This paper investigates the collaborative security control and communication resource optimization problem in multi-region communication systems under multiple cyber attacks, based on the Stackelberg game theory. Attackers weaken information interaction between subsystems by disrupting wireless transmission channels among regions, thereby altering the synchronization coefficients of regional tie-lines and threatening the overall system stability. Under energy budget constraints for both the system and attackers, this study establishes a Stackelberg game model between the system and multiple attackers with incomplete information, focusing on energy allocation efficiency of transmission channels as the core performance metric. By solving the equilibrium solution of this game model, the optimal energy allocation schemes for both attack strategies and system transmission channels are derived. These solutions determine the system coefficient matrices of subsystems and further compute the control gains that ensure asymptotic stability of the system. Finally, simulation experiments on a practical power system validate the correctness and effectiveness of the proposed method.

In highly dynamic wireless communication scenarios, video streaming transmission faces significant challenges including rapid channel state variations, high sensitivity to end-to-end latency, and coupled computational-communication resource scheduling constraints, which severely degrade transmission continuity and user experience. To address these challenges, this paper proposes a joint dynamic resource scheduling and task offloading optimization framework based on mobile edge computing (MEC), integrated with scalable video coding (SVC) mechanisms, to enable real-time network state awareness and long-term system performance optimization. To enhance system stability and long-term decision-making performance, we introduce Lyapunov stochastic optimization theory to construct a queue-stability-driven optimization model for online dynamic decision-making in resource scheduling and task offloading. Moreover, considering the poor convergence and adaptability of traditional optimization methods in dynamic environments, an enhanced quantum particle swarm optimization (HQPSO) algorithm is developed with dynamic particle search strategy adjustment, significantly improving global optimization capability and stability in complex scenarios. Simulation results demonstrate that the proposed method provides both theoretical foundations and practical guidelines for constructing efficient video transmission systems in high-mobility scenarios, exhibiting strong application prospects.

The emergence of new applications has increased the demand of mobile users for edge computing. Therefore, the optimization of base station load balancing and task offloading is particularly important. In this paper, a two-tier heterogeneous mobile edge computing (MEC) system based on Stackelberg Game (SG) is studied. The introduction of SG can improve user association flexibility while reducing base station load. The introduction of network slicing can classify users according to different types, which increases the universality of the proposed framework. To solve the above problem, we map an optimization problem of efficiency, computation delay and computation energy consumption. Due to the non-convexity of the optimization problem, the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on SG is proposed in this paper. The gradient descent strategy can converge faster and obtain the optimal solution of user association policy and unload policy. Through simulation, it is verified that the proposed scheme can obtain lower computation delay and computation energy consumption on the basis of solving the problem of base station overload.

Offloading for 5G intelligent terminals is an important requirement of mobile edge computing (MEC), which can reduce the load of terminal devices and provide more powerful computing power and services. However, due to the interference between different users and the waste of a lot of resources, the role of MEC is greatly limited. In the article, energy-saving blockchain technology is introduced into the user-centric MEC (UC-MEC) system. However, in UC-MEC, it is necessary to comprehensively consider task offloading and energy consumption optimization in the consensus process to improve overall energy efficiency. To solve these problems, a new offloading model of 5G intelligent terminal is proposed, which realizes safe and efficient resource utilization by jointly optimizing the bandwidth and computing resource allocation of access point (AP). The research also proposed a resource consensus mechanism based on Zookeeper atomic broadcast (RC-ZAB), and the parallel optimization framework of relaxed hybrid alternative direction method of multipliers (RH-ADMM) to solve the problems of resource transaction and energy consumption optimization. The experimental results show that compared with the comparison method, the total energy consumption of the proposed method is reduced by 41.39% at most, and the total delay is reduced by 29.80% at most.

