Resource Allocation and Cross-Layer Control in Wireless Networks

By Leonidas Georgiadis, Michael J. Neely, Leandros Tassiulas. Foundations and Trends® in Networking, 2006

1,557 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b1f1febaf4cdb05b35b33bd11372da48d71f6888

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Citations in the corpus

The Effect of Fading, Channel Inversion, and Threshold Scheduling on Ad Hoc Networks (2007, methodology): “A recent review of this research thrust is found in the monograph by Georgiadis, Neely, and Tassiulas [31].”
Network Optimization and Control (2008, background): “The reader is referred to [15, 16] for more detailed surveys of the optimization approach to wireless network resource allocation.”
DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (2015, methodology): “Proof: It can be similarly proven with [22] using Caratheodory’s theorem [25].”
Optimal power cost management using stored energy in data centers (2011, methodology): “This approach is based on the recently developed technique of Lyapunov optimization [8] [15] that enables the design of online control algorithms for such time-varying systems.”
FlashLinQ: A Synchronous Distributed Scheduler for Peer-to-Peer Ad Hoc Networks (2013, methodology): “FlashLinQ leverages the .ne-grained parallel channel access offered by OFDM and incorporates an analog energy-level-based signaling scheme that enables signal-to-interference ratio (SIR)-based distributed scheduling.”
Decentralized Learning in Online Queuing Systems (2021, background): “We refer the reader to [Georgiadis et al., 2006] for an extended survey on resource allocation in wireless networks.”
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks (2017, result): “We refer to [3], [4] for a survey of the key results.”
A Dynamic Offloading Algorithm for Mobile Computing (2012, methodology): “Before further discussing the decision function, we first present a Lemma in [13], which is related to the derivation of the decision function.”
Resource Slicing in Virtual Wireless Networks: A Survey (2016, background): “This is known as the resource allocation problem and is well studied in computer science and operations research [37], [38] for other research ﬁelds.”
Ergodic Stochastic Optimization Algorithms for Wireless Communication and Networking (2010, methodology): “In the context of wireless networking, the work on stochastic network optimization of [14] and [ 15 ] has similar features to ESO in that it can operate without knowledge of the fading pdf and handle constraints that are not convex in . The goal in stochastic network optimization is to determine ope”
Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation (2017, methodology): “Popular tools for solving stochastic resource allocation problems include the backpressure algorithm [3] and variants of the stochastic dual descent method [9], [23].”
Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, background): “3 Network Optimization There are quite a lot work on stochastic cross-layer network optimization and control [27], [14].”
FemtoCaching: Wireless video content delivery through distributed caching helpers (2011, background): “Under the infinite backlog assumption, for all users the policy ensures that the system operates at a desirable point of the system ergodic rate region (defined by a utility function) [17] [18].”
Fairness and Optimal Stochastic Control for Heterogeneous Networks (2005, background): “assumptions were made to simplify exposition, the same policies can be shown to offer similar performance (with modified delay expressions) for arbitrary ergodic arrivals and channels, and are robust to cases when channel probabilities or arrival rates change over time [1], [4].”
Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks (2008, methodology): “The notion of network capacity for general networks with time-varying channels and energy constraints is formalized in [15], [17], [ 18 ], where it is shown to be a function of the steady-state network topology distribution, channel probabilities, and time-average transmission rates.”