A Grey-Fuzzy Controller for Optimization Technique in Wireless Networks
In wireless and mobile communications, this progress
provides opportunities for introducing new standards and improving
existing services. Supporting multimedia traffic with wireless networks
quality of service (QoS). In this paper, a grey-fuzzy controller for radio
resource management (GF-RRM) is presented to maximize the number
of the served calls and QoS provision in wireless networks. In a
wireless network, the call arrival rate, the call duration and the
communication overhead between the base stations and the control
center are vague and uncertain. In this paper, we develop a method to
predict the cell load and to solve the RRM problem based on the
GF-RRM, and support the present facility has been built on the
application-level of the wireless networks. The GF-RRM exhibits the
better adaptability, fault-tolerant capability and performance than other
algorithms. Through simulations, we evaluate the blocking rate, update
overhead, and channel acquisition delay time of the proposed method.
The results demonstrate our algorithm has the lower blocking rate, less
updated overhead, and shorter channel acquisition delay.
[1] "http://www.3gpp.org", 2002.
[2] H. Holma and A. Toskala (eds.), WCDMA for UMTS. Wiley, 2000.
[3] 3rd Generation Partnership Project Technical Specification Group Radio
Access Network. Working Group 1, "Physical Layer - Measurements."
TS25.225 v4.0.0. 2001.
[4] 3rd Generation Partnership Project. Technical Specification Group. Radio
Access Network "Radio Interface Protocol Architecture." TS25.301
v4.2.0. 202.
[5] 3rd Generation Partnership Project. Technical Specification Group. Radio
Access Network "Radio Resource Control (RRC); Protocol
Specification." TS25.331" 4.4.0, 2002.
[6] S. K. Das, S. K. Sen and R. Jayaram, A structured channel borrowing
scheme for dynamic load balancing in cellular networks, IEEE
Distributed Computing Systems Conference, pages 116-123, 1997.
[7] J. Kim, T. Lee, and C. S. Hwang, A dynamic channel assignment scheme
with two thresholds for load balancing in cellular networks, IEEE Radio
and Wireless Conference, pages 141-145, 1999.
[8] X. Dong and T. H. Lai, Distributed dynamic carrier allocations in mobile
cellular networks: search vs. update, IEEE Distributed Computing
Systems Conference, pages 108-115, 1997.
[9] T. Lee, J. Kim, and C. S. Hwang, A dynamic channel assignment scheme
with two thresholds for load balancing in cellular networks, IEEE Radio
and Wireless Conference, pages 141-145, 1999.
[10] H. Jiang and S. S. Rappaport, CBWL: a new channel assignment and
sharing method for cellular communication systems, IEEE Transactions
on Vehicular Technology, pages 313 -322, 1994.
[11] S. Kim and P. K. Varshney, Adaptive Load Balancing with Preemption
for Multimedia Cellular Network, IEEE Wireless Communications and
Networking Conference, pages 1680-1684, 2003.
[12] T. S. Yum and M. Zhang, Comparisons of channel-assignment strategies
in cellular mobile telephone systems, IEEE Transactions on Vehicular
Technology, pages 211-215, 1989.
[13] Y. -T. Wang and J.-P. Sheu, A Dynamic Channel Borrowing Approach
with Fuzzy Logic Control in Distributed Cellular Networks, the special
issue of Simulation Modeling Practice and Theory, Vol. 12, pages 287 -
303, 2004.
[14] Y. T. Wang, A Fuzzy-Based Dynamic Channel Borrowing Scheme for
Wireless Cellular Networks, IEEE Vehicular Technology Conference,
pages 1517-1521, 2003.
[15] L. A. Zadeh, Fuzzy Algorithm. Information and Control, pages 94-102,
1968.
[16] Y. Zhang, A new adaptive channel assignment algorithm in cellular
mobile systems, IEEE Systems Sciences Conference, pages 1-7, 1999.
[17] J. S. Engel and M. Peritsky, Statistically-optimum dynamic sever
assignment in systems with interfering severs, IEEE Vehicular
Technology Conference, pages 1287-1293, 1973.