The paper mainly studies how to better apply non-orthogonal multiple access (NOMA) to mobile edge computing (MEC) system under the condition of considering offload interruption, and adopts user collaboration(UC) and wireless power transfer (WPT) technology. First, two users use WPT to collect energy from the base station equipped with the MEC server. Then, to better increase the system capacity and performance, the near user acts as a relay to help the far user offload some of the tasks to the MEC server, using the NOMA protocol for both users. To obtain an energy-efficient MEC design, we first derive the outage probability for both users, then the offloading time allocation and power allocation are jointly designed to maximize the effective computational efficiency, which is a non-convex problem. To deal with the problem, we first transform it into a relaxation problem and then solve it using the sequential convex approximation (SCA) method. Finally, numerical results demonstrate that the proposed scheme introduces NOMA and UC into MEC simultaneously, which has better advantages compared with other schemes.

With the advancement of 5G technology, user demand grows, resources become scarce and differentiated services place greater requirements on performance in slice-MEC system. However, optimizing works in the vast majority of existing research focus only on slice selection, resource orchestration, utility improvement from the perspective of users, without considering whole system's power consumption cost. Firstly, to get over this dilemma, a two-tier architectural model for slice-MEC system is proposed, which is mainly divided into the slice instance layer and infrastructure layer. Next, we establish the optimization problem of minimizing weighted power consumption cost while guaranteeing requirements of delay and rate, taking into account both communication and computational resources. Moreover, to solve the problem above, the HPCA-based resource management and computing offloading algorithm is designed. Finally, simulation results show that the proposed algorithm in this paper outperforms than benchmark algorithms and we also analyse the performance of the system by varying different parameter conditions.

In order to meet the demand for bigger data and lower time of processing data in future industry and daily life, the paper proposes a scheme combining multi-access edge computing (MEC) and mode division multiple access (MDMA). To greatly reduce the MEC system transmission and data processing time of a scenario where multiple devices offload the large amount of data onto the MEC server within the coverage area of base station, modes are used as new communication resources with frequency to form resources blocks which will be allocated. Because the model of MEC system using MDMA contains continuous and boolean variables and the exclusive constraints about using resources blocks, the paper proposes an algorithm combining modified differential evolution (DE), tournament selection (TS) and logical operation, which has a great performance.

To enhance the security in the computational offloading process of Mobile Edge Computing (MEC) systems, this paper proposes a physical layer secure transmission scheme based on Intelligent Reflecting Surface (IRS) assistance. In this scheme, IRS is added to facilitate the task transfer between the user and the MEC server, while suppressing eavesdroppers from eavesdropping on user information, thus achieving a secure computing offload for the system. In order to maximize the safe transmission rate of the system, a flow-form optimization algorithm based on alternating optimization and conjugate gradient is designed to obtain the users transmit power as well as the phase shift of the IRS. Based on this, the computational offload rate of users and the computational resources of MEC servers are optimized by heuristic algorithms to minimize the computational offload delay of MEC systems while ensuring secure transmission. Simulation results show that the proposed scheme can reduce the total computational offload delay while guaranteeing secure transmission of the MEC system compared with other benchmark schemes and traditional artificial noise assisted schemes.

Grant-free access successfully reduces the overhead and delay of signaling in the scenario of ultra-reliable low-latency communications (URLLC). However, it is also prone to conflict in the case of high load, resulting the lose of spectrum efficiency, reduced reliability and increased delay. In this paper, we realize K-multipacket reception (K-MPR) with sparse code multiple access (SCMA) technology to improve spectrum efficiency and throughput, and yet its implementation greatly increases the difficulty of ensuring the reliability of data transmission. Therefore, we propose an optimization method to maximize service quality of SCMA grant-free access with MPR. First, we investigate the behaviors of transient system via Markov chain to obtain reliability under delay constraints. Furthermore, we use dichotomy to calculate the minimum MPR ability value $K$. The simulation results show the validity of theoretical analysis. The proposed scheme maintains the increasing trend of reliability under delay constraints and tradeoff between throughput and reliability under delay constraints is presented, while optimal tradeoff between spectrum efficiency and reliability is achieved.

