B. Aazhang, B.-P. Paris, and G. C. Orsak. Neural networks for multi-user detection in code-division multiple-access communications. IEEE Transactions on Communications, 40(7):1212--1222, July 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....neural MUD operating with a criterion similar to the AMBER algorithm outperforms neural receivers using the MMSE criterion via gradient type algorithms and linear receivers with MMSE and MBER techniques. I. INTRODUCTION Neural networks have recently been used in the design of multiuser receivers [1 3] for DS CDMA systems. Neural receivers employing the minimum mean squared error (MMSE) 1 3] criterion usually show good performance and have simple adaptive implementation, at the expense of a higher computational complexity. The deployment of non linear structures, such as neural networks and ....

....using the MMSE criterion via gradient type algorithms and linear receivers with MMSE and MBER techniques. I. INTRODUCTION Neural networks have recently been used in the design of multiuser receivers [1 3] for DS CDMA systems. Neural receivers employing the minimum mean squared error (MMSE) [1 3] criterion usually show good performance and have simple adaptive implementation, at the expense of a higher computational complexity. The deployment of non linear structures, such as neural networks and decision feedback, can mitigate more effectively intersymbol interference, caused by the ....

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B. Aazhang, B. P. Paris and G. C. Orsak,"Neural Networks for Multiuser Detection in Code-Division-Multiple-Access Communications," IEEE Transactions on Communications, vol. 40, No. 7, July 1992.

....to find the optimum is a NP hard problem as the number of users grows. Many authors proposed suboptimal linear and nonlinear solutions such as Decorrelating Detector, MMSE (Minimum Mean Square Error) detector, Multistage Detector, Hoppfield neural network or Stochastic Hoppfield neural network [1, 2, 3, 4], and the references therein. One can find a comparison of the performance of the above mentioned algorithms in [5] Nonlinear sub optimal solutions provide quite good performance, however, only asymptotically. Quantum computation based algorithms seem to be able to fill this long felt gap. ....

B. Aazhang, B.-P. Paris, G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. on Communications, vol. 40, pp. 1212--1222, July 1992.

....is an NP hard problem as the number of users grows. Many authors proposed sub optimal linear and nonlinear solutions such as Decorrelating Detector, MMSE (Minimum Mean Square Error) detector, Multistage Detector, Hoppfield neural network or Stochastic Hoppfield neural network [1] 2] 3] [4], and the references therein. One can find a comparison of the performance of the above mentioned algorithms in [5] Nonlinear sub optimal solutions provide quite good performance, however, only asymptotically. Quantum computation based algorithms seem to be able to fill this long felt gap. Beside ....

B. Aazhang, B.-P. Paris, G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. on Communications, vol. 40, pp. 1212--1222, July 1992.

....to find the optimum is a NP hard problem as the number of users grows. Many authors proposed sub optimal linear and nonlinear solutions such as Decorrelating Detector, MMSE (Minimum Mean Square Error) detector, Multistage Detector, Hoppfield neural network or Stochastic Hoppfield neural network [1, 2, 3, 4], and the references therein. One can find a comparison of the performance of the above mentioned algorithms in [5] Nonlinear sub optimal solutions provide quite good performance, however, only asymptotically. Quantum computation based algorithms seem to be able to fill this long felt gap. ....

B. Aazhang, B.-P. Paris, G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. on Communications, vol. 40, pp. 1212--1222, July 1992.

....University of Southampton, High eld, Southampton SO17 1BJ, U.K. Contact author: S. Chen, Tel. Fax: 44 (0)23 8095 6660 4508; E mail: sqc ecs.soton.ac.uk. separable. As nonlinear separable cases are common in DS CDMA channels, neural networks have been considered as nonlinear MUDs. In the work [15], a multilayer perceptron (MLP) was applied to a CDMA system without intersymbol interference (ISI) The experience shows that the MLP MUD has better performance than the linear MUD but training times are long and unpredictable. Mitra and Poor [16] applied a RBF network to the same problem. An ....

B. Aazhang, B.P. Paris and G.C. Orsak, \Neural networks for multiuser detection in code-division multiple-access communications, " IEEE Trans. Communications, Vol.40, No.7, pp.1212-1222, 1992.

....recognized a linear detector can only work when the underlying noise free signal classes are linearly separable. As nonlinear separable cases are common in DS CDMA channels, better performance can be obtained by using a nonlinear MUD and neural networks have been considered as nonlinear MUDs [6] [9] Training times for these nonlinear MUDs, however, are often long and unpredictable. Furthermore, the structures of these neural network MUDs are usually determined by trial and error. In our previous works [10] 11] the SVM technique [12] 14] has been applied to construct kernel based ....

