A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems

Abstract

In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR) is low.

Robust wireless statistic division multiplexing and its performance analysis

“…With regard to the receiver in WSDM, the source signals are retrieved by blind source separation (BSS) based on independent component analysis (ICA, such as Fastica and Infomax). 1–23 However, the conventional ICA model does not take into account the influence of noise, and the robustness of performance is weak.…”

Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

“…In real scientific applications, many of the observed mixed signals are modeled as a suite of sensors output, with receiving different linear combinations of the underlying source signals. Therefore, the interested source signals are expected to be separated or extracted from the observed data directly with the aid of BSS exempting from extra parameter estimation, such as channel state information and synchronization parameters for wireless receiving processing [4], [7], [9], [10], [12]- [14].…”

“…A typical example can be found in wireless communication, which benefits a lot from BSS by helping wireless communication system arrive at requirements of strong anti-interference and high spectral efficiency due to its blind and adaptive features for future green and intelligent communication implementation. In wireless communication systems, a number of receiving models can be constructed as a BSS framework or conceived as a BSS problem, such as DS-CDMA (direct sequencecode division multiple access) [7], [8], OFDM (orthogonal frequency division multiplexing) [9]- [11], MIMO (multiple input multiple output) [12]- [14] and wireless sensor network (WSN) [23]- [25], cognitive radio [26], and so on [4]. In a general way, those received models can be considered as mixtures of independent source and unknown channel condition.…”

“…The researchers have made tremendous efforts to improve the performance of blind separation of the noisy ICA model. Some investigators hold the view of adding the number of received sensors through noise estimation operation [1], [3], or constructing overdetermined model using principal component analysis (PCA) [7] for suppressing the influence of noise . But this processing will boost the complexity and the cost of the receiver.…”

Performance Analysis of ICA in Sensor Array

“…The simple and generic nature of this assumption allows ICA to be successfully applied in a diverse range of research fields [ 5 ]. For the last three decades, ICA has received attention from varied domains, including the biomedical applications [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] such as the single-channel electromyogram (EMG) classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders in [ 6 ], the identification of simple and complex finger flexion movements using surface electromyography (sEMG) and muscle activation strategy with Subband Decomposition ICA (SDICA) in [ 7 ] and the driver fatigue classification with ICA by Entropy Rate Bound Minimization (ICA-ERBM) in an electroencephalography (EEG)-based system in [ 8 ]; the audio source separation like the noisy speech recognition [ 14 ], the multichannel blind deconvolution and speech separation [ 15 ]; the cooperative or non-cooperative communication like the semi-blind signal extraction with possible constrains [ 16 ], the blind adaptive detection in systems like Direct Sequence Code Division Multiple Access (DS-CDMA) [ 17 ]; other applications also include those in chemical sensor arrays [ 18 ], the target decomposition (TD) in image processing [ 19 ] and the estimation of modal parameters in vibration systems [ 20 ].…”