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.
A parallel work @cite_8 introduced a network utility maximization (NUM) formulation on the rate control problem when specified to safety-awareness. A distributed algorithm was proposed to adjust the rate with the objective to maximize the utility function. Similarly, @cite_1 also provided a NUM formulation on the rate control problem and proposed a fair adaptive beaconing rate for intervehicular communications (FABRIC) algorithm, which essentially is a particular scaled gradient projection algorithm to solve the dual of the NUM problem.
@cite_1 @cite_8
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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 present a fast distributed gradient method for a convex optimization problem with linear inequalities, with a particular focus on the network utility maximization (NUM) problem. Most existing works in the literature use (sub)gradient methods for solving the dual of this problem which can be implemented in a distributed manner. However, these (sub)gradient methods suffer from an O(1 √k) rate of convergence (where k is the number of iterations). In this paper, we assume that the utility functions are strongly concave, an assumption satisfied by most standard utility functions considered in the literature. We develop a completely distributed fast gradient method for solving the dual of the NUM problem. We show that the generated primal sequences converge to the unique optimal solution of the NUM problem at rate O(1 k). Allocating limited resources such as bandwidth and power in a multi-hop wireless network can be formulated as a Network Utility Maximization (NUM) problem. In this approach, both transmitting source nodes and relaying link nodes exchange information allowing for the NUM problem to be solved in an iterative distributed manner. Some previous NUM formulations of wireless network problems have considered the parameters of data rate, reliability, and transmitter powers either in the source utility function which measures an application's performance or as constraints. However, delay is also an important factor in the performance of many applications. In this paper, we consider an additional constraint based on the average queueing delay requirements of the sources. In particular, we examine an augmented NUM formulation in which rate and power control in a wireless network are balanced to achieve bounded average queueing delays for sources. With the additional delay constraints, the augmented NUM problem is non-convex. Therefore, we present a change of variable to transform the problem to a convex problem and we develop a solution which results in a distributed rate and power control algorithm tailored to achieving bounded average queueing delays. Simulation results demonstrate the efficacy of the distributed algorithm. The standard Network Utility Maximization (NUM) problem has a static formulation, which fails to capture the temporal dynamics in modern networks. This work considers a dynamic version of the NUM problem by introducing additional constraints, referred to as delivery contracts. Each delivery contract specifies the amount of information that needs to be delivered over a certain time interval for a particular source and is motivated by applications such as video streaming or webpage loading. The existing distributed algorithms for the Network Utility Maximization problems are either only applicable for the static version of the problem or rely on dual decomposition and first-order (gradient or subgradient) methods, which are slow in convergence. In this work, we develop a distributed Newton-type algorithm for the dynamic problem, which is implemented in the primal space and involves computing the dual variables at each primal step. We propose a novel distributed iterative approach for calculating the dual variables with finite termination based on matrix splitting techniques. It can be shown that if the error level in the Newton direction (resulting from finite termination of dual iterations) is below a certain threshold, then the algorithm achieves local quadratic convergence rate to an error neighborhood of the optimal solution in the primal space. Simulation results demonstrate significant convergence rate improvement of our algorithm, relative to the existing first-order methods based on dual decomposition.