V. Mehta

Bio: V. Mehta is an academic researcher. The author has contributed to research in topics: Spread spectrum & Interference (wave propagation). The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Adaptive Blind Narrowband Interference Cancellation for Multi-User Detection

Abstract: When overlaying spread spectrum (SS) transmission over a narrowband system, the performance of the spread spectrum system will be significantly degraded due to the interference from the narrowband signal. This paper proposes two computationally attractive and efficient adaptive techniques for narrowband interference (NBI) suppression in DS-CDMA system: adaptive linear predictor algorithm and adaptive NBI re-estimation algorithm. Unlike existing techniques in literature which use either estimator/subtracter approach or code-aided approach, the proposed methods combine these two approaches together and show that a much better performance can be achieved. In addition, the proposed algorithms are blind and do not require any training symbols and interference characteristics. The proposed methods not only provide faster convergence speed than the pure code-aided approach (without using a predictor and subtractor), but also give better BER performance

Dual-antenna-based blind joint hostile jamming cancellation and multi-user detection for uplink of asynchronous direct-sequence code-division multiple access systems

Abstract: The authors investigate the problem of blind multi-user detection (MUD) for uplink of asynchronous direct-sequence code-division multiple access (DS-CDMA) systems with hostile jamming To cope with the lack of prior knowledge of spread signals, hostile jamming and channel state information, the authors propose a scheme based on blind source separation (BSS) to solve this problem Dual receive antennas are used in the proposed scheme By exploiting the structure of the two received signals, a BSS model with dependent sources is formed, and then blind hostile jamming cancellation and MUD are obtained by solving this BSS problem The proposed scheme does not require to know any information about the hostile jamming, and can work well under multiple kinds of hostile jamming Simulation results further demonstrate that if such proposed approach is used to cancel hostile jamming and make MUD, then the bit-error-rate performance can be substantially improved as compared with earlier detectors

Narrowband interference suppression in DSSS system based on frequency shift wavelet packet transform

Abstract: A narrowband interference suppression algorithm which combined wavelet packet transform with frequency shift is proposed. It first confined the interference in the center of a subband. Then, the signal is decomposed by wavelet packet transform, and interference is suppressed under subband power ratio rule. Finally, the signal is de-noised by soft-thresholding before signal reconstructed. Both analysis and simulation demonstrate that the interference is completely suppressed by frequency shift wavelet packet transform, the signal-to-noise ratio is improved by soft threshold signal de-noising, the single dwell time of acquisition is reduced and the acquisition performance is improved.

Nonlinear Demodulation and Channel Coding in EBPSK Scheme

Abstract: The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) performance. However, the conventional threshold decision cannot achieve the optimum performance, and the SIF brings more difficulty in obtaining the posterior probability for LDPC decoding. In this paper, we concentrate not only on reducing the BER of demodulation, but also on providing accurate posterior probability estimates (PPEs). A new approach for the nonlinear demodulation based on the support vector machine (SVM) classifier is introduced. The SVM method which selects only a few sampling points from the filter output was used for getting PPEs. The simulation results show that the accurate posterior probability can be obtained with this method and the BER performance can be improved significantly by applying LDPC codes. Moreover, we analyzed the effect of getting the posterior probability with different methods and different sampling rates. We show that there are more advantages of the SVM method under bad condition and it is less sensitive to the sampling rate than other methods. Thus, SVM is an effective method for EBPSK demodulation and getting posterior probability for LDPC decoding.

Nonlinear detection for a high rate extended binary phase shift keying system.

Abstract: The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.