Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series. > <s> BIB001 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> In this paper, a bit-error-rate (BER) analysis for closed-loop transmit diversity in a time-selective Rayleigh fading channel containing feedback delay is presented. In the absence of feedback delay, closed-loop transmit diversity always outperforms open-loop transmit for a given transmitted signal energy. This is no longer true in the presence of feedback delay. We derive closed-form expressions of the average BER for this case assuming QPSK and BPSK signaling. The results of the analysis are instrumental for comparing closed-loop with open-loop schemes under given operating conditions. In particular, we demonstrate that, for a given transmitted energy and number of transmit antennas, open-loop outperforms closed-loop at sufficiently fast channel fading. We also show that, for a given transmitted signal energy and fading rate, closed-loop outperforms open-loop for sufficiently large numbers of transmit antennas while the total average transmitted signal energy is kept constant. For some special cases, closed-form expressions for the fading rate at which the performance of open-loop is equal to closed-loop are obtained. <s> BIB002 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> 1. Introduction 2. The wireless channel 3. Point-to-point communication: detection, diversity and channel uncertainty 4. Cellular systems: multiple access and interference management 5. Capacity of wireless channels 6. Multiuser capacity and opportunistic communication 7. MIMO I: spatial multiplexing and channel modeling 8. MIMO II: capacity and multiplexing architectures 9. MIMO III: diversity-multiplexing tradeoff and universal space-time codes 10. MIMO IV: multiuser communication A. Detection and estimation in additive Gaussian noise B. Information theory background. <s> BIB003 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Autoregressive stochastic models for the computer simulation of correlated Rayleigh fading processes are investigated. The unavoidable numerical difficulties inherent in this method are elucidated and a simple heuristic approach is adopted to enable the synthesis of accurately correlated, bandlimited Rayleigh variates. Startup procedures are presented, which allow autoregressive simulators to produce stationary channel gain samples from the first output sample. Performance comparisons are then made with popular fading generation techniques to demonstrate the merits of the approach. The general applicability of the method is demonstrated by examples involving the accurate synthesis of nonisotropic fading channel models. <s> BIB004 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction. Three different algorithms, namely the real time recurrent learning (RTRL), the global extended Kalman filter (GEKF) and the decoupled extended Kalman filter (DEKF) are used for training the recurrent neural network (RNN) based channel predictor. Our simulation results show that the GEKF and DEKF training schemes have the potential of converging faster than the RTRL training scheme as well as attaining a better MSE performance. <s> BIB005 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Adaptive transmission methods can potentially aid the achievement of high data rates required for mobile radio multimedia services. To realize this potential, the transmitter needs accurate channel state information (CSI) for the upcoming transmission frame. In most mobile radio systems, the CSI is estimated at the receiver and fed back to the transmitter. However, unless the mobile speed is very low, the estimated CSI cannot be used directly to select the parameters of adaptive transmission systems, since it quickly becomes outdated due to the rapid channel variation caused by multipath fading. To enable adaptive transmission for mobile radio systems, prediction of future fading channel samples is required. Several fundamental issues arise in the design and testing of fading prediction algorithms for adaptive transmis- sion systems. These include complexity, robustness, choice of an appropriate channel model for algorithm validation, chan- nel estimation and noise reduction required for reliable prediction, and design and analysis of adaptive transmission methods aided by fading prediction algorithms. We use these criteria in the review of recent advances in the area of fading channel prediction. We also demonstrate that reliable fading prediction makes adaptive transmission feasible in diverse wireless communication systems. <s> BIB006 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> This paper employs a high resolution quantization framework to study the effects of finite-rate feedback of the channel state information (CSI) on the performance of multiple-input-multiple-output (MIMO) systems over independently and identically distributed (i.i.d.) Rayleigh flat fading channels. The contributions of this paper are twofold. First, we extend the general distortion analysis of vector quantizers to deal with complex source variables. Necessary and sufficient conditions