Effects of Interference Mitigation and Scheduling on Dense Small Cell Networks

Abstract

This article compares the potential performance gains from downlink scheduling and interference mitigation in an LTE-Advanced dense small cell network. The study combines theoretical considerations and system-level simulations with a dynamic traffic model and different offered loads. It is shown that intra-cell scheduling can provide a 22% throughput gain in a narrow traffic load region, while the plausible gains from an ideal inter-cell resource management mechanism can be greater than 50% for a wider range of traffic loads, reaching 300% for some of the cases. The results from this research provide valuable insight for the design of resource management algorithms targeted to small cell scenarios on dedicated carriers.

Interference characterization and mitigation benefit analysis for LTE-A macro and small cell deployments

“…As the system is unstable in this region and the results are therefore highly dependent on parameters such as the simulation time, we will focus on the feasible region for the remainder of the article. In line with the results presented in [15] and [20] for the dense small cell case, the scheduling algorithm which provides the best results in terms of these two metrics is GPF, both for the macro-cell and the small cell scenario. The reason is that this scheduler assigns a higher priority to users under better SINR conditions than PF and BET.…”

“…In particular, the impact of the strongest interferer and the potential benefit from cancelling it are evaluated. This investigation continues the work begun in [15], which evaluated the behaviour of the intra-layer interference in an LTE-A dense small cell network. The analysis is extended here to a network based on a regular macro-cell deployment, and we delve deeper into the reasons for the observed interference patterns.…”

Joint Cell Assignment and Scheduling for Centralized Baseband Architectures

“…The two greedier scheduling metrics, MT and FU, increase the performance the most. The reason is that prioritizing the users that can finish the transmission faster and leave the network frees up resources and reduces the generated interference [3]. By performing comparisons with the optimal cell association provided by the Hungarian algorithm, it was found that the suboptimal approach presented in this paper results in less than a 5% average data rate decrease, while significantly reducing the complexity as discussed in Section III.…”

“…The study employs the dense small cell scenario presented in [3]. The scenario considers the deployment of C small cells, divided into several clusters.…”

“…Dense scenarios can also suffer from an uneven use of resources leading to a suboptimal performance. The reason is that having a few very loaded cells in a dense deployment can harm the performance of the rest of the cells because of the interference they generate [3]. Load balancing has been widely studied for LTE [4] but the solutions proposed in the literature have, for the most part, used fairly slow adapting schemes.…”