| Summary: | 博士 === 國立臺灣大學 === 電信工程學研究所 === 100 === Next generation cellular systems are expected to support data rate higher than 1 Gbps for static user equipment (UE) and 100 Mbps for mobile UE [1], even when the number of UEs increases significantly. One way to achieve this tough criterion is to introduce more base stations (BS) (e.g., relay base station, pico base station and femto base station) into coverage of existing macro base station, to decrease the transmission distance and to reduce the load of macro base station. Currently, such approach has been adopted by advanced cellular systems such as 3GPP LTE and
LTE advanced. Otherwise, aggressive technologies such as multiple input and multiple output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) are also integrated into both BSs and UEs to enhance spectrum utilization efficiency. As a result, future cellular systems are expected to include more BSs and UEs with higher complexity, and thus induces two critical challenges on energy efficiency and interference control. It is due to the increasing number of advanced BSs and UEs significantly impacts operator energy cost and global environment. On the other hand, due to the trend to allow every BS to reuse system spectrum to maximize spectrum efficiency, the inter-cell interference emerges as a serious challenge. In this
dissertation, both kinds of challenges are investigated based on OFDMA systems. For energy efficiency, we start from minimizing overall energy consumption of base station via proper trade-off between computation energy and execution energy. It is due to that, the components of a system fall into two types as computation units and execution units, and the energy consumption of execution units can be minimized by the optimization implemented in computation units. It can be expected that, the higher the complexity of the optimization algorithm executed in computation units results in lower energy consumption in execution units. It implies
the overall energy consumption can be minimized by proper trade-off between the energy consumption for computation and the energy consumption for execution. In addition to the energy efficiency of BS, we further consider the energy efficiency of UE. We note that there are multiple frequency bands in one time slot, and the reception schedule of each UE is determined by BS. The duration that each UE has
data to receive can be reduced by allowing BS to arrang transmissions to each UE into fewer time slots. If UE can turn off its circuit when there is no data to be receive, we can enhance the UE energy efficiency.
For interference control, we focus on the scenario that femto base stations are deployed under coverage of macro base station. Due to the difficulties of backhaul coordinations among macro base station and femto base stations, distributed interference mitigation schemes are necessary. Three approaches are proposed as backhaul constrained resource allocation, spatial channel separation and cognitive resource management. In the first approach, we note that the backhaul throughput of femto base station is usually lower than air interface, and thus each femto base station could occupy partial system spectrum. A spectrum selection mechanisms based on Gibbs sampler is proposed, by which the overall interference can be minimized. In the second approach, we propose to separate the transmissions of
macro base station and femto base station by spatial channels, if multiple antennas are available. In the third approach, we exploit the cognitive radio for each femto
base station to avoid interference. A generalized likelihood ratio test is proposed to identify which spectrum is not being used by other base stations, and a resource allocation
is proposed for femto base station to allocate spectrum among UEs. As all of the proposed approaches are evaluated under the simulation environment specified by 3GPP LTE/LTE-Advanced, they are ready for advanced cellular systems.
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