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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

2. Dynamic Resource Allocation

verfasst von : Shaowei Wang

Erschienen in: Cognitive Radio Networks

Verlag: Springer International Publishing

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Abstract

Dynamic Resource Allocation is an essential technique to exploit the time-space-frequency variation in wireless channels by adaptively distributing precious radio resources, such as spectrum and power, to either maximize or minimize the concerned network performance metrics. In traditional static resource allocation strategies, subchannels are distributed in a predetermined manner; that is, each user is assigned fixed frequency bands regardless of the channel status. In this case, the resource allocation problem reduces to power allocation or bits loading on each subchannel, which fails to fully exploit the potential of multiuser diversity in wireless environment.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Spectral-Efficient Resource Allocation in CR Systems
Literatur
1.
T. S. Rappaport, Wireless Communications. Prentice Hall PTR, 2002.
2.
A. Duel-Hallen, S. Hu, and H. Hallen, “Long-range prediction of fading signals,” IEEE Signal Process. Mag., vol. 17, pp. 62–75, May 2000.
3.
A. Forenza and R. W. Heath, “Link adaptation and channel prediction in wireless OFDM systems,” in Proc. 45th IEEEMWSCAS, vol. 3, pp. 211–214, Aug. 2002.
4.
M. Sternad and D. Aronsson, “Channel estimation and prediction for adaptive OFDM downlinks [vehicular applications],” in Proc. IEEEVTC, vol. 2, pp. 1283–1287, Oct. 2003.
5.
I. C. Wong, A. Forenza, R. W. Heath, and B. L. Evans, “Long range channel prediction for adaptive OFDM systems,” in Proc. IEEE ACSSC, vol. 1, pp. 732–736, Nov. 2004.
6.
I. C. Wong and B. L. Evans, “Joint channel estimation and prediction for OFDM systems,” in Proc. IEEE Globecom’05, vol. 4, pp. 2255–2259, Dec. 2005.
7.
D. Schafhuber and G. Matz, “MMSE and adaptive prediction of time varying channels for OFDM systems,” IEEE Trans. Wireless Commun., vol. 4, pp. 593–602, Mar. 2005.
8.
I. C. Wong and B. L. Evans, “Low-complexity adaptive high-resolution channel prediction for OFDM systems,” in Proc. IEEE Globecom’06, Nov. 2006.
9.
S. Sadr, A. Anpalagan, and K. Raahemifar, “Radio resource allocation algorithms for the downlink of multiuser OFDM communication systems,” IEEE Commun. Surv. & Tutor., vol. 11, no. 3, pp. 92–106, Sep. 2009.
10.
J. Jang and K. B. Lee, “Transmit power adaptation for multiuser OFDM systems,” IEEE J. Select. Areas Commun., vol. 21, pp. 171–178, Feb. 2003.
11.
Y. Chen, S. Zhang, S. Xu, and G. Li, “Fundamental trade-offs on green wireless networks,” IEEE Commun. Mag., vol. 49, no. 6, pp. 30–37, June 2011.
12.
D. Feng, C. Jiang, G. Lim, L. Cimini, Jr., G. Feng, and G. Li, “A survey of energy-efficient wireless communications,” IEEE Commun. Surv. & Tutor., vol. PP, no. 99, pp. 1–12, 2012.
13.
G. Miao, N. Himayat, G. Li, and S. Talwar, “Low-complexity energy efficient scheduling for uplink OFDMA,” IEEE Trans. Commun., vol. 60, no. 1, pp. 112–120, Jan. 2012.
14.
C. Xiong, G. Li, S. Zhang, Y. Chen, and S. Xu, “Energy- and spectral- efficiency trade off in downlink OFDMA networks,” IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3874–3886, Nov. 2011.
