Joint Downlink and Uplink Tilt-Based Self-Organization of Coverage and Capacity Under Sparse System Knowledge

Concurrent self-organization of coverage and capacity in both downlink (DL) and uplink (UL) is an ambitious task since 1) Coverage and capacity are conflicting key performance indicators (KPIs), and 2) an adequate DL performance does not necessarily entail an adequate UL performance because the DL and UL interference scenarios are fundamentally different. However, considering the UL when self-organizing the network is crucial because the UL transmission becomes more important due to the emergence of new applications and services, such as social networking, video calls, or sensor networks, which require either parity between DL and UL traffic or even more UL than DL traffic. In this paper, we propose a general concept for the self-organization of multiple KPIs while having only very sparse knowledge about the network. Using this concept, we propose an effective tilt-based algorithm that manages to jointly optimize coverage and capacity in DL and UL. In an urban scenario with real Long-Term Evolution (LTE) site locations, we were able to increase the cell-edge user throughput by 70% (DL) and 24% (UL) while concurrently decreasing the number of uncovered users by 21% (DL) and 25% (UL), compared with a well-chosen reference tilt setting.