Wireless Communications and Signal Processing for the Internet of Things

物联网无线通信和信号处理

• 批准号: RGPIN-2019-06237
• 负责人: Blostein, Steven
• 金额: $ 2.84万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752042
• 关键词: Wireless; Communications; Signal; Processing; Internet

Reliable high-rate wireless and mobile personal communications of multimedia has been largely achieved. Its success is due to physical signal layer improvements, Internet packet (IP) switching combined with channel sensing, and multiple access protocols with re-transmission of packet errors. The future is faced with new challenges motivated by automation in manufacturing, smart city infrastructure, transportation systems, e-health, environmental monitoring, and energy efficiency demands. Technical challenges standing in the way include communication among ever-larger numbers of devices rather than among people, low latency requirements rather than best effort service, and dense networks rather than minimum coverage achievement. These issues are starting to be addressed worldwide by fifth-generation (5G) standardization efforts targeted for year 2020. By necessity, the aggressive 5G time frame is narrowly focused. However, the above-mentioned future challenges are longer term, much broader and require comprehensive solutions. The proposed research program addresses several of these challenges by exploring the following themes: (i) increasing simultaneous communication capacity with low power devices well beyond the number of base station antennas, (ii) low delay event detection and change detection monitoring from a distributed sensor network with restricted communication, energy cost, error rate, failure rate, and resilience to malicious users, and (iii) multiple access protocols for dense networks to associate groups of devices to multi-antenna access points that have performance optimization in terms of throughput and fairness. To delineate our proposed research, we note first that communication with low-rate power-limited devices is not well modeled by conventional signalling assumptions used in classical information theory by Shannon nor is well served by classical maximum signal-to-noise ratio beamforming criteria. The large numbers of devices require signaling dimensions that far exceed the number of available antennas. Second, unlike classical Shannon principals, communication latency (or delay) is constrained severely so as to enable new applications, e.g., real-time remote control of complex networked systems, driver-less vehicular networks, and operation of networked robots. In addition, sensor networks will increasingly require monitoring tasks that transmit decisions with reliability, speed and security, and solutions are needed to address protocol overhead. Third, networks face formidable challenges in scaling of size and density. Resource allocation in dense networks faces computational explosion. Current protocols that enable dynamic spectrum resource sharing, while effective for current systems, are limited in the long term due to the many possible groupings of devices. The proposed research program will train 4 PhD, 4 MASc, 5 M.Eng. and 3 undergraduate students and enable future defense and industry related collaborations.

可靠的高速率无线和移动的个人多媒体通信已基本实现。它的成功是由于物理信号层的改进，与信道检测相结合的互联网分组（IP）交换，以及具有分组错误重传的多址协议。未来将面临制造业自动化、智能城市基础设施、交通系统、电子健康、环境监测和能源效率需求带来的新挑战。技术挑战包括越来越多的设备之间的通信而不是人与人之间的通信，低延迟要求而不是尽力而为的服务，以及密集的网络而不是最小的覆盖范围。这些问题正开始通过目标为2020年的第五代（5G）标准化工作在全球范围内得到解决。出于必要，积极的5G时间框架集中在狭窄的范围内。然而，上述未来挑战是长期的、广泛得多的，需要全面的解决办法。拟议的研究计划通过探索以下主题来解决其中的几个挑战：（i）增加与远超过基站天线数量的低功率设备的同时通信容量，（ii）来自分布式传感器网络的低延迟事件检测和变化检测监视，其具有受限的通信、能量成本、错误率、故障率和对恶意用户的弹性，以及（iii）用于密集网络的多接入协议，以将设备组关联到多天线接入点，其在吞吐量和公平性方面具有性能优化。为了描述我们提出的研究，我们首先注意到，与低速率功率受限的设备的通信是不好建模的传统信令假设中使用的经典信息理论的香农，也不是很好地服务于经典的最大信噪比波束成形标准。大量设备需要远远超过可用天线数量的信令尺寸。其次，与经典的香农原理不同，通信延迟（或延迟）受到严格约束，以便能够实现新的应用，例如，复杂网络系统的实时远程控制、无人驾驶车辆网络以及网络机器人的操作。此外，传感器网络将越来越多地要求监控任务以可靠、快速和安全的方式传输决策，并且需要解决方案来解决协议开销。第三，网络在规模和密度的扩展方面面临巨大挑战。密集网络中的资源分配面临计算爆炸。实现动态频谱资源共享的当前协议虽然对当前系统有效，但由于设备的许多可能分组而在长期内受到限制。拟议的研究计划将培养4名博士，4名硕士，5名工程硕士和3名本科生，并使未来的国防和工业相关的合作。