15.
G. Miao, N. Himayat, G. Li, and S. Talwar, “Distributed interference aware energy-efficient power optimization,” IEEE Trans. Wireless Commun., vol. 10, no. 4, pp. 1323–1333, Apr. 2011.
16.
D. Ng, E. Lo, and R. Schober, “Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 11, no. 9, pp. 3292–3304, Sep. 2012.
17.
D. W. K. Ng, E. S. Lo, and R. Schober, “Energy-efficient resource allocation in multi-cell OFDMA systems with limited backhaul capacity,” IEEE Trans. Wireless Commun., vol. 11, no. 10, pp. 3618–3631, Oct. 2012.
18.
Y. Pei, Y.-C. Liang, K. C. Teh, and K. H. Li, “Energy-efficient design of sequential channel sensing in cognitive radio networks: Optimal sensing strategy, power allocation, and sensing order,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1648–1659, Sep. 2011.
19.
Y. Otani, S. Ohno, K. Ann Donny Teo, and T. Hinamoto, “Subcarrier allocation for multi-user OFDM system,” in Proc. Asia-Pasific Conf. on Commun., pp. 1073–1077, 2005.
20.
W. Rhee and J. M. Cioffi, “Increase in capacity of multiuser OFDM system using dynamic subchannel allocation,” in Proc. IEEE VTC’00, vol. 2, pp. 1085–1089, May 2000.
21.
Z. Shen, J. G. Andrews, and B. L. Evans, “Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEE Trans. Wireless Commun., vol. 4, pp. 2726–2737, Nov. 2005.
22.
C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, “Multiuser OFDM with adaptive subcarrier, bit and power allocation,” IEEE J. Select. Areas Commun., vol. 17, pp. 1747–1758, Oct. 1999.
23.
G. Zhang, “Subcarrier and bit allocation for real-time services in multiuser OFDM systems,” in Proc. IEEE ICC’04, vol. 5, pp. 2985–2989, June 2004.
24.
L. Xiaowen and Z. Jinkang, “An adaptive subcarrier allocation algorithm for multiuser OFDM system,” in Proc. IEEE VTC’03, vol. 3, pp. 1502–1506, Oct. 2003.
25.
G. Song and Y. G. Li, “Cross-layer optimization for OFDM wireless networks-Part I: theoretical framework,” IEEE Trans. Wireless Commun., vol. 4, pp. 614–624, Mar. 2005.
26.
G. Song and Y. G. Li, “Cross-layer optimization for OFDM wireless networks-Part II: Algorithm development,” IEEE Trans. Wireless Commun., vol. 4, pp. 625–634, Mar. 2005.
27.
Z. Shen, J. G. Andrews, and B. L. Evans, “Optimal power allocation in multiuser OFDM systems,” in Proc. IEEE Globecom’03, vol. 1, pp. 337–341, Dec. 2003.
28.
I. C. Wong, Z. Shen, B. L. Evans, and J. G. Andrews, “A low complexity algorithm for proportional resource allocation in OFDMA systems,” in Proc. IEEE Workshop on Signal Processing Systems, Oct. 2004.
29.
H. Yin and H. Liu, “An efficient multiuser loading algorithm for OFDM based broad band wireless systems,” in Proc. IEEE Globecom’00, vol. 1, pp. 103–107, Nov. 2000.
30.
G. Song and Y. G. Li, “Utility-based joint physical-MAC layer optimization in OFDM,” in Proc. IEEE Globecom’02, vol. 1, pp. 671–675, Nov. 2002.
31.
G. Song and Y. G. Li, “Adaptive subcarrier and power allocation in OFDM based on maximizing utility,” in Proc. IEEE VTC, vol. 2, pp. 905–909, Apr. 2003.
32.
M. Tao, Y.-C. Liang, and F. Zhang, “Resource allocation for delay differentiated traffic in multiuser OFDM systems,” IEEE Trans. Wireless Commun., vol. 7, no. 6, pp. 2190–2201, June 2008.
33.
