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.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems

An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System

Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

Channel Estimation for OTFS System over Doubly Spread Sparse Acoustic Channels

This paper addresses sparse channels estimation problem for the generalized linear models(GLM)in the orthogonal time frequency space(OTFS)underwater acoustic(UWA)system.OTFS works in the delay-Doppler domain,where timevarying channels are characterized as delay-Doppler impulse responses.In fact,a typical doubly spread UWA channel is associated with several resolvable paths,which exhibits a structured sparsity in the delayDoppler domain.To leverage the structured sparsity of the doubly spread UWA channel,we develop a structured sparsity-based generalized approximated message passing(GAMP)algorithm for reliable channel estimation in quantized OTFS systems.The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm.In addition,the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance.Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.

Piezo1 channel exaggerates ferroptosis of nucleus pulposus cells by mediating mechanical stress-induced iron influx

To date,several molecules have been found to facilitate iron influx,while the types of iron influx channels remain to be elucidated.Here,Piezo1 channel was identified as a key iron transporter in response to mechanical stress.Piezo1-mediated iron overload disturbed iron metabolism and exaggerated ferroptosis in nucleus pulposus cells(NPCs).Importantly,Piezo1-induced iron influx was independent of the transferrin receptor(TFRC),a well-recognized iron gatekeeper.Furthermore,pharmacological inactivation of Piezo1 profoundly reduced iron accumulation,alleviated mitochondrial ROS,and suppressed ferroptotic alterations in stimulation of mechanical stress.Moreover,conditional knockout of Piezo1(Col2a1-CreERT Piezo1^(flox/flox))attenuated the mechanical injury-induced intervertebral disc degeneration(IVDD).Notably,the protective effect of Piezo1 deficiency in IVDD was dampened in Piezo1/Gpx4 conditional double knockout(cDKO)mice(Col2a1-CreERT Piezo1^(flox/flox)/Gpx4^(flox/flox)).These findings suggest that Piezo1 is a potential determinant of iron influx,indicating that the Piezo1-iron-ferroptosis axis might shed light on the treatment of mechanical stress-induced diseases.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Sedimentary elements,evolutions and controlling factors of the Miocene channel system:a case study of the deep-water Taranaki Basin in New Zealand

Deep-water channel systems are important petroleum reservoirs,and many have been discovered worldwide.Understanding deep-water channel sedimentary elements and evolution is helpful for deep-sea petroleum exploration and development.Based on high-resolution 3D seismic data,the Miocene channel system in the deep-water Taranaki Basin,New Zealand,was analyzed by using seismic interpretation techniques such as interlayer attribute extraction and strata slicing.The channel system was divided into five composite channels(CC-I to CC-V)according to four secondary level channel boundaries,and sedimentary elements such as channels,slump deposits,inner levees,mass transport deposits,and hemipelagic drape deposits were identified in the channel system.The morphological characteristics of several composite channels exhibited stark variances,and the overall morphology of the composite channels changed from relatively straight to highly sinuous to relatively straight.The evolution of the composite channels involved a gradual and repeated process of erosion and filling,and the composite channels could be divided into three evolutionary stages:initial erosion-filling,later erosion-filling(multistage),and channel abandonment.The middle Miocene channel system may have formed as a consequence of combined regional tectonic activity and global climatic change,and its intricate morphological alterations may have been influenced by the channel's ability to self-regulate and gravity flow properties.When studying the sedimentary evolution of a large-scale deep-water channel system in the Taranaki Basin during the Oligocene-Miocene,which transitioned from a passive margin to plate convergence,it can be understood how tectonic activity affected the channel and can also provide a theoretical reference for the evolution of the deepwater channels in areas with similar tectonic conversion environments around the world.

Lithium-Ion Charged Polymer Channels Flattening Lithium Metal Anode

The concentration difference in the near-surface region of lithium metal is the main cause of lithium dendrite growth.Resolving this issue will be key to achieving high-performance lithium metal batteries(LMBs).Herein,we construct a lithium nitrate(LiNO_(3))-implanted electroactiveβphase polyvinylidene fluoride-co-hexafluoropropylene(PVDF-HFP)crystalline polymorph layer(PHL).The electronegatively charged polymer chains attain lithium ions on the surface to form lithium-ion charged channels.These channels act as reservoirs to sustainably release Li ions to recompense the ionic flux of electrolytes,decreasing the growth of lithium dendrites.The stretched molecular channels can also accelerate the transport of Li ions.The combined effects enable a high Coulombic efficiency of 97.0%for 250 cycles in lithium(Li)||copper(Cu)cell and a stable symmetric plating/stripping behavior over 2000 h at 3 mA cm^(-2)with ultrahigh Li utilization of 50%.Furthermore,the full cell coupled with PHL-Cu@Li anode and Li Fe PO_(4) cathode exhibits long-term cycle stability with high-capacity retention of 95.9%after 900 cycles.Impressively,the full cell paired with LiNi_(0.87)Co_(0.1)Mn_(0.03)O_(2)maintains a discharge capacity of 170.0 mAh g^(-1)with a capacity retention of 84.3%after 100 cycles even under harsh condition of ultralow N/P ratio of 0.83.This facile strategy will widen the potential application of LiNO_(3)in ester-based electrolyte for practical high-voltage LMBs.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Distribution of 26 Metals in the Waters of the Aquatic Ecosystems of the Cotonou Channel and Lake Nokoué, Benin

