A radio resource allocation framework is proposed for underlay spectrum sharing. The ergodic capacity maximization problem in orthogonal frequency division multiple access-based network on point to multi point transmission is formulated and solved. Heterogeneous traffic is also considered in which two types of traffic is assumed: streaming traffic which has strict delay requirements, and elastic traffic with flexible delay requirements. Considering the effect of channel state information (CSI) imperfection in the evaluation of the secondary users’ expected rate, we further assume that the estimated CSI between the secondary users and secondary base station (secondary channel) is not perfect. Moreover three different cases are considered depending on the availability of the CSI between the secondary base station and the primary receivers (interference channel). Using simulations, we evaluate the impact of streaming traffic and imperfect CSI on the sum capacity of the secondary elastic users for different values of parameter systems.
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Haykin S. (2005) Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications 23(2): 201–220
Peha J. M. (2005) Approaches to spectrum sharing. IEEE Communications Magazine 43(2): 10–12
Zhao Q., Sadler B. M. (2007) A survey of dynamic spectrum access. IEEE Signal Processing Magazine 24(3): 79–89
Ho-Van, K., (2012). Performance evaluation of underlay cognitive Multi-hop Networks over Nakagami-m fading channels. Wireless personal communication. doi:10.1007/s11277-012-0690-7.
Yaacoub E., El-Hajj A. M., Dawy Z. (2010) Weighted ergodic sum-rate maximisation in uplink orthogonal frequency division multiple access and its achievable rate region. IET Communications 4(18): 2217–2229
Wang B., Zhao D. (2010) Scheduling for long term proportional fairness in a cognitive wireless network with spectrum underlay. IEEE Transactions on Wireless Communications 9(3): 1150–1158
Fraimis, I. G., & Kotsopoulos, S. A. (2012). Fair radio resource allocation for MIMO OFDM-based multicast systems. Wireless personal communication. doi:10.1007/s11277-012-0782-4.
Lun T., Jing-lin Y., Qing L., Qian-bin C., Wang Huan W. (2012) Power allocation with max-min and min-max fairness for cognitive radio networks with imperfect CSI. Wireless Personal Communication 65(3): 671–687
Del Re, E. Gorni, G. Ronga, L. S., & Suffritti, R. (2009). Resource allocation in cognitive radio networks: A comparison between game theory based and heuristic approaches. Wireless Personal Communication. doi:10.1007/s11277-009-9689-0.
Le L. B., Hossain E. (2008) Resource allocation for spectrum underlay in cognitive radio networks. IEEE Transactions on Wireless Communications 7(12): 5306–5315
Song G., Li Y. (2005) Cross-layer optimization for OFDM wireless networks part i: Theoretical framework. IEEE Transactions on Wireless Communications 4(2): 614–624
Wong I. C., Evans B. L. (2008) Optimal downlink OFDMA resource allocation with linear complexity to maximize ergodic capacity. IEEE Transactions on Wireless Communications 7(3): 962–971
Wong I. C., Evans B. L. (2009) Optimal resource allocation in the OFDMA downlink with imprefect channel knowledge. IEEE Transactions on Communications 57(1): 232–241
Musavian L., Assa S. (2009) Capacity and power allocation for spectrum sharing communications in fading channels. IEEE Transactions on Wireless communications 8((1): 148–156
Suraweera H. A., Smith P. J., Shafi M. (2010) Capacity limits and performance analysis of cognitive radio with imprefect channel knowledge. IEEE Transactions on Vehicular Technology 59(4): 1811–1822
Mokari N., Navaie K., Khoshkholgh M. G. (2011) Downlink radio resource allocation in OFDMA spectrum sharing environment with partial channel state information. IEEE Transactions on Wireless Communications 10: 3482–3495
Dashti, M., Azmi, P., & Navaie, K. Radio resource allocation for OFDMA based underlay cognitive radio networks utilizing weighted ergodic rates. IET Communications (accepted).
Ghasemi A., Sousa E. S. (2007) Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications 6(2): 649–658
Tse D., Viswanath P. (2004) Fundamentals of wireless communication. Cambridge University Press, Cambridge
Mokari N., Javan M. R., Navaie K. (2010) Cross-layer resource allocation in OFDMA systems for heterogeneous traffic with imperfect CSI. IEEE transactions on vehicular technology 59: 1011–1017
Goldsmith A. J. (2004) Wireless communications. Cambridge University Press, Cambridge
Yu W., Lui R. (2006) Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Transactions on Communications 54((7): 1310–1322
Boyd S., Vandenberghe L. (2004) Convex optimization. Cambridge University Press, Cambridge
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Dashti, M., Azmi, P., Navaie, K. et al. Ergodic Sum Rate Maximization for Underlay Spectrum Sharing with Heterogeneous Traffic. Wireless Pers Commun 71, 589–610 (2013). https://doi.org/10.1007/s11277-012-0832-y
- Elastic traffic
- Streaming traffic
- Resource allocation
- Underlay spectrum sharing
- Cognitive radio