Citation
AlSamawi, Aida Ismail Ahmed
(2015)
Green network planning and operational power consumption optimization in LTEA using artificial intelligence.
Doctoral thesis, Universiti Putra Malaysia.
Abstract
Demands for high bandwidth and data rates put wireless communication industry
in a leading position as energy demanding industry which contributes in carbon
dioxide volume. Therefore, Green wireless network concept has emerged to provide
the foundation of energyefficient wireless network. Base stations in wireless network
are considered as the major part responsible for the network power consumption.
Therefore, this work is dedicated to introduce a new perspective for the heterogeneous
network power efficiency improvement through network planning optimization
and relay station switching. A cascaded multiobjective genetic algorithm network
optimization (CMOGANO) is developed to optimize the network number of base
station, their location and configuration in the first stage to provide full coverage.
The second stage of the developed algorithm optimizes the average number of relay
station per base station and their distance from the respective base station to
meet the capacity constraint of the network operator. To optimize the relay station
switching, a detailed mathematical model assuming linear power consumption
model for the transmitters in the network is developed. In this model, the rate of
active relay stations is defined and integrated in the model as a varying function in
time. Optimization of the rate of active relay stations, in this model, is treated as a
variation concept. A simplified fuzzy logic solution to the optimization of the rate
of active RS is introduced.
The CMOGANO optimization showed that a power reduction up to 40% is feasible
in the network by reducing the number of base stations by 47% as compared to the
operator plan. This reduction of the number of base stations is achieved without
losses in the network coverage. The second stage of CMOGANO added a total of 516
relay stations in the network to improve its capacity up to 98%. The rate of active relay station optimization process revealed that the optimum rate of active relay
station obeys a linear first order ODE. Moreover, the optimum rate of active relay is a
function of the traffic pattern, average relay station load factor, their derivatives, and
the relative RS to BS capacity factor. The mathematical model yielded a significant
power saving up to 30% and 46% can be achieved in RS idling and sleeping modes
respectively. Moreover, the amount of power saving is a function of the number of
relay stations per base station. The effect of the traffic rate derivative is to activate
the relay stations for longer time resulting in less power saving in the network.
Similarly, power saving up to 41% can be achieved by the fuzzy logic solution.
In conclusion, complying with green network concept in heterogeneous network systems
is feasible. This compliance starts from adopting the green network concept in
the planning stage of the systems and continues through the optimization of the network
power expenditure during its operation. However, the introduction of the relay
base stations in heterogeneous network significantly increased the complexity of the
network. This complexity can be handled by developing a sophisticated heuristic
mathematical tools that account for diverse parameters in the network such that the
demanded Quality of Service (QoS) is achieved under the umbrella of green network
operation.
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