arXiv subjects

Feifei Gao

Publications and source records attributed to Feifei Gao.

At least 91 records · Page 5Linked to original sources

arXiv2022

Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems. By employing ResNet to extract features from the channel matrices, two neural networks, i.e., the antenna selection network (ASNet) and the hybrid beamforming network (BFNet), are respectively proposed for dynamic antenna selection and hybrid beamformer design. Furthermore, a deep probabilistic subsampling trick and a specially designed quantization function are respectively developed for ASNet and BFNet to preserve the differentiability while embedding discrete constraints into the network structures. With the aid of a flexibly designed loss function, ASNet and BFNet are jointly trained in a phased unsupervised way, which avoids the prohibitive computational cost of acquiring training labels in supervised learning. Simulation results demonstrate the advantage of the proposed approach over conventional optimization-based algorithms in terms of both the achieved rate and the computational complexity.

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arXiv2021

Federated Dynamic Neural Network for Deep MIMO Detection

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detector and the OAMPNet detector as two independent network branches, the DDNet detector performs sample-wise dynamic routing to adaptively select a better one between the IDetNet and the OAMPNet detectors for every samples under different system conditions. To avoid the prohibitive transmission overhead of dataset collection in centralized learning (CL), we propose the federated averaging (FedAve)-DDNet detector, where all raw data are kept at local clients and only locally trained model parameters are transmitted to the central server for aggregation. To further reduce the transmission overhead, we develop the federated gradient sparsification (FedGS)-DDNet detector by randomly sampling gradients with elaborately calculated probability when uploading gradients to the central server. Based on simulation results, the proposed DDNet detector consistently outperforms other detectors under all system conditions thanks to the sample-wise dynamic routing. Moreover, the federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the transmission overhead by at least 25.7\% while maintaining satisfactory detection accuracy.

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arXiv2021

Resilient UAV Swarm Communications with Graph Convolutional Neural Network

In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external disruptions (UEDs). Firstly, to cope with the one-off UEDs, we propose a graph convolutional neural network (GCN) and find the recovery topology of the USNET in an on-line manner. Secondly, to cope with general UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions.

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arXiv2021

Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions

Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications. Since the number of coefficients of the cascaded channels in such systems is closely dependent on the product of the number of base station antennas and the number of RIS elements, the pilot overhead would be prohibitively high. In this letter, we propose a cascaded channel estimation framework for an RIS assisted mmWave multiple-input multiple-output system, where the wideband effect on transmission model is considered. Then, we transform the wideband channel estimation into a parameter recovery problem and use a few pilot symbols to detect the channel parameters by the Newtonized orthogonal matching pursuit algorithm. Moreover, the Cramer-Rao lower bound on the channel estimation is introduced. Numerical results show the effectiveness of the proposed channel estimation scheme.

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arXiv2021

Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

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arXiv2021

AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beamforming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.

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arXiv2021

Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.

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arXiv2021

Deep Learning Based RIS Channel Extrapolation with Element-grouping

Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. To reduce the pilot overhead, we adopt the element-grouping strategy, where each element in one group shares the same reflection coefficient and is assumed to have the same channel condition. We analyze the channel interference caused by the element-grouping strategy and further design two deep learning based networks. The first one aims to refine the partial channels by eliminating the interference, while the second one tries to extrapolate the full channels from the refined partial channels. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme provides significant gain compared to the conventional element-grouping method without interference elimination.

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arXiv2021

Understanding Deep MIMO Detection

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most DL-based detection algorithms are lack of theoretical interpretation on internal mechanisms and could not provide general guidance on network design. In this paper, we analyze the performance of DL-based MIMO detection to better understand its strengths and weaknesses. We investigate two different architectures: a data-driven DL detector with a neural network activated by rectifier linear unit (ReLU) function and a model-driven DL detector from unfolding a traditional iterative detection algorithm. We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes model expert knowledge to alleviate the impact of channels and establish a relatively reliable detection method with a small set of training data. Due to its model specific property, the performance of model-driven DL detector is largely determined by the underlying iterative detection algorithm, which is usually suboptimal compared to the MAP detector. Simulation results confirm our analytical results and demonstrate the effectiveness of DL-based MIMO detection for both linear and nonlinear signal systems.

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arXiv2021

Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. Firstly, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.

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arXiv2021

Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

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arXiv2021

Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions

With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO. In these systems, how to acquire accurate channel state information (CSI) is difficult and becomes a bottleneck of the communication links. In this article, we introduce the concept of channel extrapolation that relies on a small portion of channel parameters to infer the remaining channel parameters. Since the substance of channel extrapolation is a mapping from one parameter subspace to another, we can resort to deep learning (DL), a powerful learning architecture, to approximate such mapping function. Specifically, we first analyze the requirements, conditions and challenges for channel extrapolation. Then, we present three typical extrapolations over the antenna dimension, the frequency dimension, and the physical terminal, respectively. We also illustrate their respective principles, design challenges and DL strategies. It will be seen that channel extrapolation could greatly reduce the transmission overhead and subsequently enhance the performance gains compared with the traditional strategies. In the end, we provide several potential research directions on channel extrapolation for future intelligent communications systems.

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arXiv2021

Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

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arXiv2020

Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulations results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data in various wireless communication scenarios.

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arXiv2020

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, where each learning task aims to predict the downlink CSI from the uplink CSI for one single environment. Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments 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 in terms of both prediction accuracy and stability, which validates its effectiveness and superiority.

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arXiv2020

FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots

In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots. Specifically, we design a novel dual-input neural network, called FusionNet, to extract and exploit the features from sub-6GHz channel and a few mmWave pilots to accurately predict mmWave beam. To further improve the beamforming performance and avoid over-fitting, we develop two data pre-processing approaches utilizing channel sparsity and data augmentation. The simulation results demonstrate superior performance and robustness of the proposed strategy compared to the existing one that purely relies on the sub-6GHz information, especially in the low signal-to-noise ratio (SNR) regions.

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arXiv2020

Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication

To capture the communications gain of the massive radiating elements with low power cost, the conventional reconfigurable intelligent surface (RIS) usually works in passive mode. However, due to the cascaded channel structure and the lack of signal processing ability, it is difficult for RIS to obtain the individual channel state information and optimize the beamforming vector. In this paper, we add signal processing units for a few antennas at RIS to partially acquire the channels. To solve the crucial active antenna selection problem, we construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning (DL) based schemes, i.e., the channel extrapolation scheme and the beam searching scheme, to enable the RIS communication system. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels received by the active RIS antennas, while the latter adopts a fully-connected neural network to achieve the direct mapping between the partial channels and the optimal beamforming vector with maximal transmission rate. Simulation results are provided to demonstrate the effectiveness of the designed DL-based schemes.

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arXiv2020

Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO

In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode. To overcome the bottleneck of the limited number of radio links in hybrid beamforming, we utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs. The probabilistic sampling theory is utilized to approximate the discrete antenna selection as a continuous and differentiable function, which makes the back propagation of the deep learning feasible. Then, we design the proper off-line training strategy to optimize both the antenna selection pattern and the extrapolation NNs. Finally, numerical results are presented to verify the effectiveness of our proposed massive MIMO channel extrapolation algorithm.

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