Gibbs-Sampling-based Optimization for the Deployment of Small Cells in 3G Heterogeneous Networks
The growing popularity of mobile data services has placed great demands for wireless cellular networks to support higher-throughput. One way to meet the rapidly growing traffic demand is through heterogeneous network deployment, which uses a mixture of macro cells and small cells (i.e., micro-/ pico-cells) to further enhance the spatial reuse and thus improves network throughput. In this paper, we propose a Gibbs-Sampling based optimization method for finding the optimal deployment of a given number of small cells in 3G networks. The Gibbs Sampling method intelligently balances two potentially conflicting considerations of placing small-cell BSs close to hotspots and avoiding interference with the macro-cell BSs & other small cell BSs. We show that it converges to the deployment decision with the maximum total system throughput with high probability. We also describe two low-complexity algorithms, the greedy EcNo and the greedy hotspot algorithms. Both algorithms are widely used in industry and can be used as the benchmark for comparing our Gibbs sampling-based (GSB) design. We have conducted extensive simulations based on real traffic traces from the 3G data network. Our numerical results show that the GSB placement outperforms the greedy solutions. The GSB approach produces 10% higher throughput and 30% higher off-loading factor than the greedy solutions. Since the cost of deploying small nodes could be expensive and each city may need a large number of small nodes, the proposed results thus represent significant cost savings when compared to the existing greedy solutions.
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