that guarantee a concise high-resolution distortion analysis in the complex domain is presented. Second, as an application of the proposed complex distortion analysis, tight lower bounds on the capacity loss due to the finite-rate channel quantization are provided for MIMO systems employing a fixed number of equal power spatial beams. Based on the obtained closed-form analytical results, it is shown that the system capacity loss decreases exponentially as the ratio of the quantization rate to the total degrees of freedom of the channel state information to be quantized. Moreover, MIMO CSI-quantizers using mismatched codebooks that are only optimized for high-signal-to-noise ratio (SNR) and low-SNR regimes are also investigated to quantify the penalties incurred by the use of mismatched codebooks. In addition, the analysis is extended to deal with MIMO systems using multi-mode spatial multiplexing transmission schemes with finite-rate CSI feedback. Finally, numerical and simulation results are presented which confirm the tightness of the derived theoretical distortion bounds. <s> BIB007 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems. <s> BIB008 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> In this paper, we analyze the outage probability and diversity order of opportunistic relay selection in a scenario based on decode and forward and where the available channel state information (CSI) is outdated. The study is conducted analytically by obtaining a closed-form expression for the outage probability, which is defined as the probability that the instantaneous capacity is below a target value. We derive high-SNR approximations for the outage probability. By doing so, we demonstrate that the diversity order of the system is reduced to 1 when CSI is outdated, being this behavior independent of the level of CSI accuracy. A physical explanation for this extreme loss of diversity is provided along with numerical results to support the analytical study. <s> BIB009 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms. <s> BIB010 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Multiple-input multiple-output (MIMO) communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multiuser MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multiceli network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects. <s> BIB011 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> The quality of channel state information (CSI) has large impact on the performance of coordinated multi-point (CoMP) systems. In this paper we study the impact of channel prediction on the performance of coherent CoMP transmission in time-varying channels with large backhaul latency. We resort to large system analysis to derive an explicit expression of the average user rate of the CoMP system when predicted channels are employed for downlink precoding. By comparing with the Non-CoMP systems, we find that channel prediction brings much higher performance gain to CoMP systems with respect to channel estimation. As a result, a CoMP system can perform well for mobile users even under large backhaul latency, if channel prediction will be used instead of channel estimation. Simulation results are provided to validate our analysis. <s> BIB012 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Prior research has shown that delayed channel state information (CSI) severely degrades the throughput performance of multi-user (MU) multiple-input multiple-output (MIMO) transmission, resulting in a preference for single-user (SU) transmission. This paper investigates how the relative performance of MU-MIMO and SU-MIMO is affected by 1) different Doppler spectra, and 2) high-order prediction methods for channel estimation. Simulation results reveal that the often-used Jakes-Clarke spectrum is the worst case for MU performance. By evaluating the impact of other Doppler spectra and high-order channel prediction on MU-MIMO performance, we demonstrate that MU can tolerate larger CSI delays and retain its theoretical advantage over SU in practice. <s> BIB013 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> This paper investigates the effect of imperfect channel state information (CSI) on the outage performance of cooperative distributed beamforming (DBF). The acquisition of perfect CSI is disturbed by outdated CSI and channel estimation errors. Decode-and-forward relays are considered with individual power constraints at each relay. Considering a training-and-feedback strategy to acquire CSI, a closed-form approximate expression for outage probability and the achievable diversity gain are first provided under Rayleigh fading channels. Numerical observations verify that the approximation is very tight. It is shown that outdated CSI seriously degrades the performance, and therefore, the outage probability is bounded and no diversity is achieved. Conversely, channel estimation errors cause slight performance degradation, and full diversity order is still achievable. However, even when CSI is outdated, it is shown that the asymptotic outage decreases as the number of relays $K$ increases, where the