Robust blind PIC with adaptive despreader and improved interference estimator

Abstract

SUMMARYAn effective design of multistage parallel interference cancellation (PIC) receiver using blind adaptive (BA) despreader and pre-respreader interference estimator for uplink CDMA is proposed and analysed. A novel algorithm is designed, which exploits constant modulus (CM) property of the users' transmitted signals and inherent channel condition to perform adaptive despreading based on minimum error variance criteria. This is carried out by BA weighting of each chip signal for accurate tracking of the desired user's signal power and hence for more improved data detection at the output of each stage of PIC. Furthermore, the despreader weights are used within the adaptive pre-respreader interference estimation and cancellation to obtain online scaling factors during every symbol period, without any knowledge of users' channels or the use of training sequences. It is found that this way of estimation is optimal in minimum mean squared error sense, and hence, significant reduction in interference and noise variance is observed in detection and estimation of the desired users' signals compared with conventional PIC. Bit error probability of the proposed PIC is obtained using Gaussian Approximation method. Extensive simulation results are shown, which demonstrate impressive performance advantage in fading environments, high system loading, and severe near far conditions.

Distributed Base Station Association and Power Control for Heterogeneous Cellular Networks

“…User i can estimate R i (p, k) if it has information about the total interference and noise power [i.e., the denominator of (1)] and the channel power gain h ki according to the SINR expression in (1) (since the current transmit power level p i is readily available). In addition, the channel power gains h ki can be estimated by BS k and sent back to each user by using the pilot signal and any standard channel estimation technique [30]- [32]. Moreover, the total interference and noise power for each user can be estimated as follows.…”

Adaptive system identification using robust LMS/F algorithm

“…Adaptive system identification (ASI) includes many applications such as interference cancelation [1][2][3], spectral subtraction [4,5], wireless localization [6][7][8][9], channel equalization [10][11][12][13], and adaptive beamforming [14]. One of most popular algorithms is least mean square (LMS), which is proposed by Widrow et al [15].…”

Fuzzy step size least mean square algorithm tuned linearly constrained constant modulus algorithm for multi‐carrier code division multiple access blind multiuser detector

“… and Li and de Lamare have demonstrated the robustness of the blind adaptive techniques with the CCM criterion against nonstationary environments, which can be implemented with a stochastic gradient algorithms or a recursive least squares algorithm. MUD schemes, such as parallel interference cancellation , can be used to effectively solve this problem. For this reason, the interference suppression is recognized as one of the viable techniques and has already been addressed in a wide range of research effort.…”