Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.

/pdf/deep-learning-for-fading-channel-prediction-li1o8x8djm.pdf

Deep Learning for Fading Channel Prediction

By adapting transmission parameters such as the constellation size, coding rate, and transmit power to instantaneous channel conditions, adaptive wireless communications can potentially achieve great performance. To realize this potential, accurate channel state information (CSI) is required at the transmitter. However, unless the mobile speed is very low, the obtained CSI quickly becomes outdated due to the rapid channel variation caused by multi-path fading. Since outdated CSI has a severely negative impact on a wide variety of adaptive transmission systems, prediction of future channel samples is of great importance. The traditional stochastic methods, modeling a time-varying channel as an autoregressive process or as a set of propagation parameters, suffer from marginal prediction accuracy or unaffordable complexity. Taking advantage of its capability on time-series prediction, applying a recurrent neural network (RNN) to conduct channel prediction gained much attention from both academia and industry recently. The aim of this article is to provide a comprehensive overview so as to shed light on the state of the art in this field. Starting from a review on two model-based approaches, the basic structure of a recurrent neural network, its training method, RNN-based predictors, and a prediction-aided system, are presented. Moreover, the complexity and performance of predictors are comparatively illustrated by numerical results.

/pdf/neural-network-based-fading-channel-prediction-a-2vc8rox28q.pdf

Neural Network-Based Fading Channel Prediction: A Comprehensive Overview

Signal modulation identification (SMI) plays a very important role in orthogonal frequency-division multiplexing (OFDM) systems. Currently, SMI methods are often implemented via feature extraction based on machine learning. However, the traditional methods encounter a bottleneck where the probability of correct classification (PCC) is very limited and hence it is hard to implement in practical OFDM systems due to the fact that traditional methods are difficult to extract feature of the OFDM signals. In order solve these problems, we propose a deep learning (DL) based SMI method for identifying OFDM signals. Specifically, convolutional neural network (CNN) is adopted to train in-phase and quadrature (IQ) samples for OFDM signals. Then we choose dropout layer to prevent overfitting and improve its identification accuracy. In addition, datasets with different modulation modes are adopted to verify our trained CNN. Experiments are conducted to show that our proposed method achieves higher accuracy and better consistency than traditional methods. Moreover, extensive results confirm that the proposed method performs robustly in different datasets.

/pdf/deep-learning-based-signal-modulation-identification-in-ofdm-420vhukbnz.pdf

Deep Learning-Based Signal Modulation Identification in OFDM Systems

This two-part paper investigates the application of artificial intelligence (AI) and, in particular, machine learning (ML) to the study of wireless propagation channels. In Part I of this article, we introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and antenna-channel optimization, and in this part (Part II), we review the state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state of the art, the future challenges of AI-/ML-based channel data processing techniques are given as well. 

/pdf/artificial-intelligence-enabled-radio-propagation-for-2orfvv33.pdf

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Heterogeneous network (HetNet) with energy harvesting is a promising technique to provide perpetual power supplies and ubiquitous coverage as well as high data rate for next-generation wireless communications. In this article, we consider a robust power allocation and power splitting (PS) problem for downlink simultaneous wireless information and power transfer (SWIPT)-enabled HetNets. The robust energy-efficiency (EE) maximization problem of femtocell users (FUs) is formulated under the outage-probability interference power constraint of macrocell user (MU), the maximum allowable transmission power of FU, and the EE-based outage constraint of each FU. The originally fractional optimization problem with the probabilistic constraint is NP-hard and difficult to solve. Without knowing the distribution of uncertain parameters, a min–max probability machine approach is first introduced to convert the semi-infinite optimization problem into a deterministic one which is transformed into a deterministic convex one by using the Dinkelbach method and the quadratic transformation approach. An iterative power allocation and PS scheme is obtained based on convex optimization methods. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results from the perspective of EE and robustness.

Robust Resource Allocation and Power Splitting in SWIPT Enabled Heterogeneous Networks: A Robust Minimax Approach

Frequency division duplex (FDD) systems dominate current cellular networks due to its advantages of low latency and strong anti-interference ability. However, the computation and the feedback overheads for predicting the downlink channel state information (DL-CSI) are the major bottlenecks to further improve the cellular FDD systems performance. To deal with these problems, in this paper, a convolutional long short-term memory network (ConvLSTM-net)-based deep learning method is proposed for predicting the DL-CSI from the uplink channel state information (UL-CSI) directly. In detail, our proposed ConvLSTM-net consists of two modules: one is the feature extraction module that learns spatial and temporal correlations between the DL-CSI and the UL-CSI, and the other one is the prediction module that maps the extracted features to the reconstructions of the DL-CSI. To evaluate the outperformance of the ConvLSTM-net, a long short-term memory network (LSTM-net) and a convolutional neural networks (CNN)-based schemes are simulated for comparisons. The simulation experiments consist of two parts. One part is that the hyper parameters of the proposed ConvLSTM-net are analyzed to explore their effects on the prediction performance. Another part is that experiments are conducted in the time domain and frequency domain, respectively, for selecting a more proper domain to predict the DL-CSI accurately. From the experiment results above, it can be verified that the proposed ConvLSTM-net with proper hyper parameters outperforms the compared schemes at predicting DL-CSI according to UL-CSI in the cellular FDD systems, especially in the time domain.

/pdf/ul-csi-data-driven-deep-learning-for-predicting-dl-csi-in-4p4dk5oq4n.pdf

UL-CSI Data Driven Deep Learning for Predicting DL-CSI in Cellular FDD Systems

Recently, the massive use of invoices has increased the burden on financial staff. In order to ensure high accuracy, conventional financial reimbursement is mainly manual, which wastes lots of human and material resources. This paper proposes an intelligent reimbursement system based on a deep learning method. The system mainly contains face recognition, invoice identification, and information storage. Face recognition ensures the security in the invoice reimbursement, and invoice identification has acceptable accuracy and operating speed. The experimental results indicate that the proposed system achieves both high accuracy and fast running speed.

/pdf/deep-learning-aided-system-design-method-for-intelligent-kswgrysqjr.pdf

Deep Learning Aided System Design Method for Intelligent Reimbursement Robot

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UL-CSI Data Driven Deep Learning for Predicting DL-CSI in Cellular FDD Systems

Deep Learning Aided System Design Method for Intelligent Reimbursement Robot