A Specially Designed Modulation-Induced Cyclostationarity Scheme for Blind SISO Channel Identification and Equalization

A special Modulation-Induced Cyclostationarity(MIC)scheme is designed for the identification and equaliza-tion of FIR Single-Input-Single-Output(SISO)channel,with the property that the transmit power is constant and the re-ceiver needs only one antenna.The cyclic Wiener equalizer is presented based on the estimated channel.

Blind Multiuser Channel Identification Based on Rate Doubling and Modulation-Induced Cyclostationarity

In this paper, a novel approach is put forward to the multiuser channelidentification. The approach makes use of the modulation-induced Cyclostationarity to separate thesecond order cyclic statistics for every user, with the special features of one subspace for oneuser, so as to be able to identify individual channels of different users. In order to form aSingle-Input-Two-Output (SITO) system, the transmission rate is doubled by repeating at thetransmitters. The approach is rather simple, suitable for the multiuser uplink . And the channelidentifiability conditions with its proof are included in the paper, And finally the identificationalgorithm is proposed with simulation results.

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output （SIMO） systems of sensor networks （both time-invariant and time-varying networks）. At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.

Blind Signal Processing in Telecommunication Systems Based on Polynomial Statistics

The tendencies of the contemporary communication systems development are characterized by the increasingly stringent requirements for maximum channel utilization. Considering discrete communication systems in channels with intersymbol interference identification with the use of training signal is the key technology to create various types of equalizers. However, the time (from 20% to 50%) spent on training signal is increasingly attractive resource for upgrading standards TDMA, especially in mobile systems. An alternative method to training signal is blind signal processing.

Blind MIMO Channel Estimation Based on Modulation-Induced Two-Cycle Cyclostationarity

In this paper, a novel approach is presented to the multiuser channelidentification . The approach makes use of the modulation-induced Cyclostationarity, capable ofidentifying individual channels of different users. By means of the decomposition of the cyclicspectrum method, the blind estimation of the channel can be achieved . The approach is rathersimple, with considerable advantages over existing techniques, and suitable for the multiuser uplink. The identifiabilily condition and its proof are also concluded in the paper. And finally the.simulation of identification algorithm is given.

Blind Multipath Identification: A Weighted Linear Prediction Approach

Blind channel identification exploits the measurable channel output signaland some prior knowledge of the statistics of the channel input signal. However, in many scenarios,more side information is available, In digital communication systems, the pulse-shaping filter inthe transmitter and the anti-aliasing filter in the receiver are often known to the receiver.Exploitation of this prior knowledge can simplify the channel identification problem. In this paper,we pose the multipath identification problem as solving a group of linear equations. While we solvethe linear equations in the least-square meaning, a weight matrix can be introduced to improve theperformance of the estimator. The optimal weight matrix is derived. Compared with the existingLinear Prediction (UP) based multipath identification approach, the proposed approach offers asubstantial performance gain.

Joint Angle and Delay Estimation (JADE) in Antenna Array CDMA Systems

The estimate of signals parameters is very important in wireless communications. In this paper, we combine subspace based blind channel estimation algorithm with the extension of the JADE WSF algorithm to jointly estimate the Angles of Arrival ( AOAs ) and delays of multipath signals arriving at an antenna array in Code Division Multiple Access ( CDMA ) systems. Our approach uses a collection of estimates of a consistent chip sample of space time vector channel. The channel estimates are assumed to have constant path AOA and delay over a finite number of symbols. Unlike the traditional MUltiple SIgnal Classification ( MUSIC ) and Estimation of Signal Parameters via Rotational Invariance Techniques ( ESPRIT ) algorithms for the estimation of signals parameters, the proposed method can work when the number of paths exceeds the number of antennas. The Cramer Rao Bound ( CRB ) and simulations are provided.