Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Load imbalance, together with inefficient utilization of system resource, constitute major factors responsible for poor overall performance in Long Term Evolution (LTE) network. In this paper, a novel scheme of joint dynamic resource allocation and load balancing is proposed to achieve a balanced performance improvement in 3rd Generation Partnership Project (3GPP) LTE Self-Organizing Networks (SON). The new method which aims at maximizing network resource efficiency subject to inter-cell interference and intra-cell resource constraints is implemented in two steps. In the first step, an efficient resource allocation, including user scheduling and power assignment, is conducted in a distributed manner to serve as many users in the whole network as possible. In the second step, based on the resource allocation scheme, the optimization objective namely network resource efficiency can be calculated and load balancing is implemented by switching the user that can maximize the objective function. Lagrange Multipliers method and heuristic algorithm are used to resolve the formulated optimization problem. Simulation results show that our algorithm achieves better performance in terms of user throughput, fairness, load balancing index and unsatisfied user number compared with the traditional approach which takes resource allocation and load balancing into account, respectively

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

An Efficient Requirement-Aware Attachment Policy for Future Millimeter Wave Vehicular Networks

The automotive industry is rapidly evolving towards connected and autonomous vehicles, whose ever more stringent data traffic requirements might exceed the capacity of traditional technologies for vehicular networks. In this scenario, densely deploying millimeter wave (mmWave) base stations is a promising approach to provide very high transmission speeds to the vehicles. However, mmWave signals suffer from high path and penetration losses which might render the communication unreliable and discontinuous. Coexistence between mmWave and Long Term Evolution (LTE) communication systems has therefore been considered to guarantee increased capacity and robustness through heterogeneous networking. Following this rationale, we face the challenge of designing fair and efficient attachment policies in heterogeneous vehicular networks. Traditional methods based on received signal quality criteria lack consideration of the vehicle's individual requirements and traffic demands, and lead to suboptimal resource allocation across the network. In this paper we propose a Quality-of-Service (QoS) aware attachment scheme which biases the cell selection as a function of the vehicular service requirements, preventing the overload of transmission links. Our simulations demonstrate that the proposed strategy significantly improves the percentage of vehicles satisfying application requirements and delivers efficient and fair association compared to state-of-the-art schemes.Comment: 8 pages, 8 figures, 2 tables, accepted to the 30th IEEE Intelligent Vehicles Symposiu

Self Organizing strategies for enhanced ICIC (eICIC)

Small cells have been identified as an effective solution for coping with the important traffic increase that is expected in the coming years. But this solution is accompanied by additional interference that needs to be mitigated. The enhanced Inter Cell Interference Coordination (eICIC) feature has been introduced to address the interference problem. eICIC involves two parameters which need to be optimized, namely the Cell Range Extension (CRE) of the small cells and the ABS ratio (ABSr) which defines a mute ratio for the macro cell to reduce the interference it produces. In this paper we propose self-optimizing algorithms for the eICIC. The CRE is adjusted by means of load balancing algorithm. The ABSr parameter is optimized by maximizing a proportional fair utility of user throughputs. The convergence of the algorithms is proven using stochastic approximation theorems. Numerical simulations illustrate the important performance gain brought about by the different algorithms.Comment: Submitted to WiOpt 201