Deep Learning-Based Sphere Decoding

Mohammadkarimi, Mostafa; Mehrabi, Mehrtash; Ardakani, Masoud; Jing, Yindi

doi:10.1109/twc.2019.2924220

Cited by 73 publications

(82 citation statements)

References 35 publications

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“…where A N \L denotes the alphabet A N excluding the tabu vectors kept in the tabu list L, and θ min is the minimum distance between two constellation points in a plane. Furthermore, the ML metric φ(x) can be expressed as [4] φ After the best neighbor is determined with (21), it becomes the candidate in the next iteration, and the determination of the best neighbor of a new candidate is performed. By this iterative manner, the final solutionŝ T S is determined as the best candidate visited so far, i.e.,…”

Section: :ŝmentioning

confidence: 99%

“…When n e is sufficiently small, the complexity involved in findings T S with (27) becomes much smaller than that for findings with (26), as well as that for the conventional TS algorithm with (21). This is becauseS is a subset of S, and hence, unlike the conventional TS algorithm, in the ith iteration, 1 ≤ i ≤ t, a reduced number of neighboring vectors are examined, which implies that the complexity required during t iterations to finds T S can be low.…”

Section: ) Dl-aided Initial Solutionmentioning

confidence: 99%

“…Specifically, Ye et al in [20] show that the detection scheme based on DL can address channel distortion and detect the transmitted symbols with performance comparable to that of the minimum mean-square error (MMSE) receiver. In [21], the DL-based SD scheme is proposed. In particular, the DL-based SD with the radius of the decoding hypersphere learned by a DNN achieves significant complexity reduction with respect to the conventional SD with a marginal performance loss.…”

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confidence: 99%

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Deep Learning-Aided Tabu Search Detection for Large MIMO Systems

Nguyen

Lee

2020

IEEE Trans. Wireless Commun.

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In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net.Furthermore, in this algorithm, an adaptive early termination algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a 32 × 32 MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance. Index TermsMIMO, deep learning, deep neural network, tabu search.

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Section: :ŝmentioning

confidence: 99%

Section: ) Dl-aided Initial Solutionmentioning

confidence: 99%

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confidence: 99%

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Deep Learning-Aided Tabu Search Detection for Large MIMO Systems

Nguyen

Lee

2020

IEEE Trans. Wireless Commun.

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“…3: Rearrange the sub-trees according tog. 4: Set the initial radius:f = min(λ 1g1 , f 0.999 ). 5: for p = 1 to |S| do 6: Perform the depth-first search in the pth sub-tree.…”

Section: Algorithm 1 the Proposed Dpp-sd Algorithmmentioning

confidence: 99%

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Weon

Lee

2020

IEEE Access

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In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multipleinput-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 subtrees 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. Index TermsMIMO, sphere decoding, tree search, machine learning, deep learning, neural network.The authors are with the 2 interest [2]. To maximize the achievable data rates in large MIMO systems, the base station needs to receive as many symbols as possible simultaneously from multiple terminals, which leads to enhanced multiplexing gains. In this circumstance, a near-optimal receiver like SD plays an important role in approaching the channel capacity. However, the complexity of SD significantly increases with the number of antennas [3], which makes it difficult to apply to large MIMO systems.Recently, deep-learning (DL) techniques have been applied in various fields, exhibiting eminent performance. Motivated by the performance of DL technologies in other fields, there have been attempts to apply DL to MIMO detection [4]-[8]. In particular, the DL-based sphere decoding (DL-SD) algorithm is derived to choose the optimal hypersphere radius [4]. In addition, a deep network architecture, called DetNet, is proposed to estimate the solution of MIMO detection [5]. Furthermore, the sparsely connected neural network (ScNet) is developed to simplify the structure of DetNet for massive MIMO systems [6]. The application of a deep neural network to reduce the computational complexity of the conventional belief propagation detector is proposed in [7], and the orthogonal approximate message-passing network (OAMP-Net) architecture is proposed to improve the performance of the iterative detection algorithm with trainable variables [8].In this paper, a novel learning-aided SD algorithm is proposed. The main idea of the proposed algorithm is to predict the minimum path metric among "deep" paths of each sub-tree in a large tree structure by using a neural network (NN). 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...

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“…A brute-force search is a computationally inefficient way of solving the ML detection problem specially for a large number of lattice points. Thus, in the past decade, the sphere decoding (SD) algorithm has been frequently employed and studied in order to reduce the computational complexity of exact or near ML decoding in the integer LS problem [2].…”

Section: Introductionmentioning

confidence: 99%

A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

Neinavaie

Derakhtian

Vorobyov

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphere decoding algorithm is pruned via intersecting one dimensional spheres with the hypersphere of the SD algorithm, and the radii are chosen to guarantee the DMT optimality. In contrast to the conventional DMT optimal SD algorithm, which is known to have a polynomial complexity at high SNR regime, we show that the proposed method achieves the DMT optimality by solely visiting a single branch of the search tree at high SNR regime. The simulation results are corroborated with the claimed characteristics of the algorithm in two different scenarios.

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Deep Learning-Based Sphere Decoding

Cited by 73 publications

References 35 publications

Deep Learning-Aided Tabu Search Detection for Large MIMO Systems

Deep Learning-Aided Tabu Search Detection for Large MIMO Systems

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

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