論文 - 大槻　知明

2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Bouazizi M., Ye C., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 13 ） 10872 - 10890 2022年07月

　概要を見る

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

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A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Zhao R., Gui G., Xue Z., Yin J., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 12 ） 9960 - 9972 2022年06月

　概要を見る

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

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Fully Convolutional Neural Network-Based CSI Limited Feedback for FDD Massive MIMO Systems

Fan G., Sun J., Gui G., Gacanin H., Adebisi B., Ohtsuki T.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 2 ） 672 - 682 2022年06月

　概要を見る

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state information (CSI) directly from its reciprocal uplink CSI. Hence, the estimated downlink CSI needs to be continuously fed back to the base station (BS) from the user equipment (UE), consuming valuable bandwidth resources. This is exacerbated, in massive MIMO, with the increase of the antennas at the BS. This paper propose a fully convolutional neural network (FullyConv) to compress and decompress the downlink CSI. FullyConv will improve the reconstruction accuracy of downlink CSI and reduce the training parameters and computational resources. Besides, we add a quantization module in the encoder and a dequantization module in the decoder of the FullyConv to simulate a real feedback scenario. Experimental results demonstrate that the proposed FullyConv is better than the baseline on reconstruction performance and reduction of the storage and computational overhead. Furthermore, the FullyConv added quantization and dequantization modules is robust to quantization error in real feedback scenarios.

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An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

Arumugasamy M.K., Bouazizi M., Ohtsuki T.

Sensors （Sensors）  22 （ 10 ）  2022年05月

ISSN  14248220

　概要を見る

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

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An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

Zhao H., Tang J., Adebisi B., Ohtsuki T., Gui G., Zhu H.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 3 ） 2939 - 2948 2022年03月

ISSN  00189545

　概要を見る

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement.

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Lightweight Automatic Modulation Classification Based on Decentralized Learning

Fu X., Gui G., Wang Y., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 1 ） 57 - 70 2022年03月

　概要を見る

Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model consolidation and lightweight design. Specifically, the model consolidation is realized by a central device (CD) for edge device (ED) model averaging (MA) and multiple EDs for ED model training. The lightweight is designed by separable convolutional neural network (S-CNN), in which the separable convolutional layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolutional neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about N times of it, where N is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset D1 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} and about 5 times that of S-CNN-based CentAMC in dataset D2 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM}), while the communication overhead is reduced more than 35%.

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Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

Bouazizi M., Ye C., Ohtsuki T.

Information (Switzerland) （Information (Switzerland)）  13 （ 3 ）  2022年03月

　概要を見る

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

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Blockchain-Based Task Offloading for Edge Computing on Low-Quality Data via Distributed Learning in the Internet of Energy

Liu Y., Guan X., Peng Y., Chen H., Ohtsuki T., Han Z.

IEEE Journal on Selected Areas in Communications （IEEE Journal on Selected Areas in Communications）  40 （ 2 ） 657 - 676 2022年02月

ISSN  07338716

　概要を見る

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

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Dementia Detection Using Language Models and Transfer Learning

Bouazizi M., Zheng C., Ohtsuki T.

ACM International Conference Proceeding Series （ACM International Conference Proceeding Series）     152 - 157 2022年01月

　概要を見る

Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.

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Dynamic Antenna Control for HAPS Using Geometry-based Method in Multi-Cell Configuration

Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

In this research, we propose a novel antenna control method for reducing the number of low throughput User Equipments (UEs) caused by the movement and rotation of High-altitude platform station (HAPS). We assume that each HAPS has three antenna arrays for serving one region that consists of three cells and that we know the UE locations in each cell. In the proposed method we first redesign the cell configuration for each HAPS and then divide all UEs into three cells based on the UE locations. Based on the radius and center location information of three new cells, we can mathematically calculate the antenna parameters by the desired coverage model. Thus, each antenna array will be controlled to serve a new cell, respectively. We evaluate the proposed method under 5 different UE distribution scenarios. The simulation results show that, in 5 different UE distribution scenarios, the proposed method can reduce the number of UEs with low throughput. Compared with the conventional method, the proposed method can achieve good throughput performance in all the scenarios.

