Abstract
In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the “deep” paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.
Highlights
In multiple-input multiple-output (MIMO) systems, the sphere decoding (SD) algorithm is known as an efficient signal-detection scheme, which performs close to the maximum-likelihood detection (MLD) receiver [1]
To satisfy the increasing demand of ultra-high data rates in mobile communication systems, large MIMO systems, in which a large number of antennas are employed at a base station for data transmission and reception, have been of great research interest [2]
The complexity of SD significantly increases with the number of antennas [3], which makes it difficult to apply to large MIMO systems
Summary
In multiple-input multiple-output (MIMO) systems, the sphere decoding (SD) algorithm is known as an efficient signal-detection scheme, which performs close to the maximum-likelihood detection (MLD) receiver [1]. In large MIMO systems, the required information to estimate the path metrics can have large dimension, which can significantly increase the complexity of the NN. To resolve this problem, the size of the input vector to the NN is optimized based on the property of large MIMO channels. The predicted minimum path metrics of sub-trees, which are generated by the designed NN architecture, are used to determine the search order of SD They are used for early termination and optimization of the initial radius of SD, which can potentially reduce the overall complexity. (·) and (·) denote the real and imaginary parts of a complex matrix, respectively
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