Distributed algorithms for resource allocation of physical and transport layers in wireless cognitive ad hoc networks

Abstract

In this paper, by integrating together congestion control, power control and spectrum allocation, a distributed algorithm is developed to maximize the aggregate source utility and increase end-to-end throughput. Despite the inherent difficulties of non-convexity and non-separability of variables in the original optimization problem, we are still able to obtain a decoupled and dualdecomposable convex formulation by applying an appropriate transformation and introducing some new variables. The objective is accomplished by the interaction and coordination among three sub-algorithms of the algorithm through the congestion prices. The convergence properties of the three sub-algorithms are also proved. Simulation results illustrate several other desirable properties of the proposed algorithm, including the impacts of node mobility and path and packet losses on convergence and robustness. This work is a preliminary attempt towards a systematic approach to jointly designing a congestion control subalgorithm and a power control sub-algorithm coupled with a spectrum allocation sub-algorithm.

Optimal Resource Allocation for Cross-layer Utility Maximization in Ad Hoc Networks

“…A joint power and subcarrier/end-to-end rate control algorithm for cognitive radio networks was proposed in [14] by restricting the interference to licensed users while fairly maintaining a satisfied data rate or ensuring the interference produced to the primary user within a given limit. In [15,16], a distributed algorithm was developed to maximize the aggregate source utility and increase end-to-end throughput, by integrating together congestion control, power control and spectrum allocation. In [17], Qu et.al.…”

“…, (17), (21) and (22), respectively. Thus the problems (15), (17), (21), (22) and (30) can be solved at each node (including source node) in a distributed manner by using local message ls  . This link price adjustment rule follows the law of supply and demand in a fashion similar to that for congestion control in wireline networks.…”

Simple Cross-Layer Network Utility Maximization Framework for Multiflow Mobile Ad-Hoc Networks

“…The linear relation between the session total flow rate and its sub-flows makes the utility function not strictly concave, even if the utility function U (.) is strictly concave, introducing instability in the NUM algorithms [38,56]. Constraint (5.2) states that the aggregate sub-flows allocated to the link must be less than or equal to the link throughput.…”