Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing (1602.06456v2)

Abstract: As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars (RAdio Detection and Ranging) are used for object detection, visual cameras as virtual mirrors, and LIDARs (LIght Detection and Ranging) for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as dedicated short-range communication (DSRC) and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This paper makes the case that millimeter wave (mmWave) communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

• The paper demonstrates that mmWave communication can meet the gigabit-per-second data rates needed for raw automotive sensor transmission.
• The authors propose integrating side information from DSRC and onboard sensors to mitigate beam alignment challenges in dynamic vehicular environments.
• The study outlines future research directions, including enhanced channel modeling and beamforming strategies to boost safety and efficiency in automated driving systems.

Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing

The paper "Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing" by Junil Choi et al. presents an examination of the role millimeter wave (mmWave) communications could play in the future landscape of vehicular communication networks, particularly in supporting the high data rates generated by advanced automotive sensors like radar, cameras, and LIDAR. As vehicles increasingly integrate these sensors to enhance automation and safety, the need to transmit large volumes of raw sensor data between vehicles and infrastructure becomes paramount.

Context and Motivation

The standard sensor technologies currently deployed in vehicles are reaching their limitations, primarily due to their inability to support the gigabit-per-second data rates necessary for effective communication of raw sensor data. Technologies like DSRC and 4G cellular networks, while currently in use or proposed for vehicle communications, offer maximum data rates which fall short of the demands anticipated in the coming years. The high-volume data generation from various sensors underscores the need for a broader communication bandwidth, which mmWave technology could potentially fulfill.

Proposed Approach and Challenges

The authors assert that mmWave communication is a promising solution for vehicular networks, highlighting its feasibility due to the high bandwidth available at mmWave frequencies. The paper suggests integrating mmWave alongside existing DSRC or 4G systems to facilitate efficient data exchange between vehicles (V2V) and between vehicles and infrastructure (V2I). The challenges identified in employing mmWave in vehicular scenarios are manifold, including the difficulty in beam alignment due to high vehicular mobility and the lack of comprehensive vehicular channel models at mmWave frequencies.

Notably, the authors propose using side information derived from existing communications or onboard sensors to mitigate the overhead associated with beam alignment—a critical bottleneck in mmWave vehicle communications. They detail the potential for leveraging DSRC or sensor data to inform mmWave beam configurations, thereby reducing alignment time and improving communication reliability.

Implications and Future Directions

The implications of adopting mmWave technology are significant for both practical and theoretical perspectives. On the practical side, deploying mmWave could dramatically enhance the capabilities and safety of automated driving systems through improved data sharing. This upgrade would play a crucial role in enabling applications like adaptive platooning and cloud-driven automated driving, contributing to transportation efficiency and safety.

Theoretically, the integration of mmWave into vehicular communications invites further research into channel modeling, beamforming strategies, and network protocols that can withstand the unique challenges posed by automotive environments. Future work will need to address the optimal architecture for mmWave vehicle networks, potentially involving new standards or modifications to existing protocols like IEEE 802.11ad.

In conclusion, Junil Choi et al. provide a compelling case for the integration of mmWave into vehicular communication networks. Although challenges remain, the potential benefits make it a promising area of research and technological development in the pursuit of safer, more efficient connected automotive systems.