In this paper, we address the joint maximum-likelihood detection of power-domain non-orthogonal multiple access and transform the detection problem into a binary optimization problem. Then, we solve it by a quantum algorithm, Grover adaptive search (GAS), and further accelerate the algorithm. Assuming fault-tolerant quantum computing, GAS provides a quadratic speedup for binary optimization problems in terms of query complexity. The proposed method designs the number of Grover operators using a time-invariant distribution of the number of solutions, which can be obtained in advance. Simulation results demonstrate that the proposed method is capable of reducing the query complexity in the classical domain while achieving the optimal performance.


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    Title :

    Grover Adaptive Search for Joint Maximum-Likelihood Detection of Power-Domain Non-Orthogonal Multiple Access


    Contributors:


    Publication date :

    2023-06-01


    Size :

    1035556 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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