Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
This work addresses coverage map reconstruction for cellular networks, offering incremental improvements over offline methods by enabling adaptive online processing with side information.
The paper tackles the problem of reconstructing coverage maps from path-loss measurements in cellular networks by proposing two kernel-based adaptive online algorithms, which incorporate side information like user trajectories to improve convergence and estimation quality, with simulations showing fast and robust estimates in real-world scenarios.
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity-awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data which is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast, robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.