The implementation of heterogeneous cloud radio access network (H-CRAN) architecture is faced with practical challenges, such as the capacity and time-delay limitations of the fronthaul links. This paper considers the use of device-to-device (D2D) communication to offload the remote radio heads (RRHs) located in the coverage region of high-power nodes (HPNs). We propose an H-CRAN with non-uniformly deployed D2D communication, in which D2D links are only utilized outside a specified distance from any HPN. Based on the analytical framework provided in this paper, the coverage and the average ergodic rate of a typical user equipment (UE) are characterized. Through defining the exclusion area appropriately, the proposed non-uniform D2D deployment can achieve performance improvement compared with uniform D2D deployment. In addition, to account for the capacity constraint of fronthaul, we characterize the average traffic delivery latency experience by a typical UE when served by RRHs as a quality-of-service metric. Our results show that for a lower fronthaul capacity regime, the proposed non-uniform D2D deployment achieves lower average traffic delivery latency compared with both the uniform D2D deployment and the pure H-CRAN scenarios.INDEX TERMS Heterogenous cloud radio access networks (H-CRANs), device-to-device (D2D), Poisson point process (PPP), coverage probability.

The fog radio access network (F-RAN) is a promising paradigm for the fifth generation wireless communication systems to provide high spectral efficiency and energy efficiency. Characterizing users to select an appropriate communication mode among fog access point (F-AP), and device-to-device (D2D) in F-RANs is critical for performance optimization. Using evolutionary game theory, we investigate the dynamics of user access mode selection in F-RANs. Specifically, the competition among groups of potential users space is formulated as a dynamic evolutionary game, and the evolutionary equilibrium is the solution to this game. Stochastic geometry tool is used to derive the proposals' payoff expressions for both F-AP and D2D users by taking into account the different nodes locations, cache sizes as well as the delay cost. The analytical results obtained from the game model are evaluated via simulations, which show that the evolutionary game based access mode selection algorithm can reach a much higher payoff than the max rate based algorithm.

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