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In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is employed to help cancel multi-user interference, and reconstructed interference cancelation (RIC) is used to help eliminate inter-cell interference. Because RIC is opportunistic, base station (BS) muting is used to coordinate the residual strong interference. Since user association affects residual interference and is coupled with BS muting while both are operated in a very different timescale from content caching, we jointly optimize user association and BS muting for a given caching policy to maximize the number of users simultaneously served by the transmission scheme. By transforming the formulated problem into a maximal independent set problem with constructed conflict graph, the global optimal solution is found with graph theory methods. By exploiting the topology feature of heterogeneous networks, we proceed to propose two low-complexity algorithms, respectively, implemented in a centralized and distributed manner, which are viable for large-scale networks. Simulation results show that the optimized transmission scheme achieves a remarkable performance gain over the existing schemes.

Future average channel gains are recently reported predictable within a minute-level horizon for a mobile user. Predictive resource allocation for non-realtime service with future average channel gains in a time window of one or more minutes long has been demonstrated effective in improving user experience and network throughput as well as reducing energy consumption. Nonetheless, existing studies for predictive resource allocation never consider inter-cell interference (ICI), which severely limits the user experience and network performance in densely-deployed cellular networks. This paper investigates predictive resource allocation in heterogeneous networks, where some base stations generate strong ICIs to some mobile users requesting non-realtime service. Optimizing the resource allocation in a predictive manner for interference networks is challenging, since how to allocate future resources depends on the future signal to interference-plus-noise ratio, which in turn relies on the assigned resources. To deal with this difficulty, we introduce a predictive interference coordination scheme to divide all BSs and users into groups, where the BS-user pairs in each group can communicate simultaneously in a second-level frame. Then, we optimize a common resource allocation plan for all users in each group. The plan essentially determines which BSs in the group should be muted and the users should be associated with which active BS in each frame of the prediction window. By resorting to graph theory, we obtain the optimal solution and derive a low-complexity algorithm. Simulation results show that the proposed scheme outperforms existing relevant methods in terms of user satisfactory rate in heterogeneous networks with heavy traffic load.