Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment (1705.05942v3)

Abstract: Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a position-aided approach where the vehicle's position (e.g., available via GPS) is used to query the multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the larger beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.

• The paper introduces a position-based beam alignment method that uses precomputed multipath fingerprints to significantly reduce real-time training overhead.
• It details two fingerprint types paired with AvgPow and MinMisProb selection methods to optimize beam selection based on historical channel data.
• Simulations in realistic urban scenarios demonstrate that the MinMisProb technique improves alignment accuracy and minimizes power loss compared to traditional methods.

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

The paper "Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment" offers a position-aided strategy for efficient beam alignment in millimeter wave (mmWave) vehicular communications, specifically in vehicle-to-infrastructure (V2I) settings. Through the utilization of multipath fingerprints that are indexed by location, this method reduces beam alignment overhead, a critical aspect for optimized communication in dynamic vehicular environments.

Overview of the Methodology

The authors propose an inverse approach to traditional fingerprinting localization. Instead of deducing the most probable location from measured channel characteristics, they use a known position (e.g., from GPS data) to query a multipath fingerprint database. This approach provides probable beam direction candidates essential for reliable beam alignment. The database contains long-term channel characteristics, or fingerprints, which are precalculated from known locations. This novel use of position data facilitates preemptive adjustment, potentially reducing beam training time by narrowing down candidate beam directions that need to be evaluated in real-time.

Problem Formulation and Innovations

Efficient beam alignment in mmWave systems poses significant challenges due to the narrow beamwidths and rapid environmental dynamics in vehicular contexts. The paper introduces two fingerprint types: Type A fingerprints capture exhaustive, coherence-contained measurements, preserving inter-beam correlations, while Type B fingerprints average received power over multiple coherence periods, foregoing the need for exhaustive searches within a single coherence time. For optimizing beam alignment, two selection methods are suggested:

1. Average Power Ranking (AvgPow): Beam pairs are ranked by their average received power over the training samples. Suitable for Type B fingerprints, this heuristic method is straightforward but less precise than methods that leverage detailed correlation data.
2. Minimizing Misalignment Probability (MinMisProb): This method uses Type A fingerprints to minimize the probability of misalignment by selecting beam pairs based on calculated probabilities derived from historical optimal beam observations.

Evaluation and Results

Simulations were conducted in a realistic city scenario developed using a commercial ray-tracing simulator, assessing the system under varied vehicular traffic and environmental conditions. Results underscored the effectiveness of the proposed methodology, particularly the MinMisProb selection method when leveraging Type A fingerprints, which demonstrated superior rates and reduced power loss probabilities compared to traditional hierarchical beam search methods such as those in IEEE 802.11ad. Analysis suggested that while the efficacy increases with array size, the proposed system maintains its low overhead, contrasting with the increasing overhead of pre-existing methodologies in comparable settings.

Implications and Future Directions

The research delineates a significant step towards the efficient implementation of mmWave communications in V2I systems, vital for autonomous driving technologies. By offloading some computations using a pre-stored fingerprint database, the method exploits infrastructural knowledge for adaptive beam alignment, making it suitable for high-speed, data-intensive vehicular contexts. Future works may explore optimization of the database update mechanisms, handling discrepancies due to dynamic traffic density or operational environments. Additionally, coupling this approach with machine learning techniques might further enhance the agility and efficiency of real-time beam selection by predicting environmental patterns and vehicular dynamics.

Overall, this paper lays a robust foundation for an adaptable, minimally invasive beam alignment strategy in vehicular environments, underscoring a vital avenue for research in the ever-evolving domain of wireless communications.