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 (HetNet) deployment, which uses a mixture of macro cells and small cells (also known as micro- or 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 the deployment of small cells in 3G networks. To our best knowledge, this work is the first to optimize the locations of multiple small cells with the goal of maximizing a given network utility function. The Gibbs sampling based (GSB) method intelligently balances two potentially conflicting considerations: (i) placing small cells close to congested areas; and (ii) minimizing interference with the existing macro cells and other small cells. We also describe two low-complexity algorithms, the greedy EcNo and the greedy hotspot algorithms. Both algorithms are widely used in industry and will be used as the performance benchmark. Extensive simulations have been conducted based on real traffic traces from the 3G data network. The numerical results show that the GSB placement leads to 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 represent significant cost savings compared to greedy solutions.