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Optimal Power Allocation in a Relay-aided Cognitive Network

Published: 12 March 2019 Publication History

Abstract

In this paper, we address a power allocation problem in a relay-aided cognitive network. The network is composed by a primary and secondary user/destination pair and a relay, which helps the communication between the secondary user and its destination. The transmission of the secondary user and the helping relay is allowed provided that a minimum quality of service (QoS) constraint is met at the primary user. First, we derive the achievable rate regions under Decode-and-Forward (DF) and Compress-and-Forward (CF) relaying schemes. Then, we provide analytic expressions of the optimal power allocation policies at the secondary user and the relay. Remarkably, if the secondary direct link is negligible - the communication takes place only via the relay - DF is proven to always outperform CF, irrespective from the system parameters. If the secondary direct link is not negligible, our numerical results illustrate that DF outperforms CF only when the relay is close to the secondary user.

References

[1]
E. V. Belmega, B. Djeumou, and S. Lasaulce. 2010. Power allocation games in interference relay channels: Existence analysis of nash equilibria. EURASIP Journal on Wireless Communications and Networking 2010 (2010), 114.
[2]
T. M. Cover and A. E. El Gamal. Sept. 1979. Capacity theorems for the relay channel. IEEE Transactions on Information Theory 25, 5 (Sept. 1979), 572--584.
[3]
Mingjun Dai, Peng Wang, Shengli Zhang, Bin Chen, Hui Wang, Xiaohui Lin, and Cong Sun. 2014. Survey on cooperative strategies for wireless relay channels. Transactions on Emerging Telecommunications Technologies 25, 9 (2014), 926--942.
[4]
A. Khina, O. Ordentlich, U. Erez, Y. Kochman, and G. W. Wornell. 2012. Decode-and-forward for the Gaussian relay channel via standard AWGN coding and decoding. IEEE Information Theory Workshop (ITW), Lausanne, Sweiss (2012).
[5]
H. Kim, S. Lim, H. Wang, and D. Hong. 2012. Optimal power allocation and outage analysis for cognitive full duplex relay systems. IEEE Transactions on Wireless Communications 11, 10 (2012), 3754--3765.
[6]
J. Lee, H. Wang, J. G. Andrews, and D. Hong. 2011. Outage probability of cognitive relay networks with interference constraints. IEEE Transactions on Wireless Communications 10, 2 (2011), 390--395.
[7]
L. Li, X. Zhou, H. Xu, G. Y. Li, D. Wang, and A. Soong. 2011. Simplified relay selection and power allocation in cooperative cognitive radio systems. IEEE Transactions on Wireless Communications 10, 1 (2011), 33--36.
[8]
R. Masmoudi, E. V. Belmega, and I. Fijalkow. 2016. Efficient spectrum scheduling and power management for opportunistic users. EURASIP Journal on Wireless Communications and Networking 2016, 1 (2016), 97.
[9]
P. Mertikopoulos and E. V. Belmega. 2014. Transmit without regrets: online optimization in MIMO-OFDM cognitive radio systems. IEEE Journal on Selected Areas in Communications 32, 11 (2014), 1987--1999.
[10]
D. T. Ngo and T. Le-Ngoc. 2011. Distributed resource allocation for cognitive radio networks with spectrum-sharing constraints. IEEE Transactions on Vehicular Technology 60, 7 (2011), 3436--3449.
[11]
G. Poltyrev. Mar. 1994. On coding without restrictions for the AWGN channel. IEEE Trans. on Information Theory 40, 2 (Mar. 1994), 409--417.
[12]
B. Rankov and A. Wittneben. 2006. Achievable rate regions for the two-way relay channel. IEEE International Symposium Information Theory (ISIT) (2006).
[13]
C. A. Rogers. 1959. Lattice coverings of space. Mathematica 6 (1959), 33--39.
[14]
Onur Sahin and Elza Erkip. 2007. Achievable rates for the Gaussian interference relay channel. In Global Telecommunications Conference, 2007. GLOBECOM'07. IEEE. 1627--1631.
[15]
A. Savard and L. Clavier. 2018. On the two-way diamond relay channel with lattice-based Compress-and-Forward. IEEE Wireless Communications and Networking Conference (WCNC) (2018).
[16]
A. Savard and C. Weidmann. 2015. Lattice coding for the Gaussian one- and two-way relay channels with correlated noises. IEEE International Symposium Information Theory (ISIT) (2015).
[17]
A. Savard and C. Weidmann. 2016. On the Gaussian multiway relay channel with intra-cluster links. EURASIP Journal on Wireless Communications and Networking, SpringerOpen 52 (2016), 1--17.
[18]
A. Scaglione, D. L. Goeckel, and J. N. Laneman. 2006. Cooperative communications in mobile ad hoc networks. IEEE Signal Processing Magazine 23, 5 (2006), 18--29.
[19]
G. Scutari and D. P. Palomar. 2010. MIMO cognitive radio: A game theoretical approach. IEEE Transactions on Signal Processing 58, 2 (2010), 761--780.
[20]
Y. Song and N. Devroye. Aug. 2013. Lattice codes for the Gaussian relay channel: Decode-and-forward and Compress-and-Forward. IEEE Transactions on Information Theory 59, 8 (Aug. 2013), 4927--4948.
[21]
S. Sridharan, S. Vishwanath, S. A. Jafar, and S. Shamai. 2008. On the capacity of cognitive relay assisted Gaussian interference channel. IEEE International Symposium Information Theory (ISIT) (2008).
[22]
L. T. Tan, L. Ying, and D. W. Bliss. 2017. Power allocation for full-duplex relay selection in underlay cognitive radio networks: Coherent versus non-coherent scenarios. arXiv preprint arXiv:1703.01527 (2017).
[23]
E. C. van der Meulen. 1971. Three-terminal communication channels. Adv. AppL. Prob. 3 (1971), 120--154.
[24]
M. Wu, J. Lou, D. Liu, T. Luo, and G. Yue. 2010. Power allocation for cognitive relay networks. In Network Infrastructure and Digital Content, 2010 2nd IEEE International Conference on. 41--45.
[25]
R. Zamir. 2009. Lattices are everywhere. Proc. Inf. Theory Appl. Wkshp., La Jolla, CA (2009), 392--421.
[26]
Z. Zhang, W. Qihui, and W. Jinlong. 2012. Energy-Efficient Power Allocation Strategy in Cognitive Relay Networks. Radioengineering 21, 3 (2012).
[27]
G. Zhao, C. Yang, G. Y. Li, D. Li, and A. C. Soong. 2011. Power and channel allocation for cooperative relay in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing 5, 1 (2011), 151--159.
[28]
Y. Zou, Y. D. Yao, and B. Zheng. 2012. Cooperative relay techniques for cognitive radio systems: Spectrum sensing and secondary user transmissions. IEEE Communications Magazine 50, 4 (2012).