Computational offloading, as one of the means to reduce latency and energy consumption in mobile edge computing (MEC), can reduce industrial costs through reasonable offloading decisions. A mixed-integer nonlinear optimization problem that minimizes task completion time is constructed for the smart grid scenario where there are power terminals with insufficient computing power to deal with high latency arising from low latency and high reliability applications, and a joint optimization strategy for task offloading decision and resource allocation is proposed. The strategy first decomposes the problem into two sub-problems, resource allocation and task offloading, and first adopts the Lagrange multiplier method to obtain the optimal solutions for computing resources as well as spectrum resource allocation, and then uses an adaptive genetic algorithm to formulate the offloading decision under the condition of determining the optimal solution for each resource allocation. The simulation results show that the proposed algorithm can minimize the task latency of electric terminals under the condition of limited resources and improve the service experience of end users.

The coexistence of URLLC and eMBB services are very important for high data rate and low latency services in 5th Generation(5G) mobile network. But it is a difficult challenge to optimize resource allocation through effective scheduling in this case. Therefore, this paper proposes a coexistence scheme of URLLC and eMBB with the puncturing mechanism. Considering rate demands of different users, we introduce a QoE-aware utility function. Then, we formulate an optimization problem that maximize the average system utility while meeting URLLC requirements. Considering the non-deterministic polynomial(NP) hard nature of the problem, we divide the problem into two parts. In order to solve the problems, we take an iterative way, which further use an effective heuristic algorithm for resource allocation and URLLC puncturing. Simulation results show the advantage of this method compared with other ways.

In this paper, we investigate the issue of resource allocation for secure communications in Divice-to-divice(D2D) network with full-duplex (FD) radio, for maximizing the overall secure energy efficiency(SEE). Under the power constraint and secrecy capacity requirement, the resource allocation problem involves the cluster head selection and subcarrier power allocation. Since the considered optimization problem is non-liner which is difficult to solve, it is decomposed into two subproblems. Firstly, we select the best cluster head with the VIKOR algorithm based on the attributes we propose. Secondly, we transform it into convex optimization by exploiting a series of mathematical tools, such as Parametric programming, Lagrange dual method and Karush-Kuhn-Tucker (KKT) optimal conditions, resulting in an efficient iterative algorithm to achieve the suboptimal allocation of power. Simulation results illustrate the suboptimal SEE can be guaranteed by our proposed algorithm.

Although deep learning has been dominant in the field of target detection, there are still some challenges in the field of detection of ships: horizontal boundary box contains too much redundancy and noise in the scene of large scale and dense arrangement of ships, and there are many interferences in remote sensing optical images that affect the bounding box regression. Recent neural network for ship detection is generally used to extract the pixel information in the image to achieve rapid positioning and boundary box regression, or to process the dataset, so as to train a more robust network. However, these methods do not focus on the original feature map and loss functions of the target, which directly lead to the effects of ship positioning and regression. In this paper, we propose a ship detection network based on feature filter and Kullback-Leibler(KL) divergence loss function. In this paper, we propose to use mask filters on the output of the region of interest(ROI) network to remove the noise around the rotating bounding box, which is conducive to the regression of the rotating bounding box and angle in the second stage. In order to get a better bounding box, we propose to use KL loss function for regression of bounding box parameters. With the optimization of KL loss, the bounding box can better surround the ship. We carried out experiments on our own remote sensing ship image dataset for ship detection, it contains 5,126 remote sensing satellite images and more than 23800 ships in 6 categories. The dataset contains a variety of scenarios and challenges. Experiments show that the method is accurate and effective, and the bounding box has good wrapping property.

Non-orthogonal multiple access (NOMA) and heterogeneous network (HetNet) have been regarded as two promising technologies in 5G, which can support massive connectivity. Integrating NOMA technology into HetNet is an inevitable trend to achieve better spectral efficiency. However, interference control becomes more complicated due to the combination of these two technologies and the optimization of energy efficiency comes at the expense of spectral efficiency. Therefore, we develop a novel network scenario, which exploits the benefits of sectorization. In this paper, we aim to maximize the energy efficiency of the entire small cells in an uplink underlaying two-tier NOMA heterogeneous network with sectorization. Since this energy efficiency maximization problem is a mixed integer non-linear programming (MINLP) problem which is difficult to solve, it is decomposed into two subproblems. Firstly, we use the Lagrange dual method and Karush-Kuhn-Tucker (KKT) optimal conditions to obtain the closed-form power allocation solution. Secondly, a low-complexity subchannel allocation algorithm based on the greedy strategy is proposed. Simulation results show that our proposed scheme obtain significant energy efficiency gain and is superior to other existing schemes.

