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.
Other related work includes the database approach proposed in @cite_7 , where the optimal broadcast rates and transmission power are calculated offline based on the network configurations. Also, @cite_18 proposed an environment and context-aware distributed congestion control (DCC) algorithm, which jointly control the rate and power to improve cooperative awareness by adapting to both specific propagation environments (such as urban intersections, open highways, suburban roads) as well as application requirements (e.g., different target cooperative awareness range). However, the stability and convergence of the algorithm are not proved mathematically. Besides the rate control algorithm IVTRC, the authors also proposed range control algorithms in @cite_11 @cite_13 @cite_12 where the objective is to adapt the transmission ranges to achieve a specific threshold. The motivation of limiting channel loads below the threshold is to control channel congestion to maximize effective channel throughput. However, fair resource allocation among vehicles to increase the safety awareness of all vehicles are not considered, and the stability of the algorithms is subject to certain conditions @cite_12 .
@cite_18 @cite_7 @cite_13 @cite_12 @cite_11
273025837 2100920052 2139529640 2038609352 2015380071
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. Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently under field study. These algorithms adapt transmission rate and power based on network measures such as channel busy ratio. We examine two such algorithms and study their dynamic behavior in time and space to evaluate stability (in time) and fairness (in space) properties of these algorithms. We present stability conditions and evaluate stability and fairness of the algorithms through simulation experiments. Results show that there is a trade-off between fast convergence, temporal stability and spatial fairness. The proper ranges of parameters for achieving stability are presented for the discussed algorithms. Stability is verified for all typical highway density cases for static traffic as well as real scenarios. Fairness is shown to be naturally achieved for some algorithms and its analysis is under study in another work of us. Under the same conditions other algorithms may have problem to maintain fairness in space. We have shown that this can be resolved by a distributed measurement of CBR and is verified. 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. 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. Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally define the notion of fairness of a beaconing rate allocation in vehicular networks. The NUM model provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. In this context, we propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. Simulation results validate our approach and show that, unlike recent proposals, FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios. We propose an intervehicle communication framework for the cooperative active safety system (CASS) whose operation is based on the dissemination of each vehicle's state information through a wireless network. Such a CASS requires each subject vehicle to be aware of its surroundings, particularly of the motion and position of other vehicles in its proximity. In this paper, we assume that all vehicles are equipped with onboard communication devices. In such situations, the wireless channel is simultaneously shared by a large number of vehicles, and one of the most difficult challenges in designing CASS is to maintain real-time tracking accuracy of neighboring vehicles while avoiding network congestion and failure. To address this issue, we analyze the problem that multiple scalar linear time-invariant dynamical systems track each other over a multiaccess channel, and then, we propose a rate adaptation algorithm to distributively control the self-information broadcast behavior of each vehicle. The proposed algorithm uses a closed-loop control concept and accounts for the lossy channel. Simulation results show that, if the message generation rate is dynamically adjusted in an on-demand fashion, more accurate and robust tracking performance can be achieved under various traffic conditions. In order for the large-scale realization of vehicular networks to be feasible, the problem of congestion control must be addressed to ensure the reliability of safety applications. The latter rely on single-hop broadcasts of safety packets in the control channel to acquire up-to-date knowledge of the local neighbourhood. However, high transmission ranges of onboard radios and the highly dynamic mobility of vehicles may result in fast-forming pockets of high node density in the network. Subsequently, the excessive load caused by safety packets broadcasts may degrade the network performance and subsequently reduce the level safety provided by applications. Existing congestion control schemes in the literature aim to reach a fair rationing of available channel resources throughout the network. However, a particular vehicle, depending on its distance and relative velocity with respect to its neighbours may require less or more network resources than another vehicle to achieve the same level of safety benefit. We examine the problem of adapting the probability of transmission of each node under a slotted p-persistent vehicular broadcast medium access control (MAC) scheme. A network utility maximization (NUM) problem is formulated, in which utility incorporates both the expected delay and a notion of safety benefit. A distributed algorithm is proposed to solve this problem in a decentralized manner and its performance is studied through simulations. Vehicular ad hoc networks play a critical role in enabling important active safety applications such as cooperative collision warning. These active safety applications rely on continuous broadcast of self-information by all vehicles, which allows each vehicle to track all its neighboring cars in real time. The most pressing challenge in such safety-driven communication is to maintain acceptable tracking accuracy while avoiding congestion in the shared channel. In this article we propose a transmission control protocol that adapts communication rate and power based on the dynamics of a vehicular network and safety-driven tracking process. The proposed solution uses a closed-loop control concept and accounts for wireless channel unreliability. Simulation results confirm that if packet generation rate and associated transmission power for safety messages are adjusted in an on-demand and adaptive fashion, robust tracking is possible under various traffic conditions. 