1.
Mumtaz, Muhammad Zeeshan; Mohammadi, Mohammad; Ngo, Hien-Quoc; Matthaiou, Michalis
Optimized energy harvesting in cell-free massive MIMO using Markov process evolution Journal Article
In: IEEE Transactions on Green Communications and Networking, 2025, ISSN: 2473-2400.
Abstract | Links | BibTeX | Tags: Cell-free massive MIMO, energy harvesting, Markov process evolution
@article{mumtaz_optimized_2025,
title = {Optimized energy harvesting in cell-free massive MIMO using Markov process evolution},
author = {Muhammad Zeeshan Mumtaz and Mohammad Mohammadi and Hien-Quoc Ngo and Michalis Matthaiou},
url = {https://www.scopus.com/pages/publications/105005454819},
doi = {10.1109/TGCN.2025.3570574},
issn = {2473-2400},
year = {2025},
date = {2025-05-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Green Communications and Networking},
abstract = {This paper investigates a discrete energy state transition model for energy harvesting (EH) in cell-free massive multiple-input-multiple-output (CF-mMIMO) networks. A Markov chain-based stochastic process is conceived to characterize the temporal evolution of the user equipment (UE) energy level by leveraging state transition probabilities (STP) based on the energy differential (E) between the EH and consumed energy within each coherence interval. Tractable mathematical relationships are derived for the STP cases using a new stochastic model of non-linear EH, approximated using a Gamma distribution. This derivation leverages closed-form expressions for the mean and variance of the harvested energy. To improve the positive STP of the minimum energy UE among all network UEs, we aim to maximize the E for this UE using two power allocation (PA) schemes. The first scheme is a heuristic PA using the relative channel characteristics to this UE from all access points (APs). The second scheme is the optimized PA based on the solution of a second-order conic problem to maximize the E using a responsive primal-dual interior point method (PD-IPM) algorithm with modified backtracking line-search, iterating over multiple PA periods. Our simulation results illustrate that both the proposed PA schemes enhance the dynamic minimum UE energy level by around four-fold over full power control, along with the performance improvement attributed to spatial resource diversification of CF-mMIMO systems.},
keywords = {Cell-free massive MIMO, energy harvesting, Markov process evolution},
pubstate = {published},
tppubtype = {article}
}
This paper investigates a discrete energy state transition model for energy harvesting (EH) in cell-free massive multiple-input-multiple-output (CF-mMIMO) networks. A Markov chain-based stochastic process is conceived to characterize the temporal evolution of the user equipment (UE) energy level by leveraging state transition probabilities (STP) based on the energy differential (E) between the EH and consumed energy within each coherence interval. Tractable mathematical relationships are derived for the STP cases using a new stochastic model of non-linear EH, approximated using a Gamma distribution. This derivation leverages closed-form expressions for the mean and variance of the harvested energy. To improve the positive STP of the minimum energy UE among all network UEs, we aim to maximize the E for this UE using two power allocation (PA) schemes. The first scheme is a heuristic PA using the relative channel characteristics to this UE from all access points (APs). The second scheme is the optimized PA based on the solution of a second-order conic problem to maximize the E using a responsive primal-dual interior point method (PD-IPM) algorithm with modified backtracking line-search, iterating over multiple PA periods. Our simulation results illustrate that both the proposed PA schemes enhance the dynamic minimum UE energy level by around four-fold over full power control, along with the performance improvement attributed to spatial resource diversification of CF-mMIMO systems.
2.
Fu, Jiafei; Mobini, Zahra; Ngo, Hien Quoc; Zhu, Pengcheng; Matthaiou, Michail
WMMSE-Based Processing in Cell-Free Massive MIMO Systems Journal Article
In: IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 330–334, 2025, ISSN: 2162-2345.
