Dynamic Channel Allocation Using a Genetic Algorithm for a TDD Broadband Fixed Wireless Access Network

Algorithm is evaluated and compared with two existing DCA methods namely the Least Interfered (LI) and Channel Segregation (CS). These three methods are chosen from the same category in the proposed Channel Allocation Matrix. I. INTRODUCTION The rapid increase in the data rate in the Internet backbone is not paralleled with the increase in the data rate in the last mile. This causes a bottleneck for the majority of Internet users that do not enjoy fast access networks. Broadband fixed wireless access (BFWA) networks can be rapidly deployed and provide data rates that current Internet users demand. Typical BFWA network components are the Control Server (CSVR), the Access Point (AP) and the Subscriber Unit (SU) and they are arranged as shown in Fig. 1. The SU is mounted on the subscriber's site and uses a directional antenna with a horizontal beamwidth of 20° to communicate with its corresponding AP. Each AP has an antenna with a horizontal beamwidth of 60°. A CSVR is connected to K APs. The CSVR is a server that provides configuration, authentication and management systems.

[1]  Nelson Sollenberger,et al.  Spectrum resource allocation for wireless packet access with application to advanced cellular Internet service , 1998, IEEE J. Sel. Areas Commun..

[2]  Kin K. Leung,et al.  Dynamic allocation of downlink and uplink resource for broadband services in fixed wireless networks , 1999, IEEE J. Sel. Areas Commun..

[3]  Yoshihiko Akaiwa,et al.  Channel Segregation - A Self-Organized Dynamic Channel Allocation Method: Application to TDMA/FDMA Microcellular System , 1993, IEEE J. Sel. Areas Commun..

[4]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[5]  Antonio Capone,et al.  Channel assignment problem in cellular systems: a new model and a tabu search algorithm , 1999 .

[6]  K. A. West,et al.  An aggressive dynamic channel assignment strategy for a microcellular environment , 1994 .

[7]  S. Haykin,et al.  A Q-learning-based dynamic channel assignment technique for mobile communication systems , 1999 .

[8]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[9]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[10]  Sajal K. Das,et al.  Dynamic resource assignment using network flows in wireless data networks , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[11]  Mahmoud Naghshineh,et al.  Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey , 2000, IEEE Communications Surveys & Tutorials.

[12]  G. G. Coghill,et al.  Channel assignment through evolutionary optimization , 1996 .

[13]  Li-Fung Chang,et al.  Wireless dynamic channel assignment performance under packet data traffic , 1999, IEEE J. Sel. Areas Commun..

[14]  Kumar N. Sivarajan,et al.  Dynamic channel assignment in cellular radio , 1990, 40th IEEE Conference on Vehicular Technology.

[15]  Dietmar Kunz Transitions from DCA to FCA behavior in a self-organizing cellular radio network , 1999 .

[16]  Sandeep K. S. Gupta,et al.  Distributed dynamic channel allocation in mobile networks: combining search and update , 1999, 1999 IEEE International Performance, Computing and Communications Conference (Cat. No.99CH36305).