) is proposed for vector perturbation precoding (VPP) in multiple input multiple output (MIMO) communications systems. The mathematical framework of VPP is presented, outlining the problem formulation and the benefits of lattice reduction algorithms. Lattice reduction aided quantum vector perturbation (LRAQVP) is designed by harnessing physical quantum hardware, and the optimization of hardware parameters is discussed. We observe a 5dB gain over lattice reduction zero forcing precoding (LRZFP), which behaves similarly to a quantum annealing algorithm operating without a lattice reduction stage. The proposed algorithm is also shown to approach the performance of a sphere encoder, which exhibits an exponentially escalating complexity.