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We develop several efficient algorithms for the classical Matrix Scaling} problem, which is used in many diverse areas, from preconditioning linear systems to approximation of the permanent. On an input n× n matrix A, this problem asks to find diagonal (scaling) matrices X and Y (if they exist), so that X A Y ε-approximates a doubly stochastic matrix, or more generally a matrix...
This paper proposes a low complex hardware accelerator algorithmic modification for n-dimensional (nD) FastICA methodology based on Coordinate Rotation Digital Computer (CORDIC) to attain high computation speed. The most complex and time consuming update stage and convergence check required for computation of the nth weight vector are eliminated in the proposed methodology. Using the Gram-Schmidt...
The ℓp norm-constrained proportionate normalized least-mean-square (LP-PNLMS) using the modified filtered-x structure is proposed for active noise control. It is shown that better performance is obtained for primary and secondary paths having a wide range of sparseness levels when compared with competing sparsity-inducing algorithms at a price of moderate complexity increase.
We study streaming principal component analysis (PCA), that is to find, in O(dk) space, the top k eigenvectors of a d× d hidden matrix \bold \Sigma with online vectors drawn from covariance matrix \bold \Sigma.We provide global convergence for Ojas algorithm which is popularly used in practice but lacks theoretical understanding for k≈1. We also provide a modified variant \mathsf{Oja}^{++}...
In this paper, the problem of echo cancellation in long acoustic impulse responses (AIRs) is highlighted. Three of the mostly-used recent NLMS-based sparse adaptive filtering algorithms are presented; and their performances in the context of acoustic echo cancellation (AEC) are studied and compared. The algorithms of interest include the improved proportionate normalized least mean square (IPNLMS),...
We introduce a low-complexity layered iterative sampling algorithm for near-optimal detection in uplink massive multiple-input multiple-output (MIMO) systems based on the Gibbs sampling. In contrast to the most of Gibbs-sampling-based detectors in the previous arts, which assuming the numbers of transmitting and receiving antennas are similar, in this paper we assume the number of receiving antennas...
Stochastic turbo decoder is a new scheme for turbo codes. But the long decoding latency and high complexity are two main challenges for fully parallel stochastic turbo decoders. In this paper, we proposed a novel stochastic turbo decoder scheme with two high accuracy stochastic operator modules, including no-scaling stochastic addition and stochastic normalization operator, which can improve the decoding...
In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce...
This paper describes new algorithms that incorporates the non-uniform norm constraint into the zero-attracting and reweighted modified filtered-x affine projection or pseudo affine projection algorithms for active noise control. The simulations indicate that the proposed algorithms can obtain better performance for primary and secondary paths with various sparseness levels with insignificant numerical...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
We present a time domain discontinuous Galerkin (TDDG) method for electromagnetics problem that directly discretizes space and time by unstructured grids satisfying a specific causality constraint. This enables a local and asynchronous solution procedure. We show that the numerical method is dissipative, thus ensuring its stability. Numerical results show the convergence rate of 2p + 1 for energy...
It has been shown that the Discrete Fourier Transform (DFT) can be computed in sublinear time from a sublinear number of samples when the target spectrum is sparse. However, this is usually only expressed qualitatively in terms of the order of number of computations/samples. Here we investigate the explicit time-data tradeoff for the Sparse Fourier Transform (SFT) algorithm proposed by Pawar and Ramchandran...
Parallel and distributed processing is employed to accelerate training for many deep-learning applications with large models and inputs. As it reduces synchronization and communication overhead by tolerating stale gradient updates, asynchronous stochastic gradient descent (ASGD), derived from stochastic gradient descent (SGD), is widely used. Recent theoretical analyses show ASGD converges with linear...
Due to the asymptotically orthogonal channel, minimum mean square error detection algorithm is near-optimal for uplink massive MIMO systems, but it involves matrix inversion with high complexity. This paper proposes a high-parallelism detection algorithm in an iterative way to avoid the complicated matrix inversion. The parallelism level is analyzed and convergence is proved in detail. The proposed...
Low rank matrix approximation, in the presence of missing data and outliers, has previously shown its significance as a theoretic foundation in a wide spectrum of tabulated information processing applications. To fit low rank models, minimizing the nuclear norm of matrices is a popular scheme, the computational load of which, however, is heavy. While bilinear factorization can largely mitigate the...
In networked control systems, the presence of input quantization is a major source of destabilization and performance deterioration of the closed-loop system. In this paper, a continuous state-feedback controller is designed to guarantee not only the stability of the closed-loop system under input quantization but additionally the enforcement of prescribed performance attributes on the output tracking...
Write-once memory (WOM) is a storage device consisting of binary cells which can only increase their levels. A t-write WOM code is a coding scheme which allows to write t times to the WOM without decreasing the levels of the cells. The sum-rate of a WOM code is the ratio between the total number of bits written to the memory and the number of cells. It is known that the maximum sum-rate of a t-write...
We study the resource allocation problem in RAN-level integrated HetNets. This emerging HetNets paradigm allows for dynamic traffic splitting across radio access technologies for each client, and then for aggregating the traffic inside the network to improve the overall resource utilization. We focus on the max-min fair service rate allocation across the clients, and study the properties of the optimal...
Indirect measurements of physical parameters of interest often require a mathematical model in which these parameters are estimated accordingly to the gathered measurements. Within the Least Squares estimation, the parameters are estimated through a regression problem. The presence of dynamics, multiple sensors and high sampling rates lead to high dimensional regression matrices. This paper deals...
This paper presents the asymptotic analysis of random lattices in high dimensions to clarify the distance properties of the considered lattices. These properties not only indicate the asymptotic value for the distance between any pair of lattice points (with and without noise corruption) in high-dimension random lattices, but also describe the convergence behavior of how the asymptotic value approaches...
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