Selective transmission and channel estimation in massive MIMO systems

Massive MIMO systems have got extraordinary spectral efficiency using a large number of base station antennas,but it is in the challenge of pilot contamination using the aligned pilots.To address this issue,a selective transmission is proposed using time-shifted pilots with cell grouping,where the strong interfering users in downlink transmission cells are temporally stopped during the pilots transmission in uplink cells.Based on the spatial characteristics of physical channel models,the strong interfering users are selected to minimize the inter-cell interference and the cell grouping is designed to have less temporally stopped users within a smaller area.Furthermore,a Kalman estimator is proposed to reduce the unexpected effect of residual interferences in channel estimation,which exploits both the spatial-time correlation of channels and the share of the interference information.The numerical results show that our scheme significantly improves the channel estimation accuracy and the data rates.

基于联邦学习的毫米波大规模MIMO的混合波束赋形和资源分配

针对大规模毫米波(millimeter wave,mm-Wave)多输入多输出(multiple input multiple output,MIMO)系统中的混合波束赋形在中心式机器学习(centralized machine learning,CML)中导致的通信开销过大问题,提出了分层联邦学习(federated learning,FL)框架下的混合波束赋形与基于合同理论的资源分配优化方法。首先,在分层系统中对多用户计算系统开销,并通过优化分配资源实现系统的效益最大化;然后,用户利用分配的资源对信道数据和相应的预编码数据进行反向传播神经网络(back propagation neural network,BPNN)模型训练,利用边缘服务器(edge server,ES)收集用户训练的权值和参数进行边缘聚合,达到一定精度后上传到云服务器(cloud server,CS)进行云聚合,直到取得最优的模型。实验结果表明,资源优化极大地降低了通信开销,并且基于FL的混合波束赋形不仅取得了和CML类似的和速率,而且其和速率要优于基于正交匹配追踪(orthogonal matching pursuit,OMP)的混合波束赋形以及全数字波束赋形方案。

基于多智能体深度强化学习的无人机路径规划

为解决多无人机(unmanned aerial vehicle, UAV)在复杂环境下的路径规划问题,提出一个多智能体深度强化学习UAV路径规划框架.该框架首先将路径规划问题建模为部分可观测马尔可夫过程,采用近端策略优化算法将其扩展至多智能体,通过设计UAV的状态观测空间、动作空间及奖赏函数等实现多UAV无障碍路径规划;其次,为适应UAV搭载的有限计算资源条件,进一步提出基于网络剪枝的多智能体近端策略优化(network pruning-based multi-agent proximal policy optimization, NP-MAPPO)算法,提高了训练效率.仿真结果验证了提出的多UAV路径规划框架在各参数配置下的有效性及NP-MAPPO算法在训练时间上的优越性.

面向绿色低碳的工业互联网:发展与挑战

当前绿色工业互联网系统在能效性、隐私性、资源管理等方面存在诸多问题,须融合更多不同领域的技术来实现绿色工业互联网的发展。因此,总结了当前绿色工业互联网的研究现状和实现技术,并对绿色工业互联网的发展和挑战进行了展望。首先,介绍了工业互联网的基本架构和技术特点,围绕绿色通信、移动边缘计算、区块链等前沿网络技术,多角度地讨论了工业互联网“绿色化”的实现途径;其次,详细综述了绿色工业互联网的发展现状以及实际场景建设中所涉及的关键技术;最后,针对现阶段绿色工业互联网发展过程中所面临的问题与挑战,讨论了构建绿色工业互联网系统的解决方案和发展趋势。

