Scope and Purpose: For cell
planning in wireless mobile communication, it is essential to consider
the location and capacity of each base station to cover traffic demands
in a specified region. The location of each base station is related to
the coverage area and the coverage is mainly determined by the minimum
received signal power from subscribers. Also the total traffic demand in
the region has to be covered by selected base stations in a certain level.
We consider the cell planning to cover increased traffic demands. The problem
is formulated as an integer linear programming.
A tabu search heuristic is investigated
to solve the problem. Intensification procedure is employed to select proper
location of each base station. The capacity is controlled in the process
of diversification. Cell planning in AMPS (Advanced Mobile Phone Service)
and CDMA (Code Division Multiple Access) environments are examined with
the procedure presented. The effectiveness of the tabu search is illustrated
by various computational results.
Abstract: A cell planning
problem with capacity expansion is examined in wireless communications.
The problem decides the location and capacity of each new base station
to cover expanded and increased traffic demand. The objective is to minimize
the cost of new base stations. The coverage by the new and existing base
stations is constrained to satisfy a proper portion of traffic demands.
The received signal power at the base station also has to meet the receiver
sensitivity. The cell planning is formulated as an integer linear programming
problem and solved by a Tabu Search algorithm.
In the tabu search intensification
by add and drop move is implemented by short-term memory embodied by two
tabu lists. Diversification is designed to investigate proper capacities
of new base stations and to restart the tabu search from new base station
locations.
Computational results show that
the proposed tabu search is highly effective. 10% cost reduction is obtained
by the diversification strategies. The gap from the optimal solutions is
approximately 1?5 % in problems that can be handled in appropriate time
limits.