logarithm of the outage probability decreases at the rate of $K\log K$ . This paper shows that, when the destination re-estimates the current CSI for decoding, the effect of outdated CSI is greatly mitigated and consequently achieves a diversity gain of one. To re-estimate the current CSI with small overhead, the beamformed training sequence (BFTS) strategy is proposed, in which a new closed-form formula for minimum-mean-square-error estimation is presented using Gaussian approximation. Numerical results verify that the outage performance with the proposed BFTS scheme is close to that of a perfect estimation. A closed-form outage expression of DBF with the proposed BFTS scheme is also presented under an assumption of high-signal-to-noise ratio (SNR), but the expression is very tight even in low-to-moderate SNR regimes. <s> BIB014 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> This letter investigates the effect of imperfect channel state information (CSI) on the performance of radio resource management for a downlink multi-user OFDMA system. It is shown that the imperfect CSI introduces a signal-and-power dependent noise term, which makes the symbol error rate (SER) constellation-point dependent, and yields an error floor on the SER. In addition, a two-step multi-user channel, power and constellation allocation algorithm is proposed to maximize the total network throughput. <s> BIB015 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Channel prediction is an important process for channel compensation in a fading environment. If a future channel characteristic is predicted, adaptive techniques, such as pre-equalization and transmission power control, are applicable before transmission in order to avoid degradation of communications quality. Previously, we proposed channel prediction methods employing the chirp z-transform (CZT) with a linear extrapolation as well as a Lagrange extrapolation of frequency-domain parameters. This paper presents a highly accurate method for predicting time-varying channels by combining a multilayer complex-valued neural network (CVNN) with the CZT. We demonstrate that the channel prediction accuracy of the proposed CVNN-based prediction is better than those of the conventional prediction methods in a series of simulations and experiments. <s> BIB016 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> In this work we consider the multiple-input multiple-output (MIMO) interference broadcast channel (IBC) and analyse the performance of interference alignment (IA) under imperfect channel state information (CSI), where the variance of the CSI error depends on the signal-to-noise ratio (SNR). First, we derive an upper bound on asymptotic mean loss in sum rate compared to the perfect CSI case and then we quantify the achievable degrees of freedom (DoF) with imperfect CSI. Both sum rate loss and achievable DoF are found to be dependent on the number of cells in the system and the amount of users per cell, in addition to the CSI error parameters themselves. Results show that when error variance is inversely proportional to SNR, full DoF are achievable and the asymptotic sum rate loss is bounded by a derived value. Additionally if the CSI imperfection does not disappear for asymptotically high SNR, then the full DoF gain promised by IA cannot be achieved; we quantify this loss in relation to the CSI mismatch itself. The analytically derived bounds are validated via system simulation, with the cellular counterparts of the maximum signal-to-interference-plus-noise ratio (Max-SINR) and the minimum weighted leakage interference (Min-WLI) algorithms being the IA techniques of choice. Secondly, inspired by the CSI mismatch model used to derive the bounds, we present a novel Max-SINR algorithm with stochastic CSI error knowledge (Max-SINR-SCEK) for the MIMO IBC. Simulations show that the proposed algorithm improves performance over the standard one under imperfect CSI conditions, without any additional computational costs. <s> BIB017 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> This paper investigates the prediction of multiple-input–multiple-output (MIMO) narrow-band multipath fading channels for mobile-to-mobile (M-to-M) wireless communication systems. Using a statistical model for M-to-M communication in urban and suburban environments, we derive a parameterized double directional model and utilize a multidimensional extension of the ESPRIT algorithm to jointly estimate the angles of departure (AoD), angles of arrival (AoA), and effective Doppler frequencies. A simple method is also proposed for mobile velocity estimation. The parameter estimates are then used to forecast the M-to-M channel. The bound on the prediction error is derived using a vector formulation of the Cramer–Rao lower bound (CRLB). Simulations are used to evaluate the performance of the prediction scheme and to compare it to the derived error bound. <s> BIB018 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> The concept of physical layer security builds on the pivotal idea of turning the channel’s imperfections, such as noise and fading, into a source of security. This