[18] H. Haas and S. McLaughlin, A novel decentralized DCA concept for a
TDD network applicable for UMTS. IEEE Transactions on Vehicular
Technology, pages 881-885, 2001.
[19] J. Karlsson and B. Eklundh, A cellular mobile telephone system with load
sharing-an enhancement of directed retry, IEEE Transactions on
Communications, pages 530-535, 1989.
[20] S. Mitra and S. DasBit, A load balancing strategy using dynamic channel
assignment and channel borrowing in cellular mobile environment, IEEE
Personal Wireless Communications Conference, pages 278-282, 2000.
[21] J. L. Deng , Control problem of grey systems, System and Control Letters,
Vol. 1, pages 288-294, 1982.
[22] Ren C. Luo and Tse Min Chen , Autonomous Mobile Target Tracking
System Based on Grey-Fuzzy Control Algorithm, IEEE Transactions on
Industrial Electronics, VOL. 47, NO. 4, pages 920-931, 2000.
[23] C.-Y. Kung, K.-T. Hsu, T.-M. Yan and P.-W. Liu, An Application of the
Grey Prediction Theory to the Annual Medical Expense of Taiwan-s
National Health Insurance, Journal of Grey System, Vol. 9, No. 2, pages
75-86, 2006.
[24] W.-N. Pi and L.-C. Liou, Electric Power Demand Forecasting in Taiwan
via Grey Prediction, Journal of Science and Engineering Technology,
Vol. 3, No. 2, pages 11-18, 2007.
[25] Y.-T. Wang and K.-M. Hung "Fuzzy Logic Based Neural Network Model
for Load Balancing in Wireless Networks. " KICS Communications
Society, International Journal of Communications and Networks, Vol. 10,
pp.38- 43, 2008.
[1] "http://www.3gpp.org", 2002.
[2] H. Holma and A. Toskala (eds.), WCDMA for UMTS. Wiley, 2000.
[3] 3rd Generation Partnership Project Technical Specification Group Radio
Access Network. Working Group 1, "Physical Layer - Measurements."
TS25.225 v4.0.0. 2001.
[4] 3rd Generation Partnership Project. Technical Specification Group. Radio
Access Network "Radio Interface Protocol Architecture." TS25.301
v4.2.0. 202.
[5] 3rd Generation Partnership Project. Technical Specification Group. Radio
Access Network "Radio Resource Control (RRC); Protocol
Specification." TS25.331" 4.4.0, 2002.
[6] S. K. Das, S. K. Sen and R. Jayaram, A structured channel borrowing
scheme for dynamic load balancing in cellular networks, IEEE
Distributed Computing Systems Conference, pages 116-123, 1997.
[7] J. Kim, T. Lee, and C. S. Hwang, A dynamic channel assignment scheme
with two thresholds for load balancing in cellular networks, IEEE Radio
and Wireless Conference, pages 141-145, 1999.
[8] X. Dong and T. H. Lai, Distributed dynamic carrier allocations in mobile
cellular networks: search vs. update, IEEE Distributed Computing
Systems Conference, pages 108-115, 1997.
[9] T. Lee, J. Kim, and C. S. Hwang, A dynamic channel assignment scheme
with two thresholds for load balancing in cellular networks, IEEE Radio
and Wireless Conference, pages 141-145, 1999.
[10] H. Jiang and S. S. Rappaport, CBWL: a new channel assignment and
sharing method for cellular communication systems, IEEE Transactions
on Vehicular Technology, pages 313 -322, 1994.
[11] S. Kim and P. K. Varshney, Adaptive Load Balancing with Preemption
for Multimedia Cellular Network, IEEE Wireless Communications and
Networking Conference, pages 1680-1684, 2003.
[12] T. S. Yum and M. Zhang, Comparisons of channel-assignment strategies
in cellular mobile telephone systems, IEEE Transactions on Vehicular
Technology, pages 211-215, 1989.
