Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a positionaided 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.

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Accurate beam alignment is essential for beambased millimeter wave communications. Conventional beam sweeping solutions often have large overhead, which is unacceptable for mobile applications like vehicle-to-everything. Learningbased solutions that leverage sensor data like position to identify good beam directions are one approach to reduce the overhead. Most existing solutions, though, are supervised-learning where the training data is collected beforehand. In this paper, we use a multi-armed bandit framework to develop online learning algorithms for beam pair selection and refinement. The beam pair selection algorithm learns coarse beam directions in some predefined beam codebook, e.g., in discrete angles separated by the 3dB beamwidths. The beam refinement fine-tunes the identified directions to match the peak of the power angular spectrum at that position. The beam pair selection uses the upper confidence bound (UCB) with a newly proposed riskaware feature, while the beam refinement uses a modified optimistic optimization algorithm. The proposed algorithms learn to recommend good beam pairs quickly. When using 16x16 arrays at both the transmitter and receiver, it can achieve on average 1dB gain over the exhaustive search (over 271x271 beam pairs) on the unrefined codebook within 100 time-steps with a training budget of only 30 beam pairs.

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We consider secret key agreement based on radio propagation characteristics in a two-way relaying system where two legitimate parties named Alice and Bob communicate with each other via a trusted relay. In this system, Alice and Bob share secret keys generated from measured radio propagation characteristics with the help of the relay in the presence of an eavesdropper. We present four secret key agreement schemes: an amplify-and-forward (AF) scheme, a signal-combining amplify-and-forward (SC-AF) scheme, a multiple-access amplify-and-forward (MA-AF) scheme, and an amplify-and-forward with artificial noise (AF with AN) scheme. In these schemes, the basic idea is to share the effective fading coefficients between Alice and Bob and use them as the source of the secret keys. The AF scheme is based on a conventional amplify-and-forward two-way relaying method, whereas in the SC-AF scheme and the MA-AF scheme, we apply the idea of physical-layer network coding to the secret key agreement. In the AF with AN scheme, the relay transmits artificially generated noise, as well as channel information signal, in order to conceal the latter. Simulation results show that the MA-AF scheme outperforms the other schemes in Rayleigh fading channels, whereas the AF with AN scheme is suitable for Rician fading channels.Index Terms-Physical-layer network coding, physical-layer security, radio propagation, secret key agreement, two-way relaying.

Modern vehicular wireless technology enables vehicles to exchange information at any time, from any place, to any network -forms the vehicle-to-everything (V2X) communication platforms. Despite benefits, V2X applications also face great challenges to security and privacy -a very valid concern since breaches are not uncommon in automotive communication networks and applications. In this survey, we provide an extensive overview of V2X ecosystem. We also review main security/privacy issues, current standardization activities and existing defense mechanisms proposed within the V2X domain. We then identified semantic gaps of existing security solutions and outline possible open issues.We also discuss possible open issues (Section VIII), summarize multiple industry/academic/government initiatives for securing V2X communications (Section IX-A) and compare our work with related surveys (Section IX-B). II. V2X PLATFORM : AN OVERVIEWThis section provides an overview of V2X communication interfaces (Section II-A) and discuss various network/communication models (Section II-B). A. Communication InterfacesThe internal architecture of a vehicle is interconnected with ECUs (electronic control units -embedded computing platform that monitor/control automotive systems) coupled with sensors and actuators. The communication between the vehicle and the outside world such as other vehicles or roadside units (RSUs) is performed via external interfaces (see Fig. 1). These vehicular external interfaces are attached to the telematics control unit (TCU) -also referred to as on-board unit (OBU) 1 -an ECU that provides wireless connectivity [7], [8]. A vehicle control unit coordinates with the OBU to collect and disseminate vehicular data [9]. The current standards for V2X communication are DSRC (dedicated short range communication) [10] in the United States, C-ITS (cooperative intelligent transport systems) [11] in Europe and ITS Connect [12] in Japan. Both DSRC and C-ITS operating in the 5.9 GHz ITS band while ITS Connect operating in 760 MHz band (refer to Section II-B1 for details). An alternative to DSRC/C-ITS is the next generation of cellular wireless mobile telecommunications technology (see Section II-B2). OBUs can also be equipped with interfaces for long-range communication. These long-range wireless channels can be classified as broadcast channels (signals can be broadcast to multiple vehicles without knowledge of the receiver's address) and addressable channels (where messages are sent to vehicles with specific addresses.) [13]. Examples of broadcast channels include the global navigation satellite system (GNSS), traffic message/satellite radio receivers, etc. Addressable channels are typically used for long-range voice/data transmissions and are intended to be used for cellular communications for mobile broadband [8].

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