Channel State Information Prediction Research Articles

Amplitude prediction from uplink to downlink CSI against receiver distortion in FDD systems

In frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems, the reciprocity mismatch caused by receiver distortion seriously degrades the amplitude prediction performance of channel state information (CSI). To tackle this issue, from the perspective of distortion suppression and reciprocity calibration, a lightweight neural network-based amplitude prediction method is proposed in this paper. Specifically, with the receiver distortion at the base station (BS), conventional methods are employed to extract the amplitude feature of uplink CSI. Then, learning along the direction of the uplink wireless propagation channel, a dedicated and lightweight distortion-learning network (Dist-LeaNet) is designed to restrain the receiver distortion and calibrate the amplitude reciprocity between the uplink and downlink CSI. Subsequently, by cascading, a single hidden layer-based amplitude-prediction network (Amp-PreNet) is developed to accomplish amplitude prediction of downlink CSI based on the strong amplitude reciprocity. Simulation results show that, considering the receiver distortion in FDD systems, the proposed scheme effectively improves the amplitude prediction accuracy of downlink CSI while reducing the transmission and processing delay.

A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network

Topology control plays a vital role in cooperative underwater sonar detection networks (USDNs) to ensure detection applications. By incorporating spatio-temporal uncertainties in acoustic channel and limited resources in underwater networks, a large number of node deployment schemes have been proposed to overcome these difficulties through topology control. However, the channel uncertainty has not been solved fundamentally. In order to effectively deal with the time-space-frequency uncertainty of the acoustic channel, this paper proposes a time-varying acoustic channel-aware (TVAC) topology control mechanism to deploy sonar nodes in USDNs. Initially, a time-varying acoustic channel state information (CSI) estimation algorithm and a prediction algorithm are proposed by applying three sub-modules: a feature stitching deep neural network-based temperature prediction model that we designed, a deep neural network-based sound velocity estimation model, and a BELLHOP-based propagation loss calculation model. After that, a TVAC topology control mechanism is adopted to adaptively deploy nodes based on the CSI estimation and prediction results. Using the improved weighted set covering algorithm, the current CSI is adopted to determine the working modes of sonar nodes, and the predicted CSI is adopted to obtain the cycle of topology update. What is more, we develop an offline–online CSI prediction model to improve the accuracy and stability of node working modes management. Simulation results show that compared with other topology control schemes, TVAC can obtain superior coverage performance, full connectivity, and prolong the network lifetime with guaranteed coverage and connectivity.

A CSI Prediction Scheme for Satellite-Terrestrial Networks

Recently, low earth orbit (LEO) satellites have been suggested as an promising solution for global coverage of 5G networks. However, long propagation delay makes obtained channel state information (CSI) (which is employed for efficient spectrum utilization) outdated before usage. Note that CSI is closely related to elevation angle and relative location. Although elevation angle and relative location could be estimated by terrestrial device position and LEO ephemeris, maintaining these two information requires high cost for most IoT devices. Due to regular and high-speed orbital movement of LEO, for an arbitrary terrestrial device, LEOs repeat the same behavioral pattern (e.g., satellite rise and satellite set) again and again. Satellite rise and set result in different temporal correlations in elevation angle and relative location. By the aid of the temporal correlations mentioned above, we proposed a CSI prediction scheme for satellite-terrestrial networks without terrestrial device position and LEO ephemeris. Simulation results show that our scheme has rather low prediction error under various LEO altitude/obit, terrestrial device location/mobility, ground environments, weather and elevation angle when handover.

PEACH: Proactive and Environment-Aware Channel State Information Prediction with Depth Images

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation, up to 6 dB, when compared to traditional approaches.

A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.

