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Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels : with or without Successive Interference Cancellation

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EN
Abstrakty
EN
This paper considers a fading cognitive multiple access channel (CMAC), where multiple secondary users (SUs), who share the spectrum with a primary user (PU), transmit to a cognitive base station (CBS). A power station is assumed to harvest energy from the nature and then provide power to the SUs. We investigate the power allocation problems for such a CMAC to maximize the SU sum rate under the interference power constraint, the sum transmit power constraint and the peak transmit power constraint of each individual SU. In particular, two scenarios are considered: with successive interference cancellation (SIC) and without SIC. For the first scenario, the optimal power allocation algorithm is derived. For the second scenario, a heuristic algorithm is proposed. We show that the proposed algorithm with SIC outperforms the algorithm without SIC in terms of the SU sum rate, while the algorithm without SIC outperforms the algorithm with SIC in terms of the number of admitted SUs for a high sum transmit power limit and a low peak transmit power limit of each individual SU.
Twórcy
autor
  • Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
autor
  • Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Bibliografia
  • [1] J. Xue, Z. Feng, and K. Chen, “Beijing spectrum survey for cognitive radio applications,” in IEEE Vehicular Technology Conference Fall, 2013, pp. 1–5.
  • [2] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
  • [3] X. Kang, Y.-C. Liang, A. Nallanathan, K. Garg, and R. Zhang, “Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity,” IEEE Transactions on Wireless Communications, vol. 8, no. 2, pp. 940–950, 2009.
  • [4] L. Qun and X. Ding, “Power allocation for ergodic capacity and outage probability tradeoff in cognitive radio networks,” IEICE Transactions on Communications, vol. 98, no. 10, pp. 1988–1995, 2015.
  • [5] R. Zhang, S. Cui, and Y.-C. Liang, “On ergodic sum capacity of fading cognitive multiple-access and broadcast channels,” IEEE Transactions on Information Theory, vol. 55, no. 11, pp. 5161–5178, 2009.
  • [6] X. Kang, Y.-C. Liang, and H. K. Garg, “Fading cognitive multiple access channels: Outage capacity regions and optimal power allocation,” IEEE Transactions on Wireless Communications, vol. 9, no. 7, pp. 2382–2391, 2010.
  • [7] W. Wang, W. Wang, Q. Lu, K. G. Shin, and T. Peng, “Geometry-based optimal power control of fading multiple access channels for maximum sum-rate in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 9, no. 6, pp. 1843–1848, 2010.
  • [8] D. Li and Y.-C. Liang, “Power allocation for interference-limited cognitive multiple access channels,” IEEE Wireless Communications Letters, vol. 2, no. 3, pp. 291–294, 2013.
  • [9] L. Zhang, Y. Xin, Y.-C. Liang, and H. V. Poor, “Cognitive multiple access channels: optimal power allocation for weighted sum rate maximization,” IEEE Transactions on Communications, vol. 57, no. 9, pp. 2754–2762, 2009.
  • [10] X. Kang, “Optimal power allocation for fading cognitive multiple access channels: a two-user case,” IEEE Wireless Communications Letters, vol. 2, no. 6, pp. 683–686, 2013.
  • [11] D. Xu and Q. Li, “On the effective capacity region for cognitive radio multiple access channels,” AEU-International Journal of Electronics and Communications, vol. 69, no. 6, pp. 958–961, 2015.
  • [12] X. Kang, H. F. Chong, Y.-K. Chia, and S. Sun, “Ergodic sum-rate maximization for fading cognitive multiple-access channels without successive interference cancelation,” IEEE Transactions on Vehicular Technology, vol. 64, no. 9, pp. 4009–4018, 2015.
  • [13] D. Xu and Q. Li, “Power allocation for two-user cognitive multiple access channels under primary user outage constraint,” International Journal of Communication Systems, to be published.
  • [14] W. Chung, S. Park, S. Lim, and D. Hong, “Optimal transmit power control for energy-harvesting cognitive radio system,” in IEEE Vehicular Technology Conference Fall, 2013, pp. 1–5.
  • [15] V. Rakovic, D. Denkovski, Z. Hadzi-Velkov, and L. Gavrilovska, “Optimal time sharing in underlay cognitive radio systems with RF energy harvesting,” in IEEE International Conference on Communications, 2015, pp. 7689–7694.
  • [16] D. T. Hoang, D. Niyato, P. Wang, and D. I. Kim, “Opportunistic channel access and RF energy harvesting in cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 11, pp. 2039–2052, 2014.
  • [17] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
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Bibliografia
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