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Open AccessArticle

Performance Evaluation of Uplink Cell-Free Massive MIMO Network Under Weichselberger Rician Fading Channel

by Birhanu Dessie, Javed Shaikh, Georgi Iliev, Maria Nenova, Umar Syed and K. Kiran Kumar

Mathematics 2025, 13(14), 2283; https://doi.org/10.3390/math13142283 - 16 Jul 2025

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Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

(This article belongs to the Special Issue Advanced Algorithms in Wireless Communication and Internet of Things (IoT), 2nd Edition)

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13 pages, 5613 KB

Open AccessArticle

Control of DC Motors to Guide Unmanned Underwater Vehicles

by Timothy Sands

Appl. Sci. 2021, 11(5), 2144; https://doi.org/10.3390/app11052144 - 28 Feb 2021

Cited by 27 | Viewed by 4821

Abstract

Many research manuscripts propose new methodologies, while others compare several state-of-the-art methods to ascertain the best method for a given application. This manuscript does both by introducing deterministic artificial intelligence (D.A.I.) to control direct current motors used by unmanned underwater vehicles (amongst other applications), and directly comparing the performance of three state-of-the-art nonlinear adaptive control techniques. D.A.I. involves the assertion of self-awareness statements and uses optimal (in a 2-norm sense) learning to compensate for the deleterious effects of error sources. This research reveals that deterministic artificial intelligence yields 4.8% lower mean and 211% lower standard deviation of tracking errors as compared to the best modeling method investigated (indirect self-tuner without process zero cancellation and minimum phase plant). The improved performance cannot be attributed to superior estimation. Coefficient estimation was merely on par with the best alternative methods; some coefficients were estimated more accurately, others less. Instead, the superior performance seems to be attributable to the modeling method. One noteworthy feature is that D.A.I. very closely followed a challenging square wave without overshoot—successfully settling at each switch of the square wave—while all of the other state-of-the-art methods were unable to do so. Full article

(This article belongs to the Special Issue Underwater Vehicles)

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