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Smart Resource Allocation with Concurrent Learning Scheme for Heterogeneous LTE Smallcell Networks

The efficient radio resource management and interference coordination schemes became an important requirement for the successful adoption of heterogeneous networks with small cells. In this approach, a large number of low-power devices is deployed to increase the spatial frequency reuse of the selected area. In this paper, we propose a distributed multi-agent strategy, where small cells locally control resource usage to maximize the overall system capacity. The main goal is to provide each cell with the ability to make its decision autonomously while taking into account the resource occupation of the surrounding cells. We study the coexistence with non-cooperative macroenvironment and propose a mechanism to increase the efficiency of learning with a smart safe-shift procedure. We illustrate the application of this distributed learning strategy for the subband allocation and propose several mechanisms to improve the convergence speed in the absence of communication. The performances of the proposed method are evaluated in the case of Long Term Evolution (LTE) setup and compared to a number of traditional resource allocation schemes. System level simulations show that it achieves a considerable improvement in system performance for heterogeneous deployment with non- cooperating agents, without compromising the efficiency of the system.