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
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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. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. 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.