In order to compensate the performance loss caused by obstacle blocking in mobile edge computing (MEC) system in 6G-enabled “intelligent Internet of Things”,this paper proposes a partial task offloading scheme supported by aerial reconfigurable intelligent surface (RIS).Firstly,we investigate the joint design of the RIS phase shift vector,the proportion of offloading task,time slot allocation,the transmit power of users and the position of UAV,formulating a non-convex problem for minimization of the total energy consumption of users.Then,the original non-convex problem is decomposed into four subproblems,and the proximal policy optimization (PPO) method in deep reinforcement learning (DRL) is utilized to provide time slot allocation.The alternative optimization (AO) is leveraged to decouple the original problem into four subproblems,including the RIS phase shift design,the convex optimization of transmit power and offloading task amount,and the UAV altitude optimization.Simulation results show that the proposed PPO model can be trained quickly,the total energy consumption of users can be reduced by about 23% and 5.3%,compared with the fully-offload strategy and fixed-UAV-height strategy,respectively.

With the rapid development of Internet of Things(IoT),various IoT mobile devices(IMDs) need to process more and more computing-intensive and delay-sensitive tasks,which puts forward new challenges for the mobile edge networks.To address these challenges,the MEC-equipped ultra-dense IoT has emerged.In such networks,IMDs can save their computation resources and reduce their energy consumption by offloading computing-intensive tasks to edge computing servers for processing.However,it will result in additional transmission time and higher delay.In view of this,an optimization problem is formulated for finding the trade-off between energy consumption and delay,which jointly considers the user(IMD) association,computation offloading and resource allocation for ultra-dense MEC-enabled IoT.To further balance the network load and fully utilize the computation resources,the optimization problem is finally modeled as multi-step computation offloading one.At last,an intelligent algorithm,adaptive particle swarm optimization(PSO),is utilized to solve the proposed problem.Compared with traditional PSO,the total cost of adaptive PSO reduces by 20%~65%.

Reconfigurable intelligent surface(RIS) can intelligently change the wireless propagation environment to significantly improve the performance of wireless communication systems.Compared with traditional relay systems,it has the characteristics of low cost,low power consumption and easy deployment.It is regarded as one of the potential key technologies of 6G.Since RIS can dynamically change the phase characteristics of radio waves,the scalability of the network can be achievedby adjusting the phase shift reasonablely,and massive IoT nodes in the network can be flexibly served.In order to further improve the performance of the RIS-assisted IoT transmission system,a two-way RIS-assisted transmission system is proposed.By introducing full-duplex and self-interference cancellation technology,the system capacity and transmission efficiency are effectively improved.The analy-tical expressions for the outage probability,average bit error rate and average channel capacity of the proposed system are derived,and the relationship between system performance and system parameters such as the number of RIS reflecting elements in the system is obtained.The accuracy of the derivation and the performance advantages of the proposed scheme have been verified by Monte Carlo simulation.

With the rapid development of the Internet of vehicles,the emerging new types of in-vehicle tasks put forward higher requirements for communication and computing capabilities.The development of satellite communication technology and the large-scale deployment of 5G millimeter-wave base stations provide safer and more reliable services for highway vehicle users.At the same time,mobile edge computing technology deploys mobile edge computing(MEC) servers with computing and storage capabi-lities around user terminals to provide computing services for on-board tasks while reducing transmission delays.Aiming at the problem of offloading decision-making and communication resource allocation of vehicle tasks in highway scenarios,the joint optimization problem of computing and communication resources is modeled as a 0-1 mixed integer linear programming problem.Firstly,the original optimization problem is decoupled into the resource block allocation sub-problem and the offloading decision sub-problem.Secondly,the sub-problems are solved by using the water injection algorithm and the particle swarm algorithm.Finally,the sub-problems are iteratively solved based on the heuristic algorithm to obtain the optimal resource block allocation scheme and offload decision vector.Simulation results show that the algorithm minimizes the average system delay while meeting the requirements of all on-board missions.

