Load balancing in self-organized heterogeneous LTE networks: A statistical learning approach

Abstract

The continuous evolution of cellular communication networks into dense, dynamic and heterogeneous networks has posed new challenges for system configuration as well as coverage and capacity optimization, especially in areas with unequal user traffic distribution. In a mixed macro/small (or heterogeneous) cell scenario, load balance is one of those challenges since users typically select the base station with the highest received signal power. Hence, the higher transmit power of macro-cells causes difficulties in offloading a sufficient number of users to small cells. This paper propose a Self-Optimizing Cell Range Expansion Scheme based on a statistical learning approach for an LTE heterogeneous network. System level simulations show the effectiveness of this approach in dynamically expanding the small cell coverage according to traffic conditions, balancing traffic load, reducing cell congestion, and diminishing packet losses.

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

“…In the context of cellular networks, supervised learning can be applied in several domains, such as: mobility prediction [27]- [30], resource allocation [31]- [33], load balancing [34], HO optimization [35], [36], fault classification [37], [38] and cell outage management [39]- [42] Supervised learning is a very broad domain and has several learning algorithms, each with their own specifications and applications. In the following, the most common algorithms applied in the context of cellular networks are presented.…”

“…One solution, proposed in [34], aims to enable a heterogeneous LTE network to learn and adjust dynamically the CRE offsets of small cells according to traffic conditions and to balance the load between macro and femtocells. The algorithm utilizes a regression method in order to learn its parameters and then uses its model to adjust the CRE offsets.…”

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

“…In [20][21][22], learning methods are used to bias cells adaptively in a distributed manner. In [20], Q-learning is used to determine the best step size used for CIO adjustment between a congested cell and its neighboring cells.…”

“…In [21], a reinforcement Q-learning algorithm is proposed to resolve cell congestion problem by predicting the load status of each cell. By taking a statistical learning approach, the authors in [22] propose a cell range expansion method for LTE heterogeneous networks. Stochastic geometry is also used to model and analyze a load balancing algorithm [23,24].…”

Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks

“…The traffic offloading through cell range expansion has been considered as a widely adopted solution (eg, see other works). In this procedure, cell transmission power is virtually boosted through the assignment of cell individual offset (CIO) parameter .…”