Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks

By Seung-Jun Kim, Georgios B. Giannakis. IEEE Transactions on Information Theory, 2008

317 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ca47d195fb566cb7e029b6833289f414a50fde7f

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “lized optimization is feasible with the global knowledge of all the channels in the SU network, as well as those from different SU-TXs to all PUs, another heuristic algorithm for (P5) was proposed in [38]. By rewriting the SU transmit covariance matrices into their equivalent precoding vectors and p”
Optimum Co-Design for Spectrum Sharing between Matrix Completion Based MIMO Radars and a MIMO Communication System (2015, methodology): “Viewing the radar system as the primary user of a cognitive radio system and the MIMO communication system as the secondary user, techniques similar to those of [31]–[33], [49] can be used to estimate and feed back the channel information between the primary and secondary systems.”
Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (2016, unknown): “IV.”
A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing (2015, methodology): “IEEE SIGNAL PROCESSING MAGAZINE [64] jANuARy 2016 algorIThMS covered By The BSuM FraMework Now that we have a good understanding of the theoretical properties of the family of BSUM algorithms, we demonstrate in this section the wide applicability of the BSUM framework by revealing its connection to ”
Optimal Joint Base Station Assignment and Beamforming for Heterogeneous Networks (2014, background): “With the increase in the number of base stations simultaneously operating within the same frequency band, interference has become a major performance limiting factor for HetNets.”
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization (2012, unknown): “The BSUM approach has been extensively used for resource allocation in wireless networks, for example [8], [16], [18], [23], [27], and [26].”
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011, methodology): “Note that the ILA algorithm [5,8] allows only one user to update its transmit covariance matrix at each iteration.”
Learning to optimize: Training deep neural networks for wireless resource management (2017, methodology): “…type algorithms [3], [4], interference pricing [5], SCALE [6]), transmit/receive beamformer design (e.g., the WMMSE algorithm [7], pricing-based schemes [8], semideﬁnite relaxation based schemes [9]), admission M. Hong is supported in part by NSF under grant CCF-1526078, AFOSR under grant 15RT0767.”
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013, methodology): “Besidethe application of the classical gradient projection algorithm to the sum-rate maximization problem over MIMO ICs [26], parallel iterative algorithms (with message passing) for DSL/ad-hoc SISO networks and MIMO broadcast interfering channels were proposed in [27]–[29] and [30], respectively.”