5G Air Interface: Ultra Reliable Low Latency Communications
One of the key use cases of 5G is to deliver ultrareliable low latency communication (URLLC). Besides antennaand frequency diversity techniques, hybrid automatic repeat request (HARQ) is a powerful tool to increase reliability. However, due to the low latency requirement, only a very small number of retransmissions can be carried out. In this work, we consider the help of additional supporting user equipment (sUE) devices. The sUE listens to the transmitter user equipment (tUE) and relays the retransmitted data in case the transmission fails between the tUE and the base station. The proposed approach improves reliability since the sUE offers an additional source of antenna diversity and it can provide benefits because of better path gain conditions compared to the tUE. Hence, the number of required transmissions will be significantly reduced thanks to the side-link assistance. In this way, the complete setup can be considered as a distributed smart antenna system. The simulation results show that the considered sidelink-assisted framework achieves a significant reduction of the latency compared to the scenario relying on direct-link-only.
Connected Vehicles: Distributed Dynamic Spectrum Access Mechanism
We propose a novel distributed cooperative sensing algorithm for connected vehicles. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. The proposed optimization approach minimizes the probability of incorrect detection as a function of both the probability of false alarm and the probability of missed detection. By considering both probabilities when computing the probability of incorrect detection, the proposed approach is novel. During the energy detection process, large-scale fading, multipath fading, Doppler effect, and transmission errors affecting the control messaging process are accounted in order to provide a practical solution for connected vehicles. Once the available channels have been detected, the vehicles share this information using broadcast control messages. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors, where the credibility of each neighbor is weighted during the voting process. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor as well as the vehicle’s own credibility. The voting mechanism is switched between the proposed voting mechanism and the traditional voting approach obtained from the current state-of-the-art in order to maintain a balance between the computational cost/latency and robust spectrum sensing.
Connected Vehicles: Distributed Congestion Control
Safety and efficiency applications in vehicular networks rely on the exchange of periodic messages between vehicles. These messages contain position, speed, heading, and other vital information that makes the vehicles aware of their surroundings. The drawback of exchanging periodic cooperative messages is that they generate significant channel load. Decentralized Congestion Control (DCC) algorithms have been proposed to minimize the channel load. However, while the rationale for periodic message exchange is to improve awareness, existing DCC algorithms do not use awareness as a metric for deciding when, at what power, and at what rate the periodic messages need to be sent in order to make sure all vehicles are informed. We propose an environment- and context-aware DCC algorithm combines power and rate control in order to improve cooperative awareness by adapting to both specific propagation environments (e.g., urban intersections, open highways, suburban roads) as well as application requirements (e.g., different target cooperative awareness range). Studying various operational conditions (e.g., speed, direction, and application requirement), ECPR adjusts the transmit power of the messages in order to reach the desired awareness ratio at the target distance while at the same time controlling the channel load using an adaptive rate control algorithm. By performing extensive simulations, including realistic propagation as well as environment modeling and realistic vehicle operational environments (varying demand on both awareness range and rate), we show that ECPR can increase awareness by 20% while keeping the channel load and interference at almost the same level. When permitted by the awareness requirements, ECPR can improve the average message rate by 18% compared to algorithms that perform rate adaptation only.
For the open source simulator: GEMV^2
Connected Vehicles: Propagation Models for Vehicle-to-Infrastructure Communications
Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator and make the source code publicly available.
For the open source simulator: GEMV^2
Multi-hopping Connected Vehicles: From Channel Modeling to Relaying Optimization
We propose a statistical channel model for decode-and-forward relaying vehicular ad hoc networks (VANETs) with single-input-single-output (SISO) antennas. The proposed model uses a sum-of-sinusoids (SoS) Rician model, which is derived for highway scenarios with line-of-sight (LOS) components, time-varying conditions and multipath excess delay. Since the time delay differences between multipath components are considered in this work as opposed to previous studies, the power of channel impulse response is conserved. One of the advantages of the proposed channel model is that it does not depend on parameters such as distance and angle between vehicles, which changes continuously in a vehicular transmission environment. To analyze the performance of relaying communications versus point-to-point communications, we use two performance metrics: the probability of error and latency.
Once the benefit of multi-hopping in vehicular communications is explored, we propose a selective message relaying algorithm to enable efficient information sharing of multi-hopping connected vehicles. We use a clustering mechanism to group messages that include the same or very similar information, and the number of clusters is adaptively varied based on the proximity of the messages. Supported by the clustering mechanism, a relay vehicle only rebroadcasts a few messages from each cluster.
Decode-and-Forward MIMO Relaying Systems: Capacity Bounds
The source and relay transmit covariance matrices are jointly optimized for a fading multiple-antenna relay channel when the transmitters only have partial channel state information (CSI) in the form of covariance feedback. For both full-duplex and half-duplex transmissions, we evaluate lower and upper bounds on the ergodic channel capacity. These bounds require a joint optimization over the source and relay transmit covariance matrices. The methods utilized in the previous literature cannot handle this joint optimization over the transmit covariance matrices for the considered Decode-and-Forward (DF) relaying system. Therefore, we utilize matrix differential calculus and propose iterative algorithms that find the transmit covariance matrices to solve the joint optimization problem. In this method, there is no need to specify first the eigenvectors of the transmit covariance matrices. The algorithm updates both the eigenvectors and the eigenvalues at each iteration.