B. Aazhang, B.P. Paris and G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. Communications, Vol.40, No.7, pp.1212--1222, 1992.

....detection [1] yield poor performance in the case of numerous or differing power users. On the other hand, to implement the optimal Bayesian decision meets severe combinatorial limits. Some authors proposed the use of neural architectures, like Hopfield network, to overcome these difficulties [2], 3] 4] in order to provide an alternative method for optimization. Unfortunately, the Hopfield model can get easily stuck into a local minimum, therefore it may not yield the optimal detection, although its performance is good enough. The authors have developed a brand new detection scheme, ....

B. Aazhang, B.-P. Paris, G.C. Orsak, "Neural Networks for Multi-User Detection in Code-Division Multiple-Access Communications" IEEE Trans. Commun. , vol. 40, pp. 1212-1222, July 1992.

....[1] 5] is widely used, as its adaptive implementation is very simple. The linear MUD, however, can only work when the underlying noise free signal classes are linearly separable. As nonlinear separable cases are common in DS CDMA channels, neural networks have been considered as nonlinear MUDs [6] [9] Training times for these nonlinear MUDs, however, are often long and unpredictable. Furthermore, the structures of these neural network MUDs are usually determined by trial and error. A learning technique known as the support vector machines SVM has gained popularity due to its many ....

B. Aazhang, B.P. Paris and G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. Communications, Vol.40, No.7, pp.1212--1222, 1992.

....detector based on neural techniques which is able to outperform some well known classical receiver structures like MMSE receivers [1] 2] In fact, one promising approach to interference suppression in a DS CDMA system is based on using neural networks. This is due to the contribution of Aazhang [3] who have demonstrated that the performance of a multilayer perceptron in this context is comparable to that of the optimum receiver [4] Recently, 5] 6] Hussein and Kaushik [7] have proposed a Decision Feedback Functional Link Equalizer (DFFLE) for mitigating Intersymbol Interference (ISI) and ....

B. Aazhang, B.P. Paris, G.C. Orsak, "Neural networks for multiuser detection in code-division multiple-access channels," IEEE Trans. Commun., vol. 40, pp. 1212-1222, July 1992.

....be implemented using standard adaptive lter techniques. Linear detectors, however, can only work when the underlying noise free signal classes are linearly separable. As nonlinear separable cases are common in DS CDMA channels, neural networks have been considered as nonlinear MUDs. In the work [10], a multilayer perceptron (MLP) was applied to a CDMA system without intersymbol interference (ISI) The experience shows that the MLP MUD has better performance than the linear MUD but training times are long and unpredictable. Mitra and Poor [11] applied a RBF network to the same problem. An ....

B. Aazhang, B.P. Paris and G.C. Orsak, \Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. Communications, Vol.40, No.7, pp.1212-1222, 1992.

....to provide remarkable performance when compared to conventional receivers. Verd u also showed that the optimal multiuser detector is resistant to near far effects. Due to the complexity of optimum multiuser detection, suboptimum multiuser receivers have been developed in order to reduce complexity [9, 10, 11, 12, 13]. However, all of these methods require knowledge of some of the parameters of the users received signals, with the common requirement of known time delay. Thus the performance of multiuser detectors is very sensitive to the accuracy of the parameter estimates [14, 15] Currently, many authors ....

B. Aazhang, B. Paris, and G. Orsak, "Neural networks for multiuser detection in codedivision multiple-access communications," IEEE Trans. on Communications, vol. COM40, pp. 1212--1222, July 1992.

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B. Aazhang, B.-P. Paris, and G. C. Orsak. Neural networks for multi-user detection in code-division multiple-access communications. IEEE Transactions on Communications, 40(7):1212--1222, July 1992.

No context found.

B. Aazhang, B.P. Paris and G.C. Orsak, \Neural networks for multiuser detection in code-division multiple-access communications, " IEEE Trans. Communications, Vol.40, No.7, pp.1212-1222, 1992.

No context found.

B. Aazhang, B.P. Paris and G.C. Orsak, \Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. Communications, Vol.40, No.7, pp.1212-1222, 1992.

No context found.

B. Aazhang, B.P. Paris and G.C. Orsak, \Neural networks for multiuser detection in code-division multiple-access communications, " IEEE Trans. Communications, Vol.40, No.7, pp.1212-1222, 1992.

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