arXiv subjects

Siavash Bayat

Publications and source records attributed to Siavash Bayat.

4 recordsLinked to original sources

arXiv2026

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices. Among various BCI paradigms, EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing algorithms developed over the past decade for decoding brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance EEG-based MI-BCI systems.

eess.SP↗

arXiv2021

Multi-objective Resource Allocation for D2D and Enabled MC-NOMA Networks by Tchebycheff Method

This paper considers a resource allocation problem in device-to-device (D2D) communications sharing the same frequency spectrum. In particular, the CUs utilize non-orthogonal multiple access (NOMA) while DUs adopt the orthogonal frequency division multiple access (OFDMA). A multi-objective optimization problem (MOOP) is formulated, which jointly maximizes the sum rate of D2D and CUs (CUs) in uplink communications while taking into account the maximum transmit power budget and minimum data rate requirement for D2D and CUs. This MOOP is handled by the weighted Tchebycheff method, which converts it into a single-objective optimization (SOOP). Then, the monotonic optimization approach is employed to solve this SOOP optimally. Numerical results unveil an interesting tradeoff between D2D and CUs.

cs.IT↗

arXiv2020

Novel Relay Selection Algorithms for Machine-to-Machine Communications with Static RF Interface Usage

Machine-to-Machine (M2M) communications have been introduced to improve the communication capacity in dense wireless networks. One of the most important concerns for network designers is maintaining the high performance of the network when the quality of connections between sources and their destinations is poor. Thus the careful selection of relays between data sources and their destinations is a very important issue. The possibility of simultaneous use of different Radio Frequency (RF) interfaces for transmitting data, which communication devices are equipped with them, can increase the capacity of data transmission over the network. In this paper, two novel M2M relay selection algorithms are proposed, named as Optimal Relay Selection Algorithm (ORSA) and Matching based Relay Selection Algorithm (MRSA). ORSA is a centralized algorithm for the optimal selection of relays by transforming the main problem to a k-cardinality assignment problem that can be solved using the Hungarian algorithm. MRSA is a distributed algorithm that leverages concepts from matching theory to provide a stable solution for the relay selection problem. In both proposed algorithms static RF interfaces usage is applied to enable simultaneous use of different interfaces for data transmission. The simulations show that ORSA is optimally solving the relay selection problem. MRSA has an optimal stable result, that when there is no restriction on the number of channels, is only about 1% lower than ORSA. Besides, MRSA provides better results than direct transmission Without any Relay Selection Algorithm (WRSA) and Random Relay Selection Algorithm (RRSA), about 15% and 98%, respectively.

cs.NI↗

arXiv2013

Dynamic Decentralized Algorithms for Cognitive Radio Relay Networks

We propose a distributed spectrum access algorithm for cognitive radio relay networks with multiple primary users (PU) and multiple secondary users (SU). The key idea behind the proposed algorithm is that the PUs negotiate with the SUs on both the amount of monetary compensation, and the amount of time the SUs are either (i) allowed spectrum access, or (ii) cooperatively relaying the PU's data, such that both the PUs' and the SUs' minimum rate requirement are satisfied. The proposed algorithm is shown to be flexible in prioritizing either the primary or the secondary users. We prove that the proposed algorithm will result in the best possible stable matching and is weak Pareto optimal. Numerical analysis also reveal that the distributed algorithm can achieve a performance comparable to an optimal centralized solution, but with significantly less overhead and complexity.

cs.NI↗