Load Balancing (LB)

Abstract— Load balancing (LB) is an important use case in long-term evaluation (LTE) self-optimizing networks (SONs). To overcome the potential ping-pong load transfer and low-convergence issues, we propose a hand over optimization in lte son to solve the asymmetry traffic distribution among multiple cells and delay.The Multi objective bat method along with priori expert knowledge reduces the search space and computation time.In PSO each particle (distortion types) flies in the search space with a velocity adjusted by its own flying memory and its companion’s flying experience. Each particle (distortion types) has its objective function value which is decided by a fitness function. The PSO process stops once the termination criteria have

In an LTE SON, LB is usually referred to as mobility load balancing (MLB)which is

automatically performed to use overall network resources by setting the cell individual offset (CIO) value and is applied between the overloaded cell and a possible target cell if an omni directional antenna is used to achieve optimal system performance. The sensor nodes have the ability to detect data from the environment, and work together with other sensor nodes to transmit the data to a managing center , known as the sink, using multi-hop wireless communication. WSNs are homogeneous sensor networks hardly exist. Even homogeneous sensors have different levels of abilities like initial energy, depletion rate, etc. In divergant sensor networks, typically, a large number of inexpensive nodes perform sensing, while a few nodes having comparatively more energy perform data separation from unwanted noise ,integration, fusion and transmission. In 3GPP TS 36.331, handovers (HOs) can be utilized by a number of events .we are concerned with one particular event known as event A3, which defines the entering and leaving conditions of HOs

After that initializes the pulse frequency, pulse rate and loudness. In every iteration, the new solutions are generated by adjusting frequency and update the velocity and position of bats. Select the best solution based on the fitness value. Rank the bats and find the current best.

• In the multi objective BAT algorithm system, an innovative technique is introduced which is called multi objective bat algorithm for combines a priori expert knowledge with Multi-Objective BAT algorithm, which allows to considerable reduce the search space and the computational time required for designing the MLB SON controller. After that, the dynamicity of the optimization phase is addressed.

• To deal with potential risks of ping-pong and slow-convergence problems in the conventional MLB schemes, we propose a cluster based load balancing algotithm, which implements multicell-to-multicell LB to achieve an optimal load redis-tributions and low blocking probability.

This paper is organized as follows. In Section II, the network model and some preliminaries are given. In Section III, the Multi Objective Bat algorithm and PSO is described. The Hybridised Bat is given in Section IV. Finally, the simulation results and a comparative analysis are presented in Section VI.

II. NETWORK MODEL AND PROBLEM DEFINITION

A. Network