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. Machine-to-machine (M2M) communications are bringing new challenges to congestion control in the Internet of Things. One key issue is to facilitate the proper functioning of a wide range of M2M applications with drastically different throughput demands. Traditional Internet congestion control algorithms aim at sharing bandwidth among traffic flows equally, limiting their suitability for M2M communications. To maintain comparable levels of QoS of heterogeneous M2M applications when congestion is present, we propose a distributed congestion control algorithm which allocates transmission rates to M2M flows in proportion to their demands, through the use of a simple technique which we call “proportional additive increase”. To ease M2M application development, we make a further attempt to stabilize the throughputs of M2M flows controlled by the algorithm. We present simulation results to illustrate the effectiveness of the algorithm in achieving the desired rate allocation, as well as the challenge we face in stabilizing throughputs. In a wireless ad hoc network with multihop transmissions and interference-limited link rates, can we balance power control in the physical layer and congestion control in the transport layer to enhance the overall network performance, while maintaining the stability, robustness, and architectural modularity of the network? We present a distributive power control algorithm that couples with the original TCP protocols to increase the end-to-end throughput and energy efficiency of the network. Under the rigorous framework of nonlinearly constrained optimization, we prove the convergence of this coupled system to the global optimum of joint power control and congestion control, for both synchronized and asynchronous implementations. The rate of convergence is geometric and a desirable modularity between the transport and physical layers is maintained. In particular, when the congestion control mechanism is TCP Vegas, that a simple utilization in the physical layer of the router buffer occupancy information suffices to achieve the joint optimum of this cross layer design. Both analytic results and simulations illustrate other desirable properties of the proposed algorithm, including robustness to channel outage and to path loss estimation errors, and flexibility in trading-off performance optimality for implementation simplicity. An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network?s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes. Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency. The current framework of network utility maximization for rate allocation and its price-based algorithms assumes that each link provides a fixed-size transmission "pipe" and each user's utility is a function of transmission rate only. These assumptions break down in many practical systems, where, by adapting the physical layer channel coding or transmission diversity, different tradeoffs between rate and reliability can be achieved. In network utility maximization problems formulated in this paper, the utility for each user depends on both transmission rate and signal quality, with an intrinsic tradeoff between the two. Each link may also provide a higher (or lower) rate on the transmission "pipes" by allowing a higher (or lower) decoding error probability. Despite nonseparability and nonconvexity of these optimization problems, we propose new price-based distributed algorithms and prove their convergence to the globally optimal rate-reliability tradeoff under readily-verifiable sufficient conditions. We first consider networks in which the rate-reliability tradeoff is controlled by adapting channel code rates in each link's physical-layer error correction codes, and propose two distributed algorithms based on pricing, which respectively implement the "integrated" and "differentiated" policies of dynamic rate-reliability adjustment. In contrast to the classical price-based rate control algorithms, in our algorithms, each user provides an offered price for its own reliability to the network, while the network provides congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which we prove to be a globally optimal one for channel codes with sufficiently large coding length and utilities whose curvatures are sufficiently negative. Under these conditions, the proposed algorithms can thus generate the Pareto optimal tradeoff curves between rate and reliability for all the users. In addition, the distributed algorithms and convergence proofs are extended for wireless multiple-inpit-multiple-output multihop networks, in which diversity and multiplexing gains of each link are controlled to achieve the optimal rate-reliability tradeoff. Numerical examples confirm that there can be significant enhancement of the network utility by distributively trading-off rate and reliability, even when only some of the links can implement dynamic reliability. We consider QoS-aware spectrum sharing in cognitive wireless networks where secondary users are allowed to access the spectrum owned by a primary network provider. The interference from secondary users to primary users is constrained to be below the tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. When network load is high, admission control needs to be performed to satisfy both QoS and interference constraints. We propose an admission control algorithm which is performed jointly with power control such that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. When all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power rate allocation with max-min fairness criterion as an optimization problem. We show how to transform it into a convex optimization problem so that its globally optimal solution can be obtained. Numerical results show that the proposed admission control algorithm achieves performance very close to the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for both admission control and rate power allocation problems. In wireless cellular networks and wireless local area networks, nonlinear network utility maximization need to be conducted over both user rates and transmit powers. For each of the three cases considered in this paper, we present an algorithm that converges to the jointly optimal pair of rate vector and power vector. For the simple case when data rates are not limited by interferences, for example in single-cell downlink transmissions, we propose algorithm 1, which is an iterative bidding mechanism between the base station and mobile users, where knowledge about channel conditions and individual user utility functions is only needed locally at each user but not needed at the base station. In the case when data rates are limited by interferences, the utility maximization problem is complicated both by nonlinear coupling between powers and rates, and by interference among powers. Through centralized iterative steps, we propose algorithm 2, which converges to a joint and global optimum over the solution space of rates and powers. We then consider end-to-end transmissions in cellular networks, which traverse both wireless fading channels and many hops of wired links shared by other traffic. There is a tradeoff between attaining air-interface capacity in the wireless hop and controlling congestion in the wired backbone wide area network. We formulate this end-to-end resource allocation problem in such hybrid networks, and present a solution to obtain the Pareto optimal tradeoff between attaining wireless multi-access fading channel capacity and maximizing global network utility. This article describes the use of predictive control for the decentralized cooperative control of unmanned aerial vehicles in an unfamiliar three-dimensional environment. It is assumed that each vehicle is equipped with an autopilot and a trajectory control unit. The autopilot insures the stability of the vehicle. The setpoints of the autopilot are calculated by the trajectory control unit, thus forming a cascade control structure. The trajectory control unit relies on a predictive control algorithm to calculate the optimal commands (autopilot setpoints) such that the vehicle will reach fixed targets at known positions while avoiding static obstacles that are detected en route. The advantage in using predictive control is that it offers great flexibility in the objective function to optimize while respecting constraints such as command limits, limits on the displacement that a vehicle can carry out, and the constraints that allow obstacle avoidance. The principle proposed in order to avoid static obstacles (that are assumed ellipsoid) is to verify that the predicted vehicle trajectory does not intersect these obstacles. With the objective to increase performance, cooperation between vehicles must also be privileged. Thus, if some vehicles are within the communication range, they can share the position and shape of the obstacles they have detected. Simulations illustrate the method and higlight the benefits of cooperation. Cooperative vehicle safety (CVS) systems rely on vehicular ad-hoc networks operating in broadcast mode to deliver vehicle tracking and safety information to neighboring cars. This information is used to enable collision avoidance and warning systems. One of the main challenges of the eventual large scale deployment of such systems is network congestion, which could critically degrade the quality of a CVS system. In this paper, we present a method for congestion monitoring and control based on limited feedback from the network. We study the relationship between channel occupancy, as a readily available feedback measure, controllable network parameters, and network performance. We describe a performance measure relevant to CVS systems and present a congestion control method, based on channel occupancy measurements, that robustly maintains the system performance near optimal operation points. We examine the convergence properties of the congestion control algorithm and provide guidelines for the design of such systems based on network and traffic density conditions. Through simulation experiments we show significant gains in performance when closed loop congestion control methods are applied. Nowadays it is vital to design robust mechanisms to provide QoS for multimedia applications as an integral part of the network traffic. The main goal of this paper is to provide an efficient rate control scheme to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in congested networks. Towards this, we introduce a content-aware time-varying utility function, in which the quality impact of video content is incorporated into its mathematical expression. Moreover, we analytically model the buffer requirements of video sources in two ways: first as constraints of the optimization problem to guarantee a minimum rate demand for each source, and second as a penalty function embedded as part of the objective function attempting to achieve the highest possible rate for each source. Then, using the proposed analytical model, we formulate a dynamic network utility maximization problem, which aims to maximize the aggregate hybrid objective function of sources subject to capacity and buffer constraints. Finally, using primal-dual method, we solve DNUM problem and propose a distributed algorithm called CA-DNUM that optimally allocates the shared bandwidth to video streams. The experimental results demonstrate the efficacy and performance improvement of the proposed content-aware rate allocation algorithm for video sources in different scenarios. Vehicle Safety Communications (VSC) is advancing rapidly towards product development and field testing. While a number of possible solutions have been proposed, the question remains open as how such a system will address the issue of scalability in its actual deployment. This paper presents a design methodology for congestion control in VSC as well as the description and evaluation of a resulting rate adaption oriented protocol named PULSAR. We start with a list of design principles reflecting the state of the art that define why and how vehicles should behave while responding to channel congestion in order to ensure fairness and support the needs of safety applications. From these principles, we derive protocol building blocks required to fulfill the defined objectives. Then, the actual protocol is described and assessed in detail, including a discussion on the intricate features of channel load assessment, rate adaptation and information sharing. A comparison with other state-of-the-art protocols shows that “details matter” with respect to the temporal and spatial dimensions of the protocol outcome.