Efficient Road Traffic Estimation for Proactive Beam Allocation in an ISAC Setup

• Published in: IEEE access 2024, Vol.12, p.84952-84967
• Main Authors: Amiri, Wesam Al, Guo, Terry N., Mackenzie, Allen B.
• Format: Article
• Language: English
• Subjects: Communication; communication signals of opportunity; Communications systems; Estimation; Integrated sensing and communication; integrated sensing and communication (ISAC); Intelligent transportation system (ITS); Intelligent transportation systems; Jensen-Shannon (JS) divergence; least-squares estimation (LSE); Millimeter waves; Monitoring; OFDM; Orthogonal Frequency Division Multiplexing; proactive mmWave beam allocation; Resource management; Road traffic; Roads; Sensors; System effectiveness; Traffic; traffic estimation; Traffic speed; Traffic volume; Vehicles; Waveforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3415956

Building efficient and effective road traffic monitoring systems has become a major challenge in different countries, mainly due to the rapid growth of the metropolis road network and the booming of vehicles. Existing traffic monitoring methods are accurate but typically come with inherent limitations, prompting the exploration of alternative techniques. Integrated sensing and communication (ISAC) offers an effective approach to traffic monitoring by leveraging the synergy between sensing and communication to enhance system efficiency and reduce costs. In this paper, we present a particular ISAC use case tailored for radio-based traffic monitoring. Both traffic density and speed estimations take advantage of communication functionality, involving the reuse of communication waveforms for the sensing purpose. In particular, proactive millimeter-wave (mmWave) beam allocation aided by traffic density estimation is studied to enhance communication coverage of vehicular users in the area of interest for bandwidth-intensive applications. Specifically, we exploit orthogonal frequency division multiplexing (OFDM) communication signals of opportunity reflected from targets (vehicles) to efficiently estimate the road traffic density and speed in a road section. A hybrid scheme combining model-based and data-driven methods is considered to build efficient estimators that require reduced-size training data and are less computationally complex. Simulation and comparison results demonstrate that the proposed traffic estimation techniques can accurately handle a wide range of numbers of vehicles, even with a small-sized dataset. Furthermore, the proactive beam allocation analysis shows that the quality of service (QoS, in terms of outage probability) of the communication system is effectively improved.