[13] Y. -T. Wang and J.-P. Sheu, A Dynamic Channel Borrowing Approach
with Fuzzy Logic Control in Distributed Cellular Networks, the special
issue of Simulation Modeling Practice and Theory, Vol. 12, pages 287 -
303, 2004.
[14] Y. T. Wang, A Fuzzy-Based Dynamic Channel Borrowing Scheme for
Wireless Cellular Networks, IEEE Vehicular Technology Conference,
pages 1517-1521, 2003.
[15] L. A. Zadeh, Fuzzy Algorithm. Information and Control, pages 94-102,
1968.
[16] Y. Zhang, A new adaptive channel assignment algorithm in cellular
mobile systems, IEEE Systems Sciences Conference, pages 1-7, 1999.
[17] J. S. Engel and M. Peritsky, Statistically-optimum dynamic sever
assignment in systems with interfering severs, IEEE Vehicular
Technology Conference, pages 1287-1293, 1973.
[18] H. Haas and S. McLaughlin, A novel decentralized DCA concept for a
TDD network applicable for UMTS. IEEE Transactions on Vehicular
Technology, pages 881-885, 2001.
[19] J. Karlsson and B. Eklundh, A cellular mobile telephone system with load
sharing-an enhancement of directed retry, IEEE Transactions on
Communications, pages 530-535, 1989.
[20] S. Mitra and S. DasBit, A load balancing strategy using dynamic channel
assignment and channel borrowing in cellular mobile environment, IEEE
Personal Wireless Communications Conference, pages 278-282, 2000.
[21] J. L. Deng , Control problem of grey systems, System and Control Letters,
Vol. 1, pages 288-294, 1982.
[22] Ren C. Luo and Tse Min Chen , Autonomous Mobile Target Tracking
System Based on Grey-Fuzzy Control Algorithm, IEEE Transactions on
Industrial Electronics, VOL. 47, NO. 4, pages 920-931, 2000.
[23] C.-Y. Kung, K.-T. Hsu, T.-M. Yan and P.-W. Liu, An Application of the
Grey Prediction Theory to the Annual Medical Expense of Taiwan-s
National Health Insurance, Journal of Grey System, Vol. 9, No. 2, pages
75-86, 2006.
[24] W.-N. Pi and L.-C. Liou, Electric Power Demand Forecasting in Taiwan
via Grey Prediction, Journal of Science and Engineering Technology,
Vol. 3, No. 2, pages 11-18, 2007.
[25] Y.-T. Wang and K.-M. Hung "Fuzzy Logic Based Neural Network Model
for Load Balancing in Wireless Networks. " KICS Communications
Society, International Journal of Communications and Networks, Vol. 10,
pp.38- 43, 2008.
@article{"International Journal of Electrical, Electronic and Communication Sciences:64416", author = "Yao-Tien Wang and Hsiang-Fu Yu and Dung Chen Chiou", title = "A Grey-Fuzzy Controller for Optimization Technique in Wireless Networks", abstract = "In wireless and mobile communications, this progress
provides opportunities for introducing new standards and improving
existing services. Supporting multimedia traffic with wireless networks
quality of service (QoS). In this paper, a grey-fuzzy controller for radio
resource management (GF-RRM) is presented to maximize the number
of the served calls and QoS provision in wireless networks. In a
wireless network, the call arrival rate, the call duration and the
communication overhead between the base stations and the control
center are vague and uncertain. In this paper, we develop a method to
predict the cell load and to solve the RRM problem based on the
GF-RRM, and support the present facility has been built on the
application-level of the wireless networks. The GF-RRM exhibits the
better adaptability, fault-tolerant capability and performance than other
algorithms. Through simulations, we evaluate the blocking rate, update
overhead, and channel acquisition delay time of the proposed method.
The results demonstrate our algorithm has the lower blocking rate, less
updated overhead, and shorter channel acquisition delay.", keywords = "radio resource management, grey prediction, fuzzylogic control, wireless networks, quality of service.", volume = "4", number = "5", pages = "897-9", }
