دانلود رایگان مقاله کنترل پیش بینی مدل فعال بر اساس پروتکل MAC برای رادیو شناختی شبکه وابسته به رسانه یابرندگر

Abstract

Cognitive Radio (CR) is a recent network paradigm that allows Secondary Users (SUs), such as, wireless devices/users, to intelligently access portions of the radio spectrum not allocated to it, without interfering with the transmission of licensed users (Primary Users (PUs)) who are allocated certain dedicated portions of the radio spectrum. This paradigm in radio communication has been successfully used in vehicular networks wherein communication can be established within vehicles (vehicle-to-vehicle) or vehicles to static stations (vehicle-to-infrastructure) without allocating dedicated frequencies. However, the challenge in CR design lies in building intelligence that helps in efficiently sensing and transmitting data through available radio spectrum channels. This paper proposes a Model Predictive Control (MPC) based Proactive Medium Access Control protocol (ProMAC) for the SUs in a CR network. To the best of our knowledge this is the first proactive MAC reported in the literature for CR. Employing ProMAC in a architecture where the number of SUs and PUs were constant, we achieved 20%,13.5% and 12% improvement in channel utilization, backoff rate and sensing delay respectively as compared to the recently proposed PO-MAC protocol, which is so far the best reported in the literature. In an architecture where the numbers of SUs varied with time, ProMAC achieved 21% and 13.17% improvement in channel utilization and backoff rate, respectively, as compared to PO-MAC. The proposed protocol is based on a self-learning engine that can evolve and improve its prediction accuracy even after deployment on field.

8. Conclusion

An architecture that incorporates a proactive mindset and capable of predicting when and where communication pathways exist is significantly required for effective implementation of CR vehicular networks. It must be capable of devising multiple contingency plans based on available networks, radio environmental conditions, and, user quality of service, along with prediction of user mobility. It is therefore imperative for the present day CR framework to include a proactive approach to facilitate the cognitive procedure at all stages. The proposed ProMAC approach not only improves the channel utilization by effectively learning the environment but also helps the system to take better decisions over a period of successive time slots. This not only resulted in reducing the OODA loop time but also optimized the overall performance of the CR network. The above statement is justified empirically in this paper. Interestingly, ProMAC which starts with addressing the issue of channel assignment in a proactive manner, inadvertently solves the other issues addressed by the best reported PO-MAC, namely, reducing the number of channels to be sensed by a SU node by using the results from the MPC framework; increased channel utilization by assigning channels to SU node in a systematic manner; and, minimizing the energy dissipation by using predicted results to limit the exchange of control messages. As described in [34], POMAC was designed to work in a less disturbed environment. The ProMAC which works in a environment with more unpredictability and movement, also outperform PO-MAC in performance. Thus, the proposed ProMAC protocol provides a proactive channel allocation framework that incorporates some of the best practices reported in the literature for building an effective CR vehicular network.