Vol: 1 Issue: 2
MODELING AND PERFORMANCE ANALYSIS OF CONTENTION RESOLUTION OF A COGNITIVE RADIO NETWORK
Okwong Atte Enyenihi, Onoyom-Ita Emmanuel O.
INTRODUCTION
Cognitive Radio Networks (CRNs) have emerged as a promising solution to address the growing demand for wireless communication services while efficiently utilizing the limited and crowded radio frequency spectrum, Mitola, (2009). In the dynamic and unpredictable nature of the radio environment, contention arises when multiple cognitive radio devices seek to access the same frequency bands simultaneously. Contention resolution becomes a critical aspect of ensuring fair and efficient spectrum utilization in cognitive radio networks, Akbar, el at (2010). Contending devices must contend with each other to access available spectrum resources, and effective contention resolution models play a pivotal role in optimizing network performance. These models aim to manage the competition among cognitive radio nodes, dynamically allocating spectrum access based on priority, fairness, and overall network efficiency, Ramani & Sharma (2017). Helen & Susan, W, (2015).
The complexity of contention resolution in cognitive radio networks is magnified by the need to coexist with primary users and adapt to the changing radio frequency environment. Traditional contention resolution mechanisms, such as Carrier Sense Multiple Access (CSMA), may not fully address the unique challenges posed by the dynamic and cognitive nature of these networks, Rahman & Karmakar (2018), Akyildiz,el at , (2011, Uddin, & Al-Dubai, (2013),.
This introduction explores the significance of contention resolution models within the context of cognitive radio networks, highlighting the need for adaptive and intelligent mechanisms to address contention challenges. By delving into the principles, algorithms, and advancements in contention resolution, we aim to gain insights into how these models contribute to the efficient and reliable operation of cognitive radio networks in diverse and dynamic communication scenarios, Mahmoodi, el al,(2009, Digham, el at, (2007)..
Contending for spectrum access (sensing) in cognitive radio networks involves intricate decision-making processes, as devices must dynamically adapt to changing environmental conditions, avoid interference with primary users, and adhere to regulatory constraints. Contention resolution models play a crucial role in orchestrating these decisions, ensuring that cognitive radio devices cooperate effectively to share the spectrum efficiently Yucek and Arslan (2009), Khan,et al,(2015), Zang, el at. (2009),.
The challenges associated with contention resolution in cognitive radio networks are manifold. Devices must contend not only with each other but also with the uncertainties introduced by varying propagation conditions, interference levels, and the presence of dynamic primary users. Traditional contention resolution mechanisms may struggle to cope with these complexities, necessitating the development of novel models that harness the cognitive capabilities of the devices.
Moreover, contention resolution in cognitive radio networks is closely tied to the concept of spectrum sensing, where devices must accurately detect and assess the occupancy status of the spectrum. The integration of sensing information into contention resolution models adds a layer of intelligence, allowing devices to make more informed decisions about when and where to contend for spectrum access.