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Booming demand for mobile intelligent terminal equipment results in the exponential growth of the data flow in the fifth-generation mobile communication network. Small-cell networks have been considered as one of the possible solutions, where interference management and effective resource allocation are outstanding issues due to numerous small base stations. In this paper, the concept of cognitive smallcell networks is introduced, which combines technologies from cognitive radio with small cells. We aim to maximize the total throughput of the cognitive small-cell networks by jointly considering interference management, fairness-based resource allocation, average outage probability, and channel reuse radius. In order to make the optimization problem tractable, we decompose the original problem into three subproblems. First, we derive the average outage probability function of the network with respect to the spectrum sensing threshold. With a given outage probability threshold, the associated range of the channel reuse radius is obtained. In addition, to maximize the total throughput, a fairness-based distributed resource allocation (FDRA) algorithm is proposed to guarantee the fairness among cognitive small-cell base stations, which defines the satisfaction degree and the traffic requirement indicators to dynamically adjust the resource allocation process. Finally, considering the time-varying traffic load and different geographical environments, an improved FDRA (IFDRA) algorithm is proposed to further improve the throughput of the hot area. The simulation results demonstrate that the proposed FDRA algorithm and IFDRA algorithm could achieve a considerable performance improvement compared with schemes in the literature while providing better fairness among cognitive small-cell base stations. INDEX TERMS Cognitive small cell, resource allocation, channel reuse radius, fairness.

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We consider a holistic approach for dual-access cognitive small cell (DACS) networks, which uses the LTE air interface in both licensed and unlicensed bands. In the licensed band, we consider a sensing-based power allocation scheme to maximize the sum data rate of DACSs by jointly optimizing the cell selection, the sensing operation, and the power allocation under the interference constraint to macrocell users. Due to intercell interference and the integer nature of the cell selection, the resulting optimization problems lead to a nonconvex integer programming. We reformulate the problem to a nonconvex power allocation game and find the relaxed equilibria, quasi-Nash equilibrium. Furthermore, in order to guarantee the fairness of the whole system, we propose a dynamic satisfaction-based dual-band traffic balancing (SDTB) algorithm over licensed and unlicensed bands for DACSs which aims at maximizing the overall satisfaction of the system. We obtain the optimal transmission time in the unlicensed band to ensure the proportional fair coexistence with WiFi while guaranteeing the traffic balancing of DACSs. Simulation results demonstrate that the SDTB algorithm could achieve a considerable performance improvement relative to the schemes in literature, while providing a tradeoff between maximizing the total data rate and achieving better fairness among networks.

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