Dynamic Resource Allocation in Cognitive Radio Networks

By Rui Zhang, Ying-Chang Liang, Shuguang Cui. IEEE Signal Processing Magazine, 2010

350 citations recorded by the source corpus.

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/217b0e2cf857894156147e41ce15084766b447c9

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Citations in the corpus

Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization (2011, background): “ization and interference management in standard downlinks, but have also been modiﬁed or generalized to deal with designs arising from frontier scenarios, such as relay networks [3], cognitive radios [4], and multicell coordinated downlinks [5]–[7]. In transmit beamforming, a very representative pro”
MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer (2011, background): “Note that (P3) resembles a similar problem formulated in [15], [16] (see also [17] and references therein) under the cognitive radio (CR) setup, where the rate of a secondary MIMO link is maximized subject to a set of socalled interference power constraints to protect the co-channel primary receiver”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, methodology): “Moreover, convex optimization techniques for solving cognitive radio problems are studied in [326].”
Max-Min SINR Coordinated Multipoint Downlink Transmission—Duality and Algorithms (2012, background): “Multiple power constraints are especially relevant to CoMP where each base station could be subject to its own transmit power constraint or in cognitive radio networks where the secondary user needs to apply interference power constraints to protect multiple primary users [10].”
QoS-Based Transmit Beamforming in the Presence of Eavesdroppers: An Optimized Artificial-Noise-Aided Approach (2011, background): “It is interesting to note that (12) is related to a CR transmit design problem [15], [ 30 ], when we remove all the AN terms in (12) to form the following no-AN design:”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “and other network parameters, is very challenging [35].”
Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks (2013, background): “[19], [20] and references therein), notable research has been pursued on characterizing the throughput of coexisting wireless networks based on the tool of stochastic geometry.”
Radar and Communication Coexistence: An Overview: A Review of Recent Methods (2019, background): “…denoted as F c , the communication receiver provides At the heart of the CRo system lies the proprietary modulated wideband converter (MWC) board [30] that implements a sub-Nyquist analog front-end receiver, which processes signals with Nyquist rates up to 6 GHz. a spectral map spectral map and…”
Optimum Co-Design for Spectrum Sharing between Matrix Completion Based MIMO Radars and a MIMO Communication System (2015, background): “The channels are Rayleigh fading and are consistent with a flat fading model assumption [5]–[9], [31], [32], [44], [45].”