面向Kafka共识消息传输的负载均衡算法

Kafka是一种高吞吐量消息中间件,但该算法存在负载均衡导致消费者消息处理效率下降的问题。本文提出一种改进的Kafka负载均衡算法,其中协调者基于消费者和分区的对应关系,根据不同的负载均衡场景优化调整消费者数目,按照业务负载倒序的方式更新消费者和分区的对应关系,优先处理负载较大的业务分区,提高消息传输效率,并且搭建Fabric联盟链验证分析算法的性能。实验结果表明,本算法在中央处理器(CPU)资源消耗比其他算法低5%的情况下,共识速度提升了2%~7%,并且在6个Kafka节点中3个宕机的情况下仍然能共识上链,提升了Kafka负载均衡算法的效率和稳定性。

基于边缘计算与区块链的车载计算资源智能调度研究

随着车联网(IoV)的快速发展及部署,用户对网络服务质量的要求也随之提高。车联网数据计算作为网络服务的重要内容之一,越来越受到关注。移动边缘计算(MEC)作为一种允许车辆将计算任务卸载到车联网系统边缘服务器的技术,能够有效降低计算时延,提升数据处理效率。然而,车联网的数据流量日益增加,导致边缘计算设备的需求量大幅提高且存在数据安全可靠性问题。对此,本文面向车联网中移动车辆计算卸载的场景,提出一种基于区块链的停放车辆辅助计算的系统模型。通过联合考虑服务器计算资源、车辆机动性等条件,利用深度强化学习(DRL)对计算卸载和资源分配策略进行优化,减少系统能耗和数据传输时延,并提高区块链系统的交易吞吐量。仿真结果表明,本文所提优化方法可以有效提升系统性能,同时具有良好的收敛性能和稳定性。

基于Q-Learning的无线区块链广播路由算法

面向无线区块链网络场景,参考常用的区块链逻辑分层,提出一个独立灵活的分层逻辑架构,详细地介绍了每层的工作原理及作用。在现有广播路由算法的基础上结合无线区块链网络场景,针对广播过程中存在的广播冗余及广播总能耗高等缺陷,提出了一种基于Q-Learning的广播路由算法。该算法将无线区块链网络交易信息广播过程描述为多个agent的广播决策问题,通过线下学习、训练,帮助每一个当前广播节点agent做出是否广播的决策。仿真实验结果证明,该算法有效地解决了广播冗余问题,减少了广播过程的总能耗。

Deep reinforcement learning based task offloading in blockchain enabled smart city

With the expansion of cities and emerging complicated application,smart city has become an in-telligent management mechanism.In order to guarantee the information security and quality of service(QoS)of the Internet of Thing(IoT)devices in the smart city,a mobile edge computing(MEC)en-abled blockchain system is considered as the smart city scenario where the offloading process of com-puting tasks is a key aspect infecting the system performance in terms of service profit and latency.The task offloading process is formulated as a Markov decision process(MDP)and the optimal goal is the cumulative profit for the offloading nodes considering task profit and service latency cost,under the restriction of system timeout as well as processing resource.Then,a policy gradient based task of-floading(PG-TO)algorithm is proposed to solve the optimization problem.Finally,the numerical re-sult shows that the proposed PG-TO has better performance than the comparison algorithm,and the system performance as well as QoS is analyzed respectively.The testing result indicates that the pro-posed method has good generalization.

基于时间序列模型的Kafka系统智能化管理方法

区块链是分布式的数据存储系统,共识算法为区块链实现安全存储数据提供支撑和保障。Kafka作为共识算法中的一种,其高吞吐速率、低时延的特点受到青睐。但使用Kafka算法的系统接受大量交易时,易产生数据倾斜,即分布式系统的多节点结构中,大量数据集中在少数节点,导致系统资源被占用、性能下降。为解决上述问题,本文提出基于时间序列模型长短期记忆网络(LSTM)的智能优化方法。通过学习过往生产者接收到的交易量,预测下一时刻面临的交易量,动态调整生产者节点数量,减少数据集中在少数节点的情况。实验结果显示,本文方法可以将Kafka系统时延降低2~3倍,吞吐速率提升2~3倍,与优化前相比系统效率提升52.62%,比2种传统优化方法分别提升近3%和40%,能耗仅小幅提升,系统使用情况保持更加合理。

