An adaptive framework for addressing fairness issues in wireless networks

Tsibonis, V.; Georgiadis, Leonidas

doi:10.1016/j.comcom.2004.07.018

Cited by 3 publications

(1 citation statement)

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“…The concepts of maximum weight matching and differential backlog scheduling, developed in [143], play important roles in the dynamic control strategies we present in later sections. The Lyapunov drift approach has been successfully used to optimize allocation of computer resources [21,22], stabilize packet switch systems [72,80,87,103,148,150] satellite and wireless systems [4,67,110,144,145,149,158], and ad-hoc mobile networks [111]. Recently, a simple extension of Lyapunov drift theory is developed in [108,115,116] to provide both stability and performance optimization (addressed in more detail in Sections 5 and 6).…”

Section: Lyapunov Stabilitymentioning

confidence: 99%

Resource Allocation and Cross-Layer Control in Wireless Networks

2005

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Information flow in a telecommunication network is accomplished through the interaction of mechanisms at various design layers with the end goal of supporting the information exchange needs of the applications. In wireless networks in particular, the different layers interact in a nontrivial manner in order to support information transfer. In this text we will present abstract models that capture the cross-layer interaction from the physical to transport layer in wireless network architectures including cellular, ad-hoc and sensor networks as well as hybrid wireless-wireline. The model allows for arbitrary network topologies as well as traffic forwarding modes, including datagrams and virtual circuits. Furthermore the time varying nature of a wireless network, due either to fading channels or to changing connectivity due to mobility, is adequately captured in our model to allow for state dependent network control policies. Quantitative performance measures that capture the quality of service requirements in these systems depending on the supported applications are discussed, including throughput maximization, energy consumption minimization, rate utility function maximization as well as general performance functionals. Cross-layer control algorithms with optimal or suboptimal performance with respect to the above measures are presented and analyzed. A detailed exposition of the related analysis and design techniques is provided. 2 The Network Model and Operational Assumptions Consider a general network with a set N of nodes and a set L of transmission links. We denote by N and L respectively the number of nodes and links in the network. Each link represents a communication channel for direct transmission from a given node a to another node b, and is labeled by its corresponding ordered node pair (a, b) (where a, b ∈ N). Note that link (a, b) is distinct from link (b, a). In a wireless network, direct transmission between two nodes may or may not be possible and this capability, as well as the transmission rate, may change over time due to weather conditions, mobility or node interference. Hence in the most general case one can consider that L consists of all ordered pairs of nodes, where the transmission rate of link (a, b) is zero if direct communication is impossible. However, in cases where direct communication between some nodes is never possible, it is helpful to consider that L is a strict subset of the set of all ordered pairs of nodes. The network is assumed to operate in slotted time with slots normalized to integral units, so that slot boundaries occur at times t ∈ {0, 1, 2,. . .}. Hence, slot t refers to the time interval [t, t + 1). Let µ(t) = (µ ab (t)) represent the matrix of transmission rates offered over 6

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