Femtocell technology has shifted beyond indoor residential applications to cover a wider range of scenarios including metropolitan and rural areas. The term “small cell” has hence been used to denote such low-power transmission points deployed for enhancing macrocell coverage and/or capacity. While deployment of femto BSs has typically followed the bottom-up paradigm driven by the ad hoc demand of users, more and more studies have prompted a move toward a more managed deployment model for better tradeoff between performance and cost. In this paper, we investigate an optimization problem for femtocell deployment in a dense network with arbitrary topology. The goal is to determine deployment locations and operation parameters of femtocells for maximizing the number of customers supported with QoS constraints. Since the formulated problem belongs to mixed-integer non-linear programming (MINLP), we propose an anytime algorithm that transforms the joint problem into a cluster formation sub-problem (involving location selection and cell coverage) and a resource management sub-problem (involving power control and resource allocation) for effectively solving all optimization variables in an iterative fashion. Compared with other approaches for femtocell deployment, our evaluation results show that the proposed algorithm can effectively solve the target problem while striking a better performance tradeoff between computation complexity and solution quality.

Heterogeneous network with small cells has recently been regarded as a promising scenario in LTE-Advanced systems. In particular, transmission point selection for heterogeneous networks with low-power nodes has drawn attention. We consider in this paper a scenario where a set users is admitted to a macrocell, but it is yet to be determined how each user is served solely or cooperatively by the set of subordinate transmission points in the cell. Unlike related work that focuses on maximizing the system throughput, we formulate a joint optimization problem of transmission point selection and resource allocation to minimize the total transmission delay of users in the cell. The formulated problem is NP-hard, and thus we propose an algorithm based on overlapping clustering and Gibbs sampler for solving the problem. Simulation results show that, compared to related approaches, the total transmission delay can be reduced by 34% and the Jain's fairness index can be increased to 0.9 on average.