Deep-Learning-Based Spatial–Temporal Channel Prediction for Smart High-Speed Railway Communication Networks

Intelligent channel prediction plays a key role in artificial intelligence (AI)-optimized or AI-native communication networks for smart high-speed railways (HSRs). This paper investigates the spatial-temporal prediction of channel state information (CSI) and channel statistical characteristics (CSCs) based on deep-learning (DL) for the future smart HSR communication network. A propagation-graph simulation method is used to generate datasets of CSI and CSCs for massive multiple-input multiple-output (mMIMO) channels in a HSR cutting scenario, and realistic channel measurements are used to validate the datasets. Then, single-step ahead and multi-step ahead prediction problems are formulated with the consideration of both spatial and temporal information hidden in the datasets. By exploiting the temporal and spatial correlations of the HSR mMIMO channel, a novel spatial-temporal channel prediction model that combines the convolutional neural network (CNN) and convolutional long short-term memory (CLSTM) is proposed and called as Conv-CLSTM. Moreover, the hyper-parameters of the Conv-CLSTM model are determined by autocorrelation and similarity analysis and cross-validation. Finally, the performance of the Conv-CLSTM model is evaluated in terms of prediction accuracy and space and time computational complexity, and is compared with classical DL models. The evaluation results show that the proposed model has high prediction accuracy but acceptable computational complexity.

The AI-Based Channel Prediction Scheme for the 5G Wireless System in High-Speed V2I Scenarios

Now, the fifth-generation (5G) system plays a more and more important role in the high-speed vehicle-to-infrastructure (V2I) scenario. In order to realize the high-reliability and high-efficiency transmission, it is essential to obtain accurate channel state information (CSI) for the 5G system. However, due to the fast time-varying and nonstationary characteristics of the channel in high-speed V2I scenarios, channel estimation is a challenging issue. In this paper, an artificial intelligence- (AI-) based channel prediction scheme, called AI-ChannelNet, is proposed to improve the CSI prediction performance in high-speed V2I scenarios. Specifically, AI-ChannelNet is trained in real time based on the historical channel estimation on the reference signal (RS) to realize accurate channel prediction and then recovers the received signal according to the predicted channel information. The integration of the convolutional neural network (CNN) and long short-term memory (LSTM) is designed to extract temporal features of the channel. And an online RS-based training algorithm is proposed, enabling AI-ChannelNet to track the channel variation. Evaluated by experiments, the proposed scheme outperforms conventional methods a lot, and more improvement could be achieved at a higher speed. Besides, the proposed scheme performs well without modification of the 5G radio frame and loss of transmission efficiency.

Machine Learning Empowered Accurate CSI Prediction for Large-Scale 5G Networks

Wi-Fi networks rely on channel estimation to ensure their performance. The computational complexity and dependability of fifth generation telecommunication networks have significantly improved using supervised learning. In this paper, we develop a channel estimation model that uses a machine learning approach and the study uses multipath channel simulations for the estimation of channel state information (CSI) over arbitrary transceiver antennas. The simulation is conducted to test the efficacy of the model against various machine learning channel estimation models. The results of simulation show that the proposed model obtains increased channel estimation quality than other methods. Further, the bit error rate is recorded low among other methods using the machine learning model. Thus, it is seen that the proposed method achieves a reduced mismatch rate of 1.26 × 10 − 1.5 than other methodson Doppler frequency during channel estimation, where the mismatch rate is higher in existing methods.

Channel State Information Prediction for Adaptive Underwater Acoustic Downlink OFDMA System: Deep Neural Networks Based Approach

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs.

An Intelligent Radio Buffer-Aided Relaying Scheme With Adaptive Link Selection

The intelligent radio is a novel emerging paradigm catching on in many practical applications, in order to provide context awareness techniques, able to learn by the environment conditions applying machine learning frameworks. The channel state information (CSI) prediction is one of the main challenges that an intelligent radio has to face with. This paper deals with the CSI prediction in the case of a buffer aided relaying communication scheme with adaptive link selection previously proposed in the literature where a direct source-destination link is not available and with both links affected by an independent fading. In particular, the CSI prediction has been reduced here to a time series prediction problem, performed by involving the usage of the recurrent neural networks. Finally, the validation of the effectiveness of the proposed intelligent radio buffer-aided relaying scheme with adaptive link selection approach has been pursued by presenting performance comparisons with the previously proposed scheme under the assumption of an ideal and actual CSI estimations, respectively.

