Search results for "working"
showing 10 items of 2747 documents
Partial joint processing with efficient backhauling using particle swarm optimization
2012
In cellular communication systems with frequency reuse factor of one, user terminals (UT) at the cell-edge are prone to intercell interference. Joint processing is one of the coordinated multipoint transmission techniques proposed to mitigate this interference. In the case of centralized joint processing, the channel state information fed back by the users need to be available at the central coordination node for precoding. The precoding weights (with the user data) need to be available at the corresponding base stations to serve the UTs. These increase the backhaul traffic. In this article, partial joint processing (PJP) is considered as a general framework that allows reducing the amount …
Quasi-nash equilibria for non-convex distributed power allocation games in cognitive radios
2013
In this paper, we consider a sensing-based spectrum sharing scenario in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The resulting optimization problem for each cognitive user is non-convex, thus leading to a non-convex game, which presents a new challenge when analyzing the equilibria of this game where each cognitive user represents a player. In order to deal with the non-convexity of the game, we use a new relaxed equilib…
A new strategy for effective learning in population Monte Carlo sampling
2016
In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target and that this target is gradually getting closer to the true one. Computer simulations show and confirm the improvement of the proposed strategy compared to the traditional PMC method on a simple considered scenario.
Field estimation in wireless sensor networks using distributed kriging
2012
In this paper, we tackle the problem of spatial interpolation for distributed estimation in Wireless Sensor Networks by using a geostatistical technique called kriging. We present a novel Distributed Iterative Kriging Algorithm (DIKA) which is composed of two main phases. First, the spatial dependence of the field is exploited by calculating semivariograms in an iterative way. Second, the kriging system of equations is solved by an initial set of nodes in a distributed manner, providing some initial interpolation weights to each node. In our algorithm, the estimation accuracy can be improved by iteratively adding new nodes and updating appropriately the weights, which leads to a reduction i…
Power allocation in multi-channel cognitive radio networks with channel assembling
2011
Accepted version of a paper in the book: 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Published version available from the IEEE:http://dx.doi.org/10.1109/SPAWC.2011.5990485 Consider power allocation for Secondary User (SU) packet transmissions over multiple channels with variable Primary User (PU) arrival rates in cognitive radio networks. Two problems are studied in this paper: The first one is to minimize the collision probability with PUs and the second one is to maximize the data rate while keeping the collision probability bounded. It is shown that the optimal solution for the first problem is to allocate all power onto the bes…
The stacker crane problem and the directed general routing problem
2015
[EN] This article deals with the polyhedral description and the resolution of the directed general routing problem (DGRP) and the stacker crane problem (SCP). The DGRP contains a large number of important arc and node routing problems as special cases, including the SCP. Large families of facet-defining inequalities for the DGRP are described and a branch-and-cut algorithm for these problems is presented. Extensive computational experiments over different sets of DGRP and SCP instances are included.
A Stochastic Search on the Line-Based Solution to Discretized Estimation
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_77 Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the Stochastic Learning Weak Estimator (SLWE), and is based on the principles of continuous stochastic Learning Automata (LA). In this paper, we consider a new family of stochastic discretized weak estimators pertinent to tracking time-varying binomial distributions. As opposed to the SLWE, our p…
Optimal Set Points Regulation of Distributed Generation Units in Micro-grids under Islanded Operation
2010
The present work studies the problem of optimizing the power production levels of dispersed generation units in islanded microgrids. The problem is intrinsically multi-objective with non linear objectives and constraints, thus the solution approach is based on evolutionary optimization and uses the Non dominated Sorting Genetic Algorithm II. The objectives are calculated based on the solution of the load flow problem. The latter problem is more complicated when in the considered system a physical node with a sufficiently large production capability is not available, because all the generation node of the systems have similar and limited generation capability. In this paper, the issue has be…
GRASP for the uncapacitated r-allocation p-hub median problem
2014
In this paper we propose a heuristic for the Uncapacitated r-Allocation p-Hub Median Problem. In the classical p-hub location problem, given a set of nodes with pairwise traffic demands, we must select p of them as hub locations and route all traffics through them at a minimum cost. We target here an extension, called the r-allocation p-hub median problem, recently proposed by Yaman [19], in which every node is assigned to r of the p selected hubs (r@?p) and we are restricted to route the traffic of the nodes through their associated r hubs. As it is usual in this type of problems, our method has three phases: location, assignment and routing. Specifically, we propose a heuristic based on t…
Greedy versus Dynamic Channel Aggregation Strategy in CRNs: Markov Models and Performance Evaluation
2011
Part 1: - PE-CRN 2011 Workshop; International audience; In cognitive radio networks, channel aggregation techniques which aggregate several channels together as one channel have been proposed in many MAC protocols. In this paper, we consider elastic data traffic and spectrum adaptation for channel aggregation, and propose two new strategies named as Greedy and Dynamic respectively. The performance of channel aggregation represented by these strategies is evaluated using continuous time Markov chain models. Moreover, simulation results based on various traffic distributions are utilized in order to evaluate the validity and preciseness of the mathematical models.