is established through appropriately designed coding techniques and signal processing strategies. In this vein, it has been shown that fading channels can enhance the transmission of confidential information and that a secure communication can be achieved even when the channel to the eavesdropper is better than the main channel. However, to fully benefit from what fading has to offer, the knowledge of the channel state information at the transmitter (CSIT) is of primordial importance. In practical wireless communication systems, CSIT is usually obtained, prior to data transmission, through CSI feedback sent by the receivers. The channel links over which this feedback information is sent can be either noisy, rate-limited, or delayed, leading to CSIT uncertainty. In this paper, we present a comprehensive review of recent and ongoing research works on physical layer security with CSIT uncertainty. We focus on both information theoretic and signal processing approaches to the topic when the uncertainty concerns the channel to the wiretapper or the channel to the legitimate receiver. Moreover, we present a classification of the research works based on the considered channel uncertainty. Mainly, we distinguish between the cases when the uncertainty comes from an estimation error of the CSIT, from a CSI feedback link with limited capacity, or from an outdated CSI. <s> BIB019 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Using outdated channel state information (CSI) in an opportunistic relaying system (ORS) leads to wrong selection of the best relay, which substantially deteriorates its performance. In this paper, therefore, we propose a robust cooperative scheme coined opportunistic space-time coding (OSTC) to deal with the outdated CSI. A predefined number (i.e., $N$ ) of relays, instead of a single relay in ORS, are opportunistically selected from $K$ cooperating relays. At the selected relays, $N$ -dimensional orthogonal space-time coding is employed to encode the regenerated signals in a distributed manner. Then, $N$ branches coded signals are simultaneously transmitted from the relays to the destination, followed by a simple maximum-likelihood decoding at the receiver. To evaluate its performance, the closed-form expressions of outage probability and ergodic capacity are derived, together with an asymptotic analysis that clarifies the achievable diversity. Analytical and numerical results reveal that a full diversity of $K$ is reaped by the proposed scheme when the knowledge of CSI is perfect. In the presence of outdated CSI, where the diversity of ORS degrades to one, the diversity of $N$ can be kept. Moreover, OSTC’s capacity is remarkably higher than that of the existing schemes based on orthogonal transmission. From the perspective of multiplexing-diversity tradeoff, the proposed scheme is the best cooperative solution until now. <s> BIB020 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away. <s> BIB021 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> We consider an ultra-dense network where each mobile is attached to the access point, which provides the best reference signal received power considering both path-loss and small-scale fading. In order to evaluate the effect of outdated channel state information (CSI) brought by feedback delay, we first derive the estimated and accurate handover probability according to outdated and perfect CSI, respectively. Furthermore, two metrics are proposed to measure the handover failure, i.e., false handover probability and miss handover probability . Simulations show that in sparse or ultra-dense networks, the imperfection of CSI is acceptable for handover decisions; however, in between, i.e., dense networks (medium-size networks), handover decisions are very sensitive to the user velocity and CSI imperfection. <s> BIB022 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Summary ::: Multicarrier code division multiple access (MC–CDMA) is a promising wireless communication technology with high spectral efficiency and system performance. However, all multiple access techniques including MC–CDMA were most likely to have multiple access interference (MAI). So this paper mainly aims at designing a suitable receiver for MC–CDMA system to mitigate such MAI. The classical receivers like maximal ratio combining, minimum mean square error, and iterative block–decision feedback equalization fail to cancel MAI when the MC–CDMA is subjected to severe nonlinear distortions, which may occur due to saturated power amplifiers or arbitrary channel conditions. Being highly nonlinear structures, the neural network receivers such as multilayer perceptron and recurrent neural network could be better alternative for such a case. The feasibility, efficiency, and effectiveness of the proposed neural network receiver are studied thoroughly for MC–CDMA system under different nonlinear conditions. <s> BIB023 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective. <s> BIB024 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> A unified error analysis of multi-antenna systems with orthogonal space-time block coding (OSTBC) using