In this work, we present an in-depth study to measure, analyze and enhance aerial performance (here, uplink throughput) for drones flying in the low sky over two operational 5G networks in the US (AT&T and T-Mobile). Different from prior aerial 5G measurement studies, we have made three new endeavors. First, through extensive experiments in the low sky (below 120 m), we not only characterize aerial performanceobservedover operational 5G networks, but also quantitively assess performance potentialsnot observed but missedin the sky. We have several new findings that have not been reported before: higher 5G performance potentials are realized in the sky than on the ground (say, faster data speed in the sky); But surprisingly, more performance potentials are also missed in the sky (namely, 5G could have been even much faster but such potentials are not fully utilized in the sky). Second, we delve into root causes behind missed performance potentials and find that current 5G cell selection should take the blame despite the impacts of radio resource allocation in the underlying physical layer. Cell selection is designed for terrestrial scenarios and misses good 5G cells under aerial radio channel conditions. Third, we thus devise a data-driven solution called5GAir++to patch cell selection in practice.5GAir++is promising to pursue more 5G performance potentials in the low sky. We have validated its effectiveness over real-world traces with two applications of bulky file upload and video live streaming. Datasets and codes are released.

5G is much faster than 4G, offering faster data transfer and better user experience overall. Intuitively, 5G should be used as much as possible. However, in this study, we unveil a surprising finding in operational 5G networks: 5G radio access may be in a persistent ON-OFF loop which repeatedly turns 5G on and then off. We conduct extensive measurement experiments with three US operators (T-Mobile, AT&T, and Verizon) in two US cities to characterize and analyze 5G ON-OFF loop instances in the wild. Surprisingly, we find that such 5G ON-OFF loops are not rare. They are widely observed at many places, significantly hurting data performance (from several hundreds of Mbps to tens of or even zero Mbps). We further dive into their causes and uncover that inconsistent triggers to turn 5G on and off co-exist in real-world settings, repeatedly releasing 5G radio access after getting 5G back. We identify three loop types each with distinct triggering events/causes (sub-types). Inconsistent policies and mechanisms on both network and device sides, as well as ''improper'' use of certain frequency channels, are responsible for the loops observed in this study. Our datasets and artifacts have been released on Github and MI-LAB.

In this work, we conduct a measurement study with three US operators to reveal three types of problematic failure handling on secondary radio access which have not been reported before. Compared to primary radio access failures, secondary radio access failures do not hurt radio access availability but significantly impact data performance, particularly when 5G is used as secondary radio access to boost throughput. Improper failure handling results in significant throughput loss, which is unnecessary in most instances. We then pinpoint the root causes behind these three types of problematic failure handling. When 5G provides higher throughput, failures are more likely to be falsely triggered by a specific event, causing the User Equipment (UE) to unnecessarily lose well-performing 5G connections. Moreover, after failures, the recovery of secondary radio access may fail due to inconsistent parameter settings or be delayed due to missing specific signaling fields. To address these issues, we propose SCGFailure Manager (SFM), a solution to optimize the detection and recovery of secondary radio access failures. Our evaluation results demonstrate thatSFMcan effectively avoid 60%-80% of problematic failure handling and double throughput in more than half of failure instances.

In this work, we present our measurement study to characterize and analyze operational 5G performance potentials for cellular-connected drones that fly in the low sky. We not only measure aerial performance observed over an operational 5G network (here, T-Mobile, one major 5G operator in the US), but also quantitively assess potentials missed in the sky. Different from prior measurement studies, we compare 5G performance potentials realized and missed in the low sky and on the ground. We have several new findings that have not been reported before: higher 5G performance potentials are realized in the sky than on the ground (say, faster data speed in the sky); But surprisingly, more performance potentials are also missed in the sky (namely, 5G can have been even much faster but such potentials are not fully utilized in the sky). We delve into root causes behind missed potentials and find that current 5G cell selection is designed for terrestrial scenarios and misses good candidate cells under aerial radio channel conditions. We thus devise a patch solution called 5Gair to pursue more 5G potentials in the low sky and validate its effectiveness over real-world traces (released at [1]).

Carrier aggregation (CA) is an important component technology in 5G and beyond. It aggregates multiple spectrum fragments to serve a mobile device. However, the current CA suffers under both high mobility and increased spectrum space. The limitations are rooted in its sequential, cell-by-cell operations. In this work, we propose CA++, which departs from the current paradigm and explores a group-based design scheme. We thus propose new algorithms that enable concurrent channel inference by measuring one or few cells but inferring all, while minimizing measurement cost via set cover approximations. Our evaluations have confirmed the effectiveness of CA++. Our solution can also be adapted to fit in the current 5G OFDM PHY and the 3GPP framework.