Enhancing Cognitive Radio Networks using AI-Driven Stackelberg Game Strategies for Proactive Susceptibility Management

• Published in: 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) pp. 861 - 865
• Main Authors: Singh, Arjun, Bhandari, Prabhakar, Singh, Aishwarya, Kumar, V. Mathan, Subha, E., Nigam, Shalini Y.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.11.2023
• Subjects: Adaptive Wireless Communication Systems; Artificial intelligence; Behavioral sciences; Cognitive radio; Cognitive Radio Optimization; Collaboration; Games; Network Performance Enhancement; Resource management; Stackelberg Game Theory; Stochastic processes; Susceptibility Management
• DOI: 10.1109/ICCSAI59793.2023.10421377

In the rapidly advancing field of wireless communications, the concept of Cognitive Radio Network Systems (CRNS) has been enriched by the emergence of a new paradigm known as Collective CRNS Channels. This approach, stemming from the development of Federated Dialogue and Device-to-Device (D2D) systems, posits that entities can serve as collaboration beacons, thereby securing temporal and spatial rights for spectrum sensing. Contemporary research predominantly focuses on the spectrum market dynamics, delving into aspects such as spectrum leasing and trading within economic frameworks. However, these investigations have typically presupposed passive roles for relay nodes, neglecting the scenario where active intermediary nodes may opt out of collaboration with primary users due to the incentives presented by a spectrum market. This study adopts a novel approach, employing a Dynamic Stochastic General Game (DSGG) model to scrutinize the interplay between secondary users' inclinations for bandwidth leasing versus collaborative engagement. This model facilitates a deeper understanding of the strategic behaviors of users under the influence of market forces and the allocation of spectrum rights. The findings of this research underscore the necessity of integrating economic considerations into the architecture of future radio spectrum auctions. It is demonstrated that the economic behaviors of secondary users, governed by a preference for spectrum leasing, introduce complex variables that must be accounted for in the design of collaborative CRNS channels. This study paves the way for more nuanced and economically informed models in the arena of spectrum allocation and management, a critical component in the landscape of computer science, AI, ML, data mining, and wireless communication systems.