Retrospective spectrum access protocol: A payoff-based learning algorithm for cognitive radio networks

Abstract

Decentralized cognitive radio networks (CRN) require efficient channel access protocols to enable cognitive secondary users (SUs) to access the primary channels in an opportunistic way without any coordination. In this paper, we develop a distributed retrospective spectrum access protocol that can orient the network towards a socially efficient and fair equilibrium state. With the developed protocol, each SU j chooses a channel to select based on the experienced payoff in past Hj periods. Each SU is thus supposed to be equipped with bounded memory and should make its decision based on only local observations. In that sense, the SUs behavioral rules are said to be payoffbased. The protocol also models a natural human decision making behavior of striking a balance between exploring a new choice and retrospectively exploiting past successful choices. With both analytical demonstration and numerical evaluation, we illustrate the two noteworthy features of our solution: (1) the entirely distributed implementation requiring only local observations and (2) the guaranteed statistical convergence to the equilibrium state within a bounded delay.

SOSAP: A Pareto-Efficient Spectrum Access Protocol for Cognitive Radio Networks

“…In other words, the throughput obtained by each SU depends on the identity of the SUs on the same channel. In this particular case a PNE is not guaranteed to exist for the game formulated in [5] (insights on PNE existence in congestion games can be found in [18] and [19]). ‚ The system includes battery-powered devices (e.g., IoT nodes): as one can infer from Eq.…”

“…(4) and revise their strategy according to SOSAP (Algorithm 1). For performance comparison, we choose RSAP (which stands for Retrospective Spectrum Access Protocol), the payoff-based algorithm proposed in [5], where the SUs utility function is associated to their expected throughput and the equilibria are characterized by Fig. 3.…”

“…This means, at the NE the SUs will achieve approximately the same throughput with RSAP, while this is not the case with SOSAP. This is because RSAP equilibria are budget-balanced (i.e., they feature a high fairness) but not Pareto-efficient (for the game formulated in [5] PoA=PoS=1/2).…”

“…Other payoff-based algorithms have been proposed in the literature. In [5] and [3] for instance, the authors develop payoff-based mechanisms that are proven to converge to a PNE of the formulated game. In those cases however, the best PNE is not Pareto-efficient and, furthermore, no equilibrium selection is performed.…”

“…(4))) max. achievable cumulative SU throughput 7% gain SOSAP and RSAP[5] secondary throughput comparison as a function of the iteration t (average).…”