Dynamic resource management for cognitive radios using limited-rate feedback

Antonio G. Marques; Xin Wang; Georgios B. Giannakis

doi:10.1109/TSP.2009.2022009

Dynamic resource management for cognitive radios using limited-rate feedback

Antonio G. Marques, Xin Wang, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

59 Scopus citations

Abstract

Tailored for the emerging class of cognitive radio networks comprising primary and secondary wireless users, the present paper deals with dynamic allocation of subcarriers, rate and power resources based on channel state information (CSI) for orthogonal frequency-division multiple access (OFDMA). Users rely on adaptive modulation, coding and power modes that they select in accordance with the limited-rate feedback they receive from the access point. The access point uses CSI to maximize a generic concave utility of the average rates in the network while adhering to rate and power constraints imposed on the primary and secondary users to respect cognitive radio related hierarchies. When the channel distribution is available, optimum dual prices are found to optimally allocate resources across users dynamically per channel realization. In addition, a simple yet optimal online algorithm that does not require knowledge of the channel distribution and iteratively computes the dual prices per channel realization is developed using a stochastic dual approach. Analysis of the computational and feedback overhead along with simulations assessing the performance of the novel algorithms are also provided.

Original language	English (US)
Pages (from-to)	3651-3666
Number of pages	16
Journal	IEEE Transactions on Signal Processing
Volume	57
Issue number	9
DOIs	https://doi.org/10.1109/TSP.2009.2022009
State	Published - 2009

Bibliographical note

Funding Information:
Manuscript received June 25, 2008; accepted April 03, 2009. First published May 02, 2009; current version published August 12, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mats Bengtsson. Work in this paper was supported by NSF Grants CCF 0830480, CON 014658 and CNS 0831671; USDoD ARO Grant W911NF-05-1-0283; by the Government of C.A. Madrid Grant P-TIC-000223-0505; and also through collaborative participation in the Communications and Networks Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. This paper was presented in part at the EUSIPCO, Poznan, Poland, September 3–7, 2007 and the International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, NV, April 1–4, 2008. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U. S. Government.

Keywords

Adaptive signal processing
Cognitive radios
Dual formulation
Dynamic resource management
Nonlinear convex optimization
Quantization
Scheduling

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title = "Dynamic resource management for cognitive radios using limited-rate feedback",

abstract = "Tailored for the emerging class of cognitive radio networks comprising primary and secondary wireless users, the present paper deals with dynamic allocation of subcarriers, rate and power resources based on channel state information (CSI) for orthogonal frequency-division multiple access (OFDMA). Users rely on adaptive modulation, coding and power modes that they select in accordance with the limited-rate feedback they receive from the access point. The access point uses CSI to maximize a generic concave utility of the average rates in the network while adhering to rate and power constraints imposed on the primary and secondary users to respect cognitive radio related hierarchies. When the channel distribution is available, optimum dual prices are found to optimally allocate resources across users dynamically per channel realization. In addition, a simple yet optimal online algorithm that does not require knowledge of the channel distribution and iteratively computes the dual prices per channel realization is developed using a stochastic dual approach. Analysis of the computational and feedback overhead along with simulations assessing the performance of the novel algorithms are also provided.",

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Dynamic resource management for cognitive radios using limited-rate feedback

Abstract

Bibliographical note

Keywords

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