Cited By

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  • (2022)Unsupervised deep learning to solve power allocation problems in cognitive relay networks2022 IEEE International Conference on Communications Workshops (ICC Workshops)10.1109/ICCWorkshops53468.2022.9814541(331-336)Online publication date: 16-May-2022
  • (2020)Optimal power allocation policies in multi-hop cognitive radio networks2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications10.1109/PIMRC48278.2020.9217334(1-6)Online publication date: Aug-2020
  • (2020)Full-Duplex Relaying for Opportunistic Spectrum Access Under an Overall Power ConstraintIEEE Access10.1109/ACCESS.2020.30240108(168262-168272)Online publication date: 2020

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cover image ACM Other conferences
VALUETOOLS 2019: Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools
March 2019
202 pages
ISBN:9781450365963
DOI:10.1145/3306309
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • EAI: The European Alliance for Innovation
  • Universitat de les Illes Balears: Universitat de les Illes Balears

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 March 2019

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Author Tags

  1. Compress-and-Forward
  2. Decode-and-Forward
  3. Power allocation
  4. Relay-aided cognitive network

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VALUETOOLS 2019

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VALUETOOLS 2019 Paper Acceptance Rate 18 of 42 submissions, 43%;
Overall Acceptance Rate 90 of 196 submissions, 46%

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Cited By

View all
  • (2022)Unsupervised deep learning to solve power allocation problems in cognitive relay networks2022 IEEE International Conference on Communications Workshops (ICC Workshops)10.1109/ICCWorkshops53468.2022.9814541(331-336)Online publication date: 16-May-2022
  • (2020)Optimal power allocation policies in multi-hop cognitive radio networks2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications10.1109/PIMRC48278.2020.9217334(1-6)Online publication date: Aug-2020
  • (2020)Full-Duplex Relaying for Opportunistic Spectrum Access Under an Overall Power ConstraintIEEE Access10.1109/ACCESS.2020.30240108(168262-168272)Online publication date: 2020

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