Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels

Degraded broadcast channels(DBC) are a typical multiuser communication scenario, Semantic communications over DBC still lack in-depth research. In this paper, we design a semantic communications approach based on multi-user semantic fusion for wireless image transmission over DBC. The transmitter extracts semantic features for two users separately and then effectively fuses them for broadcasting by leveraging semantic similarity. Unlike traditional allocation of time, power, or bandwidth, the semantic fusion scheme can dynamically control the weight of the semantic features of the two users to balance their performance. Considering the different channel state information(CSI) of both users over DBC,a DBC-Aware method is developed that embeds the CSI of both users into the joint source-channel coding encoder and fusion module to adapt to the channel.Experimental results show that the proposed system outperforms the traditional broadcasting schemes.

Cooperative Covert Communication in Multi-Antenna Broadcast Channels

With the gradual popularization of 5G communications,the application of multi-antenna broadcasting technology has become widespread.Therefore,this study aims to investigate the wireless covert communication in the two-user cooperative multi-antenna broadcast channel.We focus on the issue that the deteriorated reliability and undetectability are mainly affected by the transmission power.To tackle this issue,we design a scheme based on beamforming to increase the reliability and undetectability of wireless covert communication in the multi-antenna broadcast channel.We first modeled and analyzed the cooperative multi-antenna broadcasting system,and put forward the target question.Then we use the SCA(successive convex approximation)algorithm to transform the target problem into a series of convex subproblems.Then the convex problems are solved and the covert channel capacity is calculated.In order to verify the effectiveness of the scheme,we conducted simulation verification.The simulation results show that the proposed beamforming scheme can effectively improve the reliability and undetectability of covert communication in multi-antenna broadcast channels.

A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.

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.

Channel estimation in integrated radar and communication systems with power amplifier distortion

To reduce the negative impact of the power amplifier(PA)nonlinear distortion caused by the orthogonal frequency division multiplexing(OFDM)waveform with high peak-to-average power ratio(PAPR)in integrated radar and communication(RadCom)systems is studied,the channel estimation in passive sensing scenarios.Adaptive channel estimation methods are proposed based on different pilot patterns,considering nonlinear distortion and channel sparsity.The proposed methods achieve sparse channel results by manipulating the least squares(LS)frequency-domain channel estimation results to preserve the most significant taps.The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion.Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns.In addition,the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges,such as low user utilization,unbalanced resource allocation,and extended adaptive allocation time.We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues.This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components.It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes.Furthermore,this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes.The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity,calculates the probability of vehicle network connectivity under different mobile cloud communication radii,and determines the amount of cloud communication resources required by vehicles in different lane scenarios.Based on the communication status of users in 5G vehicular networks,this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s formula.It determines the adaptive allocation of cloud communication resources,introduces an objective function to obtain the optimal solution after allocation,and completes the adaptive allocation process.The experimental results demonstrate that,with the application of the proposed method,the maximum utilization of user communication resources reaches approximately 99%.The balance coefficient curve approaches 1,and the allocation time remains under 2 s.This indicates that the proposed method has higher adaptive allocation efficiency.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Effect of Marine Planktonic Algal Particles on the Communication Performance of Underwater Quantum Link

As one of the main application directions of quantum technology,underwater quantum communication is of great research significance.In order to study the influence of marine planktonic algal particles on the communication performance of underwater quantum links,based on the extinction characteristics of marine planktonic algal particles,the influence of changes in the chlorophyll concentration and particle number density of planktonic algal particles on the attenuation of underwater links is explored respectively,the influence of marine planktonic algal particles on the fidelity of underwater quantum links,the generation rate of the security key,and the utilization rate of the channel is analyzed,and simulation experiments are carried out.The results show that with the increase in chlorophyll concentration and particle density of aquatic planktonic algal particles,quantum communication channel link attenuation shows a gradually increasing trend.In addition,the security key generation rate,channel fidelity and utilization rate are gradually decreasing.Therefore,the performance of underwater quantum communication channel will be interfered by marine planktonic algal particles,and it is necessary to adjust the relevant parameter values in the quantum communication system according to different marine planktonic algal particle number density and chlorophyll concentration to improve the performance of quantum communication.

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also,it utilizes pilots to offer more helpful information about the communication channel.The proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)CSEs.In addition,the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway(HSR)communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs.To this end,this paper investigates millimeter-Wave(mmWave)extra-large scale(XL)-MIMO-based massive Internet-of-Things(loT)access in near-field HSR communications,and proposes a block simultaneous orthogonal matching pursuit(B-SOMP)-based Active User Detection(AUD)and Channel Estimation(CE)scheme by exploiting the spatial block sparsity of the XLMIMO-based massive access channels.Specifically,we first model the uplink mmWave XL-MIMO channels,which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays.By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels,the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing(MIMV-CS)problem.Based on the designed sensing matrix,a B-SOMP algorithm is proposed to achieve joint AUD and CE.Finally,simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

MIMO-Terahertz in 6G Nano-Communications:Channel Modeling and Analysis

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

A 3-D Hybrid Dynamic Channel Model for Indoor THz Communications

To meet the demands for the explosive growth of mobile data rates and scarcity of spectrum resources in the near future,the terahertz(THz)band has widely been regarded as a key enabler for the upcoming beyond fifth-generation(B5G)wireless communications.An accurate THz channel model is crucial for the design and deployment of the THz wireless communication systems.In this paper,a three-dimensional(3-D)dynamic indoor THz channel model is proposed by means of combining deterministic and stochastic modeling approaches.Clusters are randomly distributed in the indoor environment and each ray is characterized with consideration of molecular absorption and diffuse scattering.Moreover,we present the dynamic generation procedure of the channel impulse responses(CIRs).Statistical properties are investigated to indicate the non-stationarity and feasibility of the proposed model.Finally,by comparing with delay spread and K-factor results from the measurements,the utility of the proposed channel model is verified.

