No abstract

All-IP network architecture is fast becoming a norm in mobile telecommunications. The International Telecommunications Union-Radio communication sector (ITU-R) recognizes a technology as 4G after haven met the International Mobile Telecommunications Advanced (IMT-A) specification of a minimum of 100 Mb/s downlink data rate for high mobility and 1 Gb/s for low mobility. The Long Term Evolution specified by the 3GPP, provides a minimum downlink data rate of 100 Mb/s and marks a new beginning in Radio Access Technologies (RATs). It also notably implements an all-IP network architecture, providing higher data rates, end-to-end Quality of Service (QoS) and reduced latency. Since the first release of the LTE standard (3GPP release 8), there have been a number of enhancements in subsequent releases. Significant improvements to the standard that enabled LTE to meet the IMT-A specifications were attained in release 10, otherwise known as LTE-Advanced. Some of the enhancements such as the use of small cells (known as femtocells) are envisioned to be the basis of fifth generation (5G) wireless networks. Thus, it is expedient to study the LTE technology and the various enhancements that will shape the migration towards 5G wireless networks. This paper aims at providing a technical overview of 3GPP LTE. With a brief overview of its architecture, this paper explores some key features of LTE that places it at the forefront in achieving the goals of wireless access evolution, enabling it to become a key element of the ongoing mobile internet growth. The migration to 5G may be radical, thus some enabling technologies that will shape the 5G cellular networks are also examined.

Post third generation (3G) broadband mobile networks such as HSPA+, LTE and LTE-Advanced offer improved spectral efficiency and higher data rates using innovative technologies such as relay nodes and femto cells. In addition, these networks are normally deployed for parallel operation with existing heterogeneous networks. This increases the complexity of network management and operations, which reflects in higher operational and capital cost. In order to address these challenges, self-organizing network operations were envisioned for these next generation networks. For LTE in particular, Self-organizing networks operations were built into the specifications for the radio access network. Load balancing is a key self-organizing operation aimed at ensuring an equitable distribution of users in the network. Several iterative techniques have been adopted for load balancing. However, these iterative techniques require precision, rigor and certainty, which carry a computational cost. Retrospect, these techniques use load indicators to achieve load balancing. This paper proposes two neural encoded fuzzy models, developed from network simulation for load balancing. The two models use both load indicators and key performance indicators for a more informed and intuitive load balancing. The result of the model checking and testing satisfactorily validates the model. General TermsAccess Network, Broadband, Models, Soft computing Wireless communication. KeywordsLoad balancing, neural network, fuzzy logic, LDI Model, USU Model.

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 intuition

Abstract-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 selfoptimization, 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 neurofuzzy 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.