Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

Deep unfolding based channel estimation for wideband terahertz near-field massive MIMO systems

The combination of terahertz and massive multiple-input multiple-output(MIMO)is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant band-width and spatial degrees of freedom.However,unique channel features,such as the near-field beam split effect,make channel estimation particularly challenging in terahertz massive MIMO systems.On one hand,adopting the conventional angular domain transformation dictionary designed for low-frequency far-feld channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain.On the other hand,most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously.To alleviate these issues,in this study,we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect.Then,a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed.In each iteration of the approximate message passing-sparse Bayesian learning algorithm,the optimal update rule is learned by a deep neural network(DNN),whose architecture is customized to effectively exploit the inherent channel patterns.Furthermore,a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations.Simulation results validate the superiority of the proposed algorithm in terms of performance,complexity,and robustness.

Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities

Atmospheric ducting has a significant impact on electromagnetic wave propagation.Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications.On one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference.On the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications.In this article,the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed.Then solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are presented.Finally,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.

An Overview on Backscatter Communications

As a key low-power communication tech-nique,backscatter communications exploits the reflected or backscattered signals to transmit data,where the backscattered signals can be the reflection of ambient radio frequency(RF)signals,the RF signals from the dedicated carrier emitter or the signal photons in the non-classical quantum entangled pairs,etc.In the past 70 years,various kinds of backscatter communication systems have been developed,which will enable the low-power communications as required in the Internet of things(IoTs)and green communications.This article provides a historical view on the development and the research achievements on backscatter communications,including the fundamental principles,the applications,the challenges,and the potential research topics.This article will benefit the researchers and engineers concerning the area of backscatter communications,especially for applications in IoTs.

Local Observations-Based Energy-Efficient Multi-Cell Beamforming via Multi-Agent Reinforcement Learning

With affordable overhead on information exchange,energy-efficient beamforming has potential to achieve both low power consumption and high spectral efficiency.This paper formulates the problem of joint beamforming and power allocation for a multiple-input single-output(MISO)multi-cell network with local observations by taking the energy efficiency into account.To reduce the complexity of joint processing of received signals in presence of a large number of base station(BS),a new distributed framework is proposed for beamforming with multi-cell cooperation or competition.The optimization problem is modeled as a partially observable Markov decision process(POMDP)and is solved by a distributed multi-agent self-decision beamforming(DMAB)algorithm based on the distributed deep recurrent Q-network(D2RQN).Furthermore,limited-information exchange scheme is designed for the inter-cell cooperation to boost the global performance.The proposed learning architecture,with considerably less information exchange,is effective and scalable for a high-dimensional problem with increasing BSs.Also,the proposed DMAB algorithms outperform distributed deep Q-network(DQN)based methods and non-learning based methods with significant performance improvement.