浅谈数据溯源性分析的风向标价值

文章基于在各功能模块之间进行数据或信息整合中,由于各功能模块规划和运作,在整体的协同层面可能发生功能间互斥或非高效运作导致的资源利用效率偏低。而实际的业务场景,各功能模块以独立的功能模块满足为基准进行相互独立运作并进行海量数据收集。本文讨论一种对已经获取的海量数据进行溯源性特征分析方法,充分利用大数据作为指引,开展改善和优化工作,可以作为一种指引和借鉴。

The adaptive distributed learning based on homomorphic encryption and blockchain

The privacy and security of data are recently research hotspots and challenges.For this issue,an adaptive scheme of distributed learning based on homomorphic encryption and blockchain is proposed.Specifically,in the form of homomorphic encryption,the computing party iteratively aggregates the learning models from distributed participants,so that the privacy of both the data and model is ensured.Moreover,the aggregations are recorded and verified by blockchain,which prevents attacks from malicious nodes and guarantees the reliability of learning.For these sophisticated privacy and security technologies,the computation cost and energy consumption in both the encrypted learning and consensus reaching are analyzed,based on which a joint optimization of computation resources allocation and adaptive aggregation to minimize loss function is established with the realistic solution followed.Finally,the simulations and analysis evaluate the performance of the proposed scheme.

Pseudo-label based semi-supervised learning in the distributed machine learning framework

With the emergence of various intelligent applications,machine learning technologies face lots of challenges including large-scale models,application oriented real-time dataset and limited capabilities of nodes in practice.Therefore,distributed machine learning(DML) and semi-supervised learning methods which help solve these problems have been addressed in both academia and industry.In this paper,the semi-supervised learning method and the data parallelism DML framework are combined.The pseudo-label based local loss function for each distributed node is studied,and the stochastic gradient descent(SGD) based distributed parameter update principle is derived.A demo that implements the pseudo-label based semi-supervised learning in the DML framework is conducted,and the CIFAR-10 dataset for target classification is used to evaluate the performance.Experimental results confirm the convergence and the accuracy of the model using the pseudo-label based semi-supervised learning in the DML framework.Given the proportion of the pseudo-label dataset is 20%,the accuracy of the model is over 90% when the value of local parameter update steps between two global aggregations is less than 5.Besides,fixing the global aggregations interval to 3,the model converges with acceptable performance degradation when the proportion of the pseudo-label dataset varies from 20% to 80%.

基于车联网和移动边缘计算的时延可容忍数据传输

以物联网和车联网为代表的智慧城市的快速发展,使网络中的数据传输与数据计算面临巨大挑战,网络资源的分配也越来越受到广泛关注,为此提出了一种基于移动边缘计算的新型网络架构,通过整合物联网与车联网,用以传输时延可容忍数据及处理数据计算任务.由于在同一网络架构下,需要融合多种网络标准和协议,基于可编程控制原理的软件定义网络技术被应用于所提网络架构中.此外,时延可容忍数据在软件定义的车联网中的传输与计算节点选择过程可建模为部分可观测马尔科夫决策过程,从而优化并获得最小化系统开销,包括最小网络开销和最短数据计算处理时间.仿真结果表明,与已有方案相比,所提方法可以有效地降低系统开销,缩短数据计算执行时间,提升数据计算效率,且在传输时延允许条件下,保证时延可容忍数据的传输到达率.

