CHANNEL ALLOCATION SCHEME BASED ON GREEDY ALGORITHM IN COGNITIVE VEHICULAR NETWORK

In the dynamic landscape of modern wireless communication, the seamless allocation of channels holds the key to robust network performance, especially in the intricate domain of Vehicular Communication Networks (VCNs). This study introduces a pioneering solution titled the "Channel Allocation Scheme Based on Greedy Algorithm and Cognitive Vehicular Network." By seamlessly integrating the power of the greedy algorithm with the cognitive capabilities of VCNs, our approach addresses the complexities of real-time channel allocation. The proposed scheme prioritizes immediate gains while cognitively adapting to the network's evolving needs, resulting in optimized channel utilization, reduced latency, and enhanced overall network efficiency. Through meticulous implementation and extensive simulations, we validate the effectiveness of our methodology. Our approach showcases remarkable improvements in channel utilization, network throughput, and latency reduction. This paper not only explores the theoretical underpinnings of channel allocation but also provides a pragmatic, real-world solution for the ever-demanding VCNs. As vehicular networks become integral to smart transportation systems, our innovative approach paves the way for responsive, adaptive, and high-performance channel allocation strategies, thereby reshaping the landscape of vehicular communication technologies.

Keywords: vehicular communication, resource allocation, latency