Small‐cell deployment over existing heterogeneous networks

Abstract

In order to meet the extra traffic demand of hot-spot users not expected in the original network planning, it is desirable to deploy new small cells on top of the existing heterogeneous network (HetNet) without replanning the overall network. In this paper, we propose to maximize the minimum user throughput in a HetNet with unexpected recurring hot-spots by jointly optimizing the number and locations of new small cells and user associations of all cells. A reduced-complexity iterative algorithm is devised to solve the joint optimization problem. The simulation results show that the proposed iterative algorithm significantly outperforms the random deployment of new small cells and achieves performance very close to numerically solving the joint optimization in terms of minimum user throughput and required number of new small cells, especially for a large number of unexpected hot-spot users.Introduction: The appropriate deployment of small cells is crucial to the success of heterogeneous networks (HetNets). Existing deployment strategies mainly focus on designing the whole HetNet to offer good service quality at low cost [1], [2]. After a HetNet has been established, it is desirable to meet the extra traffic demands from recurring hot spots (HSs) of user equipments (UEs), which were not expected in the original network planning, by deploying new small cells on top of the existing HetNet without replanning the whole network. Mobile small cells mounted on vehicles can be used in this situation, however, the operators need to know the optimal number and locations of new small cells to be deployed. In this paper, we propose to maximize the minimum UE throughput in a HetNet with unexpected recurring HSs by jointly optimizing the number and locations of new small cells and user associations of all cells. Due to the high computational complexity of the joint optimization problem, we propose a reduced-complexity iterative algorithm to solve this optimization. Performance of the iterative new small-cell deployment algorithm in terms of minimum UE throughput and required number of new small cells is evaluated through simulations, in comparison with numerically solving the joint optimization and the random deployment of new small cells.

Dynamic and energy‐efficient ICI mitigation techniques for mobility‐based 5G HetCN

“…The fixed and mobile small cells (SCs) are overlaid within macrocells (MCs) coverage area, not only for load balancing but also to provide services to high data demand place such as at hotspot, railway stations, residential homes, offices etc. [1–3]. The mobile SCs also seem to be mounted on vehicles to provide services at remote and outdoor locations [4, 5].…”

Effect on energy efficiency with small cell deployment in heterogeneous cellular networks

“…Poison Point Process (PPP) based on SG allows spatial modeling and analysis of network entities without affecting preexisting structures and environment . Due to low‐powered nature, cost‐effective and self‐interference management, SCs has been overlaid with preexisting high‐powered macro‐centric base stations (BSs) . Typically, SCs (eg, Picocells) are backhauled to the core network via digital subscriber line, cable, etc., and are used for both poor indoor (such as in offices, residential homes, stations, etc.)…”

“…and outdoor (eg, mobile SCs in vehicles) coverage. SCs not only offload the traffic in the cell but also enhance link quality by reducing communicating distance, and allow utilization of spectrum in an efficient manner …”