6G智能内生无线通信网络:现状、挑战、系统设计和架构

移动通信系统已经成为支持各领域信息传输需求的基础设施,而6G的功能预计将超越简单的数据传输。6G智能内生无线通信网络具有提供智能包容性的潜在能力,可以让任何终端用户随时随地获得人工智能服务。现有的分布式人工智能架构中,执行算法的终端间相对独立,无法为整个无线通信提供内生智能系统性功能。针对这一问题提出了一个能够为6G提供智能内生功能的基本系统框架,使6G网络可以更好地实现高性能安全计算和通信,构建一个智能体可相互合作的生态架构。首先简述了无线通信网络的发展背景和现状,然后从数据、智能和网络的维度讨论了实现6G智能内生架构的挑战,最后提出了一个基本的6G智能网络的系统架,包括了网络层、数据层、智能层和应用层四层结构。

Intelligent handoff decision in radio heterogeneous network

Abstract： In order to improve the accuracy of vertical handoff decision for radio heterogeneous network, this paper proposes an intelligent adaptive multi-criteria vertical handoff （AMVHO） decision algorithm. This algo- rithm uses a fuzzy inference system （FIS） and a modified Elman neural network （MENN）. The FIS adopts the crucial criteria of vertical handoff as input variables and makes handoff decision based on the defined rule base. The MENN helps to predict the number of users of the after-handoff network, which is a pivotal variable of the FIS. Simulation results show that, compared with the conventional method, the AMVHO decision algorithm a- chieves better performance in guaranteeing the quality of service （QoS） of the after-handoff communication.

Intelligent Traffic Allocation Algorithm for Multiple Networks

In recent years, wireless communication systems have experienced tremendous growth in data traffic. Many capacity-enhancing techniques are applied to elevate the gap between the amount of traffic and network capacity, and more solutions are required to minimize the gap. Traffic allocation among multiple networks is regarded as one of the most effective methods to solve the problem. However, current studies are unable to derive the quantity of traffic that each network should carry. An intelligent traffic allocation algorithm for multiple networks is proposed to obtain the optimal traffic distribution. Multiple factors affecting traffic distribution are considered in the proposed algorithm, such as network coverage, network cost, user habit, service types, network capacity and terminals. Using evaluations, we proved that the proposed algorithm enables a lower network cost than load balancing schemes. A case study of strategy rmldng for a 2G system refarming is presented to further illustrate the applicability of the proposed algorithm. We demonstrated that the new algorithm could be applied in strategy rmldng for telecommunication operators.

Traffic Scheduling Mechanism Based on Interference Avoidance for Meter Data Collection in Wireless Smart Grid Communication Networks

Meter Data Collection Building Area Network(MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.

Intelligent GPS-Less Speed Detection and Clustering in VANET

Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.

Multiple Mobile Sinks Data Dissemination Mechanism for Large Scale Wireless Sensor Network

WSN has been developing from traditional environment monitoring applications to the ubiquitous information services such as the Congestion-oriented Intelligent Transportation System （ColTS）. However, the mobility of nodes makes data dissemination a hard nut to crack. In this paper, we propose MSDD, a multiple mobile sinks data dissemination mechanism for solving the dissemination problem. The main ideas of MSDD are constructing a two-tier grid structure by a designated sink, exploiting a hierarchical monitoring mechanism, and employing a global agent to track the sink locations in order to make the emergencies reported to the sinks immediately, In addition, MSDD supports the query-driven data dissemination. Being compared with TTDD, MSDD is theoretically proved to have less communication overhead. We also validate MSDD outperforms TTDD in reliability and the emergency delivery latency bv simulations.