Fractional Frequency Reuse in Multi-tier Networks: Performance Analysis and Optimization
- Publisher:
- Springer Science and Business Media LLC
- Publication Type:
- Journal Article
- Citation:
- International Journal of Wireless Information Networks, 2020, 27, (1), pp. 164-183
- Issue Date:
- 2020-03-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Lam-Sandrasegaran2020_Article_FractionalFrequencyReuseInMult.pdf | Published version | 2.48 MB |
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Frequency Reuse (FR) is an efficient approach to improve the network performance in a multi-cell cellular network. In this paper, we investigate the performance of heterogeneous networks utilising two well-known frequency reuse algorithms, called Strict FR and Soft FR, with a reuse factor of Δ (Δ> 1). Based on a two-phase operation of the FR algorithm, we develop a new approach to analyse cellular networks based on the Poisson Point Process (PPP) model which successfully demonstrates the impact of the network parameters (such as the number of allocated Resource Blocks (RBs) and bias factor) and FR parameters (such as Base Station (BS) transmit power) on the performance of a user and overall network. Compared to related works, we propose the following novel approaches: (i) we investigate flexible FR networks in which users have connections with the BSs which deliver the highest performance; (ii) the BS observes SINR on the data channel to classify each user into either a Cell-Edge User (CEU) or Cell-Center User (CCU); (iii) the initial state of the network is considered to establish the initial network interference when new users arrive and request connections to the BSs. In the case of a single-tier network, it is proved that our analytical approach is more accurate than previous works. In the case of a two-tier network, the analytical results indicate that compared to the 3GPP model, our proposed model not only reduces up to 40.79% and 3.8% power consumption of a BS on the data channel but also achieves 16.08% and 18.63% higher data rates in the case of Strict FR and Soft FR respectively. Furthermore, the paper presents an approach to find an optimal value of SINR thresholds and bias factor to achieve the maximum network performance.
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