AITF: Learning and Adapting Sparse Recovery Algorithms for RF Spectrum Sensing

无线通信技术在社会中发挥着关键作用，支持个人通信、商业、国防和安全、家庭连接和娱乐。随着新应用的出现，对连接的需求也在快速增长。这一发展给通信可用的资源带来了压力：频谱是一种有限的自然资源；对移动应用最有用的部分大约在700 MHz到6 GHz之间。其中很大一部分被分配给了主要用户，其中许多人扮演着社会关键角色。为了满足日益增长的无线连接需求，需要能够以机会主义方式感知和利用可用频谱，同时不干扰主用户的设备。挑战在于如何在移动设备上以既省时又节能的方式做到这一点。一种通用的方法承诺，在快速检测大型干扰信号时，能效将提高数量级，它结合了新硬件和重要算法的组合，将频谱作为一个整体进行少量测量，并使用重要的算法来解释这些测量结果。该项目从机器学习发展到学习算法，以适应硬件传感器的特定特征，改进对非线性的处理，产生更低功率的传感器，并提高灵敏度。研究人员正在指导研究生和本科生，他们的工作跨越了学科界限，并通过新课程开发和新教科书传播结果。该项目研究学习和调整用于射频频谱感知的稀疏恢复算法的方法，利用与人工神经网络的已知连接，其中算法的结构决定网络的拓扑和权重。然后可以调整和优化这些权重，以适应物理传感器的特性。这种方法的一个主要前景是能够适应建模错误，同时生成更敏感、更健壮且可以简单高效的方式实现的恢复方法。该项目正在制定一种有原则和透明的方法，包括从理论上说明何时以及为什么有可能学习重建和支持在典型和最坏情况下都有效的恢复程序。该项目研究了线性逆问题和非线性问题的这些问题，既可以一次感知频谱，也可以随着时间的推移整合信息。该项目通过实验评估了这些方法对实现为集成电路的硬件传感器的效率和灵敏度的影响。

Wireless communications technology plays a critical role in society, supporting personal communication, business, defense and security, family connectivity, and entertainment. As new applications emerge, the demand for connectivity is increasing at a rapid pace. This development has put a strain on the available resources for communications: the spectrum is a finite natural resource; the most useful portions for mobile applications lie roughly from 700 MHz to 6 GHz. Large portions of it have been allocated to primary users, many of whom play socially critical roles. To accommodate the growing demand for wireless connectivity, there is a need for devices that can sense and utilize available spectrum in an opportunistic manner, while not interfering with primary users. The challenge is to do this in a time- and energy-efficient manner, on mobile devices. A general approach, which promises order-of-magnitude improvements in energy efficiency for rapidly detecting large interfering signals, uses a combination of new hardware to take a small number of measurements of the spectrum as a whole, and nontrivial algorithms to interpret those measurements. This project develops from machine learning to learn algorithms that are adapted to the specific characteristics of the hardware sensor, improving handling of non-linearities, yielding lower power sensors with improved sensitivity. The researchers are mentoring graduate and undergraduate students, whose work crosses disciplinary boundaries, and disseminating the results through new course development and a new textbook. The project studies methodologies for learning and adapting algorithms for sparse recovery for RF spectrum sensing, leveraging a known connection to artificial neural networks, in which the structure of the algorithm dictates the topology and weights of the network. These weights can then be adapted and optimized to fit the characteristics of a physical sensor. A major promise of this approach is the ability to adapt to modeling errors, while simultaneously producing recovery methods that are more sensitive, more robust, and implementable in a simple and efficient manner. The project is developing a principled and transparent methodology, including theoretical characterizations of when and why it is possible to learn reconstruction and support recovery procedures that are effective in both typical and worst-case senses. The project studies these problems both for linear inverse problems and for nonlinear problems, both for sensing the spectrum at a single time, and for integrating information over time. The project experimentally evaluates the impact of these methodologies on the efficiency and sensitivity of hardware sensors, realized as integrated circuits.