Jul 2026· Wireless personal communications· Vol 146, pp. 5265 - 5300· 0 citations· 28 references
TL;DR
Comparison shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.
Abstract
Massive Multiple Input Multiple Output (MIMO) is an essential technology that can significantly improve the performance of 5G wireless networks by using multiple antennas in base stations, improving coverage, reducing interference, and increasing data throughput. In this comprehensive study, we propose and analyze advanced optimization techniques for resource allocation in 5G MIMO networks, focusing on three distinct approaches: simple sorting, Hungarian Algorithm, and Minimum Cost Flow Algorithm. Simulations are performed using the publicly available DeepMIMO dataset, where we evaluate each method under both static and dynamic scenarios, aiming to optimize bandwidth distribution and minimize power consumption. A key contribution of this work is the formulation and comparative evaluation of the resource allocation problem as an assignment-based model, allowing the examined methods to be compared under common DeepMIMO-based static and dynamic scenarios. The technical contribution of this work lies in the common assignment-based formulation and comparative evaluation of simple sorting, Hungarian, and Minimum Cost Flow allocation methods under the same DeepMIMO-based static and dynamic 5G MIMO scenarios. Our comparative analysis shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.
Non-orthogonal multiple access (NOMA) is a kind of 5G and 6G radio access technology, which not only enhances spectrum efficiency but also enables several users at the same time to access the network and share the same frequency resource. This paper studies the problem of jointly optimizing power allocation and channel resource assignment in the downlink multi-carrier NOMA system, with the aim of maximizing the weighted sum rate under individual quality-of-service (QoS) constraints, per-user minimum rate requirements, and total transmit power budget. We cast the problem as a mixed-integer non-linear programming (MINLP) task and decompose it into two tractable subproblems: A low-complexity channel allocation step using a bipartite matching framework, followed by an successive convex approximation (SCA) solution to the power control step with Lagrangian duality. A closed-form expression for the optimal power ratio under fixed channel assignment is derived to achieve efficient iteration between the two stages. To further reduce the computational burden for dense deployment of the network, we combined the iterative scheme with a DRL module based on the deep deterministic policy gradient (DDPG) algorithm to enable the system to respond to changes in channel state without having to solve the optimization problem at each time slot. Simulation results show that when deployed in a 3GPP-compliant urban macro-cell environment, the proposed joint scheme can achieve 38 percent more sum throughput than orthogonal frequency-division multiple access (OFDMA) baselines, a 22 percent increase over fixed NOMA power allocation, and converges within 15 iterations under moderate user density. The energy efficiency gain is 3.62 bits/J/Hz when combining the DRL-based dynamic policy, and the practical feasibility of the proposed framework for next-generation network deployment is verified.
Yuming Fu, Xiaofeng Chang, Wanze Gan· Digital Signal and Computer...· 0 citations
Rate-Splitting Multiple Access (RSMA) has emerged as a robust interference management strategy for future wireless networks. This paper investigates the performance of a hierarchical RSMA scheme in the downlink of a multi-antenna system, designed to efficiently serve clustered user deployments. We derive exact and asymptotic closed-form expressions for the outage probability of users under Nakagami- $m$ fading channels, considering a two-layer message splitting architecture (systemcommon, group-common, and private streams). Furthermore, to ensure fairness and reliability, we formulate a min-max power allocation problem to minimize the worst-case outage probability among users. A Geometric Programming-based algorithm is proposed to solve the resulting non-convex optimization problem. The numerical results validate the theoretical analysis and demonstrate the impact of different strategies for using this model, such as the number of users per group, user allocation strategies, and the number of base station transmit antennas.
Raphael Parreira De Souza, E. Olivo· International Mediterranean...· 0 citations
This paper proposes an advanced resource allocation technique based on multi-objective optimization (MOO) to jointly optimize spectrum and power, mitigating nonlinear impairments and enhancing network performance, thereby improving the optical signal-to-noise ratio.
S. A. Silva, Carmelo J. A. Bastos-Filho, Danilo R. B. Araújo et al.· Journal of Microwaves, Optoe...· 0 citations
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
K. A. Bonsu, Ebenezer Baidoo Baidoo Bediako, K. Darkwah et al.· Applied Mathematics and Stat...· 0 citations
Link scheduling remains a fundamental challenge in wireless networks, as it directly affects critical performance metrics such as throughput, delay, fairness, and energy efficiency. In this paper, we investigate the Maximum Link Scheduling (MLS) and Shortest Link Scheduling (SLS) problems with power assignment, considering two widely adopted interference models: the Signal-to-Interference-plus-Noise Ratio (SINR) model and the Rayleigh fading model. We propose two approximation algorithms: the TMP algorithm for MLS and the TSP algorithm for SLS, both of which employ a class-dependent oblivious power assignment strategy. The validity of these algorithms is established rigorously for both interference models. Our approach classifies the set of links into distinct classes and schedules them by partitioning the link deployment plane associated with each class into small triangular regions. Our theoretical analysis and simulation results demonstrate that the plane partitioning and classification framework outperforms the considered baseline schemes in terms of scheduling efficiency and resource utilization. Moreover, simulation results show that our power assignment method achieves a substantial reduction in energy consumption of nodes compared to competing algorithms.
A. Amirkhani, Neda Mohammadi· IEEE Access· 0 citations
Wireless fronthaul is a key enabler of flexible and scalable cell-free massive MIMO systems, but its limited capacity poses significant challenges for maintaining high and uniform user performance. In this work, we analyze the performance of a cell-free massive MIMO network with wireless fronthaul under realistic low physical layer functional splits. We propose a joint access and fronthaul resource allocation algorithm that maximizes the minimum user equipment (UE) spectral efficiency while satisfying fronthaul load constraints. Our analysis reveals that power allocation over the wireless fronthaul follows a modified water-filling structure, where the water level is jointly determined by the access and fronthaul channel gains. Furthermore, we show that severe fronthaul limitations not only reduce UE rates but also introduce spatial performance disparities depending on the cloud location. Finally, we demonstrate that split option 8 is impractical under wireless fronthaul constraints, underscoring the importance of dynamic fronthaul bit allocation to reduce fronthaul load and enable efficient system operation.
Ozan Alp Topal, Özlem Tuğfe Demir, Emil Björnson et al.· 0 citations