Channel access optimization with adaptive congestion pricing for cognitive vehicular networks: an evolutionary game approach

Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed

Spectrum prediction in dynamic spectrum access systems

Despite the remarkable foreseen advancements in maximizing network capacities, the in-expansible nature of radio spectrum exposed outdated spectrum management techniques as a core limitation. Fixed spectrum allocation inefficiency has generated a proliferation of dynamic spectrum access solutions to accommodate the growing demand for wireless, and mobile applications. This research primarily focuses on spectrum occupancy prediction which equip dynamic users with the cognitive ability to identify and exploit instantaneous availability of spectrum opportunities. The first part of this research is devoted to identifying candidate occupancy prediction techniques suitable for SOP scenarios are extensively analysed, and a theoretical based model selection framework is consolidated. The performance of single user Bayesian/Markov based techniques both analytically and numerically. Understanding performance bounds of Bayesian/Markov prediction allows the development of efficient occupancy prediction models. The third and fourth parts of this research investigates cooperative decision and data-based occupancy prediction. The expected cooperative prediction accuracy gain is addressed based on the single user prediction model. Specifically, the third contributions provide analytical approximations of single user, as well as cooperative hard fusion based spectrum prediction. Finally, the forth contribution shows soft fusion is superior and more robust compared to hard fusion cooperative prediction in terms of prediction accuracy. Throughout this research, case study analysis is provided to evaluate the performance of the proposed approaches. Analytical approaches and Monte-Carlo simulation are compared for the performance metric of interest. Remarkably, the case study analysis confirmed that the statistical approximation can predict the performance of local and hard fusion cooperative prediction accurately, capturing all the essential aspects of signal detection performance, temporal dependency of spectrum occupancy as well as the finite nature of the network

Social cognitive radio networks

This brief presents research results on social cognitive radio networks, a transformational and innovative networking paradigm that promotes the nexus between social interactions and cognitive radio networks. Along with a review of the research literature, the text examines the key motivation and challenges of social cognitive radio network design. Three socially inspired distributed spectrum sharing mechanisms are introduced: adaptive channel recommendation mechanism, imitation-based social spectrum sharing mechanism, and evolutionarily stable spectrum access mechanism. The brief concludes with a discussion of future research directions which ascertains that exploiting social interactions for distributed spectrum sharing will advance the state-of-the-art of cognitive radio network design, spur a new line of thinking for future wireless networks, and enable novel wireless service and applications