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Auctions have been proposed as a way to provide economic incentives to dynamically allocate unused spectrum to users in need of it. Previously proposed auction schemes do not take into account the fact that users' power and bandwidth constraints might prevent them from transmitting their bid prices to the auctioneer with high precision, and that these transmitted bid prices must travel through a noisy...
We introduce a recursive scheme for performing Compressed Sensing (CS) on streaming data and analyze, both analytically and experimentally, the computational complexity and estimation error. The approach consists of sampling the input stream recursively via overlapping windowing and making use of the previous measurement in obtaining the next one. The signal estimate from the previous window is utilized...
A major challenge in OFDMA cellular networks is to efficiently allocate scarce channel resources and optimize global system performance. Specifically, the allocation problem across cells/base-stations is known to incur extremely high computational/communication complexity. Recently, Gibbs sampling has been used to solve the downlink inter-cell allocation problem with distributed algorithms that incur...
Motivated primarily by the problem of efficient assignment of virtual machines to physical host machines in a network cloud, we consider an infinite-server system with several types of arriving customers. Multiple customers can be placed into one server for service, subject to general “packing” constraints. Service times of different customers are independent, even if served simultaneously by the...
We consider a class of multi-agent optimization problems, where each agent is endowed with a strongly convex (but not necessarily differentiable) loss function and is subject to individual constraints composed of linear equalities, convex inequalities, and convex set constraints. We derive a novel algorithm that allows the agents to collaboratively reach a decision that minimizes the sum of the loss...
A distributed algorithm is described for solving a linear algebraic equation of the form Ax = b where A is a matrix for which the equation has at least one solution. The equation is simultaneously solved by m agents assuming each agent knows only a subset of the rows of the partitioned matrix [A b], the current estimates of the equation's solution generated by its neighbors, and nothing more. Each...
In this paper, we consider the non-convex active power loss minimization problem. Under the existence of saddle points and certain mild conditions, we show that a solution to the active power loss minimization can be achieved by applying the gradient dynamics approach. An important feature of this approach is that it is naturally distributed, i.e., each bus in the network only requires the local information...
We propose a time-variant regularization in affine projection algorithms, where we update the regularization parameter with a gradient method using a momentum term parametrized by a momentum rate. To further improve the convergence properties of the algorithm in transient stages while ensuring a small final misadjustment, we adaptively estimate the momentum parameter. Then, we prove both the weak...
Spatially coupled low-density parity-check codes show an outstanding performance under the low-complexity belief propagation (BP) decoding algorithm. They exhibit a peculiar convergence phenomenon above the BP threshold of the underlying non-coupled ensemble, with a wave-like convergence propagating through the spatial dimension of the graph, allowing to approach the MAP threshold. We focus on this...
Surges during training process are a major obstacle in training a Spiking Neural Network (SNN) using Spike-Prop algorithm and its derivatives [1]. In this paper, we perform weight convergence analysis to understand the proper step size during SpikeProp learning and hence avoid surges during the training process. Using the results of weight convergence analysis, we propose an optimum adaptive learning...
This paper proposes a decentralized algorithm that allows a group of Plug-in Electric Vehicles (PEVs) to arrive at an optimal strategy to charge their batteries during the day. By communicating repeatedly with an energy coordinator, the PEVs adjust their battery-charging plans by means of a price-feedback signal that accounts for the aggregated demand. The algorithm allows PEVs to adjust their plan...
A lot of interest has recently arisen in the analysis of multiple-choice “cuckoo hashing” schemes. In this context, a main performance criterion is the load threshold under which the hashing scheme is able to build a valid hashtable with high probability in the limit of large systems; various techniques have successfully been used to answer this question (differential equations, combinatorics, cavity...
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