Estimation and equalization strategies for fiber-wireless communications in a multiuser CDMA environment

Two major issues associated with fiber-wireless technology are the nonlinear distortion of the optical link and the multipath dispersion of the wireless channel. In order to limit the effects of these distortions, estimation, and subsequently equalization of the concatenated fiber-wireless channel needs to be done. This thesis addresses three scenarios in this regard, they are: uplink estimation using pseudonoise (PN) sequences, downlink estimation using Walsh codes, and uplink equalization using a decision feedback equalizer (DFE) and series reversion, all in the presence of both wireless and optical channel noise. The training sequences used in the identification are practically feasible. These training sequences have white noise-like properties which effectively decouples the identification of the linear and nonlinear channels. Correlation analysis is then applied to identify both systems. Furthermore, we propose an algorithm to mitigate the adverse effect of multiple access interference (MAI). Numerical evaluations show a good estimation of both the linear and nonlinear systems with 10 users for the uplink and 54 users for the downlink, both with a signal-to-noise ratio (SNR) of 25 dB. Chip error rate (CER) simulations show that the proposed MAI mitigation algorithm leaves only small residual MAI.

Estimation and equalization strategies for fiber-wireless communications in a multiuser CDMA environment

Two major issues associated with fiber-wireless technology are the nonlinear distortion of the optical link and the multipath dispersion of the wireless channel. In order to limit the effects of these distortions, estimation, and subsequently equalization of the concatenated fiber-wireless channel needs to be done. This thesis addresses three scenarios in this regard, they are: uplink estimation using pseudonoise (PN) sequences, downlink estimation using Walsh codes, and uplink equalization using a decision feedback equalizer (DFE) and series reversion, all in the presence of both wireless and optical channel noise. The training sequences used in the identification are practically feasible. These training sequences have white noise-like properties which effectively decouples the identification of the linear and nonlinear channels. Correlation analysis is then applied to identify both systems. Furthermore, we propose an algorithm to mitigate the adverse effect of multiple access interference (MAI). Numerical evaluations show a good estimation of both the linear and nonlinear systems with 10 users for the uplink and 54 users for the downlink, both with a signal-to-noise ratio (SNR) of 25 dB. Chip error rate (CER) simulations show that the proposed MAI mitigation algorithm leaves only small residual MAI.

A Joint Low-Rank and Sparse Method for Reference Signal Purification in DTMB-Based Passive Bistatic Radar

In a digital terrestrial multimedia broadcasting (DTMB)-based passive bistatic radar (PBR) system, the received reference signal often suffers from serious multipath effect, which decreases the detection ability of low-observable targets in urban environments. In order to improve the target detection performance, a novel reference signal purification method based on the low-rank and sparse feature is proposed in this paper. Specifically, this method firstly performs synchronization operations to the received reference signal and thus obtains the corresponding pseudo-noise (PN) sequences. Then, by innovatively exploiting the inherent low-rank structure of DTMB signals, the noise component in PN sequences is reduced. After that, a temporal correlation (TC)-based adaptive orthogonal matching pursuit (OMP) method, i.e., TC-AOMP, is performed to acquire the reliable channel estimation, whereby the previous noise-reduced PN sequences and a new halting criterion are utilized to improve channel estimation accuracy. Finally, the purification reference signal is obtained via equalization operation. The advantage of the proposed method is that it can obtain superior channel estimation performance and is more efficient compared to existing methods. Numerical and experimental results collected from the DTMB-based PBR system are presented to demonstrate the effectiveness of the proposed method.

Implementation of DSSS system of image using MATLAB

In direct sequence spread spectrum systems, pseudo sequence random (PN) are used to spreading data bits. There are various type of PN sequences are available with good correlation properties, on mostly are generating pn by linear feedback shift register (LFSR). Will examine in this paper we describe spreading binary random and spreading the image As well as studying probability bit error rate of DSSS system with change of the channel (Add white Gaussian noise channel, Rayleigh fading channel, Rician fading channel). Our study will be performed using MATLAB. Terms of completed my articles in building system developed to security transmitter and receiver the information of theft against communication digital system talk as well as the increase of data rate information. Was this design a way process of his used in applications military and have a major role also in the most applications requiring protect high for many information binary data and image. So that the system designed as fits practice and prevent the overlap the outside after it has become send signals in communication system exhibition penetration and Intentional attacks or other breaches.

Representations of Generalized Self-Shrunken Sequences

Output sequences of the cryptographic pseudo-random number generator, known as the generalized self-shrinking generator, are obtained self-decimating Pseudo-Noise (PN)-sequences with shifted versions of themselves. In this paper, we present three different representations of this family of sequences. Two of them, the p and G-representations, are based on the parameters p and G corresponding to shifts and binary vectors, respectively, used to compute the shifted versions of the original PN-sequence. In addition, such sequences can be also computed as the binary sum of diagonals of the Sierpinski’s triangle. This is called the B-representation. Characteristics and generalities of the three representations are analyzed in detail. Under such representations, we determine some properties of these cryptographic sequences. Furthermore, these sequences form a family that has a group structure with the bit-wise XOR operation.