Building Semantic Communication System via Molecules:An End-to-End Training Approach

The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources.In this paper,we propose an end-to-end(E2E)semantic molecular communication system,aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information.Specifically,following the joint source channel coding paradigm,the network is designed to encode the task-relevant information into the concentration of the information molecules,which is robust to the degradation of the molecular communication channel.Furthermore,we propose a channel network to enable the E2E learning over the non-differentiable molecular channel.Experimental results demonstrate the superior performance of the semantic molecular communication system over the conventional methods in classification tasks.

Co-Sharing Waveform Design for Millimeter-Wave Radar Communication Systems

Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.

Semantic segmentation-based semantic communication system for image transmission

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.

Joint Optimization of Resource Allocation and Trajectory Based on User Trajectory for UAV-Assisted Backscatter Communication System

The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme.

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.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

Extended context-based semantic communication system for text transmission

Context information is significant for semantic extraction and recovery of messages in semantic communication.However,context information is not fully utilized in the existing semantic communication systems since re-lationships between sentences are often ignored.In this paper,we propose an Extended Context-based Semantic Communication(ECSC)system for text transmission,in which context information within and between sentences is explored for semantic representation and recovery.At the encoder,self-attention and segment-level relative attention are used to extract context information within and between sentences,respectively.In addition,a gate mechanism is adopted at the encoder to incorporate the context information from different ranges.At the decoder,Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery.Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.

Variational Neural Inference Enhanced Text Semantic Communication System

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,etc.While efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and decoder.However,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart devices.Furthermore,existing work tends to prioritize explicit semantics,neglecting the potential of implicit semantics.This paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder structures.We propose a novel semantic communication system with variational neural inference for text transmission.Specifically,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received text.This information is then utilized to assist in the decoding process.The simulation results show a significant enhancement in system performance and improved robustness.

Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

A cooperative passive sensing framework for millimeter wave(mmWave)communication systems is proposed and demonstrated in a scenario with one mobile signal blocker.Specifically,in the uplink communication with at least two transmitters,a cooperative detection method is proposed for the receiver to track the blocker’s trajectory,localize the transmitters and detect the potential link blockage jointly.To facilitate detection,the receiver collects the signal of each transmitter along a line-of-sight(LoS)path and a non-line-of-sight(NLoS)path separately via two narrow-beam phased arrays.The NLoS path involves scattering at the mobile blocker,allowing its identification through the Doppler frequency.By comparing the received signals of both paths,the Doppler frequency and angle-of-arrival(AoA)of the NLoS path can be estimated.To resolve the blocker’s trajectory and the transmitters’locations,the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations.Finally,a gradient-descent-based algorithm is proposed for joint detection.With the reconstructed trajectory,the potential link blockage can be predicted.It is demonstrated that the system can achieve decimeterlevel localization and trajectory estimation,and predict the blockage time with an error of less than 0.1 s.

Analysis of LDPC Code in Hybrid Communication Systems

Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.The RF spectrum is becoming denser and more employed,making its availability tough for additional channels.Optical communication,exploited for messages or indications in historical times,is now becoming famous and useful in combination with error-correcting codes(ECC)to mitigate the effects of fading caused by atmospheric turbulence.A free-space communication system(FSCS)in which the hybrid technology is based on FSO and RF.FSCS is a capable solution to overcome the downsides of current schemes and enhance the overall link reliability and availability.The proposed FSCS with regular low-density parity-check(LDPC)for coding techniques is deliberated and evaluated in terms of signal-to-noise ratio(SNR)in this paper.The extrinsic information transfer(EXIT)methodology is an incredible technique employed to investigate the sum-product decoding algorithm of LDPC codes and optimize the EXIT chart by applying curve fitting.In this research work,we also analyze the behavior of the EXIT chart of regular/irregular LDPC for the FSCS.We also investigate the error performance of LDPC code for the proposed FSCS.

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.

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,etc.Since UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs.In this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV networks.The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification.At the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize clusters.Besides,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV degree.For classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)classifier.For ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

Channel Capacity and Power Allocation of MIMO Visible Light Communication System

In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power constraints.Finding the channel capacity of MIMO VLC is shown to be a mixed integer programming problem.To address this open problem,we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO VLC.Also we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form expressions.Furthermore,by considering practical discrete constellation inputs,we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC system.Simulation results show that more discrete points are needed to achieve the channel capacity as SNR increases.Both the upper and lower bounds of channel capacity are tight at low SNR region.In addition,comparing the equal power allocation,the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.

