A Game Theoretic Framework for Cognitive Radio Networks Using Adaptive Channel Allocation Spectrum
Abstract
In this research, we propose a game theoretic framework to investigate the behavior of the cognitive radio networks for distributed adaptive channel allocation. We illustrate two separate objective functions for spectrum sharing games, which capture the benefit of selfish users and cooperative users, respectively. Based on utility definition for cooperative users, we determine that the channel allocation problem can be formed as the potential game, and converges to a deterministic channel allocation Nash equilibrium point. Alternatively, no regret learning implementation is proposed for both scenarios. Also, it is pointed to have similar performance with the possible game when cooperation is expected, but with a higher variability beyond users. The no regret learning formulation is beneficial to accommodate selfish users. Noncooperative learning games have very low overhead for information interchange in the network. We point that cooperationbased spectrum sharing protocol improves the overall network performance at expense of an extended overhead needed for information exchange.
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Introduction
With new paradigm shift in the FCC’s spectrum management policy [3] that generates opportunities for new, more competitive, reuse, cognitive radio technology sets the foundation for deployment of smart flexible networks that cooperatively adjust to increase the overall network performance. The cognitive radio terminology was invented by Mitola [15], and introduces to a smart radio which has the powers to sense the external conditions, learn from the past, and make intelligent judgments to adjust its transmission parameters according to the current state of environment. The possible participation of cognitive radios to spectrum sharing and an initial framework for precise radio protocol have been discussed in [16]. According to the suggested protocol, the users should adopt to the environment, determine the radio temperature of the channels and determine the interference contributions on their neighbors. Based on these measures, the users should respond by changing their transmission parameters if some other users may require to use the channel. While it is obvious that this behavior improves cooperation between cognitive radios, the behavior of networks of cognitive radios working distributed resource allocation algorithms is limited well known.
As cognitive radios are really autonomous agents that are absorbing their environment and optimizing their performance by changing their transmission parameters, their interactions can be modeled utilizing a game theoretic framework. In this framework, the cognitive radios are the players and their performances are the selection of different transmission parameters and new transmission frequencies, which change their own performance and the performance of neighboring players. Game theory has been widely applied in micro economics, and only recently has received attention as a useful mechanism to design and analyze distributed resource allocation algorithms. So, the spectrum sharing problem was analyzed in [7] that based on a game model among providers using bargaining strategies. In [7], the bound of price of anarchy was investigated under the assumption that users are uniformly distributed, or every AP uses same transmission power. Some game theoretic models for cognitive radio networks were conferred in [18], which has recognized potential game formulations for power control, call admission control and interference delay in cognitive radio networks. The convergence conditions for different game models in cognitive radio networks were studied in [19].
In this research, we propose a game theoretic framework of adaptive channel allocation problem for cognitive radios. Our current research assumes that radios can estimate the local interference temperature on different frequencies and can improve by optimizing the information transmission rate for a given channel quality using adaptive channel coding and by possibly changing to a different frequency channel. The cognitive radios’ decisions depend on their perceived utility associated with each possible action. We introduce two different utility definitions, which indicate the amount of cooperation enforced by the spectrum sharing protocol. We design adaptation protocols based on both potential game formulation and no regret learning algorithms.
Conclusion
In this work, we have examined the plan of channel sharing decorum for intellectual radio systems for both helpful and nonagreeable situations. Two distinct plans for the channel distribution amusement were proposed: potential diversion definition, and no-lament learning. We demonstrated that all the proposed range sharing strategies merge to a channel portion harmony, in spite of the fact that an unadulterated methodology assignment can be accomplished just for agreeable situations. Our recreation results have demonstrated that the normal execution as far as SIR or reachable throughput is fundamentally the same as for both learning and potential diversion definition, notwithstanding for the instance of narrow-minded clients. In any case, regarding decency, we demonstrated that both participation and allotment methodology assume imperative jobs. While the proposed potential amusement detailing yields the best execution, its pertinence is constrained to helpful situations and huge learning about neighboring clients is required for the usage. On the other hand, the proposed no-lament learning calculation is appropriate for non-helpful situations and requires just an insignificant measure of data trade. This work speaks to an initial phase in understanding the range sharing issue for psychological radio systems. In future work, we will stretch out the proposed answers for location increasingly handy situations, for example, the instance of clients with unequal forces, control-controlled systems, just as the instance of heterogeneous clients, described by various utility capacities.