A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems.

Modern wireless communication systems rely heavily on multiple antennas and their corresponding signal processing to achieve optimal performance. As 5G and 6G networks emerge, beamforming and beam management become increasingly complex due to factors such as user mobility, a higher number of antennas, and the adoption of elevated frequencies. Artificial intelligence, specifically machine learning, offers a valuable solution to mitigate this complexity and minimize the overhead associated with beam management and selection, all while maintaining system performance. Despite growing interest in AI-assisted beamforming, beam management, and selection, a comprehensive collection of datasets and benchmarks remains scarce. Furthermore, identifying the most-suitable algorithm for a given scenario remains an open question. This article aimed to provide an exhaustive survey of the subject, highlighting unresolved issues and potential directions for future developments. The discussion encompasses the architectural and signal processing aspects of contemporary beamforming, beam management, and selection. In addition, the article examines various communication challenges and their respective solutions, considering approaches such as centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

Genetic algorithm to estimate the input parameters of Klatt and HLSyn formant-based speech synthesizers.

Voice imitation basically consists in estimating a synthesizer's input parameters to mimic a target speech signal. This is a difficult inverse problem because the mapping is time-varying, non-linear and from many to one. It typically requires considerable amount of time to be done manually. This work presents the evolution of a system based on a genetic algorithm (GA) to automatically estimate the input parameters of the Klatt and HLSyn formant synthesizers using an analysis-by-synthesis process. Results are presented for natural (human-generated) speech for three male speakers. The results obtained with the GA-based system outperform those obtained with the baseline Winsnoori with respect to four objective figures of merit and a subjective test. The GA with Klatt synthesizer generated similar voices to the target and the subjective tests indicate an improvement in the quality of the synthetic voices when compared to the ones produced by the baseline.

New approach for T-wave end detection on electrocardiogram: performance in noisy conditions.

BACKGROUND: The detection of T-wave end points on electrocardiogram (ECG) is a basic procedure for ECG processing and analysis. Several methods have been proposed and tested, featuring high accuracy and percentages of correct detection. Nevertheless, their performance in noisy conditions remains an open problem. METHODS: A new approach and algorithm for T-wave end location based on the computation of Trapezium's areas is proposed and validated (in terms of accuracy and repeatability), using signals from the Physionet QT Database. The performance of the proposed algorithm in noisy conditions has been tested and compared with one of the most used approaches for estimating the T-wave end point: the method based on the threshold on the first derivative. RESULTS: The results indicated that the proposed approach based on Trapezium's areas outperformed the baseline method with respect to accuracy and repeatability. Also, the proposed method is more robust to wideband noise. CONCLUSIONS: The trapezium-based approach has a good performance in noisy conditions and does not rely on any empirical threshold. It is very adequate for use in scenarios where the levels of broadband noise are significant.