CV-3DCNN: Complex-Valued Deep Learning for CSI Prediction in FDD Massive MIMO Systems

In beyond fifth-generation (B5G) era, massive multiple-input multiple-output (M-MIMO) will be a key technology to offer higher network capacities. Due to the different frequency of uplink and downlink channels in FDD systems, the channel state information (CSI) feedback from user terminal to the base station is necessary, but this reduces the spectrum efficiency. This letter proposes a deep learning based solution to predict the downlink CSI in frequency division duplex (FDD) systems, which is termed as complex-valued three dimensional convolutional neural network (CV-3DCNN). The proposed network uses a complex-valued neural network in complex domain to deal with the complex CSI matrices, and adopts three-dimensional convolution operations for feature extraction. The proposed scheme aims to make full use of the hidden information of the complex matrices of the CSI data, and to minimize information loss caused by data processing. The experimental results demonstrate that the proposed architecture can improve accuracy of the downlink CSI prediction by approximately 6 dB.

Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.

Machine Learning for RF Slicing Using CSI Prediction in Software Defined Large-Scale MIMO Wireless Networks

In this paper, we investigate the machine learning approaches (sparse Bayesian linear regression (SBLR) and support vector machine (SVM)) for channel state information (CSI) prediction and dynamic radio frequency (RF) slicing for software defined virtual wireless networks in large-scale multi-input multi-output (MIMO) wireless networks. Specifically, a subset of the antennas of virtual wireless networks transmits pilot symbols for estimating the CSI and use the estimated CSI dataset to train and estimate the remaining channels and future CSI for virtual networks using machine learning algorithms. This helps not only to predict the CSI with least overhead and fulfills the service demands of users but also to reduce the power consumption and computation overhead in the network. Predicted CSI is leveraged for RF slicing for virtual wireless networks. Simulation results show that the proposed SBLR for predicting CSI results in lower BER and higher data rate for the wireless users. Furthermore, SBLR outperforms the other approaches when we have sparse CSI information and we need to generalize the prediction process.

Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach

Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation. However, they usually require high computational complexity, which makes them unsuitable for 5G wireless communications due to employing many new techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)). In this paper, we propose an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems. Specifically, we first identify several important features affecting the CSI of a radio link and a data sample consists of the information of the features and the CSI. We then design a learning framework that is an integration of a CNN (convolutional neural network) and a long short term with memory (LSTM) network. We also further develop an offline-online two-step training mechanism, enabling the prediction results to be more stable when applying it to practical 5G wireless communication systems. To validate OCEAN's efficacy, we consider four typical case studies, and conduct extensive experiments in the four scenarios, i.e., two outdoor and two indoor scenarios. The experiment results show that OCEAN not only obtains the predicted CSI values very quickly but also achieves highly accurate CSI prediction with up to 2.650-3.457 percent average difference ratio (ADR) between the predicted and measured CSI.

Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems

Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm.

Static CSI extraction and application in the tomographic channel model

In this paper, the statistical properties of parameters of each path in wireless channel models are analyzed to prove that there is the static part in channel state information (CSI) which can be extracted from huge amounts of CSI data. Based on the analysis, the concept of the Tomographic Channel Model (TCM) is presented. With cluster algorithms, the static CSI database can be built in an off-line manner. The static CSI database can provide prior information to help pilot design to reduce overhead and improve accuracy in channel estimation. A new CSI prediction method and a new channel estimation method between different frequency bands are introduced based on the static CSI database. Using measurement data, the performance of the new channel prediction method is compared with that of the Auto Regression (AR) predictor. The results indicate that the prediction range of the new method is better than that of the AR method and the new method can predict with fewer pilot symbols. Using measurement data, the new channel estimation method between different frequency bands can estimate the CSI of one frequency band based on known CSI of another frequency band without any feedback.

