Interference management for Small Cell (SC) networks has drawn great attention from both academia and industry due to its importance in Long-Term Evolution (LTE) and emerging 5G networks. Although various approaches have been proposed in the literature, many of them face practical limitations such as showing gains only in specific scenarios, relying on ideal fronthaul, or becoming computationally intractable as the network size increases. In this paper, a novel framework is proposed for interference management in centralized SC networks by jointly utilizing graph theory and optimization theory. It extends the classic center-edge frequency reuse schemes into a more fine-grained scale, thus capturing the complex interference relationships in SC networks. The cell-level resource partition can be semi-statically updated according to the traffic dynamics while existing Medium Access Control (MAC) schedulers can be used for subframe-level resource allocation, not requiring fiber fronthaul. LTE system-level simulation results show that the proposed scheme can significantly improve both cell edge and mean user throughput with practical complexity.
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Guo et al. (2020) studied this question.