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Computer Knowledge and Technology

MIMO Detection Based on Quantum Neural Network

Qianqian Yang

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

The optimal solution of MIMO signal detection is a NP problem.Making use of the properties of quantum mechanics such as quantum parallelism and entanglement,Quantum Neural network(QNN) can solve this problem efficiently.This paper adopts method of Hopfield neural network to realize MIMO signals detection.Basic on mapping between detecting sequence Maximum a posteriori Probality(MAP) optimum reception likelihood function and energy function of Hopfield nerve net,we conformat a kind of quantum neural network MIMO singal detector.Computer simulation results show that the method has powerful property in bit error rate.

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The optimal solution of MIMO signal detection is a NP problem.Making use of the properties of quantum mechanics such as quantum parallelism and entanglement,Quantum Neural network(QNN) can solve this problem efficiently.This paper adopts method of Hopfield neural network to realize MIMO signals detection.Basic on mapping between detecting sequence Maximum a posteriori Probality(MAP) optimum reception likelihood function and energy function of Hopfield nerve net,we conformat a kind of quantum neural network MIMO singal detector.Computer simulation results show that the method has powerful property in bit error rate.
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.
Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is considered as candidates for future broadband wireless services. In this paper a novel signal detection scheme based on Grover's quantum search algorithm is proposed for MIMO-OFDM systems. Grover's quantum search algorithm is based on the concept and principles of quantum computing, such as quantum bit, quantum register and quantum parallelism. An analysis is given to the theoretical basis of Grover's algorithm and the performance of Grover's algorithm is evaluated. A novel signal detector based on Grover's algorithm (GD) for MIMO-OFDM system is proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than MMSE detector and VBLAST-MMSE detector. The performance of the proposed GD detector is close to optimal when the failure probability is 0.001. When the failure probability is 0.00001, the performance of GD detector declines. In this case, our proposed improved Grover's algorithm based detector is still close to the optimal ML detector. The complexity of GD and IGD is O(√N). It's much better than classical ML detector which complexity is O(N).
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.
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.
In this paper, the potential benefit of employing quantum neural networks (QNNs) for cell-free MIMO is explored. In particular, QNN-based scheme are used to optimize transmitter-user assignment in cell-free MIMO. The objective of the optimization is to maximize the minimum achieved sum rate. Although QNN has received increasing research attention owing to the potential benefit of quantum computation, its utilization for multi-transmitter scenario is still limited. As such, in this paper, we consider the QNN-based algorithm for optimal resourcea allocation in cell-free MIMO systems. We also demonstrate the advantage of our proposed QNN-based algorithm through the numerical results.