Abstract
In this paper, a novel power allocation algorithm to maximize the throughput under the bit error rate (BER) constraint and the total power constraint in cognitive radio is proposed. Although water filling (WF) algorithm is the optimal power allocation in theory, it ignores the fact that the allocated power in use is discrete, and the algorithm doesnt take the waste power into consideration. In the improved algorithm, the total power is asymmetrically quantized to apply to the practice and reduce the computation complexity before adopting the dynamic programming which is commonly used to solve the knapsack problem, so this improved algorithm is called as asymmetrically quantized dynamic programming (AQDP). Moreover, AQDP reused the residual power to maximize the throughput further. The simulation results show that AQDP algorithm has improved the transmit throughput of all CR users compared with the classical power allocation algorithms referred as WF algorithm in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee JW, Mazumdar RR, Shroff NB (2006) Joint resource allocation and base-station assignment for the downlink in CDMA networks [J]. IEEE/ACM Trans Netw 14(1):1 C 14
Knopp R, Humblet PA (1995) Information capacity and power control in single cell multiuser communications [C]. In: IEEE International conference on communications (ICC95), vol 1. Seattle, USA, p 335
Zhang Y, Leung C (2009) Resource allocation in an OFDM-based cognitive radio system [J]. IEEE Trans Commun 57(7):1928–1931
Xiang J, Zhang Y, Admission Skeie T (2010) Power control for cognitive radio cellular networks: a multidimensional knapsack solution [C]. In: (2010) 3rd international symposium on applied sciences in biomedical and communication technologies (ISABEL). IEEE, pp 1–5
Xiong XH, Ning AB, Liang M, Wang AB (2009) Competitive decision algorithm for multidimensional knapsack problem [C]. In: 2009 international conference on management science and engineering. ICMSE. IEEE, pp 161-167
Ejaz W, Hasan NU, Kim HS (2012) KENSS: knapsack-based energy-efficient node selection scheme for cooperative spectrum sensing in cognitive radio sensor networks [J]. Iet Commun 6(17):2998–3005
Gao L, Cui S (2009) Power and rate control for delay-constrained cognitive radios via dynamic programming [J]. IEEE Trans Veh Technol 58(9):4819 C 4827
Xiang X, Wan J, Lin C et al (2013) A dynamic programming approximation for downlink channel allocation in cognitive femtocell networks [J]. Comput Netw 57(15):2976 C2991
Sinha A, Chaporkar P (2012) Optimal power allocation for a renewable energy source [C]. In: 2012 national conference on communications (NCC). IEEE, pp 1–5
Kellerer H (1999) A polynomial time approximation scheme for the multiple knapsack problem [J]. Lect Notes Comput Sci 1671:51–62
Goldsmith AJ, Chua SG (1997) Variable-rate variable-power MQAM for fading channels [C]. IEEE Trans Commun 45(10):1218 C 1230
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Q., Chen, Q., Zhang, H., Fan, M. (2017). An Improved Dynamic Programming for Power Allocation in Cognitive Radio. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-38771-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-38771-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-38769-7
Online ISBN: 978-3-319-38771-0
eBook Packages: EngineeringEngineering (R0)