(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-9 Issue-41 March-2019
Full-Text PDF
Paper Title : Power allocation optimization for fading channels in cognitive radio networks
Author Name : Fareduddin Ahmed J S and Rohitha Ujjinimatad
Abstract :

The radio resource allocation in cognitive radio networks is an essential problem where the secondary users (SUs) use the spectrum allocated to primary users (PUs) without causing much interference to PUs. This paper studies optimal power and bandwidth allocation in a cognitive radio network under Rician and Nakagami-m fading channels. The performance metric for the network used is the ergodic-capacity of all the SUs. Further, this paper investigates the optimal power allocation schemes to achieve the primary capacity bounds of a secondary network with Rician and Nakagami-m fading channels. Specifically, the ergodic-capacity is considered. The methodology used involves deriving closed-form results for both Rician and Nakagami-m scenarios. Besides, the peak/average transmit-power constraints at the SUs and the peak/average interference power constraint imposed by the PU. The equations of optimal power allocations are also formulated under peak-power and peak-interference constraints. Further, the analysis is done for a network of SUs. Simulation results depicted with figures and tables show that the optimal power and bandwidth allocation for Rician and Nakagami-m fading channels. The investigation and analysis on optimal power and bandwidth allocation can be used for future reference of resource allocation in the cognitive radio networks over Rician and Nakagami-m fading channels.

Keywords : Cognitive radio, Spectrum allocation, Bandwidth allocation, Rician fading, Nakagami fading.
Cite this article : J S FA, Ujjinimatad R. Power allocation optimization for fading channels in cognitive radio networks. International Journal of Advanced Computer Research. 2019; 9(41):124-132. DOI:10.19101/IJACR.2018.839032.
References :
[1]Kolodzy P, Avoidance I. Spectrum policy task force. Federal Commun. Comm., Washington, DC, Rep. ET Docket. 2002; 40(4):147-58.
[Google Scholar]
[2]Mitola J, Maguire GQ. Cognitive radio: making software radios more personal. IEEE Personal Communications. 1999; 6(4):13-8.
[Crossref] [Google Scholar]
[3]Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications. 2005; 23(2):201-20.
[Crossref] [Google Scholar]
[4]Ghasemi A, Sousa ES. Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications. 2007; 6(2):649-58.
[Crossref] [Google Scholar]
[5]Musavian L, Aïssa S. Capacity and power allocation for spectrum-sharing communications in fading channels. IEEE Transactions on Wireless Communications. 2009; 8(1):148-56.
[Crossref] [Google Scholar]
[6]Kang X, Liang YC, Nallanathan A, Garg HK, Zhang R. Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Transactions on Wireless Communications. 2009; 8(2):940-50.
[Crossref] [Google Scholar]
[7]Tian F, Yuan X, Hou T, Lou W, Yang Z. Cost minimization for cooperative traffic relaying between primary and secondary networks. IEEE Transactions on Mobile Computing. 2018; 17(9):2014-27.
[Crossref] [Google Scholar]
[8]Li X, Liu J, Yan L, Han S, Guan X. Relay selection for underwater acoustic sensor networks: a multi-user multi-armed bandit formulation. IEEE Access. 2018; 6:7839-53.
[Crossref] [Google Scholar]
[9]Singh K, Gupta A, Ratnarajah T, Ku ML. A general approach toward green resource allocation in relay-assisted multiuser communication networks. IEEE Transactions on Wireless Communications. 2018; 17(2):848-62.
[Crossref] [Google Scholar]
[10]Feng Y, Leung VC, Ji F. Performance study for SWIPT cooperative communication systems in shadowed nakagami fading channels. IEEE Transactions on Wireless Communications. 2018; 17(2):1199-211.
[Crossref] [Google Scholar]
[11]Jitvanichphaibool K, Zhang R, Liang YC. Optimal resource allocation for two-way relay-assisted OFDMA. IEEE Transactions on Vehicular Technology. 2009; 58(7):3311-21.
[Crossref] [Google Scholar]
[12]Sidhu GA, Gao F, Chen W, Nallanathan A. A joint resource allocation scheme for multiuser two-way relay networks. IEEE Transactions on Communications. 2011; 59(11):2970-5.
[Crossref] [Google Scholar]
[13]Li H. A recommendation system in cognitive radio networks with random data traffic. IEEE Transactions on Vehicular Technology. 2011; 60(4):1352-64.
[Crossref] [Google Scholar]
[14]Kumar NS, Vakula D. Performance analysis of equalizers for MIMO in Rician-K and Nakagami-m fading channels. In international conference on electronics, communication and aerospace technology 2017(pp. 381-5). IEEE.
[Crossref] [Google Scholar]
[15]Sharma PK, Priya VH, Raju GK, Sai KV. Comparison of MIMO over different fading channels with and without channel state information. In second international conference on electronics, communication and aerospace technology 2018 (pp. 730-3). IEEE.
[Crossref] [Google Scholar]
[16]Ujjinimatad R, Patil SR. Spectrum sensing in cognitive radio networks with known and unknown noise levels. IET Communications. 2013; 7(15):1708-14.
[Crossref] [Google Scholar]
[17]Ahmed JF, Ujjinimatad R. Energy detection with different digital modulation techniques over Rayleigh fading channels in cognitive radio networks. In innovations in power and advanced computing technologies 2017 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[18]Fareduddin AJ, Ujjinimatad R. Fast optimal and explorative (FOX) sensing and power allocation scheme for non-cooperative cognitive radio networks. In international conference on recent trends in electronics, information & communication technology 2017 (pp. 457-61). IEEE.
[Crossref] [Google Scholar]
[19]Bertsekas DP. Nonlinear programming. Belmont: Athena Scientific; 1999.
[Google Scholar]