1,721,185 research outputs found
Minimum Bit Error Rate Multiuser Detection in Multiple Antenna Aided OFDM
No full textIn this contribution, we propose a Minimum Bit Error Rate (MBER) multiuser detector for Space Division Multiple Access (SDMA) aided Orthogonal Frequency Division Multiplexing (OFDM) systems. It is shown that the MBER detector outperforms the Minimum Mean Squared Error (MMSE) detector, since the MBER detector directly minimizes the BER, while MMSE detector minimize the mean-squared error (MSE), which does not guarantee achieving the minimum BER. When supporting two users, the proposed MBER scheme substantially outperforms the classic MMSE arrangement in the investigated propagation scenario
Adaptive Minimum-BER Linear Multiuser Detection for DS-CDMA Signals in Multipath Channels
No full textThe problem of constructing adaptive minimum bit error rate (MBER) linear multiuser detectors is considered for direct-sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style stochastic gradient adaptive algorithm is developed for training linear multiuser detectors. Computer simulation is used to study the convergence speed and steady-state BER misadjustment of this adaptive MBER linear multiuser detector, and the results show that it outperforms an existing LMS-style adaptive MBER algorithm first presented at Globecom'98 by Yeh, Lopes and Barry
Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems
No full textAdaptive training of neural networks is typically done using some stochastic gradient algorithm that tries to minimize the mean square error (MSE). For many applications, such as channel equalization and code-division multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A novel adaptive near minimum error rate algorithm called the least bit error rate (LBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to multiuser detection in CDMA communication systems. Simulation results show that the LBER algorithm has a good convergence speed and a small radial basis function (RBF) network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square (LMS) algorithm, although converging well in the MSE, can produce a poor error rate performance
Minimum bit error rate multiuser detection techniques
No full textThe Minimum Bit Error Rate (MBER) linear MUDs considered are designed for the synchronous downlink of Direct Sequence CDMA (DS-CDMA) systems, employing Binary Phase Shift Keying (BPSK) modulation as well as 4-level Quadrature Amplitude Modulation (4-QAM). An iterative non-adaptive algorithm capable of converging to the MBER solution was developed based on the Steepest Descent Gradient algorithm. A Simplified Conjugate Gradient algorithm was also proposed for the sake of improving the convergence speed of the Steepest Descent Gradient MBER algorithm. Our simulation results showed that the MBER MUD is capable of outperforming the MMSE MUD in terms of the achievable BER under various channel conditions. Furthermore, the MBER MUD is combined with Forward Error Correction (FEC) coding and several coded modulation schemes are invoked for the sake of enhancing the achievable BER performance. Adaptive versions of the MBER algorithm are also presented, which are initialised for example to the MMSE MUD weights and then iteratively adjust the weights until the MBER solution is reached. More specifically, two classes of adaptive algorithms are presented, namely block adaptive and sample-by-sample adaptive algorithms. Two algorithms belonging to the block adaptive category are referred to as the Block Adaptive Steepest-descent Gradient (BASG) and the Block Adaptive Conjugate Gradient (BACG) algorithms. Although the BACG algorithm is capable of a faster convergence to the MBER solution, this algorithm exhibits a higher sensitivity to the choice of the adaptive step size. Hence, a hybrid solution is preferred. Similarly, two sample-by-sample adaptive algorithms were derived, namely the Least Bit Error Rate (LBER) algorithm and the Approximate LBER (ALBER) arragement. Our simulation results demonstrated that both algorithms outperformed the Least Mean Square (LMS), the Difference approximation MBER (DMBER) and the Approximate MBER (AMBER) algorithms m terms of the attainable convergence speed and the steady state BER.</p
Barangan kraf Plaster of Paris (P.O.P) / Zawiah Samingan
Full text linkMelihat kepada keindahan seni kraf seramik sudah tentu menarik minat kita untuk memilikinya. Secara psikologi keindahan yang dipamerkan oleh barangan kraf tersebut dapat memberikan ketenangan dan kedamaian kepada mereka yang mahu menghayatinya. Penghasilan penulisan ini adalah bertujuan untuk mengetahui kegunaan lain bagi bahan Plaster of Paris selain digunakan dalam pembuatan acuan dalam industri seramik. Berbagai jenis barangan kraf seramik boleh didapati di pusat-pusat membeli belah, pusat pameran dan jualan oleh Kraftangan Malaysia, Infokraf dan sebagainya. Barangan kraf yang semakin mendapat perhatian pada masa kini ialah barangan kraf yang dihasilkan oleh bahan Plaster of Paris
Optimal Decision Feedback Equalizer for M-PAM Signals Using a Support Vector Machine Solution
No full textThe authors propose a method of designing the linear-combiner decision feedback equalizer (DFE) for -PAM signals using a support vector machine (SVM) technique. This SVM design achieves asymptotically the minimum symbol error rate (MSER) solution and can be computed efficiently
Adaptive Minimum-BER Linear Multiuser Detection for CDMA Signals in Multipath Channels with 4-QAM Constellation
No full textAdaptive Multiuser Receiver Using a Support Vector Machine Technique
No full textThe paper investigates the application of an emerging learning technique, called support vector machines (SVMs), to construct an adaptive nonlinear multiuser detector (MUD) for direct-sequence code-division multiple-access (DS-CDMA) signals transmitted through multipath channels. Computer simulation is used to study this adaptive SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector, using a relatively small training data block
Adaptive Minimum Symbol-Error-Rate CDMA Linear Multiuser Detector for Pulse-Amplitude Modulation
No full textAn adaptive minimum symbol error rate (MSER) linear multiuser detector (MUD) is proposed for direct sequence code division multiple access (DS-CDMA) systems employing multilevel pulse-amplitude modulation (-PAM) scheme. Based on a kernel density estimation for approximating the symbol error rate (SER) from training data, a least mean squares (LMS) style stochastic gradient algorithm called the least SER (LSER) is developed for training linear MUDs. Computer simulation is used to investigate the performance of this LSER MUD
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