Metallic elements have various origins: natural and anthropogenic sources as geochemical, marine and atmospheric sources resulting from the fallout of pollutants emitted or dust raised and which are transported by water and air currents. Thus marine, brackish and fresh continental waters may have high metal concentrations. In addition, some essential metals can become toxic above certain concentration values in aquatic environments. The aquatic ecosystems of Cotonou channel and lake Nokoué receive the pollutants charges from the town cities of Cotonou, Abomey-Calavi and town hall of So Ava. The aim of this study is to analyze waters from Eighteen (18) stations identified in the two ecosystems (nine by ecosystem). The concentrations of magnesium (Mg), calcium (Ca), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), beryllium (Be), aluminum (Al), strontium (Sr), molybdenum (Mo), silver (Ag), tin (Sn), barium (Ba), platinum (Pt), mercury (Hg), thallium (Tl), lead (Pb), thorium (Th) and uranium (U) were measured after acid digestion of the water samples using the inductively coupled plasma source mass spectrometer (ICP-MS). The results of the analyses indicate an unequal distribution of metals in the different ecosystems. However, atypical concentrations were observed at some stations of the lake and the channel. Magnesium, calcium and manganese have very high values in Lake Nokoué respectively at Ganvié market station GAN_M (2990 ± 105 mg/L), Ganvié center, station GAN_C (4991 ± 177 mg/L) and Lake middle station MLak4 (10662 ± 17.03 μg/L). On the other hand, iron, aluminum and strontium have very high concentrations in the Cotonou Channel respectively at Agbato station AGB (5236 ± 103 and 8289 ± 519 μg/L) and at the estuary station EST (6118 ± 68 μg/L). The concentrations were compared to wells and cborehole waters in sixth neighborhood of Cotonou. We have used statistical analyzers such as MANOVA which have made it possible to classify the waters and metals in the ecosystems studied compared to groundwater and Well water waters. We use hierarchical clustering on principal components to identify similarities between stations based on metal concentration with R software packages “FactoMineR” and “factoextra”. In general, we can conclude that most of the metals have an anthropogenic source except strontium and major elements (Ca and Mg) which could respectively provide from marine waters and geochemical sources.

Influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel

The influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel is investigated.The DarcyBrinkman model is used to characterize the fluid flow dynamics in porous materials.The analytical solutions are obtained for the unidirectional and completely developed flow.Based on a normal mode analysis,the generalized eigenvalue problem under a perturbed state is solved.The eigenvalue problem is then solved by the spectral method.Finally,the critical Rayleigh number with the corresponding wavenumber is evaluated at the assigned values of the other flow-governing parameters.The results show that increasing the Darcy number,the Lewis number,the Dufour parameter,or the Soret parameter increases the stability of the system,whereas increasing the inclination angle of the channel destabilizes the flow.Besides,the flow is the most unstable when the channel is vertically oriented.

Research progress on the water vapor channel within the Yarlung Zsangbo Grand Canyon, China

The Second Tibetan Plateau Scientific Expedition and Research Program tasked a research team with the“Investigation of the water vapor channel of the Yarlung Zsangbo Grand Canyon(INVC)”in the southeastern Tibetan Plateau(TP).This paper summarizes the scientific achievements obtained from the data collected by the INVC observation network and highlights the progress in investigating the development of heavy rainfall events associated with water vapor changes.The rain gauge network of the INVC can represent the impacts of the Yarlung Zsangbo Grand Canyon(YGC)topography on precipitation at the hourly scale.The microphysical characteristics of the precipitation in the YGC are different than those in the lowland area.The GPM-IMERG(Integrated MultisatellitE Retrievals for Global Precipitation Measurement)satellite precipitation data for the YGC region should be calibrated before they are used.The meridional water vapor flux through the YGC is more important than the zonal flux for the precipitation over the southeastern TP.The decreased precipitation around the YGC region is partly due to the decreased meridional water vapor flux passing through the YGC.High-resolution numerical models can benefit precipitation forecasting in this region by using a combination of specific schemes that capture the valley wind and water vapor flux along the valley floor.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.