transmit antenna selection (TAS) over Nakagami- $m$ channels in the presence of feedback delay and channel estimation error is presented, where the number of transmit antennas is arbitrarily selected. For imperfect channel state information (CSI), the Nakagami- $m$ channel is modeled as the sum of Nakagami- $\hat{m}$ channel and independent Gaussian channel error. By this model, accurate and closed-form approximate bit error rate (BER) expressions of TAS-OSTBC for M-ary quadrature amplitude modulation and M-ary phase-shift keying are derived by using the moment generating function of received SNR. With the approximate BER formula, the number of antennas to be selected for transmission can be adapted by minimizing the BER of the system with imperfect CSI. The diversity and coding gains of TAS-OSTBC with $K$ receive antennas and $M$ antennas selected from $N$ transmit antennas for transmission are analyzed at high SNR. For noninteger $mK$ , the upper and lower bounds of the diversity gain for different CSI and estimation error scenarios are derived. But for integer $mK$ , the results indicate that the upper and lower bounds are equal achieving the full diversity order of $mKN$ for perfect CSI, zero diversity order and partial diversity order of $mMK$ for estimation error variance being fixed and being a decreasing function of average SNR, respectively. The coding gain is affected by $M$ , $N$ , $K$ , Nakagami parameter $m$ , code rate, modulation, and channel correlation coefficients. Simulation results verify the validity of the theoretical analysis and effectiveness of the adaptive antenna selection scheme. <s> BIB025 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Outdated channel state information (CSI) has a severe impact on a wide variety of wireless techniques. Making use of artificial intelligence, a multi-antenna channel predictor is proposed in this paper. It can accurately forecast the future CSIs up to tens of symbols ahead in a fast fading channel. By tuning the number of neurons in the input and output layers of neural network (NN) in terms of the number of antennas, it congenitally suits a multi-antenna system. Relying on a NN with real-valued weights, this predictor is simpler but substantially outperforms existing complex-valued NN predictors. <s> BIB026 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Channel state information (CSI) plays a vital role in fading-adaptive wireless systems, whereas acquired CSI is generally inaccurate due to feedback delay. Channel prediction that is able to forecast upcoming CSI provides a promising approach to tackle this problem. Exploiting the capability of time-series prediction in neural network, a fading channel predictor is proposed in this paper. Since the number of neurons in the input and output layers is tunable to adapt the number of antennas, the predictor well suits multi- antenna systems. Numerical results in antenna selection system reveal that a substantial performance gain can be achieved by applying channel prediction. <s> BIB027 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> This paper presents a multi-time channel prediction system based on backpropagation (BP) neural network with multi-hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances. <s> BIB028 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Accurate channel state information (CSI) is a prerequisite to reap the benefits of fading-adaptive wireless communications. In practice, however, the available CSI is generally outdated due to processing and feedback delays, which deteriorate system's performance severely. Channel prediction that is able to forecast future CSI provides a promising solution. In addition to statistical methods, namely modelling a time-varying channel as an autoregressive process and using a Kalman filter to predict, artificial intelligence techniques with the capability of time-series prediction are also being discussed recently. This paper compares performance and complexity of these two kinds of predictors. The numerical results on prediction accuracy measured by mean squared error in both noiseless and noisy Rayleigh fading channels, together with their achieved performance in a transmit antenna selection system, are comparatively illustrated. <s> BIB029 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on optimizing the network performance in cognitive radio networks. In this paper, an EE+SE tradeoff based target is considered for the primary users (PUs) and the secondary users (SUs). First of all, considering the orthogonal frequency division multiple access-based resource allocation (RA) for the underlying SUs, we formulate an objective function through minimizing a weighted sum of the secondary interference power, where the network performance of both PUs and SUs are guaranteed by the constraints on quality of service, power consumption and data rate. However, it is a NP-hard problem. In order to solve it, we propose a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning. Specifically, a feed-forward neural network is devised and an analogous back propagation algorithm is developed to