Load Balancing for Wireless network by using Min-Max algorithm

Network overload is one of the key challenges in wireless LANs. This goal is typically achieved when the load of access points is balanced. Recent studies on operational WLANs, shown that access point’s load is often uneven distribution i.e.it will be a crucial task to handle the load of overloaded server. To identify such overloaded server many kind of techniques like load balancing have been proposed already. These methods are commonly required proprietary software or hardware at the user side for controlling the user-access point association. In this proposed system we are presenting a new load balancing method by controlling the size of WLAN cells, which is conceptually similar to cell breathing in cellular networks. This method does not require any modification to the users neither the IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We have develop a set of polynomial time algorithms which find the optimal beacon power settings which minimizes the load of the congested access point. We have also considered the problem of network-wide min-max load balancing. Simulation results show that the performance of the proposed method is comparable with or superior to the best existing association-based method

Power-optimised multi-radio network under varying throughput constraints for rural broadband access

The use of complementary radio access technologies within a network allows the advantages of each technology to be combined to overcome individual limitations. In this paper we show how 5~GHz and ``TV White Space'' overlay networks can be combined to provide fixed wireless access coverage within a rural environment. By creating a model of the whole network we derive the optimum assignment of stations between the two overlay networks to maximise the capacity of individual stations given a desired individual station data rate. Through simulation we show how the power consumption of a base station can be minimised by dynamically adjusting station assignments based on network data rate requirements changing over the course of a day

A novel load-balancing scheme for cellular-WLAN heterogeneous systems with cell-breathing technique

This paper proposes a novel load-balancing scheme for an operator-deployed cellular-wireless local area network (WLAN) heterogeneous network (HetNet), where the user association is controlled by employing a cell-breathing technique for the WLAN network. This scheme eliminates the complex coordination and additional signaling overheads between the users and the network by allowing the users to simply associate with the available WLAN networks similar to the traditional WLAN-first association, without making complex association decisions. Thus, this scheme can be easily implemented in an existing operator-deployed cellular-WLAN HetNet. The performance of the proposed scheme is evaluated in terms of load distribution between cellular and WLAN networks, user fairness, and system throughput, which demonstrates the superiority of the proposed scheme in load distribution and user fairness, while optimizing the system throughput. In addition, a cellular-WLAN interworking architecture and signaling procedures are proposed for implementing the proposed load-balancing schemes in an operator-deployed cellular-WLAN HetNet

An integrated priority-based cell attenuation model for dynamic cell sizing

A new, robust integrated priority-based cell attenuation model for dynamic cell sizing is proposed and simulated using real mobile traffic data.The proposed model is an integration of two main components; the modified virtual community – parallel genetic algorithm (VC-PGA) cell priority selection module and the evolving fuzzy neural network (EFuNN) mobile traffic prediction module.The VC-PGA module controls the number of cell attenuations by ordering the priority for the attenuation of all cells based on the level of mobile level of mobile traffic within each cell.The EFuNN module predicts the traffic volume of a particular cell by extracting and inserting meaningful rules through incremental, supervised real-time learning.The EFuNN module is placed in each cell and the output, the predicted mobile traffic volume of the particular cell, is sent to local and virtual community servers in the VC-PGA module.The VC-PGA module then assigns priorities for the size attenuation of all cells within the network, based on the predicted mobile traffic levels from the EFuNN module at each cell.The performance of the proposed module was evaluated on five adjacent cells in Selangor, Malaysia. Real-time predicted mobile traffic from the EFuNN structure was used to control the size of all the cells.Results obtained demonstrate the robustness of the integrated module in recognizing the temporal pattern of the mobile traffic and dynamically controlling the cell size in order to reduce the number of calls dropped