Performance evaluation of artificial neural network-based learning schemes for cognitive radio systems

Abstract

Over the last decade the world of wireless communications has been undergoing some crucial changes, which have brought it at the forefront of international research and development interest, eventually resulting in the advent of a multitude of innovative technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh networks and software defined radio. Such a disparate and highly varying radio environment calls for intelligent management, allocation and usage of a scarce resource, namely the radio spectrum. One of the most prominent emerging technologies that promise to handle such situations is cognitive radio. Cognitive radio systems are based on software defined radio technology and utilize intelligent software packages that enrich their transceivers with the highly attractive properties of self-awareness, adaptability and capability to learn. A cognitive radio system has the ability to adjust its operating parameters, observe the results and, eventually take actions, that is to say, decide to operate in a specific radio configuration (i.e. radio access technology, carrier frequency, modulation type, etc.), expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms that are capable of exploiting measurements sensed from the environment, gathered experience and stored knowledge, are judged as rather beneficial for guiding decisions and actions. Framed within this statement, this paper introduces and evaluates learning schemes that are based on artificial neural networks and can be used for discovering the performance (e.g. data rate) that can be achieved by a specific radio configuration in a cognitive radio system. Interesting scenarios, which include both commercial off-the-shelf and simulation hardware/software products, are mobilized for the performance assessment work, conducted in order to design and use an appropriate neural network structure, while indicative results are presented and discussed in order to showcase the benefits of incorporating such learning schemes into cognitive radio systems.