Bin Lin

The Steady-State Mean-Square Error Analysis for Least Mean

Based on series expansion, the steady-state mean-square error (MSE) analysis for real and complex least mean p-order (LMP) algorithm is developed, and some closed-form analytical expressions for the steady-state MSE and the corresponding restrictive conditions for step-size are given. Moreover, in Gaussian noise environments, its steady-state performance is also investigated. The analyses for some well-known algorithms and the computer simulation validate the accuracy of the results.

p

A unified approach to the steady-state mean square error (MSE) and tracking performance analyses for real and complex adaptive filtes with error nonlinearities is developed. Some general clofied-form analytical expressions for the steady-state performances are given. Our analyses are based on Taylor series expansion and and so-called complex Brandwood-form series expansion (BSE). Under these general explicit expressions, some well-known adaptive filters can be viewed as special cases. In addition, the closed-form analytical expressions for the steady-state performance for real and complex least-mean p-power (LMP) algorithm with different choices of parameter p are also given. A mass of simulations show the accuration of our analyses.

-Order Algorithm

Without appealing to the circularity assumption of the a priori estimation error in , two closed-form analytical expressions for the steady-state excess mean-square error (EMSE) in a noise-free environment are derived again based on Taylor series expansion for real and complex Bussgang algorithms, respectively; and the restrictive conditions of these two expressions for the steady-state EMSE are also given.

Steady-state performance analysis for adaptive filters with error nonlinearities

This paper is mainly to deal with the problem of direction of arrival ( DOA ( estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α -stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms, the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.

The Excess Mean-Square Error Analyses for Bussgang Algorithm

The stochastic blind equalization based on the Parzen window method (SQD), which forces the probability density function (PDF) at the equalizer output to match the known constellation PDF, has a good performance. However, when the signals with high frequency band efficiency are used, the computational complexity is very huge. In this paper, a low complexity SQD algorithm (LCSQD) is presented. The judged region of the proposed algorithm is only the part of the whole constellation region, whose center is the equalization output and radius can be adjusted experimentally or adaptively. It has a low computational requires which do not increase when the modulation signal order increases. Simulations for 16QAM and 64QAM show that the proposed algorithm has a fast convergence speed and more lower residual ISI than SQD algorithm.

DOA estimation based on sparse representation of the fractional lower order statistics in impulsive noise

A new class of nonlinear filters called FIR-weighted myriad hybrid (FIR-WMyH) filters was introduced. These filters have a combination structure of linear FIR filters and weighted myriad filter, and can efficiently utilize the special strongpoint of two filters. Through the backpropagation algorithm used in the neural networks, we derive a stochastic gradient-based adaptive algorithm for determining optimal FIR-WMyH filters under the mean square error (MSE) criterion. In two impulsive noise models, the robust performance of this adaptive algorithm is demonstrated through the computer simulation results.

Low Complexity Blind Equalization Based on Parzen Window Method

In the mobile Internet and big data era, wireless networks are facing challenges to transmit the huge amount of data effectively, efficiently and reliably. To deploy a network infrastructure with larger capacity, higher data rate, lower transmission delay, higher spectral efficiency and lower energy consumption, an innovative architecture-the Cloud-Radio Access Network (C-RAN) has been proposed. In this paper, we focus on network planning deployment issue based on the Optical Mixed Diet (OMD) technology. Specifically, the ring and spur topology Optimization (RSTO) problem under the C-RAN architecture is investigated. We solve this RSTO problem using Gurobi. A series of case studies are conducted to validate the optimization framework and demonstrate the solvability and scalability of the RSTO problem. Results also show that cost saving can be achieved by taking advantage of Coordinated Multi-Processing (CoMP) technology.

FIR-MYRIAD Hybrid Filters for Signal Processing in Impulsive Noise Environments

This paper addresses the issue of time difference of arrival (TDOA) estimation of cyclostationary signal under impulsive noise environments modeled by α-stable distribution. Since α-stable distribution has not finite second-order statistics, the conventional cyclic correlation based signal-selective TDOA estimation algorithm does not work effectively. To resolve this problem, we define the generalized cyclic correntropy (GCCE) which is a robust cyclic correlation and can be reviewed as an extension of the generalized correntropy for cyclostationary signal. A robust signal-selective TDOA estimation algorithm based on GCCE is proposed. The computer simulation results demonstrate that the proposed algorithm outperforms the conventional cyclic correlation and the fractional lower order cyclic correlation based algorithms.

Infrastructure Deployment and Optimization for Cloud-Radio Access Networks

Territorial ocean safety and ocean development make it necessary to establish a large-scale, long-term, and low-energy integrated ocean monitoring sensor networks (OMSNs). The High Attitude Platform based OMSN (HAP-OMAN) has provided a promising solution for ocean monitoring. In this paper, we study the node deployment problem in a HAP-OMSN architecture and formulate the problem as a multi-objective linear programming (MOLP) to maximize network lifetime, minimize energy consumption and ensure network’s connectivity, reliability and coverage of target points. Finally, we solve the MOLP by Gurobi, which is a powerful linear programming solver. Numerical analysis through case studies is conducted to verify the feasibility and scalability of the proposed optimization framework in different scales of network scenarios.

Signal-Selective Time Difference of Arrival Estimation Based on Generalized Cyclic Correntropy in Impulsive Noise Environments

Non-Gaussian impulsive noise widely exists in the real world, this paper takes the α-stable distribution as the mathematical model of non-Gaussian impulsive noise and works on the joint direction-of-arrival (DOA) and range estimation problem of near-field signals in impulsive noise environment. Since the conventional algorithms based on the classical second order correlation statistics degenerate severely in the impulsive noise environment, this paper adopts two robust correlations, the fractional lower order correlation (FLOC) and the nonlinear transform correlation (NTC), and presents two related near-field localization algorithms. In our proposed algorithms, by exploring the symmetrical characteristic of the array, we construct the robust far-field approximate correlation vector in relation with the DOA only, which allows for bearing estimation based on the sparse reconstruction. With the estimated bearing, the range can consequently be obtained by the sparse reconstruction of the output of a virtual array. The proposed algorithms have the merits of good noise suppression ability, and their effectiveness is demonstrated by the computer simulation results.