Dual methods for nonconvex spectrum optimization of multicarrier systems

By Wei Yu, Raymond Lui. 2006

1,574 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “Then, for arbitrary constant τ ∈ [0 , 1] , we can ﬁnd a feasible DNN parameter set θ C which satisﬁes Proof: The proof proceeds similarly as in [32, Theorem 2].”
Spectrum sharing for unlicensed bands (2005, background): “The problem of optimizing resource allocations has been studied in [15, 16, 17, 18, 19].”
Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks (2018, background): “1) is still non-convex, one can easily show that it satisﬁes the so-called time-sharing condition in [39].”
Energy-Efficient Resource Allocation in OFDMA Systems with Large Numbers of Base Station Antennas (2012, methodology): “This can be easily resolved by the coordinate ascent method [25], [26].”
Proactive eavesdropping via cognitive jamming in fading channels (2015, methodology): “+(1−θ)Qb, where {Xi(ν)} denotes the corresponding {X(ν)} in (9) under the given jamming power allocation {qi(ν)}, i ∈ {a,b,c}. Proof: This lemma can be proved by using a similar proof technique as in [15], for which the details are omitted for brevity. The time-sharing condition in Lemma 3.1 ensures”
Convex Optimization in Signal Processing and Communications (2009, background): “Figure 1.3 provides some intuition for the role of convexity and the Slater condition in establishing no duality gap and the boundedness of the dual optimal solution set.”
Dynamic Spectrum Management: Complexity and Duality (2008, methodology): “When (weighted sum-rate), a so called time-sharing property was introduced in [21] which can ensure zero duality gap .”
Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach (2015, methodology): “According to [31], it is shown that the duality gap for a nonconvex optimization problem approaches zero in multichannel systems when the number of subchannels is large enough.”
Wireless Information and Power Transfer in Multiuser OFDM Systems (2013, background): “However, it has been shown that the duality gap of a similar problem to (23) without the harvested energy constrains converges to zero as the number of SCs, N , increases to infinity [19], [20].”
Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services (2014, methodology): “The timesharing relaxation was first proposed in [27], resulting in a zero-duality gap that was proved in [28] and widely used to transform non-convex combinatorial optimization problems into convex optimization problems for multiuser subchannel allocation in multichannel OFDMA systems [3], [16].”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “For example, for the non-convex OFDMA sub-channel allocation problem, the authors of [188] unveiled that under a time-sharing condition which holds for a large total number of subchannels, the duality gap between the primal and dual problems is zero.”
UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization (2017, methodology): “1 In this case, the problem is shown to satisfy the so-called time-sharing condition in [24], and thus can be optimally solved via the Lagrange dual method.”
Advanced Optimization Laboratory Title : Comments on “ Dual Methods for Nonconvex Spectrum Optimization of Multicarrier Systems (2007, methodology): “As pointed out in [7], if problem (P ) is convex, then it satisfies the time-sharing property.”
Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks (2019, methodology): “Some simplification is attained by working in the Lagrangian dual domain [1], [2] and subsequently using dual descent methods [3]–[5], or, alternatively, with heuristic optimization and scheduling methods [6]–[9].”
Wireless Information Transfer with Opportunistic Energy Harvesting (2012, background): “However, it can be verified th at all of them satisfy the “time-sharing” condition given in [21].”
Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems (2016, methodology): “The joint power and subcarrier allocation for the DL and the UL transmissions are obtained by utilizing the algorithms in [34] and [35], respectively.”
Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks (2009, methodology): “Given T1, the computational complexity of each iteration of the MC-IIWF algorithm is O(T1N(|S| + M log2 N)); indeed, solving each water-filling system has a computational burden O(N log2(N)) [26], while updating k̂ has a complexity O(N |S|).”
Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory—Part I: Nash Equilibria (2007, background): “, [12] and [13] for a special case of problem P.”
Energy- and Spectral-Efficiency Tradeoff in Downlink OFDMA Networks (2011, methodology): “And the duality gap is already quite small for practical number of subcarriers as observed by simulation [14]–[17], [19], [20].”