C. Xiong, G. Y. Li, S. Zhang, Y. Chen, S. Xu, “Energy-Efficient Resource Allocation in OFDMA Networks,” in Proc. IEEEGlobecom’11, Dec. 2011.
34.
C. Xiong, G. Y. Li, S. Zhang, Y. Chen, S. Xu, “Energy-Efficient Resource Allocation in OFDMA Networks,” IEEE Trans. Commun., vol. 60, no. 12, pp. 3767–3778, Dec. 2012.
35.
A. Zappone, G. Alfano, S. Buzzi, M, Meo, “Energy-efficient non-cooperative resource allocation in multi-cell OFDMA systems with multiple base station antennas,” in Proc. IEEE GreenCom’11, pp. 82–87, Sep. 2011.
36.
G. J. Foschini and J. Salz, “Digital communications over fading radio channels,” Bell Syst. Tech. J., pp. 429–456, Feb. 1983.
37.
A. J. Goldsmith and Soon-Ghee Chua, “Variable-rate variable-power MQAM for fading channels,” IEEE Trans. Commun., vol. 45, pp. 1218–1230, Oct. 1997.
38.
Q. Zhao and B. M. Sadler, “A survey of dynamic spectrum access,” IEEE Signal Processings. Mag., vol. 24, no. 3, pp. 79–89, May 2007.
39.
A. Goldsmith, S. A. Jafar, I. Mari’c, and S. Srinivasay, “Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,” Proc. IEEE, vol. 97, no. 5, pp. 894–914, May 2009.
40.
Z. Quan, S. Cui, and A. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE J. Select. Topics Signal Process., vol. 2, no. 1, pp. 28–40, Feb. 2008.
41.
Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, Apr. 2008.
42.
B. H. Juang, Y. Li, and J. Ma, “Signal processing in cognitive radio,” Proc. IEEE, vol. 97, no. 5, pp. 805–823, May 2009.
43.
Y. H. Zeng, Y.-C. Liang, A. T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: challenges and solutions,” EURASIP J. Advances Signal Process., 2010.
44.
P. Setoodeh and S. Haykin, “Robust transmit power control for cognitive radio,” Proc. of the IEEE, vol. 97, no. 5, pp. 915–939, May 2009.
45.
S. Wang, “Efficient resource allocation algorithm for cognitive OFDM systems,” IEEE Commun. Lett., vol. 14, no. 8, pp. 725–27, Aug. 2010.
46.
M. Ge and S. Wang, “Fast optimal resource allocation is possible for multiuser OFDM-based cognitive radio networks with heterogeneous services,” IEEE Trans. Wireless Commun., vol. 11, no. 4, pp. 1500–1509, Apr. 2012.
47.
S. Wang, Z.-H. Zhou, M. Ge and C. Wang, “Resource allocation for heterogeneous cognitive radio networks with imperfect spectrum sensing,” IEEE J. Sel. Areas Commun., vol. 31, no. 3, pp. 464–475, 2013.
48.
S. Wang, M. Ge and W. Zhao, “Energy-Efficient Resource Allocation for OFDM-based Cognitive Radio Networks,” IEEE Trans. Commun., vol. 61, no. 8, pp. 3181–3191, Aug. 2013.
49.
S. Wang, M. Ge, C. Wang, “Efficient Resource Allocation for Cognitive Radio Networks with Cooperative Relays,” IEEE J. Sel. Areas Commun., vol. 31, no. 11, pp. 2432–2441, Nov. 2013.
50.
S. Wang, Z.-H. Zhou, M. Ge and C. Wang, “Resource Allocation for Heterogeneous Multiuser OFDM-based Cognitive Radio Networks with Imperfect Spectrum Sensing,” In Proc. IEEE INFOCOM’12, pp. 2264–2272, Mar. 2012.
51.
S. Wang, F. Huang and Z.-H. Zhou, “Fast Power Allocation Algorithm for Cognitive Radio Networks,” IEEE Commun. Lett., vol. 15, no. 8, pp. 845–847, Aug. 2011.
Metadaten
Titel
Dynamic Resource Allocation
verfasst von
Shaowei Wang
Copyright-Jahr
2014
Buch
Cognitive Radio Networks

Print ISBN: 978-3-319-08935-5

Electronic ISBN: 978-3-319-08936-2

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-08936-2_2