Abstract | Links | BibTeX | Tags: Approximation algorithms, Cell-free massive MIMO, Channel estimation, Data communication, Downlink, Optimization, Power control, Precoding, Quality of service, Uplink, Vectors, weighted minimum mean square error, weighted sum-rate maximization
@article{fu_wmmse-based_2025,
title = {WMMSE-Based Processing in Cell-Free Massive MIMO Systems},
author = {Jiafei Fu and Zahra Mobini and Hien Quoc Ngo and Pengcheng Zhu and Michail Matthaiou},
url = {https://ieeexplore.ieee.org/document/10755172},
doi = {10.1109/LWC.2024.3501156},
issn = {2162-2345},
year = {2025},
date = {2025-02-01},
urldate = {2025-10-08},
journal = {IEEE Wireless Communications Letters},
volume = {14},
number = {2},
pages = {330–334},
abstract = {In this letter, we address the weighted sum-rate maximization problem in a cell-free massive multi-input multi-output (CF-mMIMO) system, subject to constraints on the minimum individual quality of service (QoS), maximum power consumption at each access point (AP), and maximum fronthaul capacity. By harnessing the low computational complexity weighted minimum mean square error (WMMSE) framework, two algorithms are proposed to solve the formulated mixed integer nonlinear programming (MINLP) problems with instantaneous/statistical channel state information (CSI). Our instantaneous CSI-based approach can be applied to jointly optimize the power control, precoding, and user association, while the statistical CSI-based approach can be utilized to jointly optimize the power control and user association. Simulation results demonstrate that the proposed instantaneous CSI-based algorithm can provide approximately 66.72% sum-rate gain compared to the scheme with random user association, equal power allocation, and fixed local MMSE-based precoding design. Additionally, the statistical CSI-based algorithm offers competitive performance compared with the instantaneous CSI-based algorithm.},
keywords = {Approximation algorithms, Cell-free massive MIMO, Channel estimation, Data communication, Downlink, Optimization, Power control, Precoding, Quality of service, Uplink, Vectors, weighted minimum mean square error, weighted sum-rate maximization},
pubstate = {published},
tppubtype = {article}
}
In this letter, we address the weighted sum-rate maximization problem in a cell-free massive multi-input multi-output (CF-mMIMO) system, subject to constraints on the minimum individual quality of service (QoS), maximum power consumption at each access point (AP), and maximum fronthaul capacity. By harnessing the low computational complexity weighted minimum mean square error (WMMSE) framework, two algorithms are proposed to solve the formulated mixed integer nonlinear programming (MINLP) problems with instantaneous/statistical channel state information (CSI). Our instantaneous CSI-based approach can be applied to jointly optimize the power control, precoding, and user association, while the statistical CSI-based approach can be utilized to jointly optimize the power control and user association. Simulation results demonstrate that the proposed instantaneous CSI-based algorithm can provide approximately 66.72% sum-rate gain compared to the scheme with random user association, equal power allocation, and fixed local MMSE-based precoding design. Additionally, the statistical CSI-based algorithm offers competitive performance compared with the instantaneous CSI-based algorithm.
3.
Sui, Zeping; Ngo, Hien Quoc; Chien, Trinh Van; Matthaiou, Michail; Hanzo, Lajos
RIS-Assisted Cell-Free Massive MIMO Relying on Reflection Pattern Modulation Journal Article
In: IEEE Transactions on Communications, vol. 73, no. 2, pp. 968–982, 2025, ISSN: 1558-0857.
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
4.
Chien, Trinh Van; Viet, Nguyen Hoang; Chatzinotas, Symeon; Hanzo, Lajos
Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–6, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Cell-free massive MIMO, Channel estimation, Closed-form solutions, Contamination, Correlation, differential evolution, Massive MIMO, Optimization, Rayleigh channels, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces, Training, Vectors
@article{chien_improved_2025,
title = {Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO},
author = {Trinh Van Chien and Nguyen Hoang Viet and Symeon Chatzinotas and Lajos Hanzo},
url = {https://ieeexplore.ieee.org/document/11080325},
doi = {10.1109/TVT.2025.3589240},
issn = {1939-9359},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
pages = {1–6},
abstract = {Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.},
keywords = {Cell-free massive MIMO, Channel estimation, Closed-form solutions, Contamination, Correlation, differential evolution, Massive MIMO, Optimization, Rayleigh channels, reconfigurable intelligent surface, Reconfigurable Intelligent Surfaces, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.
5.
Nguyen, Doan Hieu; Nguyen, Xuan Tung; Jeong, Seon-Geun; Chien, Trinh Van; Hanzo, Lajos; Hwang, Won-Joo
Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems Journal Article
In: IEEE Transactions on Vehicular Technology, pp. 1–6, 2025, ISSN: 1939-9359.
Abstract | Links | BibTeX | Tags: Cell-free massive MIMO, Convergence, Convolutional neural networks, Integrated circuit modeling, Massive MIMO, Pilot Allocation, Quantum circuit, Quantum Machine Learning, Quantum state, Qubit, Throughput, Training, Vectors
@article{nguyen_hybrid_2025,
title = {Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems},
author = {Doan Hieu Nguyen and Xuan Tung Nguyen and Seon-Geun Jeong and Trinh Van Chien and Lajos Hanzo and Won-Joo Hwang},
url = {https://ieeexplore.ieee.org/document/11091511},
doi = {10.1109/TVT.2025.3588212},
issn = {1939-9359},
year = {2025},
date = {2025-01-01},
urldate = {2025-10-08},
journal = {IEEE Transactions on Vehicular Technology},
pages = {1–6},
abstract = {A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.},
keywords = {Cell-free massive MIMO, Convergence, Convolutional neural networks, Integrated circuit modeling, Massive MIMO, Pilot Allocation, Quantum circuit, Quantum Machine Learning, Quantum state, Qubit, Throughput, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks.