Incentive mechanism for hybrid access in femtocell network with traffic uncertainty

Abstract

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide improved indoor coverage and higher voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto networks is regarded as the most ideal access control mechanism to help offload macrocell traffic to femtocell, thus enhancing overall network performance. However, without suitable incentive mechanism, the Femtocell Service Providers (FSPs) are not willing to share their femtocell resource with the Macrocell Service Provider (MSP). To address this problem, in this paper, we propose an ACcess Permission (ACP) transaction framework, in which a single MSP purchases ACP from multiple FSPs in various locations throughout T timeslots, and FSPs who have overlapped coverage compete with each other for selling their ACP. However, we are facing the challenge that the demand of MSP in each location dynamically changes at each timeslot. At the start of each timeslot, FSPs are unaware of the demand of MSP, which impedes them to choose an ideal strategy that yields high payoff. To address the problem of information incompleteness, we propose an adaptive strategy updating algorithm, which is based on online learning process and enables FSPs to obtain guaranteed payoff. We conduct simulations to evaluate the payoff and the payoff gap of the FSPs when the MSP's demand is constant, quasi-constant or probabilistic. We also show that the payoff of the FSPs is affected by the learning speed of the proposed algorithm.