A Novel Guard Zone Based Multiple Access Protocol for Autonomous D2D Cellular Communication
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 7
Abstract
In today's interconnected and digitally driven era, Device-to-Device (D2D) communication has emerged as a transformative paradigm, enabling direct and efficient interactions between nearby devices with or without traditional network infrastructure. This research introduces a novel communication protocol that autonomously facilitates D2D communication in networkconstrained environments, maximizing node engagement and ensuring reliable data transmission while addressing the inherent challenges faced by existing Medium Access Control (MAC) protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The study specifically focuses on developing a protocol that enables real-time information sharing among neighboring nodes without network assistance. To evaluate the protocol's performance, comprehensive simulations were conducted using MATLAB, assessing its effectiveness in increasing the number of active nodes and reducing collision probability. The results demonstrate that the proposed protocol significantly decreases interference while enhancing data throughput and energy efficiency, achieving a 15% increase in active node pairs and a notable 4% reduction in collision probability. By minimizing contention overhead and optimizing data transmission, the protocol effectively lowers latency, ensuring reliable communication in environments lacking network support. Furthermore, it conserves energy by reducing idle listening, thereby extending battery life and promoting sustainable wireless communication systems. This research provides a robust solution to enhance D2D communication in isolated environments, paving the way for more resilient wireless ecosystems.
Authors and Affiliations
M. M. Shahzad, Jehanzeb
Keywords
Related Articles
- EP ID EP760722
- DOI -
- Views 51
- Downloads 0
Exploring the Efficacy of CNN Architectures for Esophageal Cancer Classification Using Cell Vizio Images
Esophageal cancer, as with the global burden of disease, is usually due to Barrret's esophagus and gastroesophageal reflux disease. Fortunately, the disease is amenable to early detection; however, early diagnosis has...
Early Detection and Classification of Lung Cancer using Segment Anything Model 2 and Dense Net
Lung cancer is one of the most perilous diseases worldwide with high incidence and low survival rates due to late diagnosis. Accurate detection and diagnosis of lung nodules is important for early-stage detection. Mach...
Realistic Face Super-Resolution via Generative Adversarial Networks: Enhancing Facial Recognition in Real-world Scenarios
The accuracy of real-world facial recognition operations faces challenges because of the difficulties connected to Low-Resolution image quality. Thisindicates that super-resolution methods play a vital role ini...
An Artificial Intelligence VisionTransformer Model for Classification of Bacterial Colony
The application of AI and machine learning, particularly the vision transformer method, in bacterial detection presents a promising solution to overcome limitations of traditional methods, offering faster and more accu...
Enhanced Emotion Recognition on the FER-2013 Dataset by Training VGG from Scratch
Recognizing facial emotions is still a major obstacle in computer vision, particularly when dealing with complex datasets such as FER-2013. Advancements in deep learning have simplified the process of achieving high ac...