Abstract Video-based Cognitive radio networks (CRNs) are a sub-type in which some video users send video traffic. In video CRN context, cognitive users must find an optimal rate/power assignment strategy by solving an optimization problem to maximize their perceived quality of experience (QoE) under interference and energy constraints. Due to large dimension of parameter space in dense CRNs, solving this problem using traditional methods such as game theory or other analytical gradient descent-based approaches can lead to large computational burden. Basically, in such scenarios, each cognitive video user actually faces with mean interference effect from its surrounding nodes and must adopt its behavior (rate/power optimization) strategy based on this mean-field interference effect. Because of inherent nature of mean-field game (MFG) theory in distributed solving of high-dimensional optimization problems, it seems to be a good solution candidate in this context. So, in the current paper, we have used MFG for green (energy-efficient) quality control of cognitive users in dense CRNs. We design two different solution approaches based on a finite-difference method (named GMFQ) and machine learning (named D2GMFQ). The first approach is a standard MFG solution but lacks good scalability in very dense CRN scenarios. So, we introduce the second methodology which is fast enough to tackle such cases. Numerical results show that the proposed methods, outperforms similar ones in maximizing sum perceived cognitive user QoEs under energy-efficiency constraints. Specifically, it is determined that about 11 dB and 15 dB gains can be achieved by GMFQ in average comparing with the traditional TCP and UDP streaming scenarios respectively.
Goudarzi et al. (Tue,) studied this question.