MMSE Multiuser Receiver for Uniformly Quantized Synchronous CDMA Signals in AWGN Channels

Yih, Chi-Hsiao

doi:10.1109/lcomm.2007.070584

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…where J (M) expresses the minimum mean square error (MMSE) theorem, we can get the linear MMSE transforming matrix [6] as…”

Section: Match Fileterkmentioning

confidence: 99%

“…The optimal MUD proposed in [4] can remove the MAI as well as the near-far effect completely and enlarge the system's capacity, but the computational complexity of the optimal MUD grows exponentially with the number of users which leads to the impracticability [5]. Then, many suboptimal and substitutal MUDs are brought forth aiming at achieving the best performance to complexity tradeoff [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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An Adaline Neural Network-Based Multi-User Detector Improved by Particle Swarm Optimization in CDMA Systems

Wang

Yang

et al. 2010

Wireless Pers Commun

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The detector applied to each single user based on adaline neural network (ANN) is presented in this paper, which is equivalent to the joint one applied to all users in eliminating the multiple access interference in CDMA systems. Then, particle swarm optimization (PSO) algorithm combined with least mean square scheme is employed in the training procedure of the ANN, which can effectively remove the shortcoming of the poor dynamic adaptive behavior of conventional neural network, i.e. during the convergence procedure of the weights in conventional neural network, the training samples are usually required to be trained iteratively. However, in the improved detector, each training sample can be trained repeatedly, so that the converging speed is getting much faster. Simulation results show that, in the ANN based multi-user detector improved by PSO, the dynamic adaptive behavior has been remarkably improved.

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“…where J (M) expresses the minimum mean square error (MMSE) theorem, we can get the linear MMSE transforming matrix [6] as…”

Section: Match Fileterkmentioning

confidence: 99%