Client-Based Control Channel Analysis for Connectivity Estimation in LTE Networks

Abstract

Abstract-Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the offloading of large amounts of data can be more efficient than the local processing on a resource-constraint platform, while this latter solution is preferred in case of limited connectivity. This paper proposes a Client-Based Control Channel Analysis for Connectivity Estimation (C 3 ACE), a new passive probing mechanism to enable the client-side to estimate the connection quality of 4G networks in range. The results show that by observing and analyzing the control traffic in real-time, the number of active user equipment in a cell can be determined with surprising accuracy (with errors well below 10 −6 ). The specific challenge addressed in this paper lies in a dedicated filtering and validation of the DCI (Downlink Control Information). In a subsequent step the data rates to be expected can be estimated in order to enable decision about the choice of network and the timing of the data offloading to the cloud. The proposed methods have been implemented and validated leveraging the SDR OpenAirInterface, a real-life LTE network and a distributed load generator producing a scalable network traffic by a number of LTE User Equipment.

Implementation of platform for long‐term evolution cell perspective resource utilization analysis

“…Without accessing the private data or system parameters from encrypted data channels, LTEye facilitates LTE radio analytics by detecting RNTI and decoding all possible DCIs in an LTE subframe. To improve valid DCI extraction performance, [28,29] have proposed improved algorithms based on ref. [27]'s algorithm.…”

“…In ref. [28], two steps of detection and management of candidate RNTIs are applied. However, the threshold needs to be optimized in terms of determining valid DCI or RNTI under the various channel environments in the real field.…”

FALCON: An Accurate Real-Time Monitor for Client-Based Mobile Network Data Analytics

“…When the recursion does not detect any valid DCI, all coherent but rejected RNTIs along the recursion path are added to a histogram of uncertain RNTIs. If an RNTI appears at least five times within a sliding window of 200 ms, it is added to the active set [11]. A shortcut is taken, if decoding a shortened location (cf.…”

“…These spurious RNTIs are scattered uniformly along the entire value range of 2 16 with a very low frequency, each. The low frequency is a consequence of the small probability hitting the same RNTI multiple times across a number of coin toss experiments [11].…”

Data-Driven Evaluation of Anticipatory Networking in LTE Networks

“…Kumar et al [24] proposed LTEye, which provided the inspiration for our tool, while Xie et al [39] developed Rmon from which we adapted the technique to test OWL. A very recent solution is designed by Falkenberg et al [40] to estimate mobile phone connectivity. MobileInsight [11] is another tool meant to obtain mobile network information from the UE perspective and is developed by Li et al Of course, commercial products might offer similar features albeit at a much higher price and complexity, e.g., QXDM [41], Actix Analyzer [42], or TEMS investigation [43].…”