Distributed interference compensation for wireless networks

By Jianwei Huang, Randall A. Berry, Michael L. Honig. IEEE Journal on Selected Areas in Communications, 2006

741 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fee304bbe781c398f7811de5dc4c63e7091fa288

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In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks (2009, background): “In [21], the authors instead consider a multicarrier wireless ad hoc network model with a high signal-to-noise ratio approximation and propose a”
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013, background): “We conclude this review by mentioning the recent work [34], where the authors, developing ideas contained in [30], [33], proposed parallel schemes based of the SCA idea that are applicable (only) to the class of sum-utility problems for which a connection with a MMSE formulation can be established.”
Spectrum sharing for unlicensed bands (2005, background): “In the next section we study the structure of the optimal power allocations for any reasonable choice of global utility.”
Competition Versus Cooperation on the MISO Interference Channel (2008, methodology): “Distributed algorithms for spectrum sharing in a competitive setup (using noncooperative game theory [9]) were developed in [10] and [11].”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, methodology): “In [11], a locally optimal power allocation was obtained using a game-theoretic distributed algorithm with interference pricing.”
Convex Optimization, Game Theory, and Variational Inequality Theory (2010, background): “These classes of games have recently received great attention in the signal processing and communication communities as a useful tool to model and solve various power control problems in wireless communications and networking [18], [31], [32].”
Fast Global Optimal Power Allocation in Wireless Networks by Local D.C. Programming (2012, background): “Also, according to [5, Figs 6 and 7] the algorithms of [6], [7] failed to locate such global optimal value.”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Transmit power adaptation, energy harvesting [235] and sleep modes [236] can foster EE in cell-free MIMO networks.”
Multi-Cell MIMO Cooperative Networks: A New Look at Interference (2010, background): “This idea is called interference pricing, which has been proposed for the power spectrum adaptation problem for the wireless ad-hoc network [86], [87], [88], [89], the digital subscriber line network [90], and is also applicable to wireless multi-cell networks [78], [82].”
Cell Association and Interference Coordination in Heterogeneous LTE-A Cellular Networks (2010, background): “The work in [22] considers a multiple iteration algorithm for a related optimization problem for a class of objective functions which does not include the one in this paper.”
Competitive spectrum sharing in cognitive radio networks: a dynamic game approach (2008, methodology): “Power control schemes based on game theory were proposed in the literature [11]-[15].”
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011, methodology): “In particular, [6] has established the convergence of the interference pricing algorithm to a stationary point for a set of utility functions, which unfortunately does not include the standard Shannon rate function.”
Price-Based Spectrum Management in Cognitive Radio Networks (2007, background): “Note that a similar pricing analysis was recently conducted in [13] in the context of interference management.”
What is a Spectrum Hole and What Does it Take to Recognize One? (2009, background): “Opt-in spectrum markets are an extreme case of explicit feedback from primary users [40], but other forms of implicit feedback are also possible.”
Spectrum Leasing to Cooperating Secondary Ad Hoc Networks (2008, background): “=[P i ] i∈S : 0 ≤ P i ≤ P max } , the utility function u i (P i, P −i ) of the ith secondary node (player) in the subset S is defined as the difference between the achievable transmission rate and the cost of transmitted energy, similarly to, e.g., [19].”
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, methodology): “In time slot t, we account for the externality that link i causes to link k using a price charged to link i for generating interference to link k [5]:”