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Dynamic Antenna Control for HAPS Using Fuzzy Q-Learning in Multi-Cell Configuration

Wada K., Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   1 - 6 2022年

ISSN  15503607

　概要を見る

In the 5th generation mobile communications (5G) and 5G and beyond (B5G), a high altitude platform station (HAPS) is expected to serve as a flying base station (BS) to provide communications over wide areas. In the HAPS system, a multi-cell configuration with multiple beams is considered to increase system throughput. When the HAPS is subjected to wind pressure, the cell range moves accordingly, causing degradation of received signal power and handover to the user equipment (UE). To suppress such degradation and handover, beam control of HAPS is necessary. However, it is not easy to control the beam because multiple antenna parameters affect each other and determine the cell range. In this paper, we propose a beam control method for HAPS using fuzzy Q-learning in multi-cell configuration. In this type of learning, the variable states are controlled by the use of fuzzy sets, which allows multiple searches to be performed in one setup, thus reducing the cost of search, compared with conventional Q-learning. In the proposed beam control method, antenna parameters are controlled by fuzzy Q-learning so that the number of users having a received signal power larger than a predetermined threshold becomes larger in each cell. We evaluate the proposed method by computer simulation and show that the proposed method can improve the number of users having a received signal power larger than a predefined threshold and thus reduce the number of users with low throughput compared to before learning.

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Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification

Zhang X., Wang Y., Zhang Y., Lin Y., Gui G., Tomoaki O., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

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Cross-Person Activity Recognition Method Using Snapshot Ensemble Learning

Xu S., He Z., Shi W., Wang Y., Ohtsuki T., Guiy G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Human activity recognition (HAR) is one of the most promising technologies in the smart home, especially radio frequency (RF-based) method, which has the advantages of low cost, few privacy concerns and wide coverage. In recent years, deep learning (DL) has been introduced into HAR and these DL-based HAR methods usually have outstanding performance. However, as the recognition scenarios and target change, the model performance drops sharply. To solve this problem, we propose a generalized method for cross-person activity recognition (CPAR), which is called snapshot ensemble learning based an attention with bidirectional long short-term memory (SE-ABLSTM). Specifically, by defining the cosine annealing learning rate, the models with diversity are saved and integrated in the same training process. In addition, we provide a dataset for CPAR and simulation results show that our method improves generalization performance by 5% compared to the original method. The source code and dataset for all the experiments can be available at https://github.com/NJUPT-Sivan/Cross-person-HAR.

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Co-Evolutionary Dynamic Cell optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

High-Altitude Platform Stations (HAPSs) are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Single-cell frequency reuse using multiple cells can increase capacity to cover a wide area. Thus far, we have proposed a cell configuration optimization method based on a genetic algorithm (GA). We clarified the optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution scenario. Whereas user distributions differ depending on location. Thus, cell configuration optimization is required for a non-uniform user distribution. Each cell needs different antenna parameters for non-uniform user distributions, resulting in an increase in the number of parameters compared with a uniform user distribution, making the optimization difficult even by GA in some cases. To address this problem, we propose a co-evolutionary dynamic cell optimization algorithm. Co-evolution is one of the divide-and-conquer methods. The proposed method divides multiple cells into several groups to decrease the number of parameters to optimize at a time, and each group is optimized in order. The simulation results show that the cell configuration with the proposed method can increase the sum of the square root of throughput for non-uniform user distributions compared to that for uniform user distributions. Furthermore, the proposed method with three sub-areas can improve the sum of the square root of throughput while reducing the number of combinations performed compared to the method without subarea division for a nine-cell scenario.

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Autoencoder-based Pilot Pattern Design for CDL Channels

Yamada Y., Ohtsuki T.

APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era （APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era）     76 - 80 2022年

　概要を見る

In the pilot-based channel estimations, a large number of pilot signals enable an improvement in the channel estimation accuracy but force a decrease in the data transmission efficiency. Therefore, the pilot pattern design schemes on the resource grid have been researched to achieve high channel estimation accuracy with a small number of pilot signals. In the conventional scheme for pilot pattern design, the autoencoder which enables discrete feature selection is utilized to design pilot patterns for Vehicular-A channels. However, the pilot pattern for other channels is not reported, and it is not clear whether this scheme has good performance compared to uniform pilot patterns. In this paper, we derive the pilot patterns for Clustered Delay Line (CDL) channels specified in the 5th Generation Mobile Communication System (5G) standard using the autoencoder-based pilot pattern design scheme. We also derive the pilot patterns for CDL-A and CDL-D, which model respectively NLOS (Non-Line-of-Sight) and LOS (Line-of-Sight) environments, with different three speeds of User Equipment (UE). Through computer simulation, we show that the autoencoder-based pilot patterns improve the channel estimation accuracy compared to the uniform pilot pattern.