为鼓励视频服务提供商参与到缓存过程中,本文提出一种基于Stackelberg博弈的激励缓存资源分配算法.与传统激励缓存资源分配方案不同,本文考虑同时存在多个网络运营商和多个视频服务提供商,视频服务提供商从网络运营商处购买存储空间以缓存热门视频.针对该场景,本文将该激励缓存模型建模为多主多从Stackelberg博弈问题,分别构建主方和从方的效用函数,证明了在网络运营商价格确定的情况下,视频服务提供商之间的非合作博弈存在纳什均衡.文章利用分布式迭代算法对该博弈模型进行求解,获得了视频服务提供商的最优缓存策略和网络运营商的最优价格策略.仿真结果表明,本文提出的激励缓存机制可使视频服务提供商获得比其他缓存分配算法更高的单位成本收益.

为缓解基站的视频流量过载,本文针对时延敏感的实时视频业务,设计一种D2D协作视频多播传输方案.该方案采用可伸缩视频编码(Scalable Video Coding,SVC)对视频流进行编码处理,利用SVC流的分层结构特征来应对多播信道间的差异性.在SVC编码的基础上,为了改善用户观看体验及提升用户所接收的视频质量,所提出的协作式视频传输方案引入有效吞吐量这一概念,在一定时延约束下,根据信道反馈信息灵活地对不同信道上的不同SVC视频层进行码率调整.仿真结果表明,所提出的方案能够有效地减小端到端时延,有效丢失率,提高有效吞吐量.

User Generated Content (UGC) increases rapidly owing to the development of mobile Internet. Proactive caching can unload popular content flow and reduce content request delay to improve QoS. The effectiveness of proactive caching depends on the imformation about content popularity. In this paper, we suggest a UGC online learning proactive caching strategy. In this strategy, with the utilize of multi-level online learning and user clustering, the preference similarity among cache group members is improved, thus improving online learning efficiency and cache hit ratio. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, a novel QoE (Quality of Experience) optimization mechanism is proposed for the coexistence between LTE and WiFi networks, where traffic offloading is enabled from LTE to WiFi. Firstly, the throughput models of LTE and WiFi are analyzed, respectively. And then the users' satisfaction function is introduced to measure QoE. An optimal problem that maximizing the QoE of LTE network with guaranteeing the QoE of WiFi network, is formulated and solved by transformation and simplification. Simulation results indicate that traffic offloading can greatly improve the QoE of LTE network.

Due to the openness of the physical layer, the physical layer security has been widely discussed. It is considered as an effective way to improve the secrecy rate that we add artificial noise to D2D communications underlaying cellular networks. Compared to previous studies, in this paper we not only think of the situation where artificial noise is added to the original communication signal between the base station and the cellular user, but also consider the design of the intended information signal and artificial noise signal between corresponding D2D pair. In order to improve system secret rate under limited resources, we propose an idea of the power distribution coefficient for two legitimate communication links(one link between BS and CU and the other link between a D2D pair). Then we build convex optimization model and adopt a mixed penalty function algorithm to obtain optimal value of power distribution coefficient. On this basis, a joint optimization allocation scheme for transmit power between BS and DU-T is proposed, which satisfies the best security mechanism of physical layer in D2D communication underlying cellular networks. Simulation results show that as taking the optimal solution, system secret rate is clearly improved.

With the combination of device-to-device (D2D) communication and millimeter wave (mmWave) technology, the density of communication devices increases greatly and the communication environment becomes more complicated. A key challenge in such networks is interference, which is not only from main lobe, but also from the sidelobe power accumulation. However, the approaches for resource allocation in traditional wireless networks may not be efficient for this complex environment. In this paper, we first introduce a new scenario that employs time and space division for scheduling in mmWave and D2D networks. Then, we propose a resource allocation algorithm based on vertex coloring and redefine concurrent transmission conditions by defining a power decision threshold, which is designed to further reduce the sidelobe interference. Simulation results present that our scheduling algorithm outperforms traditional time division multiple access (TDMA) and traditional vertex coloring algorithm.