Improving Massive MIMO Message Passing Detectors With Deep Neural Network

Tan, Xiaosi; Xu, Weihong; Sun, Kai; Xu, Yunhao; Be'ery, Y.; You, Xiaohu; Zhang, Chuan

doi:10.1109/tvt.2019.2960763

Cited by 67 publications

(50 citation statements)

References 45 publications

(58 reference statements)

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“…The learnable parameter γ t can be interpreted as the step-size for the update of r t , and is γ t = 1 in each iteration in the OAMP detector. The de-correlated matrix W t is given in (19). The nonlinear estimator η t (·) in OAMP-Net2 is constructed by the divergence-free estimator…”

Section: B Oamp-net2 Detectormentioning

confidence: 99%

“…Similar to the OAMP detector and OAMP-Net, the computational complexity is dominant by the matrix inverse in (19). As N t is relatively small (e.g., 4 or 8) on small-size MIMO systems, the matrix inverse operation is always acceptable.…”

Section: Computational Complexitymentioning

confidence: 99%

“…DetNet has comparable performance with the AMP-based detector and is more robust to ill-conditioned channels [18]. To further reduce the number of learnable parameters and improve convergence, the approaches in [19] and [20] use DL techniques for the belief propagation and message passing detector, respectively. In [25], a DL-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by DNN.…”

Section: Introductionmentioning

confidence: 99%

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Model-Driven Deep Learning for MIMO Detection

Wen

Jin

et al. 2020

IEEE Trans. Signal Process.

323

164

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In this paper, we investigate the model-driven deep learning (DL) for joint MIMO channel estimation and signal detection (JCESD), where signal detection considers channel estimation error and channel statistics while channel estimation is refined by detected data and takes the signal detection error into consideration. In particular, the MIMO signal detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO signal detector can be rapidly trained with a much smaller data set. Furthermore, the proposed signal detector can be extended to soft-input soft-output detection easily. Based on numerical results, the model-driven DL based JCESD scheme significantly improves the performance of the corresponding traditional iterative detector and the signal detector exhibits superior robustness to signal-to-noise ratio (SNR) and channel correlation mismatches. Recently, it has been applied in physical layer communications [10]-[12], such as channel estimation [13]-[15], CSI feedback [16], signal detection [17]-[25], and channel coding [26], [27]. In particular, a five-layer fully connected deep neural network (DNN) is embedded into an orthogonal frequency-division multiplexing (OFDM) system for joint channel estimation and signal detection (JCESD) by treating the receiver as a black box and without exploiting domain knowledge [17]. However, training such a black-box-based network requires a lot of 1 A matrix A = UΣV is unitarily-invariant if U, Σ and V are mutually independent, and U, V are Haar-distributed. The independent and identically distributed (i.i.d.) Gaussian matrix is a typical unitarily-invariant matrix.

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Section: B Oamp-net2 Detectormentioning

confidence: 99%

Section: Computational Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Driven Deep Learning for MIMO Detection

Wen

Jin

et al. 2020

IEEE Trans. Signal Process.

323

164

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“…However, the extension to the MIMO-OFDM system is not investigated in these aforementioned works. To study the MIMO case, in [21], a deep neural network detector is introduced based on unfolding the standard belief propagation algorithm. The neural network parameters are further tuned via an offline training which requires enormous investigations for various antenna configurations.…”

Section: Introductionmentioning

confidence: 99%

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

Zhou

Liu

Chang

2020

IEEE Trans. Wireless Commun.

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Reservoir computing (RC) is a special neural network which consists of a fixed high dimensional feature mapping and trained readout weights. In this paper, we consider a new RC structure for MIMO-OFDM symbol detection, namely windowed echo state network (WESN). It is introduced by adding buffers in input layers which brings an enhanced short-term memory (STM) of the underlying neural network through our theoretical proof. A unified training framework is developed for the WESN MIMO-OFDM symbol detector using both comb and scattered pilot patterns, where the utilized pilots are compatible with the structure adopted in 3GPP LTE/LTE-Advanced systems. Complexity analysis reveals the advantages of the WESN based symbol detector over the state-of-the-art symbol detectors such as the linear the minimum mean square error (LMMSE) detection and the sphere decoder when the system is employed with a large number of OFDM sub-carriers. Numerical evaluations corroborate that the improvement of the STM introduced by the WESN can significantly improve the symbol detection performance as well as effectively mitigate model mismatch effects as opposed to existing methods.

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“…As aforementioned, at each layer of SE-SD, the search order is determined by the branch metric in (7), which only considers the metric at the corresponding layer. However, if we can use the full-path metric, i.e., z − Rx 2 , which accumulates the branch metrics from the root note to the leaf node, for ordering, the search can be more efficient.…”

Section: System Modelmentioning

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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Improving Massive MIMO Message Passing Detectors With Deep Neural Network

Cited by 67 publications

References 45 publications

Model-Driven Deep Learning for MIMO Detection

Model-Driven Deep Learning for MIMO Detection

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

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

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