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In 5G and beyond wireless systems, Network Slicing (NS) feature will enable the coexistence of extremely different services by splitting the physical infrastructure into several logical slices tailored for a specific tenant or application. In sliced Radio Access Networks (RANs), an optimal traffic sharing among cells is key to guarantee Service Level Agreement (SLA) compliance while minimizing operation costs. The configuration of network functions leading to that optimal point may depend on the slice, claiming for slice-aware traffic steering strategies. This work presents the first data-driven algorithm for slice-aware traffic steering by tuning handover margins (a.k.a. mobility load balancing) per slice and adjacency with a proportional controller based on SLA criteria. The tuning process is driven by a novel indicator, derived from connection traces, showing the imbalance of SLA compliance among neighbor cells per slice. Performance assessment is carried out with a system-level simulator implementing a realistic sliced RAN offering services with different throughput, latency and reliability requirements. Results show that the proposed algorithm improves the overall SLA compliance by 9% in only 15 minutes of network activity compared to the case of not steering traffic, outperforming two legacy mobility load balancing approaches not driven by SLA.

Cellular networks are constantly evolving, driven by changes in user behavior and device capabilities. To ensure that networks adapt to these changes, it is of vital importance for mobile operators to have a good understanding of how well their network meets subscriber needs. For this purpose, the Minimization of Drive Test (MDT) feature has been standardized, allowing operators the cost-effective provision of geolocated network performance statistics and radio events. However, in practice, positioning errors severely limit the potential of MDT measurements. In this paper, an in-depth analysis of a large MDT dataset taken from a commercial Long-Term Evolution (LTE) network shows for the first time several sources of positioning errors in MDT measurements not previously reported in the literature. To address these, a novel heuristic filtering algorithm is proposed to discard samples with inaccurate location data. Method assessment is done by checking the impact of filtering on the coverage map built with a real MDT dataset. Results show that the proposed filtering method significantly improves the accuracy of coverage maps by eliminating unreliable measurements.