Cognitive MAC Protocol for Dynamic Spectrum Access in Opportunistic Wireless Networks with Channel Modelling

Abstract

The proposed novel Cognitive Medium Access Control (MAC) protocol reproduced in this paper is optimally suited to the Dynamic Spectrum Access (DSA) in opportunistic wireless networks, and more specifically has been designed to improve spectrum efficiency, reduce interference, and enable adaptive communication strategies under the challenging conditions that have to be dealt with adversely and at a faster pace. The suggested protocol combines the real-time channel modeling by the means of Hidden Markov Model (HMM) to dynamically estimate the status of the licensed channel occupancy and predict the main user (PU) activity patterns. Using this predictive Intelligence in the MAC layer, delay sensitive applications can pre-select idle channels more reliably, allowing more efficient and collision-free communication in secondary users (SUs). Architecture uses adaptive slot scheduling, probabilistic back off mechanisms and a cooperative sensing scheme that aim to improve the throughput and reduce the access delay in multi-user cognitive radio environment. The simulation experiments carried out in MATLAB and NS-3 cloud provide the analysis of the protocol performance throughput, access latency, spectrum utilization, and collision probability. The experimental results validate that the proposed MAC protocol is far superior than traditional contention based as well as fixed- slot MAC protocol, especially when PU activity level is high, and SU deployment is dense. The channel prediction also implemented besides optimizing the spectrum use helps minimize unnecessary time wastage on duplicate sensing iterations, as well as backoff, duration. In addition, the protocol shows strong scalability and flexibility in various RF conditions thus it can be functional in deploying practical applications in smart city infrastructures, the Industrial IoT system, and the evolving wireless access networks. The study calls out existing deficits in contemporary MAC layer designs and fills them with a smart and flexible system that balances decisions on how to access the spectrum, real-time channel states, and behavior of primary users. This paper ends with information on the scalability of the protocol, the property of the trade-offs between the accuracy of sensing and the latency of the system, and suggestions on how to improve upon the system with the help of deep reinforcement learning as well as the mobility-aware scheduling of the next-generation cognitive radio networks.