V2X Content Distribution Based on Batched Network Coding with Distributed Scheduling

Content distribution is an application in intelligent transportation system to assist vehicles in acquiring information such as digital maps and entertainment materials. In this paper, we consider content distribution from a single roadside infrastructure unit to a group of vehicles passing by it. To combat the short connection time and the lossy channel quality, the downloaded contents need to be further shared among vehicles after the initial broadcasting phase. To this end, we propose a joint infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication scheme based on batched sparse (BATS) coding to minimize the traffic overhead and reduce the total transmission delay. In the I2V phase, the roadside unit (RSU) encodes the original large-size file into a number of batches in a rateless manner, each containing a fixed number of coded packets, and sequentially broadcasts them during the I2V connection time. In the V2V phase, vehicles perform the network coded cooperative sharing by re-encoding the received packets. We propose a utility-based distributed algorithm to efficiently schedule the V2V cooperative transmissions, hence reducing the transmission delay. A closed-form expression for the expected rank distribution of the proposed content distribution scheme is derived, which is used to design the optimal BATS code. The performance of the proposed content distribution scheme is evaluated by extensive simulations that consider multi-lane road and realistic vehicular traffic settings, and shown to significantly outperform the existing content distribution protocols.Comment: 12 pages and 9 figure

Adjacent Channel Interference Aware Joint Scheduling and Power Control for V2V Broadcast Communication

IEEE This paper proposes scheduling and power control schemes to mitigate the impact of both co-channel interference (CCI) and adjacent channel interference (ACI) on direct vehicle-to-vehicle broadcast communication. The objective is to maximize the number of vehicles that can communicate with the prescribed requirement on latency and reliability. The joint scheduling and power control problem is formulated as a mixed Boolean linear programming (MBLP) problem. A column generation method is proposed to reduce the computational complexity of the joint problem. From the joint problem, we formulate a scheduling-alone problem (given a power allocation) as a Boolean linear programming (BLP) problem and a power control-alone problem (given a schedule) as an MBLP problem. The scheduling problem is numerically sensitive due to the high dynamic range of channel values and adjacent channel interference ratio (ACIR) values. Therefore, a novel sensitivity reduction technique, which can compute a numerically stable optimal solution at the price of increased computational complexity, is proposed. Numerical results show that ACI, just as CCI, is a serious problem in direct vehicle-to-vehicle (V2V) communication due to near-far situations and hence should not be ignored, and its impact can be reduced by proper scheduling and power control

Radio resource management for V2V multihop communication considering adjacent channel interference

This paper investigates schemes for multihop scheduling and power control for vehicle-to-vehicle (V2V) multicast communication, taking into account the effects of both co-channel interference and adjacent channel interference, such that requirements on latency or age of information (AoI) are satisfied. Optimal performance can be achieved by formulating and solving mixed Boolean linear programming (MBLP) optimization problems for various performance metrics, including network throughput and connectivity. Fairness among network nodes (vehicles) is addressed by considering formulations that maximizes the worst-case network node performance. Solving the optimization problem comes at the cost of significant computational complexity for large networks and requires that (slow) channel state information is gathered at a central point. To address these issues, a clustering method is proposed to partition the optimization problem into a set of smaller problems, which reduces the overall computational complexity, and a decentralized algorithm that does not need channel state information is provided

Scheduling and Power Control for V2V Broadcast Communications with Co-Channel and Adjacent Channel Interference

This paper investigates how to mitigate the impact of both the co-channel interference and the adjacent channel interference (ACI) in the vehicle-to-vehicle (V2V) broadcast communication by scheduling and power control. Our objective is to maximize the number of connected vehicles. The optimal joint scheduling and power control problem is formulated as a mixed integer programming problem with a linear objective and a quadratic constraint. From the joint formulation, we derive (a) the optimal scheduling problem for fixed transmit powers as a Boolean linear programming problem and (b) the optimal power control problem for a fixed schedule as a mixed integer linear programming problem. The near-optimal schedules and power values are computed by solving first (a) and then (b) for smaller-size instances of the problem. To handle larger-size instances of the problem, we propose heuristic scheduling and power control algorithms with less computational complexity. The simulation results indicate that the heuristic scheduling algorithm yields significant performance improvements compared to the baseline block-interleaver scheduler and that performance is further improved by the heuristic power control algorithm. Moreover, the heuristic algorithms perform close to the optimal scheme for small instances of the problem

On Adjacent Channel Interference-Aware Radio Resource Management for Vehicle-to-Vehicle Communication

Safety applications play an essential role in supporting traffic safety and efficiency in next generation vehicular networks. Typical safety applications require vehicle-to-vehicle (V2V) communication with high reliability and low latency. The reliability of a communication link is mainly determined by the received interference, and broadly speaking, there are two types of interferences: co-channel interference (CCI) and adjacent channel interference (ACI). The CCI is cross-talk between transmitters scheduled in the same time-frequency slot, whereas ACI is interference due to leakage of transmit power outside the intended frequency slot. The ACI is typically not a problem in cellular communication since interference is dominated by CCI due to spectrum re-usage. However, ACI is a significant problem in near-far situations, i.e., when the channel gain from the interferer to receiver is high compared to the channel gain from the intended transmitter. The near-far situation is more common in V2V broadcast communication scenario due to high dynamic range of the channel gain and penetration loss by intermediate vehicles. This thesis investigates the impact of ACI on V2V communication and methods to mitigate it by proper radio resource management (RRM), i.e., scheduling and power control.In [Paper A], we first study ACI models for various transmission schemes and its impact on V2V communication. We propose a problem formulation for a) optimal scheduling as a Boolean linear programming (BLP) problem and b) optimal power control as a mixed Boolean linear programming (MBLP) problem. The objective of the problem formulation is to maximize the connectivity among VUEs in the network. Near-optimal schedules and power values are computed by solving first a) and then b) for smaller size instances of the problem. To handle larger-size instances of the problem, heuristic scheduling and power control algorithms with less computational complexity are proposed. We also propose a simple distributed block interleaver scheduler (BIS), which can be used as a baseline method.In [Paper B], we formulate the joint scheduling and power control problem as an MBLP to maximize the connectivity among VUEs. A column generation method is proposed to address the scalability of the network, i.e., to reduce the computational complexity of the joint problem. Moreover, the scheduling problem is observed to be numerically sensitive due to the high dynamic range of channel values and adjacent channel interference ratio (ACIR) values. Therefore, a novel method is proposed to reduce the sensitivity and compute a numerically stable optimal solution at the price of increased computational complexity.In [Paper C], we extend the RRM problem formulation to include various objectives, such as maximizing connectivity/throughput and minimizing age of information (AoI). In order to account for the fairness, we also formulate the problem to improve the worst-case throughput, connectivity, and AoI of a link in the network. All the problems are formulated as MBLP problems. In order to support a large V2V network, a clustering algorithm is proposed whose computational complexity scale well with the network size. Moreover, a multihop distributed scheduling scheme is proposed to handle zero channel state information (CSI) case