Acquisition of PN sequences using multilayer perceptron neural network adaptive processor for multiuser detection in spread‐spectrum communication systems

Abstract

A non-coherent serial acquisition scheme for direct sequence spread spectrum communication systems is analyzed and discussed in this paper. The adaptive thresholding based on constant false alarm rate and multilayer perceptron neural network (MLP-NN) techniques are combined to improve the performance of code division multiple access systems. One of the most important problems in code acquisition of pseudo-noise sequences for multiuser detection is the presence of interferences caused by the multiple access technique and multipath replicas. To solve this problem, an MLP-NN is trained and adapted to work as a constant false alarm rate detector using the error back propagation gradient descendent algorithm.It is named MLP-NN adaptive processor. The performance of this proposed algorithm is presented using the serial search acquisition system, which is chosen because of its simple hardware implementation. The performance of the MLP-NN adaptive processor algorithm in homogeneous and non-homogenous environments for additive white Gaussian noise and Rayleigh fading channels is evaluated via computer simulations. The obtained results are compared to other serial acquisition schemes using the cell-averaging adaptive processor, the order statistics adaptive processor, and the automatic censoring adaptive processor algorithms.

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

“…The feed-forward MLP consists of an input layer, an output layer and one or more hidden layers between them. Each neuron is completely connected to all the neurons in the next layer, but only forward links are available [ 13 ]. The MLP uses the back propagation learning algorithm, which aims to specify the best parameters to model the relationship between the input and output variables [ 14 ].…”

A robust auto‐adaptive approach for serial acquisition of generalized multi‐carrier spread spectrum in multipath fading mobile channels

“…7 The received signals at the MC-SS systems are extremely complex in heterogeneous background environments due to the interfering signals that resulted from the presence of multipath propagation, multiple-access interference (MAI) signals or the combination of both of them. 8 Therefore, the interfering signals that may be considered as outliers are the major factors that negatively affect the code acquisition performance, if they are not properly handled. On the other hand, if the non-adaptable detection threshold is set improperly, the detection performance might be degraded.…”

“…For this reason, a large number of different adaptive threshold mechanisms known in the Radar systems have been applied to the code acquisition problems, where these mechanisms are based on constant false alarm rate (CFAR) algorithms that are used to determine the threshold value according to the actual environmental conditions. [7][8][9][10][11][12][13] As the arithmetic averaging CFAR detector and its modifications [8][9][10] suffer a serious detection performance degradation when the reference window used to estimate the background noise level is infected by interfering signals, 11 a variety of alternatives have been proposed to develop CFAR detectors applied to the PN code acquisition to overcome these drawbacks. These alternatives include the fixed point(s) censoring CFAR detectors, where the largest ranked cells are censored, and only the remaining ranked cells are used to estimate the background noise level.…”

“…Therefore, it is difficult to get the right phase to adjust the synchronization even if the channel quality is improved. For this reason, a large number of different adaptive threshold mechanisms known in the Radar systems have been applied to the code acquisition problems, where these mechanisms are based on constant false alarm rate (CFAR) algorithms that are used to determine the threshold value according to the actual environmental conditions 7‐13 …”

“…As the arithmetic averaging CFAR detector and its modifications 8‐10 suffer a serious detection performance degradation when the reference window used to estimate the background noise level is infected by interfering signals, 11 a variety of alternatives have been proposed to develop CFAR detectors applied to the PN code acquisition to overcome these drawbacks. These alternatives include the fixed point(s) censoring CFAR detectors, where the largest ranked cells are censored, and only the remaining ranked cells are used to estimate the background noise level 2,11‐13 .…”