Showing 3 results for Noma
Mousa Abdollahvand, Sima Sobhi-Givi,
Volume 21, Issue 1 (3-2025)
Abstract
This paper introduces a new method for improving wireless communication systems by employing beyond diagonal reconfigurable intelligent surfaces (BD-RIS) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-RIS represents a departure from traditional RIS designs, providing advanced capabilities for manipulating electromagnetic waves to optimize the performance of communication. We propose a DRL-based framework for optimizing the UAV and configuration of BD-RIS elements, including hybrid beamforming, phase shift adjustments, and transmit power coefficients for non-orthogonal multiple access (NOMA) transmission by considering max-min fairness. Through extensive simulations and performance evaluations, we demonstrate that BD-RIS outperforms conventional RIS architectures. Additionally, we analyze the convergence speed and performance trade-offs of different DRL algorithms, emphasizing the importance of selecting the appropriate algorithm and hyper-parameters for specific applications. Our findings underscore the transformative potential of BD-RIS and DRL in enhancing wireless communication systems, laying the groundwork for next-generation network optimization and deployment.
Umesh Mahind, Deepak Karia, Vijay Kapure, Sankit Kassa,
Volume 22, Issue 1 (3-2026)
Abstract
This research explores the demands of compressive sensing (CS) and Machine learning (ML) in biomedical signal processing. The sparse spasmodic sampling (SSS) technique has gained significant attention in compressive sensing. The SSS samples the signal irregularly and spasmodically. Combining machine learning (ML) with Sparse Spasmodic Sampling (SSS) enhances accuracy and improves anomaly detection in biomedical signals. We propose a machine learning-based novel fusion technique that enhances sparse spasmodic sampling (ML-SSS). Mathematical analysis, extensive simulations, and experimental results show notable improvements in reconstruction accuracy and precision. The reconstruction using the proposed model achieves a high signal-to-noise ratio (SNR) of up to 42 dB at a high compression factor of 10%. The achieved accuracy is approximately 95%, and the precision is about 93.3% when detecting abnormalities. This approach paves the way for advanced applications in signal processing and medical imaging, where efficient data acquisition and processing are critical. The proposed framework offers a promising direction for bridging the gap between compressive sensing and intelligent algorithms in anomaly detection.
Mina Baghani, Reza Bahri,
Volume 22, Issue 2 (3-2026)
Abstract
In this paper, the decoding order error of successive interference cancellation (SIC) of multicarrier nonorthogonal multiple access (NOMA) due to the random walk of the users and position estimation deviation is considered in resource allocation. This factor extremely degrades the performance of NOMA in terms of sum rate and outage probability. Therefore, two optimal power allocation strategies for users are derived that maximize the sum rate and minimize the outage probability. The simulation results show that by considering the decoding order error in resource allocation, better performance can be achieved compared to the previous power allocation algorithms without considering this fact, which are a well-known water filling algorithm and a power allocation that maximizes the rate with minimum rate constraint.
