The focus of this paper is on the rate and power control algorithms in Dedicated Short Range Communication (DSRC) for vehicular networks. We first propose a utility maximization framework by leveraging the well-developed network congestion control, and formulate two subproblems, one on rate control with fixed transmit powers and the other on power control with fixed rates. Distributed rate control and power control algorithms are developed to solve these two subproblems, respectively, and are proved to be asymptotically optimal. Joint rate and power control can be done by using the two algorithms in an alternating fashion. The performance enhancement of our algorithms compared with a recent rate control algorithm, called EMBARC, is evaluated by using the network simulator ns2.
The design of rate and power control algorithms in DSRC is one of most critical problems in ITS. Error Model Based Adaptive Rate Control (EMBARC) @cite_15 is a recent rate control protocol which integrates several existing rate control algorithms including the Linear Integrated Message Rate Control (LIMERIC) @cite_16 , Periodically Updated Load Sensitive Adaptive Rate control (PULSAR) @cite_0 , and the InterVechicle Transmission Rate Control (IVTRC) @cite_19 . LIMERIC allocates the wireless channel equally among all vehicles that share the same bottleneck link while guaranteeing the channel load is below a given threshold. IVTRC generates messages and adapts transmission probabilities based on the Suspected Tracking Error (STE) calculated based on vehicle dynamics to avoid collisions. In EMBARC, the message rates are controlled by LIMERIC and are further modified to satisfy the STE requirement.
@cite_0 @cite_19 @cite_15 @cite_16
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Channel congestion is one of the major challenges for deployment of collision avoidance systems based on DSRC (Dedicated Short Range Communication) in large scale networks. If vehicles do not adapt to congestion conditions, DSRC transmissions could encounter extensive packet losses in areas of high vehicle density, leading to degradation in the performance of safety applications. In this paper, we propose a novel congestion control algorithm called Error Model Based Adaptive Rate Control (EMBARC) which adapts a vehicle's transmission rate as a function of channel load and vehicular dynamics. In particular, we extend Linear Integrated Message Rate Control (LIMERIC) algorithm's message rate adaptation with the capability to preemptively schedule messages based on the vehicle's movement. This leads to more transmission opportunities for vehicles with higher dynamics. The determination of a preemptive scheduling event is based on a novel suspected tracking error technique. Since LIMERIC maintains the channel load around a specific value, vehicles moving less dynamically will adapt to slightly reduced transmission rates in EMBARC. The extra transmit opportunities for highly dynamic vehicles reduce incidences of large tracking error compared to a pure LIMERIC approach. At the same time, EMBARC's use of adaptive rate control provides tracking error advantages over systems that transmit largely independent of channel load. We use simulations of a road with a winding segment to compare EMBARC with algorithms that do not take both channel load and vehicle dynamics into account. The results show that EMBARC has the best tracking accuracy among these algorithms over a wide range of node densities. Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios. Abstract 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. In Vehicle Safety Communications (VSC) based on IEEE 802.11p, vehicles establish a mutual awareness of their presence by periodically broadcasting status messages, aka beacons. If vehicle density is high and beaconing is not regulated, the channel can become congested, impairing reception performance and safety benefit. As a countermeasure, a number of congestion control approaches have been suggested, adapting transmit (Tx) power, beacon generation rate (Tx rate), or both. However, in general these approaches did not show what the optimal outcome for congestion control would be and how and why their solution would lead to the desired result. In this work, we analyze answers to the first question and provide a methodology for the second. We systematically derive a joint power rate control strategy for VSC which optimizes reception performance for a targeted sender-receiver distance. We start by laying out why we consider average (or percentile of) packet Inter-Reception Time (IRT) at the targeted awareness distance to be a suitable metric for our purpose. Then, we analyze a wide range of Tx parameters to identify which combinations optimize reception in a homogeneous scenario. We show that for each sender-receiver distance, there is an optimal Tx power which, unlike the corresponding Tx rate, is independent of node density. In addition, we analyze the Pareto optimal Tx parameter combinations for two groups of vehicles with different target distances adapting at the same time. We show that the majority of these combinations use the same Tx power as identified in the homogeneous case. We conclude that a simple and efficient strategy to optimize reception performance is to select Tx power w.r.t. the targeted distance and to adapt Tx rate w.r.t. channel load.