MEC和区块链赋能无人机辅助的物联网资源优化

针对物联网设备部署在较偏远地区而导致的传输链路易受损或传输覆盖范围有限等问题,在此场景中引入无人机和移动边缘计算(mobile edge computing, MEC)技术,有效改善物联网设备能源供给,优化计算资源,同时提升通信覆盖范围,减少不必要的网络开销.另外,区块链技术的引入保证了数据计算卸载与交互过程中的安全性和可靠性,实现了数据共享.因此,面向无人机辅助的物联网系统提出一种融合MEC和区块链的资源分配决策方法,以实现MEC系统和区块链系统性能的最佳权衡为目标,综合考虑频谱资源和计算资源的分配,构建问题模型,并采用基于交替方向乘子(alternating direction method of multipliers, ADMM)法的分布式优化算法求解该优化问题.仿真结果表明,所提优化框架可以有效减少MEC系统的总能耗和区块链系统的计算时延.同时,所提方法具有良好的收敛性能,系统稳定性得到充分保证.

QoS-Aware Dynamic Resource Management in Heterogeneous Mobile Cloud Computing Networks

In mobile cloud computing(MCC) systems,both the mobile access network and the cloud computing network are heterogeneous,implying the diverse configurations of hardware,software,architecture,resource,etc.In such heterogeneous mobile cloud(HMC) networks,both radio and cloud resources could become the system bottleneck,thus designing the schemes that separately and independently manage the resources may severely hinder the system performance.In this paper,we aim to design the network as the integration of the mobile access part and the cloud computing part,utilizing the inherent heterogeneity to meet the diverse quality of service(QoS)requirements of tenants.Furthermore,we propose a novel cross-network radio and cloud resource management scheme for HMC networks,which is QoS-aware,with the objective of maximizing the tenant revenue while satisfying the QoS requirements.The proposed scheme is formulated as a restless bandits problem,whose "indexability" feature guarantees the low complexity with scalable and distributed characteristics.Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing ones.

区块链技术应用于物联网:发展与展望

近年来,随着数字货币的日益兴起和商业应用,区块链技术越来越受到政府部门、工业界以及学术界的关注和认可。与此同时,移动通信网络的快速发展和广泛覆盖,促使物联网(IoT)逐渐走入日常生活,极大程度地提升了工作效率与生活品质。然而,物联网场景中仍存在能效性低、数据传输安全性差等问题,使用区块链技术可有助于促进物联网更好地发展和普及。本文对区块链技术在物联网中的应用进行了系统性综述,首先介绍了背景知识和相关研究,其次提出了区块链的基础架构模型,并且详细阐述了区块链技术应用于物联网各场景的发展现状以及相关特征,最后讨论了一些挑战及未来发展趋势。

人工智能与区块链赋能物联网:发展与展望

近年来,人工智能(artificial intelligence,AI)与区块链技术在物联网(Internet of things,IoT)场景中的应用,引起了学术界和工业界对2项技术的广泛关注和深度研究.区块链技术具有去中心化、匿名性、公开透明和不可篡改性等特点,但同时在可扩展性、系统能耗和安全性等问题上亟待改进.而AI技术作为一种强大的分析和决策工具,可以在实时场景中对数据进行预测与分析,并且做出最优决策,但AI技术的中心集中化结构以及安全可信的需求对其广泛应用带来很大的局限性.因此,在IoT场景中,应用2项技术并整合发挥各自优势,已经成为当前研究的重点.对AI与区块链技术赋能IoT场景进行了系统性综述,分别从AI辅助的区块链和基于区块链的AI两方面进行阐述和分析,并且对融合2项技术的发展现状以及相关特征应用于IoT场景进行系统性论述.最后,讨论了关于两者结合赋能给IoT带来更大的优势以及未来发展趋势和挑战.

基于RFID和区块链的预制构件管理系统

针对预制构件从生产、运输到施工过程中数据割裂和易被篡改等问题,提出一种基于RFID和区块链的预制构件管理系统。首先,对系统的设计思路和系统架构进行详细介绍;其次,结合生产、运输和施工的业务流程设计App,并分析数据存储和查询的实现思路;最后,实现了管理系统并进行测试部署。该系统利用RFID技术为每个构件进行唯一标识,并将App采集的数据存储在区块链中。利用区块链的去中心化、不可篡改等特点,为管理者提供真实可信的溯源数据,进而提高施工效率。