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Abstract The purpose of this work is to explore the decrease of total used power in cell‐free millimetre‐wave (mm‐Wave) massive multiple‐input multiple‐output (MIMO) systems, which can be regarded an essential technology for future wireless generations to improve system performance. One of the most important strategies for reducing total power consumption is to activate and deactivate radio frequency (RF) chains at each access point (AP) in the coverage region. Nonetheless, the optimization issue for this methodology is NP‐hard, and an exhaustive search method may be used to determine the ideal number of RF chains at each AP in the cell‐free network. Unfortunately, the exhaustive searching approach is prohibitively complicated, indicating that it is unworkable when there are a significant number of APs in the service region. Furthermore, present RF chain selection approaches prioritize decreasing consumed power and complexity at the price of system performance in terms of total possible rate. This research solves this issue by introducing a unique RF chain selection approach that combines Hungarian and genetic algorithms. The fact that the genetic algorithm (GA) can readily determine the ideal number of active RF chains, yet this technique generally entails significant complexity in large‐scale cell‐free networks, prompted this notion. As a result, the Hungarian method is used early in the GA to overcome the complexity problem while still retaining system performance. In addition, the suggested system employs a semi‐centralized hybrid beamforming architecture in which all analogue combiners for all APs are operated at a central processing unit using channel state information. In addition, each AP has a fully linked phase shifters network and restricted RF chains connecting to its antennas. Finally, simulation findings reveal that, when compared to state‐of‐the‐art techniques, the suggested approach achieves the maximum attainable rate and overall energy efficiency with a tolerable computational complexity.

Abstract In this paper, the cell-free massive multiple input multiple output (MIMO) network is affected by the pilot contamination phenomenon when a large number of users and a small number of available pilots exists, the quality of service (QoS) will deteriorate due to the low accuracy of the channel estimation because some of users will use the same pilot. Therefore, we address this problem by presenting two novel schemes of pilot assignment and pilot power control design based on the matching technique for the uplink of cell-free massive MIMO systems to maximize spectral efficiency. We first formulate an assignment optimization problem in order to find the best possible pilot sequence to be used by utilizing genetic algorithm (GA) and then propose a Hungarian matching algorithm to solve this formulated problem. Regarding the power control design, we formulate a minimum-weighted assignment problem to assign pilot power control coefficients to the estimated channel’s minimum mean-squared error by considering the access point (AP) selection. Then, we also propose the Hungarian algorithm to solve this problem. Simulation results show that our proposed schemes outperform the state-of-the-art techniques concerning both the pilot assignment and the pilot power control design by achieving a 15% improvement in the spectral efficiency. Finally, the computational complexity analysis is provided for the proposed schemes compared with the state-of-the-art techniques.

A hybrid network has recently been proposed as a framework for a high-speed wireless communication network. Basically, it integrates light fidelity (LiFi) with radio frequency wireless gigabit alliance (WiGig) networks that operate, simultaneously, in a completely different frequency band. To assign the best access point (AP) and provide enough resources for each user, an effective load-balancing (LB) strategy is needed. However, the traditional LB strategies involve sophisticated iterative computing procedures whenever the user distribution changes. Hence, the first contribution of this work is to offer a more adaptable, two-step, conditional, and most-correlated distribution (CMCD) algorithm. Thus, the low-complexity most-correlated distribution (MCD) LB scheme is applied, and the average data rates for all users are then calculated. If the results achieve the predefined performance threshold (PDT), the decisions will be confirmed; otherwise, the proposed scheme automatically switches to the more accurate, but more complex, consecutive assign WiGig first separate optimization algorithms (CAWFS) algorithm. The suggested algorithm provides a clear performance-complexity trade-off, which could be simply controlled by choosing the suitable performance tolerance factor. The second contribution of this paper is the correlation-weighted majority voting (CWMV) method, which attempts to benefit from as many prior decision votes as possible, instead of relying just on one vote. In the CWMV technique, the weight of each vote is calculated based on the correlation between the history distribution vectors and the new user distribution vector. A significant increase in the system performance is evident from the simulation results.

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.