Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications

Caching popular contents in vehicle-to-vehicle (V2V) communication networks is expected to play an important role in road traffic management, the realization of intelligent transportation systems (ITSs), and the delivery of multimedia content across vehicles. However, for effective caching, the network must dynamically choose the optimal set of cars that will cache popular content and disseminate it in the entire network. However, most of the existing prior art on V2V caching is restricted to cache placement that is solely based on location and user demands and does not account for the large-scale spatio-temporal variations in V2V communication networks. In contrast, in this paper, a novel spatio-temporal caching strategy is proposed based on the notion of temporal graph motifs that can capture spatio-temporal communication patterns in V2V networks. It is shown that, by identifying such V2V motifs, the network can find sub-optimal content placement strategies for effective content dissemination across a vehicular network. Simulation results using real traces from the city of Cologne show that the proposed approach can increase the average data rate by $45\%$ for different network scenarios

Exploring Spatial Motifs for Device-to-Device Network Analysis (DNA) in 5G Networks

Device-to-device (D2D) communication is a promising approach to efficiently disseminate critical or viral information across 5G cellular networks. Reaping the benefits of D2D-enabled networks is contingent upon choosing the optimal dissemination policy and resource allocation strategy subject to resource, topology, and user distribution constraints. In this paper, a resource allocation problem within a single-cell orthogonal frequency division multiple-access (OFDMA) network has been formulated to optimize the system throughput. The problem is cast as a mixed binary integer programming problem, which is challenging to solve. Therefore, in order to obtain a sub-optimal solution, the optimization problem is decomposed into two subproblems: seed selection and subchannel allocation. For the seed selection sub-problem, a novel D2D network analysis (DNA) framework is proposed to explore frequent communication patterns across D2D users, known as spatial motifs, to determine the optimal number of devices which can disseminate contents. Furthermore, to solve the second sub-problem, a heuristic algorithm is introduced. Simulation results show the effectiveness of exploring motifs to design D2D dissemination policies, and show that the proposed algorithm can achieve a performance gain of up to 40% compared with a baseline scheme that selects subchannels randomly

Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles

A new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs). In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an incentive mechanism based on contract theory is designed to improve the FL convergence speed. Simulation results using real vehicular data traces show that the proposed DFP-based controller can accurately track the target CAV speed over time and under different traffic scenarios. Moreover, the results show that the proposed DFP algorithm has a much faster convergence compared to popular FL algorithms such as federated averaging (FedAvg) and federated proximal (FedProx). The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.Comment: 30 pages, 6 figure

Federated Learning for Collaborative Controller Design of Connected and Autonomous Vehicles

The deployment of future intelligent transportation systems is contingent upon seamless and reliable operation of connected and autonomous vehicles (CAVs). One key challenge in developing CAVs is the design of an autonomous controller that can make use of wireless connectivity and accurately execute control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback controllers or traditional machine learning based controllers, solely trained by the CAV's local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics. In this paper, a new federated learning (FL) framework enabled by the CAVs' wireless connectivity is proposed for the autonomous controller design of CAVs. In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in FL and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless channel dynamics, as well as the unbalanced and non-independent and identically distributed data across CAVs. A rigorous convergence study is performed for the proposed algorithm under realistic wireless environments. Then, the impact of varying CAV participation in FL process and diverse local data quality of CAVs on the convergence is explicitly analyzed. Simulation results that use real vehicular data show that the proposed DFP-based controller can accurately track the target speed over time and under different traffic scenarios, and it yields a distance error two times smaller than controllers designed using traditional machine learning solutions trained with the CAV's local data. The results also show that the proposed DFP algorithm is well-suited for the autonomous controller design in CAVs when compared to popular FL algorithms, such as federated averaging (FedAvg) and federated proximal (FedProx) algorithms

Joint communication and control for wireless autonomous vehicular platoon systems

Abstract Autonomous vehicular platoons will play an important role in improving on-road safety in tomorrow’s smart cities. Vehicles in an autonomous platoon can exploit vehicle-to-vehicle (V2V) communications to collect environmental information so as to maintain the target velocity and inter-vehicle distance. However, due to the uncertainty of the wireless channel, V2V communications within a platoon will experience a wireless system delay. Such system delay can impair the vehicles’ ability to stabilize their velocity and distances within their platoon. In this paper, the problem of integrated communication and control system is studied for wireless connected autonomous vehicular platoons. In particular, a novel framework is proposed for optimizing a platoon’s operation while jointly taking into account the delay of the wireless V2V network and the stability of the vehicle’s control system. First, stability analysis for the control system is performed and the maximum wireless system delay requirements which can prevent the instability of the control system are derived. Then, delay analysis is conducted to determine the end-to-end delay, including queuing, processing, and transmission delay for the V2V link in the wireless network. Subsequently, using the derived wireless delay, a lower bound and an approximated expression of the reliability for the wireless system, defined as the probability that the wireless system meets the control system’s delay needs, are derived. Then, the parameters of the control system are optimized in a way to maximize the derived wireless system reliability. Simulation results corroborate the analytical derivations and study the impact of parameters, such as the packet size and the platoon size, on the reliability performance of the vehicular platoon. More importantly, the simulation results shed light on the benefits of integrating control system and wireless network design while providing guidelines for designing an autonomous platoon so as to realize the required wireless network reliability and control system stability

