Review of Adaptive Cell Selection Techniques in LTE-Advanced Heterogeneous Networks

Abstract

Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions.

Machine learning algorithms for inter-cell interference coordination

“…For the frequency resource allocation, the ICIC employs the minimal resource allocation unit called Resource Block nagaraj, & Srilakshmi, 2013; Gadam, Ahmed, & Kyun, 2016), aprendizaje estadístico (Sierra & Marca, 2015) y clasificación de patrones (Thilina, Choi, Saquib, & Hossain, 2013), entre otros.…”

“…ML defines several approaches included in the current paper and applicable to the self-optimization context. The most relevant are: reinforced learning (Moysen, Giupponi, Carl, & Gauss, 2014), Q-Learning (Kumar, Kanagaraj, & Srilakshmi, 2013;Gadam, Ahmed, & Kyun, 2016), statistical learning (Sierra & Marca, 2015), and pattern classification (Thilina, Choi, Saquib, & Hossain, 2013), among others.…”

Hybrid channel gain prioritized access‐aware cell association with interference mitigation in LTE‐Advanced HetNets

“…Hence, an adaptive bias setting that take the aforementioned problems into consideration will achieve a better load balancing and improved system performance. Adaptive cell range control algorithms to improve system performance using the load balance were reviewed in [19]. The cell-edge user throughput and average user throughput parameters, which are used as inputs to the adaptive algorithm, are associated with feedback error.…”

“…Adaptive cell range control algorithms to improve system performance using the load balance were reviewed in . The cell‐edge user throughput and average user throughput parameters, which are used as inputs to the adaptive algorithm, are associated with feedback error.…”

Particle Swarm Optimization Based Multi-Objective User Association for LTE-A Heterogeneous Networks

“…Adding a fixed bias value to the small cells is known as static biasing, where adding a low bias value only attracts a small number of the UEs to be associated with the small cells. On the contrary, adding a high bias value implies that more users will be associated with the small cells [9]. From the overall perspective of the system, resources in the small cells will either be over-utilized or under-utilized when setting the bias value of the small cells to a high or a low value, respectively.…”