Design and Implementation of Nonlinear Precoding for MIMO-SDMA Toward 6G Wireless

In this paper,we present a novel and robust nonlinear precoding(NLP)design and detection structure specifically tailored for multiple-input multipleoutput space division multiple access(MIMO-SDMA)systems toward 6G wireless.Our approach aims to effectively mitigate the impact of imperfect channel estimation by leveraging the channel fluctuation mean square error(MSE)for reconstructing a highly accurate precoding matrix at the transmitter.Furthermore,we introduce a simplified receiver structure that eliminates the need for equalization,resulting in reduced interference and notable enhancements in overall system performance.We conduct both computer simulations and experimental tests to validate the efficacy of our proposed approach.The results reveals that the proposed NLP scheme offers significant performance improvements,making it particularly well-suited for the forthcoming 6G wireless.

Outage Behaviors of Active Intelligent Reflecting Surface Enabled NOMA Communications

Active intelligent reflecting surface(IRS)is a novel and promising technology that is able to overcome the multiplicative fading introduced by passive IRS.In this paper,we consider the application of active IRS to nonorthogonalmultiple access(NOMA)networks,where the incident signals are amplified actively through integrating amplifier to reflecting elements.More specifically,the performance of active/passive IRS-NOMA networks is investigated over large and small-scale fading channels.Aiming to characterize the performance of active IRSNOMA networks,the exact and asymptotic expressions of outage probability for a couple of users,i.e.,near-end user n and far-end user m are derived by exploiting a 1-bit coding scheme.Based on approximated analyses,the diversity orders of user n and user m are obtained for active IRS-NOMA.In addition,the system throughput of active IRS-NOMA is discussed in the delay-sensitive transmission.The simulation results are carried out to verify that:i)The outage behaviors of active IRS-NOMAnetworks are superior to that of passive IRS-NOMAnetworks;ii)As the reflection amplitude factors increase,the active IRS-NOMAnetworks are capable of furnishing the enhanced outage performance;and iii)The active IRS-NOMA has a larger system throughput than passive IRS-NOMA and conventional communications.

Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

System Integration of Terrestrial Mobile Communication and Satellite Communication——The Trends, Challenges and Key Technologies in B5G and 6G

Mobile communication standards have been developed into a new era of B5G and 6G.In recent years,low earth orbit(LEO)satellites and space Internet have become hot topics.The integrated satellite and terrestrial systems have been widely discussed by industries and academics,and even are expected to be applied in those huge constellations in construction.This paper points out the trends of two stages towards system integration of the terrestrial mobile communication and the satellite communications:to be compatible with 5G,and to be integrated within 6G.Based on analysis of the challenges of both stages,key technologies are thereafter analyzed in detail,covering both air interface currently discussed in 3GPP for B5G and also novel network architecture and related transmission technologies toward future 6G.

Placement Optimization of Caching UAV-Assisted Mobile Relay Maritime Communication

Recently, due to the deployment flexibility of unmanned aerial vehicles(UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assisted mobile relay communication system is limited by the capacity of wireless backhaul link between base station and UAV. In this paper, we consider a caching UAV-assisted decode-and-forward relay communication system in a downlink maritime communication. For the general case with multiple users, the optimal placement of UAV is obtained by solving the average achievable rate maximization problem through the one-dimensional linear search. For a special case with single user, we derive a semi closedform expression of the optimal placement of UAV. Simulation results confirm the accuracy of analytical results and show that the optimal placement of UAV and the average achievable rate significantly depend on the cache capacity at UAV. We also show the difference between the performances of the air-to-ground model and the air-to-sea model.

A Comprehensive Survey of TDD-Based Mobile Communications Systems from TD-SCDMA 3G to TDLTE-Advanced 4G and 5G directions

TDD(Time Division Duplex) is one of the two duplex modes.TD-SCDMA(Time division Synchronous CDMA) is the first TDD-based cellular mobile system which is commercialized in wide area and large scale and TD-SCDMA is also the first cellular mobile system which adopted smart antenna technology,also called as beamforming.As the long term evolution of TD-SCDMA,TDLTE(A)(Time Division-Lone Term Evolution,and TD-LTE Advanced) introduced OFDM(Orthogonal Frequency Division Multiplexing)and enhanced smart antenna technology together with MIMO(Multiple Input Multiple Output),which are adopted by LTE FDD(Frequency Division Duplex) either.It is indicated that TD-SCDMA and TD-LTE(A)have opened a sustainable utilization era of TDD and smart antenna Technologies in the wireless mobile communication.This paper aims to present a systematic introduction to TDD-based mobile communications from TD-SCDMA to TD-LTE and beyond and its comparisons to FDD,with particular focuses on TDD key technologies,principles of TDD cellular mobile systems,TDD evolution path,and future TDD 5G directions.We hope that this paper will provide a comprehensive overview of TDD technology upgrade and its standard evolution,and serve as a valuable reference for research on 5G mobile communication systems.It is believed that TDD will play more important role in 5G.

Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

Grant-Free Random Access in Pilot-Reused Multicell Massive MIMO Systems with Backoff Mechanisms

Grant-free random access(RA)is attractive for future network due to the minimized access delay.In this paper,we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO)systems with pilot reuse.With backoff mechanism,user equipments(UEs)in each cell are randomly activated,and active UEs randomly select orthogonal pilots from a predefined pilot pool,which results in a random pilot contamination among cells.With the help of indicators that capture the uncertainties of UE activation and pilot selection,we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs’random behaviors.Based on the analysis,the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained.We find that the backoff mechanism is necessary for the system with large number of UEs,as it can bring significant gains on the spectral efficiency.Moreover,as UE number grows,more backoff time is needed.

Max-Min Fair RIS-Aided Rate-Splitting Multiple Access for Multigroup Multicast Communications

Rate-splitting multiple access(RSMA)can cope with a wide range of propagation conditions in multigroup multicast communications through rate splitting optimization.To breakthrough the grouprate limited bottleneck,reconfigurable intelligent surface(RIS)technique can be introduced to assist wireless communications through enhancing the channel quality.In RIS-aided RSMA multigroup multicasting,how to provide fair and high-quality multiuser service under power and spectrum constraints is essential.In this paper,we propose a max-min fair RIS-aided rate-splitting multiple access(MMF-RISRSMA)scheme for multigroup multicast communications,where the rate fairness is obtained by maximizing the minimum group-rate.In doing so,we jointly optimize the beamformers,the rate splitting vector at the transmitter,as well as the phase shifts at RIS.To solve it,we divide the original optimization problem into two subproblems and alternately optimize the variables.The beamforming and rate splitting optimization subproblem is solved by using the successive convex approximation technique.The phase shift optimization subproblem is solved through the penalty function method to achieve a rank-one locally optimal solution.Simulations demonstrate that the proposed MMF-RIS-RSMA scheme can obtain significant performance gain in terms of the minimum group-rate.

RIS-assisted MIMO secure communications with Bob's statistical CSI and without Eve's CSI

In this paper,we consider a reconfigurable intelligent surface(RIS)-assisted multiple-input multiple-output(MIMO)secure communication system,where only legitimate user's(Bob's)statistical channel state information(CSI)can be obtained at the transmitter(Alice),while eavesdropper's(Eve's)CSI is unknown.Firstly,the analytical expression of the achievable ergodic rate at Bob is obtained.Then,by exploiting Bob's statistical CSI,we jointly design the transmit covariance matrix at Alice and the phase shift matrix at the RIS to minimize the transmit power of the information signal under the quality-of-service(QoS)constraint of Bob.Finally,we propose an artificial noise(AN)-aided method without Eve's CSI to enhance the security of this system and use the residual power to design the transmit covariance for AN.Simulation results verify the convergence of the proposed method,and also show that there exists a trade-off between the secrecy rate and QoS of Bob.

The Dual Iconic Features and Key Enabling Technologies of 6G

1.Introduction Since the 1980s,when the first generation(1G)of mobile networks came into being,mobile communications have been developing with the trend of a generational upgrade occurring every decade.At present,the fifth generation(5G)is in the commercial stage and the sixth generation(6G)is in the research stage.It is expected that the standardization for 6G will debut in the year 2025,and the first version for commercialization will be launched in 2030.For the purpose of better understanding 6G.

Integrated Sensing,Computing and Communications Technologies in IoV and V2X

With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians,infrastructures,clouds,etc.),is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.

A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .

Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems

To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence,multiple-input multiple-output(MIMO)technology is a valid way.A wireless ultraviolet(UV)MIMO channel estimation approach based on deep learning is provided in this paper.The deep learning is used to convert the channel estimation into the image processing.By combining convolutional neural network(CNN)and attention mechanism(AM),the learning model is designed to extract the depth features of channel state information(CSI).The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.

Optimal AP Deployment in Cell-Free Massive MIMO Systems with LoS/NLoS Transmissions:A Stochastic Geometry Approach

Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

A Synchronization Acquisition Algorithm Based on the Frequency Hopping Pulses Combining

As modern electromagnetic environments are more and more complex,the anti-interference performance of the synchronization acquisition is becoming vital in wireless communications.With the rapid development of the digital signal processing technologies,some synchronization acquisition algorithms for hybrid direct-sequence(DS)/frequency hopping(FH)spread spectrum communications have been proposed.However,these algorithms do not focus on the analysis and the design of the synchronization acquisition under typical interferences.In this paper,a synchronization acquisition algorithm based on the frequency hopping pulses combining(FHPC)is proposed.Specifically,the proposed algorithm is composed of two modules:an adaptive interference suppression(IS)module and an adaptive combining decision module.The adaptive IS module mitigates the effect of the interfered samples in the time-domain or the frequencydomain,and the adaptive combining decision module can utilize each frequency hopping pulse to construct an anti-interference decision metric and generate an adaptive acquisition decision threshold to complete the acquisition.Theory and simulation demonstrate that the proposed algorithm significantly enhances the antiinterference and anti-noise performances of the synchronization acquisition for hybrid DS/FH communications.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.