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Jisuanji gongcheng

Multiuser Detector Based on Neural Network Quantum Algorithm

Zongsheng Jia

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

A neural network quantum algorithm(NNQA) that employs a genetic quantum algorithm(GQA) and a Hopfield neural network(HNN) for the multiuser detection problem in code-division multiple-access(CDMA) communications system is proposed.Using this algorithm,the Hopfield neural network is embedded into the GQA to improve further the fitness of the population at each generation.Such a hybridization of the GQA with the Hopfield neural network reduces its computational complexity by providing faster convergence.A better initial data estimation supplied by the GQA improves the performance of the HNN,and the embedded Hopfield neural network improves the performance of the GQA.Simulation results show that the proposed approach of multiuser detection has significant performance improvements over conventional detector and the previous detectors based on intelligent algorithms in terms of bit-error-rate,multiple access interference and near-far resistance.

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A neural network quantum algorithm(NNQA) that employs a genetic quantum algorithm(GQA) and a Hopfield neural network(HNN) for the multiuser detection problem in code-division multiple-access(CDMA) communications system is proposed.Using this algorithm,the Hopfield neural network is embedded into the GQA to improve further the fitness of the population at each generation.Such a hybridization of the GQA with the Hopfield neural network reduces its computational complexity by providing faster convergence.A better initial data estimation supplied by the GQA improves the performance of the HNN,and the embedded Hopfield neural network improves the performance of the GQA.Simulation results show that the proposed approach of multiuser detection has significant performance improvements over conventional detector and the previous detectors based on intelligent algorithms in terms of bit-error-rate,multiple access interference and near-far resistance.
A quantum neural networks(QNN) approach for mutiuser detection in CDMA system is theoretically proposed.Quantum neural networks is the combination of quantum computing and artificial neural network(ANN).Taking advantages of the properties of quantum mechanics such as quantum parallelism and entanglement.The computer simulation demonstrates that this algorithm has great advantages of better tolerance to coding error and the far-near effect.These performances of the approach in flat Rayleigh fading environment are also satisfactory.
Quantum neural network (QNN) is a promising area in the field of quantum computing and quantum information processing. A model of feedback quantum neuron (FQN) is proposed and its property is analyzed. A novel multi-user detector based on FQN and quantum register is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties both in bit error rate and near-far resistance than Hopfield neural network based detector. A multi-user detector based on improved FQN is also proposed and the simulation results have shown that the improved FQN multi-user detector is obviously superior to the FQN multi-user detector. Its performance is closer to optimal, compared with the other multi-user detector.
Future wired and wireless communication systems will employ pure or combined code division multiple access (CDMA) techniques, such as in the European 3G mobile UMTS or power line communications system, but also several 4G proposals include, for example, multicarrier (MC) CDMA. Previous investigations discovered the drawbacks of single user detectors (SUD), which are widely employed in narrowband IS-95 CDMA systems, and were forced to develop suitable multiuser detection schemes to increase the efficiency against interference. However, at this moment there are only suboptimal solutions available became of the rather high complexity of optimal detectors. One of the possible receiver technologies may be quantum assisted computing devices which allow high level parallelism in computation. The first commercial devices are estimated for the next years, which meets the advent of 3G and 4G systems. We analyze the error probability and give tight bounds in a static and dynamically changing environment for a novel quantum computation based quantum multi-user detection (QMUD) algorithm, employing a quantum counting algorithm, which provides an optimal solution.
Multi-user detection based on quantum optimization algorithm has aroused intense interests recently. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting nicking probability division initialization strategy, the population diversity is enhanced; (2) Introducing quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, the searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. Simulation results prove that the multiuser detection based on given algorithm has lower bit error, rapider converge speed, better resisting MAI ability and better Near-far Resistance ability than other multi-user detections based on quantum clone genetic algorithm and classical genetic algorithms obviously.
Abstract Using global optimization algorithm to optimize the initial weights and thresholds of traditional neural network model can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slower convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA-QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA-QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. In the proposed model, the normalized results of the predicted sample attributes are transformed into quantum states as the input of the network, and the quantum rotation gate is used to rotate the phase and control the reversal of the target qubits. Finally, the output of the network is obtained by entanglement of multiple qubits in the quantum Controlled-NOT (CNOT) gate. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD-CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA-QGCNN model. Experimental results show that the proposed intrusion detection model has lower false alarm rate and significantly accuracy compared to conventional attack detection models. And GQCNN optimized by QGA also improves the convergence performance of the model.
A model of feedback quantum neuron (FQN) is proposed. A novel multi-user detector based on feedback quantum neuron and quantum register is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties both in bit error rate and near-far resistance than Hopfield neural network based detector. A multi-user detector based on improved FQN is also proposed and the simulation results have shown that the improved FQN multi-user detector is obviously superior to the FQN multi-user detector. Its performance is close to the optimum multi-user detector.