Performance analysis of fractional frequency reuse in random cellular networks
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In a Long Term Evolution (LTE) cellular network, Fractional Frequency Reuse (FFR) is a promising technique that improves the performance of mobile users which experience low Signal-to-Interference-plus-Noise Ratios (SINRs). Recently, the random cellular network model, in which the Base Stations (BSs) are distributed according to a Poisson Point Process (PPP), is utilised widely to analyse the network performance. Therefore, this thesis aims to model and analyse performance of two well-known FFR schemes called Strict Frequency Reuse (FR) and Soft FR, in the random cellular network. Monte Carlo simulation is used throughout the thesis to verify the analytical results. The first part of this thesis follows 3rd Generation Partnership Project (3GPP) recommendations to model the Strict FR and Soft FR in downlink and uplink single-tier random cellular networks. The two-phase operation model is presented for both Cell-Center User (CCU) and Cell-Edge User (CEU). Furthermore, the thesis follows the resource allocation technique and properties of PPP to evaluate Intercell Interference (ICI) of the user. The closed-form expressions of the performance metrics in terms of classification probability and average coverage probability of the CCU and CEU are derived. Thereafter, the performance of FFR is analysed in multi-tier cellular networks which are comprised of different types of cells such as macrocells, picocells and femtocells. The focus of this part is to examine the effects of the number of users and number of Resource Blocks (RBs) on the network performance. A new network model, in which the SINR on data channels are used for user classification purpose, is proposed. The analytical results indicate that the proposed model can reduce the power consumption of the BS while improving the network data rate. This chapter introduces an approach to analyse the optimal value of SINR threshold and bias factor. The analytical results indicate that the proposed model can increase the network data rate by 16.08% and 18.63% in the case of Strict FR and Soft FR respectively while reducing power consumption of the BS on the data channel. Finally, the thesis develops an FFR random cellular network model with an FR factor of 1 using either Joint Scheduling or Joint Transmission with Selection Combining. The performance metrics in terms of average coverage probability are derived for Rayleigh fading environment. Generally, this thesis makes contributions to uplink and downlink of LTE networks in terms of performance analysis.
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