Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

This paper addresses coordinated downlink beamforming optimization in multicell time-division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201

Multiuser Service Differentiated Spectrum Allocation Scheme for High Rate UWB Systems

International audienceIn this paper, we propose a multiuser spectrum allocation scheme for high-rate UWB systems under QoS requirements. This scheme comes as a solution to the coexistence of multiple users sharing the three sub-bands of the same channel as defined in the WiMedia solution adopted for multiband UWB systems. Indeed, WiMedia solution does not allow more than three users to coexist in the same channel. Based on a constrained multiuser optimization problem, the proposed allocation algorithm allows multiple users to access the medium following a mixed sub-band assignment and priority-based scheduling approach in order to ensure an efficient differentiated spectrum sharing. The resulting time-frequency scheduling algorithm relies on the combination of two main metrics available at PHY and MAC levels: the channel quality of each user provided by the exploitation of the effective SINR method, and the QoS constraint represented by a simple weighting parameter that differentiates between two service classes. Simulation results show the efficiency of the proposed scheme and how it guarantees a good performance level for users having strict QoS requirements

Hybrid - Particle Swarm Optimization and Differential Evolution for Reduction of Real Power Loss and Preservation of Voltage Stability Limits

In this paper, a Hybrid algorithm based on - Particle Swarm Optimization (PSO) and Differential Evolution (DE) is used for solving reactive power dispatch problem. It needs progressing the population to create the individual optimal positions by means of the PSO algorithm, and then the algorithm come in DE phase and progresses the individual optimal positions by smearing the DE algorithm. In order to comprehend co-evolution of DE and PSO algorithm, an information-sharing mechanism is presented, which progresses the capability of the algorithm to fence out of the local optimum. Additionally, in optimization procedure, we espouse the hybrid inertia weight stratagem, time-varying acceleration coefficients tactic and arbitrary scaling factor stratagem. The proposed Hybrid algorithm based on - Particle Swarm Optimization and Differential Evolution (H-PSDE) has been tested on standard IEEE 30, 57,118 bus test systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss. Keywords:Optimal Reactive Power; Transmission loss; Particle Swarm Optimization; Differential Evolution; Global Search; Local Search; Inertia Weight

A Novel Work-Sharing Protocol for U-Shaped Assembly Lines

Companies worldwide try to employ contemporary manufacturing systems that can cope with changes in external competitive environments and internal process variability. Just In Time (JIT) philosophy helps achieve the required resilience by its policy of having people, machines, and material just-in-time for any given process. U-shaped assembly lines (U-lines) are used to implement JIT principles. Another principle that helps achieve competitive advantage by developing a flexible workforce that responds efficiently to change is that of work-sharing. Operators share work and help each other in a dynamic and floating way, requiring little management effort to distribute workload amongst operators, or balance the assembly line. The aim of this work is to develop an effective work-sharing protocol for U-shaped assembly lines that will provide the combined advantages of U-lines and work-sharing principles. The new protocol is based on two ideas from literature - the Cellular Bucket Brigade (CBB) system, and the Modified Work-Sharing (MWS) system. To keep the focus on developing the protocol, the scope of this work was limited to two worker systems. The methodology used is to model the protocol and U-line system as a discrete event simulation model, and then use an optimization model to maximize throughput and find optimal buffer locations and levels. A physical simulation experiment was conducted in the Toyota Production Systems lab at RIT to validate the model. Once validated, computer simulation experiments were run with industry data, and results obtained were compared with existing protocols from literature. It was found that the new protocol performed at least as well as the CBB protocol, improving the output by an average of 1%, for the scenarios tested. Increase in processing speed variability as well as larger variation among workers were found to negatively impact the performance of the protocol. The results were analyzed further to understand why these factors are significant, and why there are anomalies and patterns, or lack thereof. Finally, limitations of the protocol, and opportunities for future research in the field are presented. Major limitations of the protocol are that it is difficult to comprehend, and the assumption of an assembly line divided into equal tasks is not practical in the industry

Using swarm intelligence for distributed job scheduling on the grid

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specific time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach