Gradual Change Detection in Covariance Matrix: A Lazy Approach

Thanks to its slow-varying characteristic and relatively low requirement for estimation overhead, the covariance matrix has been extensively researched in sixth-generation (6G) wireless systems. Nevertheless, user mobility in practice will cause a gradual change in the covariance matrix, thereby deteriorating the system's performance if no update of the covariance matrix is applied. In this paper, we study the problem of efficient detection of gradual changes in the covariance matrix. We first introduce four change-point detectors that directly map the observations to change in our target KPI. Then, we propose a low-overhead detection algorithm that omits unnecessary channel estimations by adapting an AoA-based estimation trigger. Simulation results show that our proposed scheme can provide near-optimal performance while drastically reducing the estimation and computation overhead.