Existing LEO satellite network simulators widely approximate satellite coverage using a cone-based model that assumes uniform capacity distribution within a satellite's field of view. However, modern systems employ phased-array antennas that impose discrete beam constraints, fundamentally altering coverage characteristics. In this paper, we investigate the extent to which ignoring beam-level constraints biases network-level performance estimates. We introduce a simplified beam-aware model that captures beam count, capacity, and limited spatial reuse, and compare it against traditional cone-based approximations across a range of user distributions. Our results show that cone models can significantly overestimate service coverage, particularly under sparse or highly clustered demand, due to beam-count and beam-capacity exhaustion effects. We further analyze how beam-hopping and -stacking mitigate these limitations and quantify their impact on predicted coverage differences, demonstrating reductions in predicted coverage difference of 40% and 65% respectively. We position these results of our model as the first step towards higher-fidelity simulation to show the importance of modeling beams and we sketch directions for future work that further improves fidelity.

Support for roaming in today's cellular architecture involves operating a private network that sits right next to the mainstream Internet. This network is called the IP Exchange Network (IPX) and it provides Mobile Network Operators (MNOs) with a mechanism to form roaming partnerships, resolve billing and QoS, and set up tunnels as necessary. We propose a design which we call the CPX (Consolidated IPX/IXP) where the roaming network converges with the Internet by leveraging existing edge providers to provide all the benefits that the IPX network provides. In addition to convergence, the CPX architecture provides lower latency for roaming users; in our simulation of a global CPX deployment, we demonstrate an average of 318% improvement in roaming connection latency.

The 5G Radio Access Network has shifted towards virtualization and disaggregation. This change aims to reduce costs and foster innovation by promoting vendor interoperability and by expanding the ecosystem. In this environment, smaller RAN vendors and open-source projects have emerged, focusing on low-cost, modular stacks. However, challenges such as achieving state-of-the-art performance and accessing data and control knobs hinder their widespread adoption. To address these issues, we propose a middlebox architecture, called RANBooster, that enhances the RAN capabilities without modifying existing network functions, by leveraging the open fronthaul interface. To demonstrate the benefits of the RANBooster framework, we build four reference applications (distributed antenna system, distributed MIMO, RU sharing, realtime physical resource block monitoring), and evaluate them on an enterprise-scale, commercial-grade 5G testbed.

We propose Roaming-in-Place (RinP), a technique for dynamically sharing capacity across mobile network operators. RinP is a new form of infrastructure sharing that expands the traditional notion of roaming in cellular networks such that users may roam between operators with overlapping coverage areas based on load and performance conditions. Using simulation and small-scale experiments, we show that deploying RinP would allow operators to run their networks at higher utilization and provide users with higher availability and performance, while achieving 30–40% infrastructure savings in our typical evaluation scenarios. We present a design for RinP that can be incrementally deployed with modest changes to existing cellular infrastructure. We build a prototype RinP testbed, and show that our proposed design can be realized feasibly with modest changes to existing cellular infrastructure, requires no change to current protocol standards, and adds minimal latency overheads.

Query scheduling is a crucial task for analytical database systems that can greatly affect the query latency. However, existing scheduling approaches are based on heuristics and not optimal. A recent trial proposed to use reinforcement learning for automatically learning end-to-end scheduling policies. However, such trial was not capable of considering the database-specific characteristics (e.g., operator types, pipelining), and hence becomes not efficient for analytical database systems. In this paper, we try to fill this gap and introduce LSched (Learned Scheduler), a fully learned workload-aware query scheduler for in-memory analytical database systems. LSched provides an efficient inter-query and intra-query scheduling for dynamic analytical workloads (i.e., different queries can arrive/depart at any time). We integrated LSched with an efficient in-memory analytical database system, and evaluated it with TPCH, SSB, and JOB benchmarks. Our evaluation shows that LSched improves over the performance of existing state-of-the-art query schedulers and heuristic-based ones by at least 35% and 50% in both streaming and batching query workloads.