基于核的边信息覆盖图自适应在线重建
Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
摘要：
在本文中，我们解决的问题，从蜂窝网络中的路径损耗测量重建覆盖图。我们提出并评估两个基于核的自适应在线算法作为一种替代典型的离线方法。所提出的算法是强大的迭代法，如自适应投影次梯度法和最先进的自适应多核方法的应用定制的扩展。假设用户的移动轨迹是可用的，它示出了如何边信息可以被纳入到算法中，以提高其收敛性能和估计的质量。通过在算法利用测量数据的可压缩性以减少被保存和处理的数据量的意义上施加稀疏意识，显著降低了复杂性。最后，我们提出了广泛的模拟现实数据的基础上表明，我们的算法提供快速，强大的估计覆盖地图在现实世界中的场景。设想中的应用包括沿着移动用户轨迹的路径损耗预测，作为预期缓冲或流量卸载的构建块。
英文摘要：
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.
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