learn the optimal parameters of the D3D-MPA. To improve the computational efficiency of the allocation and the learning, a suboptimal RA scheme is deduced based on a damped two dimensional MPA. Finally, simulation results are provided to confirm the effectiveness of our proposed scheme. <s> BIB030 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio. <s> BIB031 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input–multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the “APoZ”-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm. <s> BIB032 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> Outdated channel state information (CSI) severely degrades the performance of adaptive transmission systems that adapt their transmissions to channel fading. In contrast with mitigation methods that sacrifice scarce wireless resources to compensate for such a performance loss, channel prediction provides an efficient solution. A few predictors for frequency-flat channels were by far proposed, whereas those suited to frequency-selective channels are seldom explored. In this paper, therefore, we propose to apply a recurrent neural network to build a frequency-domain channel predictor for wideband communications. As an application example, integrating a predictor into a multi-input multi-output orthogonal frequency- division multiplexing system to improve the correctness of antenna selection is provided. Performance assessment is carried out in multi-path fading channels defined by 3GPP Extended Vehicular A and Extended Typical Urban models. Results reveal that this predictor is effective to combat the outdated CSI with reasonable computational complexity. It outperforms the Kalman filter-based predictor notably and has intrinsic flexibility to enable multi-step prediction. <s> BIB033 </s> Neural Network-Based Fading Channel Prediction: A Comprehensive Overview <s> I. INTRODUCTION <s> 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. <s> BIB034
By adapting radio transmission parameters, e.g., the constellation size, coding rate, transmit power, precoding codeword, time and frequency resource block, transmit antennas, and relays, to instantaneous channel conditions, adaptive wireless systems can potentially aid the achievement of great performance. To fully realize this potential, accurate channel state information (CSI) is required at the transmitter. In a frequency-division duplex system, the CSI is estimated at the receiver and then fed back to the transmitter, where the obtained CSI might be already outdated before its actual usage owing mainly to the feedback delay. Although a time-division duplex system can take advantage of channel reciprocity to avoid feedback, the processing delay still leads to inaccurate CSI, especially in high mobility scenarios. It has been extensively proved that the outdated CSI severely deteriorates the performance of a wide variety of The associate editor coordinating the review of this article and approving it for publication was Guan Gui. adaptive transmission techniques, including but not limited to precoding BIB007 and multi-user scheduling BIB013 in multiple-input multiple-output (MIMO) systems, massive MIMO BIB011 , beam-forming BIB014 , interference alignment BIB017 , closed-loop transmit diversity BIB002 , transmit antenna selection BIB025 , opportunistic relaying BIB009 , orthogonal frequency-division multiplexing (OFDM) BIB015 , coordinated multi-point transmission BIB012 , mobility management BIB022 , and physical layer security BIB019 . In the era of the fifth generation (5G) system, this problem will become more serious. On the one hand, new applications and services such as Internet of Things, Tactile Internet, virtual and augmented reality, networked drones, and autonomous driving impose a great demand for extremely high-speed, ultra reliable, ubiquitous, and secure wireless connectivity, where adaptive transmission techniques are envisioned to play more critical roles, further emphasizing the importance of accurate CSI. On the other hand, the fluctuation of a fading channel will speed up if the velocity of moving objects increases or the wavelength of radio signals decreases according to the Doppler effect of electromagnetic radiations BIB003 . Some of the 5G deployment scenarios, e.g., millimeter waves (having shorter wavelength), unmanned aerial vehicles and high-speed trains (with higher moving speed), suffer from faster fading channels that fluctuate more rapidly, leading to worse availability of accurate CSI, if not impossible. To cope with the outdated CSI, a large number of mitigation algorithms and protocols have been proposed in the literature. These can be mainly categorized into two classes: passive methods BIB020 that compensate for the performance loss passively with a cost of scarce wireless resources (frequency, time, power, etc.) and suboptimal methods aiming to achieve merely a portion of the full performance potential under the assumption of imperfect CSI, e.g., the techniques based on limited feedback BIB008 . In contrast, an alternative technique referred to as channel prediction BIB006 provides an