Dynamic power allocation and routing for time varying wireless networks

By Michael J. Neely, Eytan H. Modiano, Charles E. Rohrs. IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), 2003

1,030 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b212b3b9b4f2c7d0fb185a6f35efae984e1bf0a2

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Citations in the corpus

Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures (2007, background): “• Jointly congestion control and adaptive coding or power control [18], [21], [76] • Jointly congestion and contention control [16], [61], [74], [144], [167], [168], [169] • Jointly congestion control and scheduling [1], [12], [35], [92], [129] • Jointly routing and power control [58], [100], [159] ”
A tutorial on cross-layer optimization in wireless networks (2006, background): “In the node-centric formulation, a user rate vector is in the capacity region if and only if there exists a link rate vector associated with each destination node , and the vector satisfies [15], [37] (6) where can be interpreted as the rate on link that is allocated for data towards destination .”
Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, background): “It is shown by [23], [24] that the energy-bit relation can be well approximated by the monomial function.”
Joint rate control and scheduling in multihop wireless networks (2004, background): “Note that in the framework of [3], there is no rate control, i.”
Maximizing throughput in wireless networks via gossiping (2006, background): “The optimal algorithms developed in [18, 20, 23] require solving a global optimization problem, taking into account the queue backlog information for every link in the network.”
Cross-Layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks (2006, background): “4The above joint routing and scheduling recovers the DRPC policy in reference [ 22 ], except that step 2 is scheduling here and power allocation there, and data is routed based on destination here and “commodity” there.”
Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks (2008, background): “We next present an online control algorithm that overcomes all of these challenges.”
Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G (2014, background): “The focus in [22]–[24] is on relatively static networks, where there are no drastic topology changes.”
Joint congestion control, routing, and MAC for stability and fairness in wireless networks (2006, methodology): “Next, we describe the capacity region of the network as in [15] and [21].”
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, methodology): “1A dynamic power allocation problem with time-varying channels for a different system model and network setup was studied in [10], where the delay performance of the classical dynamic backpressure algorithm was improved by exploiting the stochastic Lyapunov optimization framework.”
Energy optimal control for time-varying wireless networks (2005, methodology): “Such curves might also depend on the signal to interference ratio at the intended receiver, so that μl(~ P , ~ S) for a given link l is determined by the full vector of power allocations and channel states [22] [12] [23].”
Ergodic Stochastic Optimization Algorithms for Wireless Communication and Networking (2010, background): “The operator denotes projection to the positive orthant.”
On the Complexity of Scheduling in Wireless Networks (2006, background): “This has spurred recent interest in developing cross-layer optimization algorithms (see, for example, [34, 23, 22 , 29, 7]).”
Resource Allocation and Cross-Layer Control in Wireless Networks (2006, methodology): “This measurement can be in the form of a speci c set of attenuation coe¢ cients, or can be according to a simple channel classi cation such as Good, Medium, Bad.”