Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

基于DRL的主动RIS安全无线通信优化方法

针对可重构智能表面(reconfigurable intelligent surface,RIS)辅助的安全无线通信系统在保密率优化问题中存在的信道空间连续变化、传统数学优化方法难以逼近最优解等问题,提出一种基于深度强化学习的SEC-DDPG(security deep deterministic policy gradient)算法。通过将RIS通信系统建模为连续变化空间中的马尔可夫决策过程,联合优化传输波束赋形和反射波束赋形达到最大化用户保密率的目的。仿真实验结果显示,在不同的传输功率及反射单元数量下,SEC-DDPG算法在主动和被动RIS系统中得到的最优保密率均比传统的交替优化算法有15%~20%的提升。研究结果表明,主动RIS场景下的安全性要优于被动RIS,与交替优化算法相比,SEC-DDPG算法能显著提高安全无线通信系统的用户保密率且具有鲁棒性,接近系统的最佳保密性能。

Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks

Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

基于QMix的车辆云计算资源动态分配方法

城市交通智能化和通信技术的进步会产生大量基于车辆的应用,但目前车辆有限的计算资源无法满足车辆应用的计算需求与延迟性约束。车辆云(VC)可以高效地调度资源,从而显著降低任务请求的延迟与传输成本。针对VC环境下任务卸载与计算资源分配问题,提出一个考虑异质车辆和异质任务的计计资源分配算法。对到达的任务构建M/M/1队列模型与计算模型,并定义一个效用函数以最大化系统整体效用。针对环境中车辆地理分布的高度动态系统变化,提出基于双时间尺度的二次资源分配机制(SRA),使用两个不同时间尺度的资源分配决策动作,对其分别构建部分可观测马尔可夫决策过程。两个决策动作通过执行各自的策略获得的奖励进行连接,将问题建模为两层计算资源分配问题。在此基础上提出基于二次资源分配机制的多智能体算法SRA-QMix求解最优策略。仿真结果表明,与深度确定性策略梯度算法对比,该算法的整体效用值和任务完成率分别提高了70%、6%,对于QMix和MADDPG算法分别应用SRA后的任务完成率分别提高了13%与15%,可适用于动态的计算资源分配环境。

Performance Analysis in Cloud Radio Access Networks:User-Centralized Coordination Approach

The cloud radio access network(C-RAN) has recently been proposed as an important component of the next generation wireless networks providing opportunities for improving both spectral and energy effi ciencies. The performance of this network structure is however constrained by severe inter-cell interference due to the limited capacity of fronthaul between the radio remote heads(RRH) and the base band unit(BBU) pool. To achieve performance improvement taking full advantage of centralized processing capabilities of C-RANs,a set of RRHs can jointly transmit data to the same UE for improved spectral effi ciency. In this paper,a user centralized joint coordinated transmission(UC-JCT) scheme is put forth to investigate the downlink performance of C-RANs. The most important benefit the proposed strategy is the ability to translate what would have been the most dominant interfering sources to usable signal leading to a signifi cantly improved performance. Stochastic geometry is utilized to model the randomness of RRH location and provides a reliable performance analysis. We derive an analytical expression for the closed integral form of the coverage probability of a typical UE. Simulation results confirm the accuracy of our analysis and demonstrate that significant performance gain can be achieved from the proposed coordination schemes.

X-Band Power Amplifier for Next Generation Networks Based on MESFET

Advanced wireless standards of communication like 3GPP and LTE are becoming more and more efficient and with this evolution of communication systems mobile equipment is also become smaller and smaller. Power amplifier designing has become a very crucial task in this era where efficiency and size are the main concern of any designer. In this paper we have design and analyzed X-band Class E Metal-semiconductor field effect transistor(MESFET) based Power Amplifier. This device targets the devices which use OFDM technique to improve their spectral efficiency for the next generation communication systems. Microstrip lines are used to achieve small size for our design instead of lumped components. Load Pull measurements are used to get MESFET input and output impedances optimum values. For linear and non linear operation small signal mathematical model of the design is used. To reduce thermal losses FR4 substrate is used to increase PA efficiency. Our designs shows small values of input and output return loss of about-22.3d B and-23.716 d B achieving a high gain of about25.6 d B respectively, with PAE of about 30 % having stability factor greater than 1 and 21.894 d Bm of output power.

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing.However,the current contributions in computation offloading and resource allocation are inefficient;moreover,they merely consider the static communication mode,and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs.A joint problem of mode selection,resource allocation,and power allocation is formulated to minimize latency under various constraints.We propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs.The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier.Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.

Primitives towards verifiable computation： a survey

Verifiable computation （VC） paradigm has got the captivation that in real term is highlighted by the concept of third party computation. In more explicate terms, VC allows resource constrained clients/organizations to securely outsource expensive computations to untrusted service providers, while acquiring the publicly or privately verifiable results. Many mainstream solutions have been proposed to address the diverse problems within the VC domain. Some of them imposed assumptions over performed computations, while the others took advantage of interactivity /non-interactivity, zero knowledge proofs, and arguments. Further proposals utilized the powers of probabilistic checkable or computationally sound proofs. In this survey, we present a chronological study and classify the VC proposals based on their adopted domains. First, we provide a broader overview of the theoretical advancements while critically analyzing them. Subsequently, we present a comprehensive view of their utilization in the state of the art VC approaches. Moreover, a brief overview of recent proof based VC systems is also presented that lifted up the VC domain to the verge of practicality. We use the presented study and reviewed resuits to identify the similarities and alterations, modifications, and hybridization of different approaches, while comparing their advantages and reporting their overheads. Finally, we discuss implementation of such VC based systems, their applications, and the likely future directions.