Future vehicles will require massive sensing capability. Leveraging only onboard sensors, though, is challenging in crowded environments where the sensing field-of-view is obstructed. One potential solution is to share sensor data among the vehicles and infrastructure. This has the benefits of providing vehicles with an enhanced field-of-view and also additional redundancy to provide more reliability in the sensor data. A main challenge in sharing sensor data is providing the high data rates required to exchange raw sensor data. The large spectral channels at millimeter wave mmWave frequencies provide a means of achieving much higher data rates. This monograph provides an overview of mmWave vehicular communication with an emphasis on results on channel measurements, the physical PHY layer, and the medium access control MAC layer. The main objective is to summarize key findings in each area, with special attention paid to identifying important topics of future research. In addition to surveying existing work, some new simulation results are also presented to give insights on the effect of directionality and blockage, which are the two distinguishing features of mmWave vehicular channels. A main conclusion of this monograph is that given the renewed interest in high rate vehicle connectivity, many challenges remain in the design of a mmWave vehicular network.
A major source of difficulty when operating with large arrays at mmWave frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms that leverage prior statistical information on the angles-of-arrival and angles-of-departure. Exploiting this prior information, we also propose a precoding and combining design method to increase the received SNR during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that, using channel realizations extracted from the 5G New Radio channel model, our proposed channel tracking framework is able to achieve near-optimum data rates.
Sharing perception sensor information (e.g., camera, radar, and LIDAR) among vehicles in proximity using vehicle-to-everything (V2X) communications provides non-line-of-sight (NLOS) information about the surrounding environment, improving the safety and traffic efficiency of cooperative automated driving. However, such application requires high data rates on the order of 10s to 100s of Mbps (or 1 Gbps), which cannot be supported by the existing V2X communication technologies such as IEEE 802.11p-based dedicated short-range communication (DSRC) in 5.9 GHz band. Millimeter wave (mmWave) communication has the potential to meet the high data rate demand thanks to its wide bandwidth. In this paper, we introduce potential use cases of mmWave V2X communications. Also, we discuss technical challenges and design considerations for mmWave V2X beam management. Particularly, we elaborate on how we can utilize low-frequency communication (e.g., 5.9 GHz DSRC), onboard sensors mounted at vehicles and infrastructures, and DSRC messages (e.g., vehicle position, speed, acceleration, and path prediction) to facilitate mmWave V2X beam management.
As a secure communication method for wireless communication systems, we propose a method utilizing the interference alignment (IA) technique. In the MIMO interference channels, only the designated pairs of two radio stations can communicate without the interference from the other communication pairs, while a third party radio station which is not included in the IA control suffers the interference. Utilizing the property, secure communication can be realized. In this paper, we propose a new method of secure communication using IA. We examine the performance of the proposed method quantitatively by computer simulations [1].
In recent years, the multiple-input multiple-output (MIMO) interference channels, where multiple pairs of a transmitter and a receiver having multiple antennas communicate in parallel, have been considered to maximize transmission capacity in a multiple communication environment. In that case, Interference Alignment (IA) is a promising candidate technology since it enables multiple communications simultaneously avoiding mutual interferences [1].
We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by two reasons: First, receiver design typically focuses on the universal optimal performance for a wide range of possible channel realizations. However, in actual applications and within short time intervals, only a subset of these channel parameters is likely to occur, as macro parameters, e.g., the maximum channel delay, can assumed to be static. Second, in-the-field alterations like temporal interferences or other conditions out of the originally intended specifications can occur on a practical (real-world) transmission. While conventional (filter-based) systems would require reconfiguration or additional signal processing to cope with these unforeseen conditions, NN-based receivers can learn to mitigate previously unseen effects even after their deployment. For this, we showcase on-the-fly adaption to current channel conditions and temporal alterations solely based on recovered labels from an outer forward error correction (FEC) code without any additional piloting overhead. To underline the flexibility of the proposed adaptive training, we showcase substantial gains for scenarios with static channel macro parameters, for out-of-specification usage and for interference compensation.
By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. While map data are collected from both sensor and communicated information, exchanging a subset of the local map requires some sort of content control scheme in place to ensure that useful information is being exchanged among vehicles that would enhance tracking accuracy. This paper investigates how information sharing based on proximity of mapped objects impact the position tracking capability. It also compares deterministic and probabilistic variants of the distance based content control approaches. Preliminary results show that exchanging information about objects located near the edge of sensor range increases the tracking performance. It is also shown that the probabilistic approaches have higher mapping accuracy than their deterministic counterparts.
