Abstract
We address the problem of uplink and downlink resource allocation in heterogeneous networks where device-to-device (D2D) communication is allowed. We consider a realistic, large-scale LTE network in which users can download/upload data using different paradigms, namely, downlink/uplink transmissions from/to macro or micro base stations, and D2D communication in the uplink LTE bands. We propose an approximate dynamic programming algorithm to perform resource allocation scheduling for both upload and download data traffic, while taking into account the interference caused by resource sharing between the different data transfer paradigms. Through simulation, we compare the performance of our approach to solutions employed in today's networks, such as eICIC techniques and proportional fairness scheduling. Results show that our approach significantly improves the system performance in terms of both overall throughput and energy efficiency.
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