Mining Radio Environment Maps from Measurements in SDR Based Self-Organizing Networks

Abstract

Radio environment maps (REMs) demonstrate the geo-located information of radio performance, which is critical to coverage enhancement in self-organizing networks (SONs). However, due to the limited number of user equipments available to measurement report, constructing REMs accurately is still a crucial challenge. In this paper, an effective algorithm is proposed to construct REMs based on both historical and current measurements of Reference Signal Received Power (RSRP). Specifically, given the differentiated propagation properties across different zones, historical measurements are utilized to group concerned zones into multiple clusters, in each of which all the zones share the same large-scale propagation parameters. In this way, more accurate REMs can be built. In addition, when new measurements come, the shadowing effect parameters in each cluster are further updated via the Expectation Maximization algorithm, making REMs adaptive to time-varying changes. After implementing the proposed algorithm on an OpenAirInterface based SON platform, experiments are conducted and corresponding results show its superiority compared to other interpolation methods.