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Compressed Sensing is a novel sampling technique that can be used to faithfully recover sparse signals from fewer measurements than those proposed by the Nyquist theorem. A simple and intuitive measure of sparsity in a signal is ℓ0-norm. However, the ℓ0-norm function does not satisfy all the axiomatic properties of a true mathematical norm. The discrete and discontinuous nature of ℓ0-norm poses many...
In modern wireless networks, optimizing the association between base stations (BSs) and users effectively improves network performance. On the other hand, a frequently changing BS-user association renders considerable operational burden for network management, e.g., it consumes extra power to awaken the deactivated BSs and to support users' switching among BSs. This motivates us to balance the flexibility...
For decades, optimization has played a central role in addressing wireless resource management problems such as power control and beamformer design. However, these algorithms often require a considerable number of iterations for convergence, which poses challenges for real-time processing. In this work, we propose a new learning-based approach for wireless resource management. The key idea is to treat...
In this paper, we propose a message passing-based algorithm to reconstruct a discrete-valued vector whose elements have a symmetric probability distribution. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), borrows the idea of the approximate message passing (AMP) algorithm for compressed sensing. We analytically evaluate the performance of DAMP via state...
In this paper, a new algorithm is proposed for the design of sparse FIR filters. Traditional l1-optimization-based methods take all the coefficients into l1-norm minimization. However, it is unnecessary since some of them can only take nonzero values to satisfy design specifications. Furthermore, minimizing l1 norm of all the coefficients could drive the design results to deviate from the optimal...
Centralized joint processing has been demonstrated to achieve significant performance gain over the conventional per-cell processing by avoiding the inter-cell interference in cloud radio access networks (C-RANs), but full scale cooperation over the entire network is infeasible due to its huge computational complexity. The goal of this paper is to design remote radio head (RRH) clustering and the...
In this paper, we consider a distributed stochastic optimization problem where the goal is to minimize the time average of a cost function subject to a set of constraints on the time averages of related stochastic processes called penalties. We assume that the state of the system is evolving in an independent and non-stationary fashion and the "common information" available at each node...
The static resource allocation which is usually assumed for the DSL physical layer leaves unused a significant portion of the achievable rate region. An alternative approach is to divide time into slots of short duration, and to change the resource allocation from each time slot to the next. A crosslayer scheduler then chooses a different resource allocation setting for each time slot by defining...
Decomposition of symmetric tensors has found numerous applications in blind sources separation, blind identification, clustering, and analysis of social interactions. In this paper, we consider fourth order symmetric tensors, and its symmetric tensor decomposition. By imposing unit-length constraints on components, we resort the optimisation problem to the constrained eigenvalue decomposition in which...
This paper considers a decentralized projection free algorithm for non-convex optimization in high dimension. More specifically, we propose a Decentralized Frank-Wolfe (DeFW) algorithm which is suitable when high dimensional optimization constraints are difficult to handle by conventional projection/proximal-based gradient descent methods. We present conditions under which the DeFW algorithm converges...
We consider distributed convex optimization problems that involve a separable objective function and nontrivial convex local constraints, such as Linear Matrix Inequalities (LMIs). We propose a decentralized, computationally inexpensive algorithm to solve such problems over time-varying directed networks of agents, that is based on the concept of approximate projections. Our algorithm is one of the...
Artificial bee colony (ABC) algorithm is a swarm based meta-heuristic algorithm inspired by the foraging behavior of honey bees. Due to its simplicity, the ABC algorithm is used for minimax design of linear phase FIR fullband digital differentiators in this paper. Results in term of peak error obtained from designed digital differentiator examples indicate that the approach can reach smaller peak...
In this paper, teaching-learning-based optimization (TLBO) is used for minimax design of linear phase finite impulse response (FIR) digital Hilbert transformers. TLBO is a population-based and heuristic search algorithm which is parameter-free and exhibits a strong convergence ability. The results obtained from using TLBO to design Type 4 linear phase FIR highpass digital Hilbert transformers indicate...
Phase unwrapping is a very important process in the digital elevation model(DEM) rebuilding of the imaging area from its interferometric phase data. This paper presents a fast phase unwrapping algorithm based on minimum discontinuity optimization, which greatly enhances the efficiency compared with the original algorithm proposed by Flynn. To accelerate the optimization process, a preprocessing process...
The framework of structured sparsity which is a natural extension of the standard sparsity concept is a useful tool for studying signals where the sparse coefficients are often dependent and structured. This structure can take different shapes, the interest is focused on signals with cyclic structure as periodic random impulses. Periodic random impulse signals are suitable tools for various situations...
In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l1-optimization-based methods, the proposed algorithm minimizes l1 norm of a portion (instead of all) of nonzero coefficients. In this way, some nonzero coefficients at crucial positions are not affected by l1 norm utilized in the objective function. The proposed algorithm employs...
Differential Evolution (DE), a population-based stochastic search technique is adept at solving real-world optimization problems. Unlike most population based algorithms, the use of DE is usually inexpedient in solving expensive optimization problems as the computational costs of these simulations are excessively high. This problem can be resolved by commingling surrogate model in DE that approximates...
We investigate the performance of some sparse recovery and compressed sensing algorithms when applied to the Angle-of-Arrival (AoA) estimation problem. In particular, we review three different approaches in compressed sensing, namely Pursuit-type, Thresholding-type, and Bayesian-based algorithms. The compressed sensing algorithms reviewed herein are of vast interest when applied to AoA estimation...
Radar scientists have recently explored the application of compressed sensing for generating high resolution range profiles (HRRPs) from a limited number of measurements. The last decade has witnessed a surge of algorithms for this purpose. Among these algorithms complex-valued approximate message passing (CAMP) has attracted attention for the following reasons: (i) it converges very fast, (ii) its...
This paper develops a Radial Basis Function Neural Network (RBFN) based on Tree Seed Optimization algorithm (TSA). The values of clustering centers, width and weights of the Radial Basis Function Neural Network are optimized by Tree Seed Algorithm. The proposed Radial Basis Function Neural Network optimization algorithm is tested on the application of numerical function approximation. The experimental...
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