Scheduling in multiuser multiple input multiple output (MU-MIMO) systems is essential for efficient resource allocation and overall performance enhancement. In this work, a multiuser scheduling problem is formulated to maximize the product of user equipments'(UEs) aggregate satisfactions, which maintains user fairness. Solving such a combinatorial problem using exhaustive search (EX), which requires evaluating all possible multiuser groups within a massive number of resource blocks (RBs), is prohibitive. Instead, we propose an efficient users'satisfaction based scheduling approach (US-SA). In our US-SA, a low dimension sub-grouping matrix is constructed {at each frame}, which is used to schedule the best multiuser group in each time slot; satisfied users are eliminated from the scheduling process. Our US-SA performs close to the optimal EX method in terms of satisfaction, transmitted data amount, spectral efficiency, latency, and fairness with lower computational cost. Moreover, our experiments demonstrate that the proposed scheme outperforms competing techniques.
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
Massive Multiple Input Multiple Output (M-MIMO) technology plays an important role in Fifth-Generation (5G) and beyond communication systems. It provides more benefits from enhanced Spectral Efficiency (SE) to improve energy efficiency and more consistency. These advantages are possible with accurate Channel State Information (CSI) present at the Base Station (BS). Verifying accurate CSI is difficult because of the required size of the coherence interval and the resulting limitations on pilot sequence length. So, Pilot Contamination (PC) is introduced when reusing the pilot sequences in nearby cells, which delays the SE enhancement. PC is presented as a bottleneck that limits the achievable throughput of multi-cell massive MIMO systems. In this work, a method for assigning pilot signals and improving pilot sequences is proposed using a Dynamic Attention-based Adaptive Autoformer (DA3) to reduce the effects of PC and increase the system's SE and throughput. The DA3-based Fractional Pilot Reuse (FPR) model classifies users as cell-center or cell-edge based on their Signal-to-Interference-plus-Noise Ratios (SINRs) values. Cell-edge users having lower SINR assign orthogonal pilots to reduce inter-cell interference, while cell-center users with higher SINR reuse the same pilots across different cells to enhance SE and throughput. The DA3 parameters, distance threshold, pilot reuse factor, and pilot allocation are optimized using the Secant Optimization Algorithm (SOA), which helps to increase the SE and throughput in the MIMO system. The developed SOA achieves near-optimal performance with lower computational complexity, thus making it highly suitable for interference management and eliminating the PC issues. The performance of the proposed model is tested with the classical pilot resource allocation methods to confirm its effectiveness.
Swathi Jallu, K. Raju· International Conference Com...· 0 citations
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.
Nikolaos Prodromos, Damianos Diasakos, V. Kokkinos et al.· Wireless personal communicat...· 0 citations
Every day, more and more people are using 5G wireless networks. They also need to be spectrally efficient and fast, reliable communicators. MU‐MIMO technology improves spectral efficiency and network capacity by enabling simultaneous transmission and reception for multiple users over shared frequency resources, thereby enhancing overall system performance in dense 5G environments. This allows users who have the same used frequency resources to transmit and receive simultaneously over there. The MU‐MIMO scheme, however, has a defect of cross‐user interference and inefficient use of resource that is it supports for a limited number of users; To tackle the above mentioned problems, this research proposed a novel MU‐MIMO framework with a robust beamformer together with adaptive user clustering and intelligent infrastructure resource allocation strategies. In varying network conditions, it utilizes optimization‐driven solutions to enable responses that open channels by decreasing interference. To that end, we formulate a hybrid optimization problem with the constraints of channel state information (CSI) and traffic requirements for joint optimization of precoding matrices and user scheduling options. The performance of the proposed system is evaluated in terms of certain key performance indicators (KPIs), which include throughput, spectral efficiency, signal‐to‐interference‐plus‐noise ratio, and energy efficiency. Evaluation scenarios demonstrate that the enhanced MU‐MIMO design implements significantly better than standard approaches, particularly when 5G users are numerous, showing that the proposed approach is a suitable solution to address upcoming challenges in highly susceptible wireless communication environments.
Deyong Jiang, K. Aravind, S. N. Dhanabagyam et al.· Internet Technology Letters· 0 citations
Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is crucial to achieve full-space coverage in next-generation wireless networks. However, optimizing resource allocation in STAR-RIS-assisted systems to balance the system sum rate with user fairness, especially in the presence of imperfect channel state information (CSI), remains a significant challenge. To address this issue, this work investigates resource allocation in an STAR-RIS-assisted multiple-input single-output system under imperfect CSI and proposes a novel method based on the deep reinforcement learning (DRL) framework to solve this problem. Specifically, the DRL framework is utilized to solve the maximization problem of the weighted sum of Jain’s fairness index and the normalized system sum rate, and a segmented training strategy is employed to decouple the complexity of the original joint optimization problem. The simulation results demonstrate that the proposed solution achieves a flexible trade-off between the system sum rate and user fairness. Moreover, it effectively mitigates the performance degradation caused by imperfect CSI, thereby ensuring robust system performance.
Lifan Zeng, Yuyang Peng, Mohammad Meraj Mirza et al.· IEEE Wireless Communications...· 0 citations
Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations