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

Handoff optimization in 802.11 wireless networks

In 802.11 wireless networks, a complete handoff procedure for a mobile node requires access point (AP) selection, AP switch, call admission control (CAC), IP address re-allocation, and network re-configuration. Most current handoff schemes deal only with either AP selection or IP address re-allocation. In this paper, an integrated handoff procedure is proposed. First, AP selection is accomplished by choosing an AP with the lowest channel utilization and smaller number of associated users. The information about load of each AP is reported through modified beacon frames. In the case of adopting load-based AP selection, the average throughput can be increased up to 56%, as opposed to pure SNR-based AP selection. Next, both CAC and IP address pre-fetch are performed simultaneously through the simplified DHCP procedure. Specifically, efficient limited fractional guard channel policy (ELFGCP) is proposed for the CAC phase. By adopting ELFGCP, the failure probability can be reduced as much as 45% from conventional LFGCP. Finally, the simulation results demonstrate the applicability of the integrated approach, and the overall disconnection time due to handoff can be reduced from 2.9 to 0.004 s using traditional handoff procedures

An efficient and fair reliable multicast protocol for 802.11-based wireless LANs

Many applications are inherently multicast in nature. Such applications can benefit tremendously from reliable multicast support at the MAC layer since addressing reliability at the MAC level is much less expensive than handling errors at the upper layers. However, the IEEE 802.11 MAC layer does not support reliable multicast. This void in the MAC layer is a limiting factor in the efficacy of multicast applications. In this work, we propose a Slot Reservation based Reliable Multicast protocol that adds a novel reliability component to the existing multicast protocol in the 802.11 MAC. Our protocol builds on the existing DCF support in the IEEE 802.11 MAC to seamlessly incorporate an efficient reliable multicast mechanism. Intelligent assignment of transmission slots, minimal control packet overhead and an efficient retransmission strategy form the basis of our protocol. We evaluate the performance of our protocol through extensive simulations. Our simulation results show that our protocol outperforms another reliable multicast protocol, Batch Mode Multicast MAC, in terms of delivered throughput in various scenarios. We enhance our protocol to add a fairness component in the presence of parallel unicast and multicast flows and provide unicast friendly multicast operation. We then evaluate the performance of our Slot Reservation Based Reliable Multicast Protocol with Fairness through extensive simulations and see that the scheme ensures fairness among parallel unicast and multicas

On optimization of the resource allocation in multi-cell networks.

Chen, Jieying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 58-62).Abstract in English only.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.5Chapter 1.3 --- Contributions Of This Thesis --- p.7Chapter 1.4 --- Structure Of This Thesis --- p.8Chapter 2 --- Problem Formulation --- p.9Chapter 2.1 --- The JBAPC Problem --- p.9Chapter 2.2 --- The Single-Stage Reformulation --- p.12Chapter 3 --- The BARN Algorithm --- p.15Chapter 3.1 --- Preliminary Mathematics --- p.15Chapter 3.1.1 --- Duality Of The Linear Optimization Problem --- p.15Chapter 3.1.2 --- Benders Decomposition --- p.18Chapter 3.2 --- Solving The JBAPC Problem Using BARN Algorithm --- p.21Chapter 3.3 --- Performance And Convergence --- p.24Chapter 3.3.1 --- Global Convergence --- p.26Chapter 3.3.2 --- BARN With Error Tolerance --- p.26Chapter 3.3.3 --- Trade-off Between Performance And Convergence Time --- p.26Chapter 4 --- Accelerating BARN --- p.30Chapter 4.1 --- The Relaxed Master Problem --- p.30Chapter 4.2 --- The Feasibility Pump Method --- p.32Chapter 4.3 --- A-BARN Algorithm For Solving The JBAPC Problem --- p.34Chapter 5 --- Computational Results --- p.36Chapter 5.1 --- Global Optimality And Convergence --- p.36Chapter 5.2 --- Average Convergence Time --- p.37Chapter 5.3 --- Trade-off Between Performance And Convergence Time --- p.38Chapter 5.4 --- Average Algorithm Performance Of BARN and A-BARN --- p.39Chapter 6 --- Discussions --- p.47Chapter 6.1 --- Resource Allocation In The Uplink Multi-cell Networks --- p.47Chapter 6.2 --- JBAPC Problem In The Uplink Multi-cell Networks --- p.48Chapter 7 --- Conclusion --- p.50Chapter 7.1 --- Conclusion Of This Thesis --- p.50Chapter 7.2 --- Future Work --- p.51Chapter A --- The Proof --- p.52Chapter A.l --- Proof of Lemma 1 --- p.52Chapter A.2 --- Proof of Lemma 3 --- p.55Bibliography --- p.5

Load balancing using cell range expansion in LTE advanced heterogeneous networks

The use of heterogeneous networks is on the increase, fueled by consumer demand for more data. The main objective of heterogeneous networks is to increase capacity. They offer solutions for efficient use of spectrum, load balancing and improvement of cell edge coverage amongst others. However, these solutions have inherent challenges such as inter-cell interference and poor mobility management. In heterogeneous networks there is transmit power disparity between macro cell and pico cell tiers, which causes load imbalance between the tiers. Due to the conventional user-cell association strategy, whereby users associate to a base station with the strongest received signal strength, few users associate to small cells compared to macro cells. To counter the effects of transmit power disparity, cell range expansion is used instead of the conventional strategy. The focus of our work is on load balancing using cell range expansion (CRE) and network utility optimization techniques to ensure fair sharing of load in a macro and pico cell LTE Advanced heterogeneous network. The aim is to investigate how to use an adaptive cell range expansion bias to optimize Pico cell coverage for load balancing. Reviewed literature points out several approaches to solve the load balancing problem in heterogeneous networks, which include, cell range expansion and utility function optimization. Then, we use cell range expansion, and logarithmic utility functions to design a load balancing algorithm. In the algorithm, user and base station associations are optimized by adapting CRE bias to pico base station load status. A price update mechanism based on a suboptimal solution of a network utility optimization problem is used to adapt the CRE bias. The price is derived from the load status of each pico base station. The performance of the algorithm was evaluated by means of an LTE MATLAB toolbox. Simulations were conducted according to 3GPP and ITU guidelines for modelling heterogeneous networks and propagation environment respectively. Compared to a static CRE configuration, the algorithm achieved more fairness in load distribution. Further, it achieved a better trade-off between cell edge and cell centre user throughputs. [Please note: this thesis file has been deferred until December 2016

Association Control Based Load Balancing in Wireless Cellular Networks Using Preamble Sequences

The efficient allocation and use of radio resources is crucial for achieving the maximum possible throughput and capacity in wireless networks. The conventional strongest signal-based user association in cellular networks generally considers only the strength of the signal while selecting a BS, and ignores the level of congestion or load at it. As a consequence, some BSs tend to suffer from heavy load, while their adjacent BSs may carry only light load. This load imbalance severely hampers the network from fully utilizing the network capacity and providing fair services to users. In this thesis, we investigate the applicability of the preamble code sequence, which is mainly used for cell identification, as an implicit information indicator for load balancing in cellular networks. By exploiting the high auto-correlation and low cross-correlation property among preamble sequences, we propose distributed load balancing schemes that implicitly obtain information about the load status of BSs, for intelligent association control. This enables the new users to be attached to BSs with relatively low load in the long term, alleviating the problem of non-uniform user distribution and load imbalance across the network. Extensive simulations are performed with various user densities considering throughput fair and resource fair, as the resource allocation policies in each cell. It is observed that significant improvement in minimum throughput and fair user distribution is achieved by employing our proposed schemes, and preamble sequences can be effectively used as a leverage for better cell-site selection from the viewpoint of fairness provisioning. The load of the entire system is also observed to be balanced, which consequently enhances the capacity of the network, as evidenced by the simulation results

Efficient offloading and load distribution based on D2D relaying and UAVs for emergent wireless networks

The device to device (D2D) and unmanned aerial vehicle (UAV) communications are considered as enabling technologies of the emergent 5th generation of wireless and cellular system (5G). Consequently, it is important to determine their corresponding performance with respect to the 5G requirements. In particular, we focus on enhancing the offloading and load balancing performance in three directions. In the first direction, we study the achievable data rate of user relay assisting other users in two-tier networks. We propose a novel heuristic communication scheme called device-for-device (D4D). The D4D enables moving users to share their resource by taking advantage of cooperative communication. We study the moving user rate sensitivity to the relay selection and blocking probability. In the second direction, we study the offloading from macrocell to small cell and load balancing among small cell. Also, we design a new utility weight function that enables a balanced relay assignment. We propose a novel low complexity algorithm for centralized scheme maximizing the load among small cells as well as users subject to SINR threshold constraints. The simulations show that our proposed schemes achieve performance in load balancing compared to those obtained with the previous or traditional method. In the third direction, we study the 3D deployment of multiple UAVs for emergent on-demand offloading. We propose a novel on-demand deployment scheme based on maximizing both the operator’s profit and the quality of service. The proposed scheme is based on solving a non-convex problem by combining k-means clustering with pattern search to find the suboptimal location of UAVs. The simulation results show that our proposed scheme maximizes the operator’s profit and improves offloading traffic efficiency. Our global contribution was the development of a scheme to improve the quality of service and the performance in emergent networks through the improvement of the load distribution and resource sharing using D2D and UAV