2,072 research outputs found
Improved Distributed Estimation Method for Environmental\ud time-variant Physical variables in Static Sensor Networks
In this paper, an improved distributed estimation scheme for static sensor networks is developed. The scheme is developed for environmental time-variant physical variables. The main contribution of this work is that the algorithm in [1]-[3] has been extended, and a filter has been designed with weights, such that the variance of the estimation errors is minimized, thereby improving the filter design considerably\ud
and characterizing the performance limit of the filter, and thereby tracking a time-varying signal. Moreover, certain parameter optimization is alleviated with the application of a particular finite impulse response (FIR) filter. Simulation results are showing the effectiveness of the developed estimation algorithm
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Controlled Hopwise Averaging: Bandwidth/Energy-Efficient Asynchronous Distributed Averaging for Wireless Networks
This paper addresses the problem of averaging numbers across a wireless
network from an important, but largely neglected, viewpoint: bandwidth/energy
efficiency. We show that existing distributed averaging schemes have several
drawbacks and are inefficient, producing networked dynamical systems that
evolve with wasteful communications. Motivated by this, we develop Controlled
Hopwise Averaging (CHA), a distributed asynchronous algorithm that attempts to
"make the most" out of each iteration by fully exploiting the broadcast nature
of wireless medium and enabling control of when to initiate an iteration. We
show that CHA admits a common quadratic Lyapunov function for analysis, derive
bounds on its exponential convergence rate, and show that they outperform the
convergence rate of Pairwise Averaging for some common graphs. We also
introduce a new way to apply Lyapunov stability theory, using the Lyapunov
function to perform greedy, decentralized, feedback iteration control. Finally,
through extensive simulation on random geometric graphs, we show that CHA is
substantially more efficient than several existing schemes, requiring far fewer
transmissions to complete an averaging task.Comment: 33 pages, 4 figure
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On the Performance and Linear Convergence of Decentralized Primal-Dual Methods
This dissertation studies the performance and linear convergence properties of primal-dual methods for the solution of decentralized multi-agent optimization problems. Decentralized multi-agent optimization is a powerful paradigm that finds applications in diverse fields in learning and engineering design. In these setups, a network of agents is connected through some topology and agents are allowed to share information only locally. Their overall goal is to seek the minimizer of a global optimization problem through localized interactions. In decentralized consensus problems, the agents are coupled through a common consensus variable that they need to agree upon. While in decentralized resource allocation problems, the agents are coupled through global affine constraints. Various decentralized consensus optimization algorithms already exist in the literature. Some methods are derived from a primal-dual perspective, while other methods are derived as gradient tracking mechanisms meant to track the average of local gradients. Among the gradient tracking methods are the adapt-then-combine implementations motivated by diffusion strategies, which have been observed to perform better than other implementations. In this dissertation, we develop a novel adapt-then-combine primal-dual algorithmic framework that captures most state-of-the-art gradient based methods as special cases including all the variations of the gradient-tracking methods. We also develop a concise and novel analysis technique that establishes the linear convergence of this general framework under strongly-convex objectives. Due to our unified framework, the analysis reveals important characteristics for these methods such as their convergence rates and step-size stability ranges. Moreover, the analysis reveals how the augmented Lagrangian penalty term, which is utilized in most of these methods, affects the performance of decentralized algorithms. Another important question that we answer is whether decentralized proximal gradient methods can achieve global linear convergence for non-smooth composite optimization. For centralized algorithms, linear convergence has been established in the presence of a non-smooth composite term. In this dissertation, we close the gap between centralized and decentralized proximal gradient algorithms and show that decentralized proximal algorithms can also achieve linear convergence in the presence of a non-smooth term. Furthermore, we show that when each agent possesses a different local non-smooth term then global linear convergence cannot be established in the worst case. Most works that study decentralized optimization problems assume that all agents are involved in computing all variables. However, in many applications the coupling across agents is sparse in the sense that only a few agents are involved in computing certain variables. We show how to design decentralized algorithms in sparsely coupled consensus and resource allocation problems. More importantly, we establish analytically the importance of exploiting the sparsity structure in coupled large-scale networks
Non-convex Optimization for Resource Allocation in Wireless Device-to-Device Communications
Device-to-device (D2D) communication is considered one of the key frameworks to provide suitable solutions for the exponentially increasing data tra c in mobile telecommunications. In this PhD Thesis, we focus on the resource allocation for underlay D2D communications which often results in a non-convex optimization problem that is computationally demanding.
We have also reviewed many of the works on D2D underlay communications and identi ed some of the limitations that were not handled previously, which has motivated our works in this Thesis.
Our rst works focus on the joint power allocation and channel assignment problem in the D2D underlay communication scenario for a unicast single-input and single-output (SISO) cellular network in either uplink or downlink spectrums. These works also consider several degrees of uncertainty in the channel state information (CSI), and propose suitable measures to guarantee the quality of service (QoS) and reliability under those conditions. Moreover, we also present a few algorithms that can be used to jointly assign uplink and downlink spectrum to D2D pairs. We also provide methods to decentralize those algorithms with convergence guarantees and analyze their computational complexity. We also consider both cases with no interference among D2D pairs and cases with interference among D2D pairs. Additionally, we propose the formulation of an optimization objective function that combines the network rate with a penalty function that penalizes unfair channel allocations where most of the channels are assigned to only a few D2D pairs.
The next contributions of this Thesis focus on extending the previous works to cellular networks with multiple-input and multiple-output (MIMO) capabilities and networks with D2D multicast groups. We also present several methods to accommodate various degrees of uncertainty in the CSI and also guarantee di erent measures of QoS and reliability.
All our algorithms are evaluated extensively through extensive numerical experiments using the Matlab simulation environment. All of these results show favorable performance, as compared to the existing state-of-the-art alternatives.publishedVersio
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