Rate control for communication networks: shadow prices, proportional fairness and stability

By F. Kelly, AK Maulloo. 1998

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Citations in the corpus

Fair Resource Allocation in Federated Learning (2019, background): “Fair resource allocation has been extensively studied in fields such as network management (Ee & Bajcsy, 2004; Hahne, 1991; Kelly et al., 1998; Neely et al., 2008) and wireless communications (Eryilmaz & Srikant, 2006; Nandagopal et al.”
Distributed subgradient methods and quantization effects (2008, background): “Our work is also related to the utility maximization framework for resource allocation in networks (see [11], [12], [23]).”
Modelling incentives for collaboration in mobile ad hoc networks (2004, methodology): “This leads us naturally to the use of pricing mechanisms, which have found application in rate control in wireline networks [9, 3, 11]”
Combining Equity and Utilitarianism in a Mathematical Programming Model (2012, methodology): “Proportional fairness objectives for communication networks are closely related to the Nash bargaining solution and are discussed by Kelly et al. (1999) and Mazumdar et al. (1991), among others.”
System-level performance of downlink NOMA for future LTE enhancements (2013, background): “The proportional fairness (PF) scheduler [12] is known to achieve a good balance between system capacity and user fairness by maximizing proportional fairness, i.”
Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods (2008, methodology): “This approach was proposed in the seminal work of Kelly [11] and further developed by Low and Lapsley [18], Shakkottai and Srikant [30], and Srikant [33].”
Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control (2005, unknown): “[16], [17] that analyze network rate allocation as a distributed solution of utility maximization, TCP congestion control mechanisms have recently been analyzed as approximated distributed algorithms solving appropriately formulated network utility maximization problems (e.”
End-to-end congestion control schemes: utility functions, random losses and ECN marks (2000, methodology): “We start with the nonlinear programming formulation of a flow allocation problem suggested in [12] from which a penalty function formulation is derived in [11].”
Stability of primal-dual gradient dynamics and applications to network optimization (2010, methodology): “As a first application of the preceding result, consider the optimization-based approach to Internet congestion control introduced by Kelly et al. (1998).”
A Distributed Control Strategy for Reactive Power Compensation in Smart Microgrids (2011, unknown): “in wireless networks [40] and the traffic congestion protocol for wired data networks [41], [42].”