Research on Radio Environment Maps for Mobility Management in 5G Networks
- Publication Type:
- Thesis
- Issue Date:
- 2020
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
One main feature of the fifth generation (5G) of cellular mobile communications is the deployment of an ultra-dense cellular network architecture with much more cell towers. This will construct a multi-tier 5G network, and make ubiquitous access difficult if maintaining the same approaches to mobility management as in previous generations. In recent years, we have witnessed remarkable advancements in cognitive radio, which provides radio-environmental awareness. This awareness can be exploited to improve system performance in various aspects. This thesis studies how to incorporate radio environment maps (REM) into 5G networks with a particular emphasis on mobility management.
Our work begins halfway between Long-Term Evolution (LTE) and 5G. We propose a REM-based handover algorithm that reduces the number of unnecessary handovers in multi-tier networks. The designed handover procedure is fully backward compatible with LTE and exploits the incomplete channel states stored in a REM. We evaluate our method under two different scenarios in which we can deliver the same downlink traffic as current approaches in the literature as well as decrease the overall number of handovers by at least 33% without overloading the backhaul.
We also present a geometric model to derive the handover and handover failure regions taking into consideration imperfect location, by finding the optimal prediction time through numerical optimisation. The effect of multiple mobility-management parameters is investigated as well. The proposed scheme achieves a substantial reduction of up to 30% in the number of unnecessary handovers in multi-tier networks. We then propose to use REMs for network optimisation in a dense cellular network and obtain the coverage probability for REM cell association using stochastic geometry. The optimal prediction distance maximises the average ergodic rate, including the penalty incurred by the handovers. Our strategy increases the average ergodic rate extensively by 65% across high-mobility users when compared to state-of-the-art strategies found in the literature.
In summary, radio-environmental awareness in mobile cellular networks has not been wholly addressed yet. This thesis introduces REMs as an enabling technology that contributes to the mitigation of the number of unnecessary handovers and capacity growth for mobility management in 5G networks and beyond.
Please use this identifier to cite or link to this item: