Automated small-cell deployment for heterogeneous cellular networks

Abstract

Optimizing the cellular network's cell locations is one of the most fundamental problems of network design. The general objective is to provide the desired Quality-of-Service (QoS) with the minimum system cost. In order to meet a growing appetite for mobile data services, heterogeneous networks have been proposed as a cost-and energy-efficient method of improving local spectral efficiency. Whilst unarticulated cell deployments can lead to localized improvements, there is a significant risk posed to network-wide performance due to the additional interference.The first part of the paper focuses on state-of-the-art modelling and radio-planning methods based on stochastic geometry and Monte-Carlo simulations, and the emerging automatic deployment prediction technique for low-power nodes (LPNs) in heterogeneous networks. The technique advises a LPN where it should be deployed, given certain knowledge of the network. The second part of the paper focuses on algorithms that utilize interference and physical environment knowledge to assist LPN deployment.The proposed techniques can not only improve network performance, but also reduce radio-planning complexity, capital expenditure, and energy consumption of the cellular network. The theoretical work arXiv:1307.5182v2 [cs.NI]

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

“…We have the following obvious theorem, which specifies theand simulation results to characterize the ESE metric as the key system performance indicator and to verify the accuracy of our analysis. Furthermore, we will compare the area resource consumptions (ARCs) required by our proposed ESESACoMP and ESE-JCoMPBD to that of the practical baseline cellular designs [43]- [45], where ARC is defined in terms of power×bandwidth (i.e., the denominator of the network ESE metric) divided by the network's area, and it is measured in W ·Hz/m 2 . In particular, we will use the resource saving ratio (RSR) to quantify the gain of our optimized design over the baseline design of [43]- [45], which is defined as…”

“…We now evaluate the RSR for our proposed design strategies, namely for ESE-SACoMP and ESE-JCoMPBD, over the baseline design of [43]- [45] with a typical cellular scenario of λ b = 4 BS/km 2 . More specifically, for our proposed ESESACoMP design that optimizes the CoMP degree ρ, all the network parameters are identical to those used for the baseline design, including the BS density of λ b = 4 BS/km 2 .…”

“…More specifically, for our proposed ESESACoMP design that optimizes the CoMP degree ρ, all the network parameters are identical to those used for the baseline design, including the BS density of λ b = 4 BS/km 2 . The baseline design of [43]- [45] is not CoMP-enhanced. For our proposed ESE-JCoMPBD design that jointly optimizes the CoMP activation degree ρ and BS density λ b , except for λ b which is optimized, all the other network parameters are identical to those used for the baseline design.…”

Meeting Challenges of LTE Advanced through Small Cell Deployment

“…This is good for capacity planning as it can enhance throughput performance through an offloading effect towards the pico cell. However, range expansion also leads to harsh interference conditions for UEs in the expanded area, which needs to be reflected in the planning process [10].…”

Downlink interference estimation without feedback for heterogeneous network interference avoidance

“…Whilst this analysis has been performed before for a traditional hexagonal grid model [13], it deserves to be tested for a more realistic non-hexagonal cell distribution model, such as a realistic cellular network deployment [14]. Fig.…”