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
Optimum Resource Allocation for Full-Duplex Vehicular Communication Networks
In this article, we propose a resource allocation (RA) scheme for vehicular communication networks (VCNs). The proposed scheme exploits the spectral efficiency of full-duplex (FD) communications to fulfill the reliability constraints of vehicle-to-vehicle (V2V) links and the high capacity requirements of vehicle-to-infrastructure (V2I) links. Also, it is capable of coping with the fast variations of the channels due to the high mobility. Based on the links requirements, the RA problem is formulated as a non-convex problem, which is solved in two steps. First, the optimal power allocation (PA) is obtained by solving a system of linear equations. Second, the channel assignment (CA), which turns out to be a maximum weight bipartite matching problem, is solved using the Hungarian method. Also, a heuristic hybrid scheme, which combines the proposed FD scheme and the half-duplex (HD) scheme that optimally finds the RA, is proposed. Compared to the optimal HD-based scheme, simulation results show that the proposed FD scheme always offers higher sum of the V2I links’ capacities except for the case in which V2V links require low transmission rates, while the hybrid scheme ensures higher performance for all potential cases.
more »
« less
- Award ID(s):
- 1816112
- NSF-PAR ID:
- 10189583
- Date Published:
- Journal Name:
- IEEE Access
- Volume:
- 8
- ISSN:
- 2169-3536
- Page Range / eLocation ID:
- 146683 - 146696
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We consider intermittently connected vehicular networks (ICVNs) in which base stations (BSs) are installed along the highway to connect moving vehicles with internet. Due to the deployment cost, it is hard to cover the entire highway with BSs. To minimize the outage time in the uncovered area (UA), several cooperative store-carry-forward (CSCF) schemes have been proposed in which a vehicle is selected to act as a relay by buffering data to be relayed to a target vehicle in the UA. In this paper, we propose an energy-efficient full-duplex (FD) CSCF scheme that exploits the relay ability to receive and transmit simultaneously to improve the effective communication time, Te, between the relay and the target vehicle. Accordingly, it can minimize the outage time and deliver more data to the the target vehicle. In addition, the power allocation that minimizes the transmission cost (TC) under the required rates constraints is found. The problem is formulated as a geometric program (GP) and globally solved using the interior-point method. As compared to the half-duplex CSCF scheme, simulation results show that the proposed FD scheme offers more effective time, more successfully delivered data in the UA and lower TC.more » « less
-
In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.more » « less
-
null (Ed.)For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project.more » « less
-
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
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/ACCESS.2020.3015539
