Superresolution Time Delay Estimation for Joint Communication and Sensing Systems
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Beschreibung
Conventional design of wireless communication systems has primarily focused on enhancing data transmission performance, such as increasing throughput, reducing latency, and improving system reliability. Recent developments have not only continued to advance these core metrics but have also expanded to incorporate new features and services beyond communication. One particularly attractive service is sensing, which is motivated by its ability to exploit the information contained in the received signal for various applications, such as intruder detection, people flow monitoring, and indoor localization, apart from its potential contribution to communication performance. This leads to the concept of joint communication and sensing (JCAS) design. In addition to data transmission, a JCAS system aims to retrieve environmental information from all possible propagation paths. An essential aspect of JCAS design is the integration of sensing capabilities into existing communication systems without significant hardware modifications. One of the important information that can be extracted from received signals is the propagation delays from the transmitter to the receiver via multiple paths. These delays are associated with distances, and thus, can be utilized for ranging and localization. However, the resolution and accuracy of time delay estimation using conventional algorithms, such as cross-correlation, are constrained by signal bandwidth. This limitation often restricts their usage in existing communication systems, especially within the sub-6 GHz spectrum. The objective of this work is to explore and develop algorithms that can relax bandwidth requirements while still providing reasonable resolution and accuracy, considering practical applicability in realistic scenarios. Specifically, this work focuses on subspace-based superresolution time delay estimation algorithms and proposes several enhancements. These enhancements include the flexible utilization of frequency resources for sensing, extensions that significantly increase resolution, and approaches that reduce computational complexity. The effectiveness of the proposed methodologies, in terms of estimation accuracy and resolution compared to state-of-the-art solutions, is evaluated through numerical simulations and further verified with realistic setups. In particular, the experiments are focused on ranging applications, utilizing real-world hardware components and channel conditions, demonstrating both the feasibility and the performance gains of the proposed algorithms.
Autor*in
Zhongju Li