Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Automated optimization of service coverage and base station antenna configuration in UMTS networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Siomina, I. ; Linkoping Univ. ; Varbrand, P. ; Yuan, D.

Deployment and maintenance of UMTS networks involve optimizing a number of network configuration parameters in order to meet various service and performance requirements. In this article we address automated optimization of service coverage and radio base station antenna configuration. We consider three key configuration parameters: transmit power of the common pilot channel (CPICH), antenna tilt, and antenna azimuth. CPICH power greatly influences coverage. From a resource management point of view, satisfying the coverage requirement using minimum CPICH power offers several performance advantages. In particular, less CPICH power leads to less interference and higher system capacity. Optimal CPICH power, in its turn, is highly dependent on how the other two parameters, tilt and azimuth, are configured at radio base station antennas. Optimizing antenna tilt and azimuth network-wise, with the objective of minimizing the CPICH power consumption, is a challenging task. The solution approach in this article adopts automated optimization. Our optimization engine is a simulated annealing algorithm. Staring from an initial configuration, the algorithm searches effectively in the solution space of possible configurations in order to find improvements. The algorithm is computationally efficient; thus, we can optimize large networks without using excessive computing resources. We present a case study for a UMTS planning scenario in Lisbon. For this network, automated optimization saves up to 70 percent of the CPICH power used in the reference network configuration. In addition, the optimized network configuration offers significant performance improvement in terms of fewer overloaded cells and lower downlink load factor

Published in:

Wireless Communications, IEEE  (Volume:13 ,  Issue: 6 )