Dynamic Spectrum Access with QoS and Interference Temperature Constraints

By Yiping Xing, C. N. Mathur, M. A. Haleem, Rajarathnam Chandramouli, Koduvayur P. Subbalakshmi. IEEE Transactions on Mobile Computing, 2007

501 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/75fb8769cf68e3ac8f11ac3c91eca2657ded4e25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Rui Zhang
2. Geoffrey Y. Li
3. Dusist Niyato
4. Walid Saad
5. Ying-Chang Liang
6. Ekram Hossain
7. Beibei Wang
8. K. Liu

In Electrical and Electronic Engineering

1. Rui Zhang
2. Geoffrey Y. Li
3. Ying-Chang Liang
4. Dusist Niyato
5. Ekram Hossain
6. Zhu Han
7. K. Liu
8. Qingqing Wu

Citations in the corpus

• An Adaptive Cooperation Diversity Scheme With Best-Relay Selection in Cognitive Radio Networks (2010, background): “For its implementation, interference temperature has been proposed [1], [2] as a metric to quantify and manage the interference in a radio environment.”
• Harmonic mean rate fairness for cognitive radio networks with heterogeneous traffic (2013, background): “On the other hand, PF, which is the well-known variant of utility-based fairness, has been receiving increasing attention in recent years (e.g. [8–16]).”
• Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach (2009, background): “Specifically, the primary users can competitively offer the spectrum price and the size of spectrum share to secondary users to maximize their revenues.”
• Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach (2011, background): “…manufacturers’, and policy makers’ standpoints for the successful commercialization of cognitive radio with the dynamic spectrum management policy In [5], an optimization problem was formulated for spectrum access to obtain the solution that gives the highest utility to the cognitive radio users .”
• Device-to-Device Communications Underlaying Cellular Networks (2013, background): “In [12], both maximizing the number of admitted secondary users and maximizing the secondary throughput have been investigated through centralized geometric programming algorithm and distributed game theory algorithm.”
• Game Theory in Wireless and Communication Networks (2008, background): “The power-allocation problem for underlay dynamic spectrum access is studied from a variety of perspectives [500, 163, 515, 514].”
• Cognitive radio networks (2008, unknown): “Interference analysis has been studied by a number of authors (see, for example, [7], [8], and [36]).”
• Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks (2008, background): “challenge for realizing such a CR system is to ensure the Quality-of-Service (QoS) of the PUs yet to maximize the throughput or to ensure the QoS of the SUs [4].”
• Game theory for cognitive radio networks: An overview (2010, background): “However, the question is how to find the Nash equilibrium, especially when the system is implemented in a distributed manner.”
• Resource allocation for spectrum underlay in cognitive radio networks (2008, background): “In [13], a heuristicbased channel and power allocation algorithm was proposed where interference constraints for primary users were considered.”
• Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks (2007, background): “When multiple secondary users share a single-frequency channel advertised by the primary user, the authors in [6]-[7] utilize the game theory to maximize the sum of the utility functions of the secondary users under the interference-power constraint at some measured point, while in [8]-[9] transmit…”