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Scientia Sinica Informationis

VBLAST system signal detection based on the Grover algorithm

Wang Mi

Published Jan 1, 2013• 1 citation
OpenAlex

Abstract

In the VBLAST system, linear detection has low computational complexity, but its detection performance is poor. Conversely, Maximum Likelihood(ML) detection achieves optimal detection performance, but its computational complexity grows exponentially with the number of transmit antennas and the signal modulation order. This is a typical NP problem. To reduce the computational complexity of ML detection in the VBLAST system, while at the same time retaining optimal detection performance, an improved ML detection method is proposed in this paper. Both the quantum Grover and Grover-Long algorithms are used in the ML detection algorithm for the VBLAST system, and the complexity and performance of the algorithm are simulated. The simulation results show that not only is the computational complexity of Grover algorithm detection square accelerated, but also that the detection performance of the algorithm is close to that of ML detection. Moreover,this is the first time that a quantum algorithm is used to solve an engineering problem.

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In the VBLAST system, linear detection has low computational complexity, but its detection performance is poor. Conversely, Maximum Likelihood(ML) detection achieves optimal detection performance, but its computational complexity grows exponentially with the number of transmit antennas and the signal modulation order. This is a typical NP problem. To reduce the computational complexity of ML detection in the VBLAST system, while at the same time retaining optimal detection performance, an improved ML detection method is proposed in this paper. Both the quantum Grover and Grover-Long algorithms are used in the ML detection algorithm for the VBLAST system, and the complexity and performance of the algorithm are simulated. The simulation results show that not only is the computational complexity of Grover algorithm detection square accelerated, but also that the detection performance of the algorithm is close to that of ML detection. Moreover,this is the first time that a quantum algorithm is used to solve an engineering problem.
In the VBLAST system, linear detection has low computational complexity, but its detection performance is poor. Conversely, Maximum Likelihood (ML) detection achieves optimal detection performance, but its computational complexity grows exponentially with the number of transmit antennas and the signal modulation order. This is a typical NP problem. To reduce the computational complexity of ML detection in the VBLAST system, while at the same time retaining optimal detection performance, an improved ML detection method is proposed in this paper. Both the quantum Grover and Grover-Long algorithms are used in the ML detection algorithm for the VBLAST system, and the complexity and performance of the algorithm are simulated. The simulation results show that not only is the computational complexity of Grover algorithm detection square accelerated, but also that the detection performance of the algorithm is close to that of ML detection. Moreover, this is the first time that a quantum algorithm is used to solve an engineering problem.
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).
There are different access systems proposed for next-generation wireless communications. As the number of users in wireless systems increases, the challenge of reliable communications can be faced with the aid of exhaustive search-based signal detection. However, exhaustive maximum-likelihood searches impose a high complexity. In classical communication systems, performance improvement is achieved at the cost of substantially increased computational complexity. In this study, we consider an OFDMA downlink and SC-FDMA uplink scenario. We propose the employment of quantum search methods for signal detection. Specifically, we employ Grover's algorithm for database search in signal detection. We observe from our simulations that the error performance provided by the classical ML search is attainable by our method. As a benefit of using quantum search, the complexity imposed is substantially reduced compared to using classical ML detectors. We also compare the performance of the proposed scheme with Grover's search-based MC-CDMA detector. We investigate with multiple users as well and find that the quantum search-based scheme is effective in improving performance, while the computational burden is very low.
In this paper, we propose a quantum-native formulation of maximum likelihood detection in random access channel with overloaded multiple-input multiple-output (MIMO), where a number of user terminals share the same channel resource and asynchronously transmit signals. Classical low-complexity linear detectors suffer from significant performance degradation in this scenario, while the exhaustive-search-based maximum likelihood detection achieves the optimal performance but induces an exponential increase in time complexity. To strike this fundamental tradeoff, we formulate the maximum likelihood detection as a binary optimization problem and solve it via the Grover adaptive search (GAS), a quantum exhaustive search algorithm offering quadratic speedup. We further introduce a search space reduction technique using quantum W states and controlled-H gates, which substantially decreases complexity. In addition, we investigate the effects of efficient parameter settings in GAS. Our simulation results show that the proposed detector achieved the optimal detection performance while reducing the query complexity, thereby highlighting its potential as a solution for future quantum-accelerated overloaded MIMO systems.
Abstract Maximum like-lihood (ML) algorithm is the optimal detection scheme for the data detection of multiple-input multiple-output (MIMO) communication systems, but it has a high complexity in its preprocessing. For good performance and low complexity, a new Grover’s Quantum Search (GS) based data detection algorithm for MIMO system is proposed. It can increase the probability amplitude of solutions while reducing the probability amplitude of non-solutions. By Grover’s iterative process, the probability amplitude of solutions can reach maximum. So it can get the solutions after measurement with a high probability. The simulation results show that the proposed data detection algorithm for MIMO system has a very close to ML based data detection in Bit Error Rate (BER) performance, but it has lower complexity than ML based data detection algorithm.
We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.