The sixth generation(6G) wireless communication network will become the base of endogenous intelligence,ubiquitous connectivity,and full-scene interconnection.It is an important basis to realize dependable intelligence in the future.Blockchain is considered as the key decentralized-enabled technology to improve the performance of 6G networks.In the future,the consensus nodes of the blockchain will be composed of massive edge devices and connected through wireless networks.However,motivating self-interest edge devices to participate in the consensus process still faces the challenges of information asymmetry,resource constraints and heterogeneous wireless communication environment.To solve these challenges,a blockchain sharding framework and an incentive mechanism for trusted and dependable intelligence in 6G are proposed.Firstly,an incentive mechanism is presented based on contract theory,which aims to maximize the benefits and reliability of the blockchain sharding.By analyzing the practical byzantine fault tolerance (PBFT) based intrashard consensus mechanism,this paper design energy consumption model for auditing and transmitting the blocks in wireless networks.Secondly,in order to improve the system reliability,it proposes a reputation mechanism based on subjective logic.Finally,a set of optimal contracts under complete information and asymmetric information scnearios are abtained,which could optimize the block revenue for blockchain service requester,while ensuring some desired economic properties,i.e.,budget feasibility,individual rationality and incentive compatibility.Simulation results show that the proposed contract-based incentive mechanism can motivate edge devices to participate in the blockchain consensus process and maintain the operation of blockchain from the perspective of economics more efficiently.

The intelligent container system improves the container operation efficiency through the real-time collection and transmission of information.In order to ensure the ultra reliable and low delay communication of container terminals,this paper pro-poses to transmit the information in the form of the short packets,and studies the sum rate maximization of the uplink transmission system for the railway container terminals in the multi cells.This paper proposes a spectrum resource allocation problem.Multiple cells share spectrum resources,and the terminals in the cells obtain the spectrum through competition.The game theory model is used to construct this competition relationship,and Nash equilibrium solution is proved.The best Nash equilibrium solution is the global optimal solution for system and rate maximization.Then,a distributed iterative algorithm is designed,which only needs local information interaction.It is proved theoretically that when the smoothing coefficient is small enough,the algorithm can converge to the best Nash equilibrium point with any high probability.Finally,the proposed algorithm is verified by simulation.Simulation results show that the proposed algorithm has fast convergence speed and is better than best response dynamics(BRD) algorithm and No-regret algorithm.

As a typical industrial Internet of things (IIOT) service,demand response(DR) is becoming a promising enabler for intelligent energy management in 6G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’ load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio (PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

The introduction of electric vehicles(EVs) alleviates greenhouse gases emission.Its application has huge potential in the attempt to achieve green transportation today.However,the long charging time and charging congestion greatly affect the travel experience of EVs.To optimize EV charging,the charging station(CS) selection scheme(where to charge) and the charging scheduling strategy(when to charge) become the core of solving the problem of urban EV charging.In this paper,the preemptive charging scheduling strategy considering the charging priority(CP) is proposed.This strategy allows the preemptive charging of EVs with high urgency of charging(calculated from the charging demand and the remaining parking duration).Based on the CP charging scheduling strategy,a CS selection scheme that further combines reservation information is optimized.This scheme selects the CS with the shortest charging travel time(including one-time charging process) for EVs.Meanwhile,EVs are required to report their charging reservation information to accurately predict the congestion status of CSs,so as to efficiently allocate charging resources.The charging network is simulated through the urban traffic scene of Helsinki.The results show that the charging management scheme,CP scheduling strategy and reservation-based CS selection scheme proposed in this paper,can effectively shorten the average charging travel time of EVs and provide fully charging service for more EVs within a limited parking duration.

The rise of the 6G wireless communication provides a broader prospect for the development of virtual reality panoramic video.The tile-based panoramic video coding scheme can improve the viewing experience of 360° video under the same condition of network bandwidth.Tile partition affects the video transmission performance.Compared with small tiles,using large tiles can effectively improve coding efficiency,but it will cause more pixel overhead by transmitting a larger area to cover the viewport.The existing tile partition work is mainly designed for the rectangular viewport,but the projection of spherical video to a two-dimensional plane will stretch different areas of the viewport to varying degrees in practice.Deriving the pixel overhead associated with covering irregular viewport areas with rectangular tiles is more complicated.To address this challenge,a tile partitioning algorithm for the user's real viewport has been proposed in this paper.Firstly,the stretching distortion of the viewport caused by the projection format is analyzed,and the pixel overhead of the irregular viewport with different tile partition sizes is derived.Secondly,by trading off the pixel overhead and the coding efficiency of tiles with different granularity,an optimal tile partition scheme has been proposed for the panoramic video sequence.Finally,the proposed scheme is compared with the exhaustive search method for tile partition in the experiment,and the results show that the proposed algorithm can achieve almost the same transmission efficiency as the exhaustive search method with less computational complexity.