Jareen Shuva

Md Mahfuzur Rahman, Jareen Shuva, Nishith Tripathi et al.

We propose a machine learning (ML)-based framework for downlink performance prediction in 5G networks using real-time measurements from commercial off-the-shelf (COTS) user equipment (UE). Our experimental platform integrates the srsRAN 5G New Radio (NR) stack deployed on a Dell desktop serving as the 5G next generation nodeB (gNB), operating at 3.4 GHz. Two Google Pixel 7a smartphones are used to collect physical layer characteristics such as channel quality indicator (CQI), modulation and coding scheme (MCS), bit rate, transmission time interval (TTI), and block error rate (BLER), which are leveraged as predictors in model training. We use commercial-grade traffic generation tools, including Ookla, for stationary and mobility measurements under line-of-sight (LOS) and non-line-of-sight (nLOS) conditions. Test data includes global Ookla servers (e.g., USA, Portugal, Ghana, Egypt, Japan), iperf TCP/UDP data, and video streaming sessions from YouTube. To analyze inter-user interference, we also include scenarios with multiple UEs at the same location. We evaluate the predictive performance of five supervised regression models - linear regression, decision tree regression, random forest regression, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM). Our results demonstrate that throughput and BLER can be accurately predicted using COTS hardware and standard ML techniques in diverse real-world 5G scenarios.

Md Mahfuzur Rahman, Jareen Shuva, Nishith Tripathi et al.

We propose a machine learning (ML) and smartphone-assisted framework for uplink performance prediction in a private, realistic 5G cellular system using real-time measurements in both indoor and outdoor settings. This work presents a comprehensive data-driven evaluation of 5G performance prediction using a controllable software-defined radio test environment. The experimental platform is built on srsRAN 5G NR stack running on a Dell workstation configured as a gNB and 5G core operating at 3.4 GHz. Two commercial Google Pixel 7a devices are instrumented to capture uplink metrics, including channel quality indicator (CQI), modulation and coding scheme (MCS), throughput, transmission time interval (TTI), and block error rate (BLER). Different types of traffic are generated using industry-standard tools such as Ookla and iperf, spanning stationary, pedestrian, and mobility cases under both line-of-sight (LOS) and non-line-of-sight (nLOS) propagation environments. Additional datasets include YouTube video sessions and global server endpoints to introduce variability in path characteristics. The resulting measurements, including multi-UE interference conditions, serve as training data for several supervised regression models. Five learning algorithms-linear regression, decision tree, random forest, XGBoost, and LightGBM-are benchmarked for prediction accuracy. The study shows that reliable forecasting of throughput and BLER is feasible using only COTS smartphones and widely available ML methods, offering a practical pathway for real-world 5G network performance estimation.