Performance analysis of aircraft-to-ground communication networks in urban air mobility (UAM)

Abstract To meet the growing mobility needs in intra-city transportation, urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide on-demand service. In UAM, an aircraft can operate in the corridors, i.e., the designated airspace, that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters. For safety, a UAM aircraft will use air-to-ground communications to report flight plan, off-nominal events, and real-time movements to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance in UAM, a stochastic geometry-based spatial model is developed. In particular, the distribution of GBSs is modeled as a Poisson point process (PPP), and the aircraft are distributed according to a combination of PPP, Poisson cluster process (PCP), and Poisson line process (PLP). For this setup, assuming that any given aircraft communicates with the closest GBS, the distribution of distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ra-tio (SIR)-based connectivity probability is determined to capture the connectivity performance of the aircraft-to-ground communication network in UAM. Simulation results validate the theoretical derivations for the UAM wireless connectivity and provide useful UAM design guidelines by showing the connectivity performance under different parameter settings

Dependence control for reliability optimization in vehicular networks

Abstract Vehicular networks will play an important role in enhancing road safety, improving transportation efficiency, and providing seamless Internet service for users on the road. Reaping the benefit of vehicular networks is contingent upon meeting stringent wireless communication performance requirements, particularly in terms of delay and reliability. In this paper, a dependence control mechanism is proposed to improve the overall reliability of vehicular networks. In particular, the dependence between the communication delays of different vehicleto-vehicle (V2V) links is first modeled. Then, the concept of a concordance order, stemming from stochastic ordering theory, is introduced to show that a higher dependence can lead to a better reliability. Using this insight, a power allocation problem is formulated to maximize the concordance, thereby optimizing the overall communication reliability of the V2V system. To obtain an efficient solution to the power allocation problem, a dual update method is introduced. Simulation results verify the effectiveness of performing dependence control for reliability optimization in a vehicular network, and show that the proposed mechanism can achieve up to 25% reliability gain compared to a baseline system that uses a random power allocation

ntegrated communications and control co-design for wireless vehicular platoon systems

Abstract Vehicle platoons will play an important role in improving on-road safety in tomorrow’s smart cities. Vehicles in a platoon can exploit vehicle- to-vehicle (V2V) communications to collect information, such as velocity and acceleration, from surrounding vehicles so as to coordinate their operations and maintain the target velocity and inter-vehicle distance required by the platoon. However, due to the interference and uncertainty of the wireless channel, V2V communications within a platoon will experience a wireless transmission delay which can impair the vehicles’ ability to stabilize their speed and distances within their platoon. In this paper, the problem of integrated communication and control is studied for wireless-connected platoons. In particular, a novel approach is proposed for optimizing a platoon’s stability while taking into account, jointly, the state of the wireless V2V network and the stability of the platoon’s control system. Based on the proposed integrated communication and control strategy, the plant and string stability for the platoon are analyzed. The signal-to-interference-plus-noise-ratio (SINR) threshold, which will prevent the instability of the control system, is also determined. Moreover, the reliability of the wireless system, defined as the probability that the wireless system meets the control system’s delay needs, is derived. Simulation results shed light on the benefits of the proposed approach and the synergies between the wireless network and the platoon’s control system

Joint communication and control system design for connected and autonomous vehicle navigation

Abstract Connected and autonomous vehicles (CAVs) are able to improve on-road safety and provide convenience in our daily lives. To perform autonomous path tracking and navigation, CAVs can exploit vehicle-to-everything (V2X) communications to determine their vehicle dynamics parameters, such as location, heading angle, and curvature, which can be then used as inputs to their control system. However, the interference and uncertainty of the wireless channels can increase the transmission delay on the vehicle dynamics and, thus, impair the CAV’s ability to track its target path. In this paper, the problem of joint communication network and control system design is studied to solve the path tracking problem for CAVs. In particular, a novel approach is proposed to maximize the number of reliable V2X transmitter-receiver pairs while jointly considering the stability of the controller and the state of the wireless network. Based on the joint design, the maximum transmission delay which can prevent instability in the controller is determined. Then, the reliable V2X links maximization problem is decomposed into two equivalent sub-problems. The first sub-problem is the control mechanism design in which a dual update method is used to determine the headway distance parameter for the control system. The second sub-problem uses the outcome of the first sub-problem to optimize the power allocation for the communication system. To solve this power allocation problem, a novel risk-based approach that uses the so-called conditional value at risk (CVaR) from financial engineering is proposed. Simulation results validate the theoretical results and show that the proposed joint design can improve the number of reliable V2X pairs by as much as 70% compared to a baseline scheme that optimizes the communication and control systems independently