Advanced Techniques for Base Station Deployment Planning for Localization
The need for accurate localization of User Equipment (UE) has grown significantly in modern wireless
communication networks. This thesis addresses the problem of optimizing Base Station (BS) placement in
complex environments to enhance localization accuracy. Traditional methods often overlook the impact of
real-world environmental features such as building geometry and user distribution, leading to suboptimal
planning decisions [1].
This research proposes a novel approach that incorporates environmental data and signal propagation
characteristics into the planning process. The methodology involves simulating realistic environments using
raytracing techniques and modeling the network using a GPU-accelerated simulation framework. The goal
is to evaluate localization performance for given layouts and suggest improved deployment strategies [1].
In addition, the work explores a reinforcement learning–based optimization framework, where an intelligent
agent iteratively refines BS positions to minimize localization error. Key factors such as Time-Of-Arrival
(TOA), channel impulse responses, and user positions are leveraged to assess and improve system
performance [1].
The outcomes of this thesis include insights into how BS configurations affect localization in urban or
obstructed areas and a systematic framework for data-driven deployment planning.
Main Objectives:
- Analyze the impact of building geometry on localization accuracy in complex deployment scenarios.
- Compare the performance of brute force planning methods with a reinforcement learning–based
optimization framework for BS placement.
Proposed Steps:
- Create a 3D building map in Blender to serve as input to Sionna RT, a raytracing software.
- Place BSs at all candidate locations and implement raytracing–based propagation simulations within
a GPU-accelerated framework. - Design and train a deep reinforcement learning agent to iteratively refine BS positions, minimizing
localization error. - Benchmark localization performance for both brute force and RL-optimized BS layouts.
- Evaluate and compare deployment configurations against criteria, including positioning accuracy.
Reference
[1] J. AlTahmeesschi, M. Talvitie, H. LópezBenítez, H. Ahmadi, and L. Ruotsalainen, “MultiObjective Deep
Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable
Traffic,” in Proc. IEEE 97th Vehicular Technology Conference (VTC2023Spring), Florence, Italy, Jun. 2023,
pp. 1–5.