One of the most challenging applications targeted by evolving (beyond-)5G technology is virtual reality (VR). Particularly, 'Social VR' applications provide a fully immersive experience and sense of togetherness to users residing at different locations. To support such applications the network must deal with huge traffic demands, while keeping end-to-end latencies low. Moreover, the radio access network must deal with the volatility and vulnerability of mmWave radio channels, where even small movements of the users may have substantial effects on the Quality of Experience. We present an integral modelling framework for feasibility assessment and performance optimization of the radio access network for Social VR applications in indoor office scenarios. Using the presented modelling approach, we conduct an extensive simulation-based assessment to determine the performance impact of head motion, the frequency band (3.5 GHz, 26 GHz) and radio network configurations, and derive the required carrier bandwidth for a range of 'Social VR' scenarios. Insights into these issues are a prerequisite for setting up guidelines for network deployment and configuration as well as for the development of (AI/ML-based) methods for dynamic resource management to optimally support Social VR applications. @en

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

@en

Drone base stations can help safeguard coverage and provide capacity relief when cellular networks are under stress. Examples of such stress scenarios are events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a traffic hotspot, where agile drone positioning and load management is a critical issue. In order to address this challenge, we propose and assess a data-driven algorithm which leverages real-time measurements to dynamically optimize the 3D position of the drone as well as a cell selection bias tuned for optimized load management. We compare the performance with three benchmark scenarios: i) no drone; ii) a drone positioned above the failing site; and iii) a drone with a statically optimized position and cell selection bias. The results demonstrate that the proposed algorithm significantly improves the call success rate and achieves close to optimal performance. @en

Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources.

@en