1.
Mobini, Zahra; Mohammadi, Mohammadali; He, Jiajun; Ngo, Hien Quoc; Matthaiou, Michail
Cell-Free Massive MIMO-Assisted ISAC with Beam Scanning Proceedings Article
In: 2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC), pp. 1–5, 2025, ISSN: 1948-3252, (ISSN: 1948-3252).
Abstract | Links | BibTeX | Tags: Conferences, Copper, Integrated sensing and communication, Optimization, Programming, Protocols, Signal processing, Spectral efficiency, Three-dimensional displays, Transmitters
@inproceedings{mobini_cell-free_2025,
title = {Cell-Free Massive MIMO-Assisted ISAC with Beam Scanning},
author = {Zahra Mobini and Mohammadali Mohammadi and Jiajun He and Hien Quoc Ngo and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/11143371},
doi = {10.1109/SPAWC66079.2025.11143371},
issn = {1948-3252},
year = {2025},
date = {2025-07-01},
urldate = {2025-10-08},
booktitle = {2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC)},
pages = {1–5},
abstract = {This paper proposes a comprehensive framework for a cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) system, where the access points (APs) are partitioned into communication APs (CAPs) and the sensing APs (SAPs) to simultaneously support downlink (DL) communications and multi-static sensing. A dedicated sensing transmitter (ST) and the SAPs cooperatively sense a target within a designated zone, while the CAPs serve multiple communication users (CUs). To enable practical 3-dimensional (3D) target localization, we develop a novel beam scanning protocol and derive closed-form expressions for the DL spectral efficiency (SE), mainlobe-to-average sensing ratio (MASR), and the Cramer-Rao lower bound (CRLB) of target estimation. Moreover, we formulate a power optimization problem to improve the sensing performance under SE constraints for CUs, solving it efficiently using fractional programming (FP) techniques. Numerical results demonstrate that our approach achieves sensing performance gains of up to 20 dB and significantly reduces the CRLB.},
note = {ISSN: 1948-3252},
keywords = {Conferences, Copper, Integrated sensing and communication, Optimization, Programming, Protocols, Signal processing, Spectral efficiency, Three-dimensional displays, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a comprehensive framework for a cell-free massive multiple-input multiple-output (CF-mMIMO) integrated sensing and communication (ISAC) system, where the access points (APs) are partitioned into communication APs (CAPs) and the sensing APs (SAPs) to simultaneously support downlink (DL) communications and multi-static sensing. A dedicated sensing transmitter (ST) and the SAPs cooperatively sense a target within a designated zone, while the CAPs serve multiple communication users (CUs). To enable practical 3-dimensional (3D) target localization, we develop a novel beam scanning protocol and derive closed-form expressions for the DL spectral efficiency (SE), mainlobe-to-average sensing ratio (MASR), and the Cramer-Rao lower bound (CRLB) of target estimation. Moreover, we formulate a power optimization problem to improve the sensing performance under SE constraints for CUs, solving it efficiently using fractional programming (FP) techniques. Numerical results demonstrate that our approach achieves sensing performance gains of up to 20 dB and significantly reduces the CRLB.
2.
Singh, Jitendra; Naveen, Banda; Srivastava, Suraj; Jagannatham, Aditya K.; Hanzo, Lajos
Pareto Optimal Hybrid Beamforming for Short-Packet Millimeter-Wave Integrated Sensing and Communication Journal Article
In: IEEE Transactions on Communications, vol. 73, no. 6, pp. 4570–4585, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
3.
Gadamsetty, Pavan Kumar; Hari, K. V. S.; Hanzo, Lajos
Sum-Rate Maximization of RIS-Aided Digital and Holographic Beamformers in MU-MISO Systems Journal Article
In: IEEE Transactions on Communications, vol. 73, no. 5, pp. 3106–3118, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
4.
Soleymani, Mohammad; Jorswieck, Eduard; Schober, Robert; Hanzo, Lajos
A Framework for Fractional Matrix Programming Problems with Applications in FBL MU-MIMO Journal Article
In: IEEE Transactions on Wireless Communications, pp. 1–1, 2025, ISSN: 1558-2248.
Abstract | Links | BibTeX | Tags: Delays, Finite block length coding, fractional matrix programming, latency minimization, mean square error, Measurement, MIMO, Minimization, multi-user MIMO systems, Optimization, Performance metrics, Programming, reconfigurable intelligent surface, Resource management, spectral-energy efficiency tradeoff, Transforms, Vectors
@article{soleymani_framework_2025,
title = {A Framework for Fractional Matrix Programming Problems with Applications in FBL MU-MIMO},
author = {Mohammad Soleymani and Eduard Jorswieck and Robert Schober and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11096011},
doi = {10.1109/TWC.2025.3590162},
issn = {1558-2248},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Wireless Communications},
pages = {1–1},
abstract = {An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework’s versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics.},
keywords = {Delays, Finite block length coding, fractional matrix programming, latency minimization, mean square error, Measurement, MIMO, Minimization, multi-user MIMO systems, Optimization, Performance metrics, Programming, reconfigurable intelligent surface, Resource management, spectral-energy efficiency tradeoff, Transforms, Vectors},
pubstate = {published},
tppubtype = {article}
}
An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework’s versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics.