Cross-Layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks

Abstract

Abstract-This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multi-rate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer.We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with time-varying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dual-based algorithm remains stable and optimal when the constraint set is modulated by an irreducible finite-state Markov chain. This paper thus presents a step toward a systematic way to carry out cross-layer design in the framework of "layering as optimization decomposition" for time-varying channel models.

Cross-layer rate control, medium access control and routing design in cooperative VANET

“…In the case of multi-path flow control, many literatures have been proposed by introducing the variable of path price to maximum the total utility function of the system [6][7][8]. In [7], the author solves the optimal congestion control problem by taking the path price into consideration, and the optimal source rate * x at each source can be adjusted according to the persistence probability of each link.…”

Achieving Global End-to-End Maxmin in Multihop Wireless Networks

“…Two wireless links contend if they cannot transmit simultaneously. Based on the most popular MAC protocol, this model excludes the majority of related works [15,19,2,12,16].…”

“…Efforts have been made to adapt utility-based solutions in wireless networks by assuming each wireless node has a fixed bandwidth capacity [15], considering only single-hop flows [7], eliminating contention among neighboring nodes by using separate CDMA/FDMA channels for wireless links [19], modeling resources as maximal contention cliques instead of wireless links [18], or relying on crosslayer design to integrate end-to-end rate adaptation with MAClayer packet scheduling [2]. The maximal clique approach [18] requires that each clique's effective capacity is known, but it is not clear how to accurately measure such capacity, which is a complex function of nearby contention and environmental noise.…”

“…The maximal clique approach [18] requires that each clique's effective capacity is known, but it is not clear how to accurately measure such capacity, which is a complex function of nearby contention and environmental noise. The cross-layer approach [2] requires the nodes to dynamically establish globally coordinated (or locally approximated [12]) time-slotted transmission schedules at the MAC layer, which does not fit well with IEEE 802.11's random access model. More importantly, the utility function that approximates maxmin fairness contains an exponent approaching to infinity [14], which makes the system hard to stabilize.…”

Intelligent allocation of network bandwidth: A comparison of two generalized particle models

“…During the last several years, different approaches using economic models for resource allocation have been proposed [13][14][15][16][17][18][19]. Some of them divide the traffic into multiple priority classes, but use fixed prices for each service class.…”