Channel Estimation and Throughput Evaluation for 5G Wireless Communication Systems in Various Scenarios on High Speed Railways

The fifth generation （5G） wireless communication is currently a hot research topic and wireless communication systems on high speed railways （HSR） are important applications of 5G technologies. Existing stud- ies about 5G wireless systems on high speed railways （HSR） often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the down- link throughput of 5G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line （TDL） models through ray-tracing sim- ulations, which can be considered as a good match to practical situations. The channel estimators including least square （LS）, linear minimum mean square error （LMMSE）, and our proposed historical information based ba- sis expansion model （HiBEM）. We analyze the performance of the HiBEM estimator in terms of mean square error （MSE） and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our HiBEM estimator outperforms other estimators and that the sys-tem throughput can reach the highest point in the viaduct scenario.

A General Channel Model for Visible Light Communications in Underground Mines

In underground mines, visible light communication(VLC) system is a promising method to realize effective communication,which supports communication and illumination at the same time. Therefore, adequate research of underlying physical propagation phenomenon should be carried out to realize VLC system in underground mines. To design VLC system and evaluate its performance, accurate and efficient channel models, including large-scale fading and scattering characteristics, are needed to be established. However,the characteristics of the underlying VLC channels about fading and scattering have not been sufficiently investigated yet. In this paper, a path loss channel model, based on the recursive model, is proposed precisely. Its path loss exponent is determined by three different trajectories, which is studied in the mining roadway and working face environment. Besides, the shadowing effect for VLC has been modelled by a Bimodal Gaussian distribution in underground mines. Considering the number of transmitters in line-of-sight(Lo S) as well as non-line-of-sight(NLo S) scenarios,our simulation illustrates the fact that, as the curve fitting technique is employed, the path loss displays a linear behavior in log-domain.The path loss expression is derived, it is related to the distance. Finally, root mean square(RMS) delay spread and Mie scattering in underground mines are analyzed.

Frequency Diversity for OFDM Mobile Communication via Underwater Acoustic Channels

The major constraint on the performance of orthogonal frequency division multiplexing （OFDM） based underwater acoustic （UWA） communication is to keep subcarriers orthogonal. In this paper, Doppler estimation and the respective compensation technique along with various diversity techniques were deliberated for OFDM-based systems best suited for underwater wireless information exchange. In practice, for mobile communication, adjustment and tuning of transducers in order to get spatial diversity is extremely difficult. Considering the relatively low coherence bandwidth in UWA, the frequency diversity design with the Doppler compensation function was elaborated here. The outfield experiments of mobile underwater acoustic communication （UWAC） based on OFDM were carried out with 0.17 bit/（s-Hz） spectral efficiency. The validity and the dependability of the scheme were also analyzed.

Characteristic of Line-of-Sight in Infrastructure-to-Vehicle Visible Light Communication Using MIMO Technique

Visible Light Communication(VLC)technology is aggressive research for the next generation of communication.Currently,Radio Frequency(RF)communication has crowed spectrum.An Intelligent Transportation System(ITS)has been improved in the communication network for Vehicle-to-Vehicle(V2 V),Vehicle-to-Infrastructure(V2I),and Infrastructure-to-Vehicle(I2V)by using the visible light spectrum instead of the RF spectrum.This article studies the characterization of Line-of-Sight(LOS)optical performance in an Outdoor Wireless Visible Light Communication(OWVLC)system employing a Multiple-Input Multiple-Output(MIMO)technique for I2V communications in ITS regulations.We design the new configuration of the OWVLC-I2V system,which is an alternative approach to communication for I2V system at nighttime.The results show the Channel Impulse Response(CIR)of the LOS links in visible light communication for I2V system in ITS by investigating the receiver on the vehicle moving along the coverage communication area.Furthermore,the OWVLC-I2V system using the MIMO technique depicts the performance of throughput and Bit Error Rate(BER)vs.vehicle speed while the vehicle passes a street light.

Exploring Channel Diversity in HF Communication Systems： A Matching-Potential Game Approach

This paper investigates the channel diversity problem in high frequency(HF) communication systems. Due to the limited HF spectrum resources, a HF communication system with shared channels is considered, where each user equipment(UE) has individual communication demand. In order to maximize the communication probability of the whole system, a matching-potential game framework is designed. In detail, the channel diversity problem is decomposed into two sub-problems. One is channel-transmitter matching problem, which can be formulated as a many-to-one matching game. The other is the transmitter allocation problem which decides the transmission object that each transmitter communicates with under channel-transmitter matching result, and this sub-problem can be modeled as a potential game. A multiple round stable matching algorithm(MRSMA) is proposed, which obtains a stable matching result for the first sub-problem, and a distributed BR-based transmitter allocation algorithm(DBRTAA) is designed to reach Nash Equilibrium(NE) of the second sub-problem. Simulation results verify the effectiveness and superiority of the proposed method.

A Geometry-Based Non-Stationary MIMO Channel Model for Vehicular Communications

This paper derives a non-stationary multiple-input multiple-output(MIMO) from the one-ring scattering model. The proposed channel model characterizes vehicular radio propagation channels with considerations of moving base and mobile stations, which makes the angle of arrivals(AOAs) along with the angle of departures(AODs) time-variant. We introduce the methodology of including the time-variant impacts when characterizing non-stationary radio propagation channels through the geometrical channel modelling approach. We analyze the statistical properties of the proposed channel model including the local time-variant autocorrelation function(ACF) and the space cross-correlation functions(CCFs). We show that the model developed in this paper for non-stationary scenarios includes the existing one-ring wide-sense stationary channel model as its special case.