Concluding 50 Years of Research in Wireless Communications: Algorithms for Artificial Intelligence and Optimization in Networks Beyond 5G and Thereafter

总结无线通信 50 年的研究：5G 及以后网络中的人工智能和优化算法

• 批准号: RGPIN-2022-04417
• 负责人: Bhargava, Vijay
• 金额: $ 2.61万
• 依托单位: University of British Columbia
• 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=752040
• 关键词: Concluding; 50; Years; Research; Wireless

The proposed project will conclude our ongoing research on algorithms for networks beyond 5G and thereafter. Our specific focus will be on the topics of spectrum monitoring, resource optimization in edge networks, and device localization. In networks beyond 5G, we will continue to see a surge in the number of wireless devices. For optimal spectrum utilization, a critical step is to monitor the spectrum actively, detect all the signals present and map them to their transmitters. In recent work, we explored deep learning for this task and proposed a spectrogram-based solution to detect Wi-Fi-type signals. We will now make architectural extensions to detect other heterogeneous signals like Bluetooth, as experienced in a real-life scenario. We will pursue protocol-level and device-level classification using defining features such as the wireless packet inter-arrival times, the number of subcarriers, and hopping periods. We will also develop denoising algorithms to alleviate the performance degradation in the low signal-to-noise ratio region. The number of services offered at the network edge keeps growing. For optimal resource utility in networks beyond 5G, we need fair and efficient resource allocation algorithms at the edge. In recent work, we investigated the joint edge resource management and pricing problem and proposed a game theory-based solution. We will now extend the work to include the service placement cost and the uncertainties due to demand variability, node failures, and fluctuating resource prices. We will tackle the resulting complications using techniques from stochastic, chance-constrained, and risk-constrained optimization. We will also develop a robust optimization framework for the service provider to determine the optimal locations for service placement and the amount of resources to purchase from each location. Accurate localization will be crucial in networks beyond 5G due to the growing need for device sensing and cooperation in low-latency communications. In recent work, we proposed machine learning algorithms to locate the devices transmitting at a fixed power level in a 5G system. We will now extend the work to heterogeneous devices with diverse antenna gains and power control mechanisms. We will seek improvements in the localization accuracy through fusion techniques, which augment the signal strength with angle and time information. Towards developing efficient tracking algorithms, we will model the device mobility using appropriate state-space models and design Bayesian filters that can recursively estimate the time-varying device location. Network operators across Canada strive to deliver various location-aware services while providing quality applications at affordable rates. The proposed research will immensely benefit them in growing their subscription base. Our spectrum monitoring methods will also help detect malicious transmitters and protect our safety-critical airspaces such as airports.

拟议的项目将结束我们正在进行的关于5G及之后网络算法的研究。我们的具体重点将是频谱监控、边缘网络中的资源优化和设备本地化。在5G之后的网络中，我们将继续看到无线设备数量的激增。为了优化频谱利用率，关键步骤是主动监控频谱，检测所有存在的信号并将其映射到其发射机。在最近的工作中，我们探索了这项任务的深度学习，并提出了一种基于频谱图的解决方案来检测Wi-Fi类型的信号。我们现在将进行架构扩展，以检测其他异构信号，如蓝牙，如在现实生活中的场景中所经历的。我们将追求协议级和设备级的分类使用定义功能，如无线数据包到达间隔时间，子载波的数量和跳频周期。我们还将开发去噪算法，以减轻低信噪比区域的性能下降。 在网络边缘提供的服务数量不断增长。为了在超过5G的网络中实现最佳资源利用率，我们需要在边缘实现公平高效的资源分配算法。在最近的工作中，我们研究了联合边缘资源管理和定价问题，并提出了一个基于博弈论的解决方案。现在，我们将扩展工作，包括服务布局成本和不确定性，由于需求的变化，节点故障，和波动的资源价格。我们将使用随机、机会约束和风险约束优化技术来解决由此产生的复杂问题。我们还将为服务提供商开发一个强大的优化框架，以确定服务放置的最佳位置以及从每个位置购买的资源量。 由于对设备感知和低延迟通信合作的需求日益增长，准确定位在5G之后的网络中至关重要。在最近的工作中，我们提出了机器学习算法来定位在5G系统中以固定功率水平传输的设备。我们现在将工作扩展到具有不同天线增益和功率控制机制的异构设备。我们将寻求通过融合技术，增强信号强度与角度和时间信息的定位精度的改进。为了开发有效的跟踪算法，我们将使用适当的状态空间模型和设计贝叶斯滤波器，可以递归地估计随时间变化的设备位置的设备移动性建模。加拿大各地的网络运营商努力提供各种位置感知服务，同时以合理的价格提供高质量的应用程序。拟议的研究将极大地有利于他们扩大其订阅基础。我们的频谱监测方法还将有助于检测恶意发射机，并保护我们的安全关键空域，如机场。