An Adaptive and Parameter-Free Recurrent Neural Structure for Wireless Channel Prediction

Predicting channel state information (CSI) is a fundamental element in wireless communications systems. An accurate CSI estimation and prediction is critical to the system performance. This paper introduces a recurrent neural network (RNN) based approach for real-time prediction in real-world non-stationary channels. It uses the recent history data for online training, followed by prediction employing the trained model, in order to adapt to the changing channel and obtain a more accurate CSI prediction compared to conventional methods. Furthermore, the proposed method needs no additional knowledge, such as the internal properties of the channel itself, or the external features that affect the channel propagation, greatly facilitating its use in practical systems. Simulation results show that the proposed adaptive and parameter free recurrent neural structure (APF-RNS) outperforms the existing methods under a dynamically changing non-stationary environment. Therefore, the proposed online training based RNN approach is a promising method for channel prediction in wireless communications.

Channel-Aware Spectrum Sensing and Access for Mobile Cognitive Radio Ad Hoc Networks

In hardware-constrained cognitive radio (CR) ad hoc networks, secondary users (SUs) with limited sensing capabilities strive to discover and share available spectrum resources without impairing primary-user (PU) transmission. Sensing strategy design objectives include high CR network throughput, resolved SU competition, distributed implementation, and reliable performance under node mobility. However, these objectives have not been realized by previously investigated sensing strategies. A novel sensing strategy is analyzed, where the reward is adapted to the SU-link channel state information (CSI) prior to sensing, thus randomizing sensing decisions and boosting the network throughput. Moreover, CSI-aided sensing is combined with a novel first-come-first-served (FCFS) medium access control (MAC) scheme that resolves SU competition prior to sensing. Finally, a pilot-based CSI prediction method is developed to enable the proposed CSI-aided sensing strategies for mobile scenarios. Analytical and numerical results demonstrate that the proposed sensing and access methods significantly outperform nonadaptive sensing strategies for practical mobile CR scenarios with CSI mismatch and pilot overhead.

Comparisons of Receive Array Interference Reduction Techniques Under Erroneous Generalized Transmit Beamforming

This paper studies generalized single-stream transmit beamforming employing receive array co-channel interference reduction algorithms under slow and flat fading multiuser wireless systems. The impact of imperfect prediction of channel state information for the desired user spatially uncorrelated transmit channels on the effectiveness of transmit beamforming for different interference reduction techniques is investigated. The case of over-loaded receive array with closely-spaced elements is considered, wherein it can be configured to null specified interfering sources. Both dominant interference reduction and adaptive interference reduction techniques for statistically ordered and unordered interferers powers, respectively, are thoroughly studied. The effect of outdated statistical ordering of the interferers powers on the efficiency of dominant interference reduction is studied and then compared against the adaptive interference reduction. For the system models described above, new analytical formulations for the statistics of combined signal-to-interference-plus-noise ratio are presented, from which results for conventional maximum ratio transmission and single-antenna best transmit selection can be directly deduced as limiting cases. These results are then utilized to obtain quantitative measures for various performance metrics. They are also used to compare the achieved performance of various configuration models under consideration.

Low complexity minimum mean square error channel estimation for adaptive coding and modulation systems

Performance of the Adaptive Coding and Modulation (ACM) strongly depends on the retrieved Channel State Information (CSI), which can be obtained using the channel estimation techniques relying on pilot symbol transmission. Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little. In this paper, we investigate the performance of CSI prediction using the Minimum Mean Square Error (MMSE) channel estimator for an ACM system. To solve the two problems of MMSE: high computational operations and oversimplified assumption, we then propose the Low-Complexity schemes (LC-MMSE and Recursion LC-MMSE (R-LC-MMSE)). Computational complexity and Mean Square Error (MSE) are presented to evaluate the efficiency of the proposed algorithm. Both analysis and numerical results show that LC-MMSE performs close to the well-known MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.