arXiv subjects

Sajjad Emdadi Mahdimahalleh

Publications and source records attributed to Sajjad Emdadi Mahdimahalleh.

4 recordsLinked to original sources

arXiv2025

Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels

Automatic modulation recognition (AMR) is critical for cognitive radio, spectrum monitoring, and secure wireless communication. However, existing solutions often rely on large labeled datasets or multi-stage training pipelines, which limit scalability and generalization in practice. We propose a unified Vision Transformer (ViT) framework that integrates supervised, self-supervised, and reconstruction objectives. The model combines a ViT encoder, a lightweight convolutional decoder, and a linear classifier; the reconstruction branch maps augmented signals back to their originals, anchoring the encoder to fine-grained I/Q structure. This strategy promotes robust, discriminative feature learning during pretraining, while partial label supervision in fine-tuning enables effective classification with limited labels. On the RML2018.01A dataset, our approach outperforms supervised CNN and ViT baselines in low-label regimes, approaches ResNet-level accuracy with only 15-20% labeled data, and maintains strong performance across varying SNR levels. Overall, the framework provides a simple, generalizable, and label-efficient solution for AMR.

cs.CV↗

arXiv2025

Analysis of Scheduling schemes based on Carrier Aggregation in LTE-Advanced and their improvement

In this paper I focused on resource scheduling in the downlink of LTE-Advanced with aggregation of multiple Component Carriers (CCs). When Carrier Aggregation (CA) is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint User Scheduling (JUS), Separated Random User Scheduling (SRUS), Separated Burst-Level Scheduling (SBLS) are three different resource scheduling schemes. JUS is optimal in performance but with high complexity and not considering quality of experience (QoE) parameters. Whereas SRUS and SBLS are contrary and users will acquire few resources because they do not support CA and the system fairness is disappointing. The author propose a novel Carrier Scheduling (CS) scheme, termed as "Quality of Service and Channel Scheduling" (QSCS). Connected CCs of one user can be changed in burst level and these changes are based on checking of services priority and quality of signal that user experiences. Simulation results show that the proposed algorithm can effectively enhance throughput of users like JUS and also it chooses best CCs based on QoS and channel quality parameters. The simulation results also show that achieved QoE is much better than other algorithms.

eess.SP↗

arXiv2025

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence.

cs.LG↗

arXiv2025

Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review

These days with the rising computational capabilities of wireless user equipment such as smart phones, tablets, and vehicles, along with growing concerns about sharing private data, a novel machine learning model called federated learning (FL) has emerged. FL enables the separation of data acquisition and computation at the central unit, which is different from centralized learning that occurs in a data center. FL is typically used in a wireless edge network where communication resources are limited and unreliable. Bandwidth constraints necessitate scheduling only a subset of UEs for updates in each iteration, and because the wireless medium is shared, transmissions are susceptible to interference and are not assured. The article discusses the significance of Machine Learning in wireless communication and highlights Federated Learning (FL) as a novel approach that could play a vital role in future mobile networks, particularly 6G and beyond.

cs.LG↗