efficient approach to improve the quality of CSI directly without spending extra wireless resources, and therefore attracted much attention from researchers. Through statistical modeling of wireless channels, two classical model-based prediction, namely parametric model BIB018 and autoregressive (AR) model BIB004 , have been developed. The former assumes that a fading channel is a superposition of a finite number of complex sinusoids, and its parameters, i.e., amplitude, angles of arrival and departure, Doppler shift, and the number of scattering sources, vary much slowly relative to channels' fluctuation rate and can be estimated accurately. However, the estimation process is tedious, while estimated parameters will quickly expire once the channel changes and therefore need to be re-estimated iteratively, leading to high computational complexity. The AR model approximates a fading channel as an AR process and extrapolates the future CSI using a weighted linear combination of past and current CSI. The AR model is computationally simpler, but it is vulnerable to impairments such as additive noise BIB029 , making it unattractive in practice. In March 2016 when AlphaGo, a computer program developed by Google DeepMind BIB021 , achieved an overwhelming victory versus a human champion in the game of Go, the passion for exploring Artificial Intelligence (AI) technology was sparked almost in all scientific and engineering branches. Actually, the wireless research community started to apply AI to solve communication problems long ago. Especially in the recent period, with the development of deep neural networks, the application of AI in wireless communications was booming, such as channel estimation  , resource allocation BIB030 , modulation recognition BIB031 , multi-user detection BIB010 , multiple access BIB023 , beam-forming BIB032 , and autonomous network management BIB024 . There are a number of different neural network structures, among which recurrent neural network (RNN) has a strong capability on time-series prediction BIB001 . Reference BIB005 first proposed an application of a RNN to build a predictor for narrow-band single-antenna channels and was further extended to MIMO channels in BIB016 . The authors of BIB026 proposed to employ a real-valued RNN to implement a multi-step predictor and further verified its effectiveness in a MIMO system BIB027 . The feasibility of applying a deep neural network was also studied in BIB028 . Recently, a frequency-domain RNN predictor reported in BIB033 further extended the application range from frequency-flat to frequency-selective MIMO channels. Neural networks, as a data-driven approach, can totally avoid the tedious estimation process of propagation parameters in model-based methods, and can enable great flexibility of adapting to different prediction scenarios. Beyond the aforementioned works, a comprehensive overview that can facilitate readers to quickly grasp the main ideas and catch up with the state of the art of this promising area is of great worth but unfortunately still missing. The aim of this article is to fill this gap in time. In contrast to BIB034 that proposed a hybrid of convolutional neural network and long short-term memory to get the CSI of downlink channels according to that of uplink channels, this article focuses on predicting future CSI from its past values at the same frequency/sub-carrier. Starting from a brief review on AR and parametric models, the basic structure of a recurrent neural network, its training method based on the back-propagation algorithm, and several variants of RNN predictors applied for different scenarios ranging from frequency-flat singleantenna channels to frequency-selective MIMO channels, are presented. To exemplify the applicability, prediction-aided transmit antenna selection (TAS) in a MIMO-OFDM system is depicted. Performance assessment in fading channels specified by 3GPP models, taking into account the factors such as the Doppler shift, spatial correlation, additive noise, and interpolation error, is carried out, followed by comparisons on computational complexity. The rest of this paper is organized as follows: Section II reviews two model-based prediction schemes. The structure of a recurrent neural network and the back-propagation training algorithm are introduced in Section III. Section IV presents the variants of RNN predictors, followed by an illustration of a prediction-aided MIMO-OFDM system in Section V. Section VI compares the complexity and performance for different predictors by numerical results. Finally, concluding remarks are made in Section VII. Notations: Throughout this article, vectors are denoted by bold lower case letters and matrices are bold upper case letters. For the operation of matrices, (·) T and (·) H notate the transpose and Hermitian transpose, respectively, · stands for the Euclidean norm, and marks the Hadamard product. H andH represent time-domain and frequency-domain channel responses, respectively, whileĤ is a predicted value. The bracket (·) indicates continuous-time signals and the square bracket [·] associates with discrete-time sequences.