SR-DCSK Cooperative Communication System with Code Index Modulation:A New Design for 6G New Radios

This paper proposes a high-throughput short reference differential chaos shift keying cooperative communication system with the aid of code index modulation,referred to as CIM-SR-DCSK-CC system.In the proposed CIM-SR-DCSK-CC system,the source transmits information bits to both the relay and destination in the first time slot,while the relay not only forwards the source information bits but also sends new information bits to the destination in the second time slot.To be specific,the relay employs an N-order Walsh code to carry additional log_(2)N information bits,which are superimposed onto the SRDCSK signal carrying the decoded source information bits.Subsequently,the superimposed signal carrying both the source and relay information bits is transmitted to the destination.Moreover,the theoretical bit error rate(BER)expressions of the proposed CIMSR-DCSK-CC system are derived over additive white Gaussian noise(AWGN)and multipath Rayleigh fading channels.Compared with the conventional DCSKCC system and SR-DCSK-CC system,the proposed CIM-SR-DCSK-CC system can significantly improve the throughput without deteriorating any BER performance.As a consequence,the proposed system is very promising for the applications of the 6G-enabled lowpower and high-rate communication.

Anti-Jamming and Anti-Eavesdropping in A2G Communication System with Jittering UAV

This paper focuses on anti-jamming and anti-eavesdropping problem in air-to-ground(A2G)communication networks considering the impact of body jitter of unmanned aerial vehicle(UAV).A full-duplex(FD)active ground eavesdropper launches jamming attack while eavesdropping to stimulate the legitimate transmitter(i.e.,UAV)to increase its transmission power.The legitimate transmitter’s objective is to against the simultaneous wiretapping and jamming with a robust and power-efficient transmission scheme.The active eavesdropper aims to minimize the system secrecy rate.To study the interaction between the legitimate transmitter and the active eavesdropper,a non-cooperative game framework is formulated.Detailed,considering the impact of UAV jitter on antenna array response and secrecy performance,we first investigate the UAV’s transmission power minimization problem for the worst scenario with minimum legitimate data rate and maximum eavesdropping data rate under UAV jitter.Then,the active eavesdropper’s secrecy rate minimization problem with the worst scenario is investigated by optimizing its jamming strategy.Nash equilibrium is proved to be existed and obtained with the proposed iterative algorithm.Finally,extensive numerical results are provided to evaluate the system secrecy performance and to show the secrecy performance gains of the proposed method.

UAV-Assisted FSO Communication System with Amplify-and-Forward Protocol under AOA Fluctuations:A Performance Analysis

Free space optical(FSO)communication has recently aroused great interest in academia due to its unique features,such as large transmission band,high data rates,and strong anti-electromagnetic interference.With the aim of evaluating the performance of an FSO communication system and extending the line-of-sight transmission distance,we propose an unmanned aerial vehicle(UAV)-assisted dual-hop FSO communication system equipped with amplifyand-forward protocol at the relay node.Specifically,we consider impairments of atmospheric absorption,pointing errors,atmospheric turbulence,and link interruptions due to angle-of-arrival fluctuations in the relay system.The Gamma-Gamma and Malaga distributions are used to model the influence of atmospheric turbulence on the source-to-UAV and UAVto-destination links,respectively.We derive closedform expressions of the probability density function(PDF)and cumulative distribution function(CDF)for the proposed communication system,in terms of the Meijer-G function.Based on the precise PDF and CDF,analytical expressions for the outage probability,average bit error rate,and ergodic capacity are proposed with the aid of the extended generalized bivariate Fox’s H function.Finally,we show that there is a match between the analytical results and numerical results,and we analyze the influence of the system and channel parameters on the performance.

Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.

Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to interference.However,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further investigation.As such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication performances.The proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data rate.Various modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,respectively.The simulation outcomes are indeed viable for the practical warehouse model.

Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond technologies.The random movement of mobile terminals in the indoor environment is a challenge in the VLC system.The model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)algorithm.The optimized optical attocells were examined and compared with previous models.This novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 dB.The bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,respectively.The optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,respectively.As the results of the proposed model are optimal,it is suitable for standard office and room model applications.

Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.