Abstract--Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (E2E) Quality of Service (QoS) and its variability. In this work, we leverage large-scale crowdsourced measurements--including E2E, radio, contextual and network deployment features collected by the user equipment (UE)--to propose an uncertainty-aware and explainable approach for downlink user throughput estimation. T o address the variability of throughput, we apply NGBoost, a model that outputs both point estimates and calibrated confidence intervals, representing its first use in the field of computer communications. Finally, we use the proposed model to analyze the evolution from 4G to 5G SA, and show that throughput bottlenecks move from the RAN to transport and service layers, as seen by E2E metrics gaining importance over radio-related features. Over the past two decades, the widespread adoption of mobile broadband networks has intensified both user and industry demands for reliable and predictable Quality of Service (QoS) to support effective network management and optimization. Among QoS indicators, user throughput is a primary concern for bandwidth-intensive applications such as media streaming and large file transfers.

Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group's transmissions. We will find the optimal user grouping decisions in RAW to maximize the network's worst-case user throughput. We review existing user grouping approaches and highlight their performance limitations in the above problem. We propose formulating user grouping as a graph construction problem where vertices represent users and edge weights indicate the contention and interference. This formulation leverages the graph's max cut to group users and optimizes edge weights to construct the optimal graph whose max cut yields the optimal grouping decisions. To achieve this optimal graph construction, we design an actor-critic graph representation learning (AC-GRL) algorithm. Specifically, the actor neural network (NN) is trained to estimate the optimal graph's edge weights using path losses between users and access points. A graph cut procedure uses semidefinite programming to solve the max cut efficiently and return the grouping decisions for the given weights. The critic NN approximates user throughput achieved by the above-returned decisions and is used to improve the actor. Additionally, we present an architecture that uses the online-measured throughput and path losses to fine-tune the decisions in response to changes in user populations and their locations. Simulations show that our methods achieve $30\%\sim80\%$ higher worst-case user throughput than the existing approaches and that the proposed architecture can further improve the worst-case user throughput by $5\%\sim30\%$ while ensuring timely updates of grouping decisions.

Network complexity is ever increasing. The introduction of 5G on top of legacy 2G, 3G and 4G networks, coupled with subscribers' increasing expectations of a mobile experience close to fiber broadband, puts tremendous pressure on the communication service providers managing day-to-day operations. Service providers also face immense financial challenges due to decreasing revenue per gigabyte and market saturation, making it critical for survival to ensure maximum return on network investment decisions. How can we leverage AI to transform our approach to network investment decisions, in order to make it faster, more granular, and able to quickly assess a variety of precise what-if scenarios that take traffic forecasts, user experience and revenue potential into consideration? A typical capacity planning exercise starts with the planning strategy phase.