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Attention-CNN-Aided Specific Emitter Identification Method with Limited Radio Frequency Dataset

Wang S., Zhang Y., Sun J., Ohtsuki T., Gui G.

International Conference on Communication Technology Proceedings, ICCT （International Conference on Communication Technology Proceedings, ICCT）  2022-November-November   1536 - 1540 2022年

　概要を見る

Specific emitter identification (SEI) plays a more and more important role to recognize unknown radio frequency (RF) devices in the field of physical layer security. Existing deep learning (DL)-based SEI methods have been proposed for solving problems that enough RF dataset can be obtained at the model training stage. In many real application scenarios, however, only the limited dataset can be collected which causes the conventional DL methods unstable. To solve this problem, in this paper, we propose a robust SEI method using a self-attention mechanism and convolutional neural network (attention-CNN). To be specific, we collect a real RF dataset from the automatic dependent surveillance-broadcast (ADS-B) signal. Here, the ADS-B dataset is limited in some special cases and data augmentation is used to improve the features of the dataset. Simulation results show that the proposed method achieves better identification performance than the benchmark.

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Arterial Blood Pressure Estimation Method from Electrocardiogram Signals using U-Net

Yoshizawa R., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2689 - 2692 2022年

ISSN  1557170X

　概要を見る

Previous works proposed deep learning models to estimate blood pressure from electrocardiogram (ECG) signals. However, they can only estimate max, min, and mean arterial blood pressures and cannot estimate arterial blood pressure (ABP). This paper presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with signals of about 185 hours, we observed that the proposed method estimated ABP with high accuracy. Furthermore, the accuracies of the calculated max\min and mean ABPs were comparable to those in the previous works, even though our method also estimated ABP. In the end, we discussed the subject-overfitting problem and future work toward practical use of daily blood pressure monitoring.

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An Evaluation on Information Composition in Dementia Detection Based on Speech

Zheng C., Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  2022年

　概要を見る

In recent years, scientists are paying much attention to the research on automatic dementia detection that could be applied to the speech samples of dementia patients. In a related context, recent research has seen the fast development of Deep Learning (DL) and Natural Language Processing (NLP). The techniques developed for text classification or sentiment analysis have been applied to the field of early dementia detection by many researchers. However, text classification and sentiment analysis are different tasks from dementia detection, which makes us believe that for dementia detection, some adjustments would help improve the performance of the machine learning models. In this work, we implemented experiments with various language models including traditional n-gram language models, Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) models, and attention-based models to evaluate the speech data of dementia patients. Unlike traditional works where the text is stripped from stop words, we propose the idea of exploiting the stop words themselves, since they offer non-context information which helps to identify dementia. As a result, 3 different language models are prepared in this work: a model processing only context words, a model processing stop words and Part-of-Speech (PoS) tag sequences, and a model processing both of them. By performing the aforementioned experiments, we show that both grammar and vocabulary contribute equally to classification: The 3 models achieve an accuracy equal to 70.00%, 76.16%, and 81.54%, respectively.

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An Effective Radio Frequency Signal Classification Method Based on Multi-Task Learning Mechanism

Liu H., Hao C., Peng Y., Wang Y., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.

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Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

Gao J., Bouazizi M., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Accessing the accurate downlink channel state information (CSI) is essential to take full advantage of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its weak channel reciprocity. Meanwhile, great computational burdens will happen, which is accompanied by continuous CSI feedback. The existing compressive sensing (CS)-based and deep learning (DL)-based methods try to solve such problems, but do not achieve desired effect to get ideal CSI feedback or decrease the overhead. An adaptive deep neural network (DNN)-based CSI feedback method is proposed in this paper to address this. A classification block of the compression ratio is adopted and modified to apply to a more complex channel model named Clustered-Delay-Line (CDL), which helps decrease the computational overhead of the network. Besides, the reconstruction accuracy of the CSI feedback is further improved by proposing a new structure of the encoder. Quantization and dequantization modules are also applied to make the whole network more robust and effectively minimize the quantization distortion in the real communication scenario, respectively. The simulation results show that the proposed method performs better than the conventional ones on the CSI reconstruction accuracy in terms of normalized mean square error (NMSE), even though the quantization module is added.

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