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

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

ANFIS Modeling of Dynamic Load Balancing in LTE

Modelling of ill-defined or unpredictable systems can be very challenging. Most models have relied on conventional mathematical models which does not adequately track some of the multifaceted challenges of such a system. Load balancing, which is a self-optimization operation of Self-Organizing Networks (SON), 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. While some of them have a very buoyant theoretical basis, they are not practical. Furthermore, most of the techniques proposed the use of an iterative algorithm, which in itself is not computationally efficient as it does not take the unpredictable fluctuation of network load into consideration. This chapter 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. Three key load parameters (number of satisfied user in the net- work, virtual load of the serving eNodeB, and the overall state of the target eNodeB) are used to adjust the hysteresis value for load balancing

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

3GPP Long Term Evolution: Architecture, Protocols and Interfaces

The evolution of wireless networks is a continuous phenomenon. Some key trends in this changing process include: reduced latency, increased performance with substantial reduction in costs, and seamless mobility. Long Term Evolution (LTE) is based on an evolved architecture that makes it a candidate of choice for next generation wireless mobile networks. This paper provides an overview of both the core and access networks of LTE. Functional details of the associated protocols and interfaces are also presented

Neural-Encoded Fuzzy Models for Load Balancing in 3GPP LTE

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

Power Line Communications: A Platform for Sustainable Development

–E Abstract lectricity infrastructure together with information and communication technology (ICT) constitute a veritable platform for driving inclusive and sustainable development. However, last mile internet access in underdeveloped areas is limited by deficit telecommunications infrastructure. This is mainly due to the cost associated with deploying telecommunication distribution networks and the low returns on investments associated with underdeveloped areas. The availability of electric power grids which can be used as telecommunication distribution networks, makes the idea of using power line communication and wireless networks a realistic means of providing communications service to underdeveloped areas.On the other hand, electricity utilities needs an efficient and cost effective means of operating and managing the electric grid. This paper reviews different power line communications technologies that can used to achieve a smart grid model that provides a sustainable electricity and ICT infrastructure for development in Africa

Call Admission Control Techniques for 3GPP LTE: A Survey

Radio resources in a wireless communication network are normally shared among multiple users. When the number of users admitted into the network exceed the capacity of the network, a network congestion is said to occur. Network congestion causes degradation of Quality of Service (QoS) and Grade of Service (GoS), which results in users’ dissatisfaction. Thus, it is of uttermost importance to proactively prevent network congestion. Call Admission Control (CAC) is a radio resource management (RRM) technique that can be employed to prevent network congestion, thereby ensuring the GoS/QoS of admitted calls. In addition to preventing network congestion and ensuring QoS/GoS, it is sometimes necessary to meet other network or users’ requirements. This survey outlines different call admission control schemes used to achieve specific objectives for different deployment scenario

Android Based Smart Home System

Conventional electrical installations used in various building today poses great difficulty for physically challenged and elderly persons to operate them. Sometimes it is also inconvenient for normal person to use productively. Because of the increasing number of both the elderly and the disabled persons, an implementation of a smart home system is proposed in this paper. In addition to the convenience, the system also provides a platform for inclusion of the elderly and physically challenged individuals in both homes and offices, thereby enabling them to contribute meaningful to the development of the economy. The system gives home users wireless control over the house hold lighting systems, ventilation systems and the home main gate using an application running on an android smart phone. It also provides the user with an up to date temperature reading of the surrounding and the energy consumed by the device in the house. A centralized controller was developed around PIC 18F4550 microcontroller to handle the data acquisition and processing for the system. The overall system performance was demonstrated in controlling lamps, fans and gate of a prototyped one bedroom flat and confirmed the success of the design

K pi scattering for isospin 1/2 and 3/2 in lattice QCD

We simulate K pi scattering in s-wave and p-wave for both isospins I=1/2, 3/2 using quark-antiquark and meson-meson interpolating fields. We extract the elastic phase shifts delta at several values of the K-pi relative momenta. The resulting phases exhibit qualitative agreement with the experimental phases in all four channels. We express the s-wave phase shifts near threshold in terms of the scattering length and the effective range. Our K pi system has zero total momentum and is simulated on a single ensemble with two dynamical quarks, so results apply for mpi=266 MeV and mK=552 MeV in our simulation. The backtracking contractions in both I=1/2 channels are handled by the use of Laplacian-Heavyside smeared quarks within the distillation method. Elastic phases are extracted from the energy levels using Luscher's relations. In all four channels we observe the expected K(n)pi(-n) scattering states, which are shifted due to the interaction. In both attractive I=1/2 channels we observe additional states that are related to resonances; we attribute them to K_0^*(1430) in s-wave and K*(892), K*(1410) and K*(1680) in p-wave.Comment: 17 pages, 7 figures, version published in PR