Abstract We consider a multipoint channel charting (MPCC) algorithm for radio resource management (RRM) in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. A massive MIMO (mMIMO) infrastructure network performs logical localization of vehicles to a MPCC, based on V2I communication signals. Combining logical distances given by channel charting with V2V measurements, the network trains a function to predict the quality of a direct V2V communication link from observed V2I communication signals. In MPCC, the network uses machine learning techniques to learn a logical radio map from V2I channel state information (CSI) samples transmitted from unknown locations. The network extracts CSI features, constructs a dissimilarity matrix between CSI samples, and performs dimensional reduction of the CSI feature space. Here, we use Laplacian Eigenmaps (LE) for dimensional reduction. The resulting MPCC is a two-dimensional map where the spatial distance between a pair of vehicles is closely approximated by the distance in the MPCC. In addition to V2I CSI, the network acquires V2V channel quality information for vehicles in the training set and develops a link quality predictor. MPCC provides a mapping for any vehicle location in the training set. To use MPCC for cognitive RRM of V2I and V2V communications, network management has to find logical MPCC locations for vehicles not in the training set, based on newly acquired V2I CSI measurements. For this, we develop an extension of LE-based MPCC to out-of-sample CSI samples. We evaluate the performance of link quality prediction for V2V communications in a mMIMO millimeter-wave scenario, in terms of the relative error of the predicted outage probability.
more »
« less
Graph-Based Radio Resource Management for Vehicular Networks
This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.
more »
« less
- PAR ID:
- 10066943
- Date Published:
- Journal Name:
- IEEE International Conference on Communications
- ISSN:
- 1938-1883
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles’ positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. Theoretical and simulation results are provided to validate the feasibility of the proposed schemes.more » « less
-
Abstract—Millimeter wave wireless spectrum deployments will allow vehicular communications to share high data rate vehicular sensor data in real-time.The highly directional nature of wireless links in millimeter spectral bands will require continuous channel measurements to ensure the transmitter (TX) and receiver (RX) beams are aligned to provide the best channel. Using real-world vehicular mmWave measurement data at 28GHz, we determine the optimal beam sweeping period, i.e. the frequency of the channel measurements,to align the RX beams to the best channel directions for maximizing the vehicle-to-infrastructure (V2I) throughput.We show that in a realistic vehicular traffic environment in Austin,TX, for a vehicle traveling at an average speed of 10.5mph,a beam sweeping period of 300 ms in future V2I communication standards would maximize theV2I throughput,using a system of four RX phased arrays that scanned the channel 360 degrees in the azimuth and 30 degrees above and below the boresight.We also investigate the impact of the number of active RX chains controlling the steerable phased arrays on V2I throughput. Reducing the number of RX chains controlling the phased arrays helps reduce the cost of the vehicular mmWave hardware while multiple RX chains, although more expensive,provide more robustness to beam direction changes at the vehicle,allowing near maximum throughput over a wide range of beam sweep periods.We show that the overhead of utilizing one RX chain instead of four leads to a10% drop in mean V2I throughput over six non-line- of-sight runs in real traffic conditions, with each run being 10 to 20 seconds long over a distance of 40 to 90 meters. Index Terms—mmWave;beam management;channel sound- ing; phased arrays;V2X;V2V;5G;sidelinkmore » « less
-
The development of communication technologies in edge computing has fostered progress across various applications, particularly those involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Enhanced infrastructure has improved data transmission network availability, promoting better connectivity and data collection from IoT devices. A notable IoT application is with the Intelligent Transportation System (ITS). IoT technology integration enables ITS to access a variety of data sources, including those pertaining to weather and road conditions. Real-time data on factors like temperature, humidity, precipitation, and friction contribute to improved decision-making models. Traditionally, these models are trained at the cloud level, which can lead to communication and computational delays. However, substantial advancements in cloud-to-edge computing have decreased communication relays and increased computational distribution, resulting in faster response times. Despite these benefits, the developments still largely depend on central cloud sources for computation due to restrictions in computational and storage capacity at the edge. This reliance leads to duplicated data transfers between edge servers and cloud application servers. Additionally, edge computing is further complicated by data models predominantly based on data heuristics. In this paper, we propose a system that streamlines edge computing by allowing computation at the edge, thus reducing latency in responding to requests across distributed networks. Our system is also designed to facilitate quick updates of predictions, ensuring vehicles receive more pertinent safety-critical model predictions. We will demonstrate the construction of our system for V2V and V2I applications, incorporating cloud-ware, middleware, and vehicle-ware levels.more » « less
-
We tackle the atypical challenge of supporting postquantum cryptography (PQC) and its significant overhead in safety-critical vehicle-to-vehicle (V2V) communications, dealing with strict overhead and latency restrictions within the limited radio spectrum for V2V. For example, we show that the current use of spectrum to support signature verification in V2V makes it nearly impossible to adopt PQC. Accordingly, we propose a scheduling technique for message signing certificate transmissions (which we find are currently up to 93% redundant) that learns to adaptively reduce the use of radio spectrum. In combination, we design the first integration of PQC and V2V, which satisfies the above stringent constraints given the available spectrum. Specifically, we analyze the three PQ signature algorithms selected for standardization by NIST, as well as XMSS (RFC 8391), and propose a Partially Hybrid authentication protocol—a tailored fusion of classical cryptography and PQC—for use in the V2V ecosystem during the nascent transition period we outline towards fully PQ V2V. Our provably secure protocol efficiently balances security and performance, as demonstrated experimentally with software-defined radios (USRPs), commercial V2V devices, and road traffic and V2V simulators. We show our joint transmission scheduling optimization and Partially Hybrid design are scalable and reliable under realistic conditions, adding a negligible average delay (0.39 ms per message) against the current state-of-the-art.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC.2018.8422585
