Power Control By Geometric Programming

By Mung Chiang, Chee Wei Tan, D. P. Palomar, Daniel C. O'Neill, David Julian. IEEE Transactions on Wireless Communications, 2007

893 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/535a478744419b05fb629489ccded17b35e8165d

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Citations in the corpus

Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks (2009, methodology): “By using (10) and the transformation q = ln p [23], [35], [36], we have”
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.”
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization (2012, positive): “The BSUM approach has been extensively used for resource allocation in wireless networks, for example [8], [16], [18], [23], [27], and [26].”
Cooperative Jamming for Secure Communications in MIMO Relay Networks (2011, methodology): “The GP problems in this successive optimization method can be solved using interior-point methods with polynomial-time complexity [36], and it has been proven in [35] that the solution obtained using successive approximations for the single condensation method will efficiently converge to a point sa”
Energy-Efficient Resource Allocation in Wireless Networks (2007, background): “In [43], the authors present a centralized scheduling scheme to transmit the arriving packets within a specific time interval such that the total energy consumed is minimized whereas in [44], a distributed ALOHA-type scheme is proposed for achieving energy-delay tradeoffs.”
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.”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Finally, in Table XIII we summarize some advantages and current drawbacks of selected solutions targeting resource allocation.”
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.”
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning (2017, background): “As a result, minimizing the surrogate function becomes a standard GP [53].”
Binary Power Control for Sum Rate Maximization over Multiple Interfering Links (2008, methodology): “5The content in this section is largely based on [24], [26], where geometric power control for wireless networks is given a formal mathematical treatment.”
An introduction to convex optimization for communications and signal processing (2006, background): “) Recent progress has been made in this area for both wireline [22]–[24] and wireless applications [25].”
Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (2016, neutral): “IV.”
Waveform Design for Wireless Power Transfer (2016, background): “It cannot guarantee to converge to the global solution of the original problem, but only to yield a point fulfilling the KKT conditions [21], [22].”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, background): “Successive solution of geometric programming (GP) problems was proposed in [4] with promising results.”
A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks (2017, methodology): “-1,-2: Balanced traffic routing, sleep scheduling An iterative geometric programming algorithm [131] based on signomial programming [143] problem.”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “By rewriting the SU transmit covariance matrices into their equivalent precoding vectors and power allocation vectors , this algorithm iteratively updates the SU transmit precodi ng vectors (based on the “network duality” [39]) or the power allocation vectors (by solving geometric programming (GP) p”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “Based on this favorable feature, various power control problems in multi-user networks can be approximated as GPs in the high-SNR regime and solved using convex optimization (a detailed tutorial is given in [149]).”