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Large language model (LLM) inference at datacenter scale is fundamentally bottlenecked by memory capacity and bandwidth limits. Because model weights are read-only and identical across instances, conventional architectures that replicate these parameters across the local DRAM of tens of thousands of accelerators incur massive capital expenditures, significant static power dissipation, and redundant memory-fetch energy. A recent proposal to use optical fiber loops as delay-line memory alleviates these issues, but its recirculating loop must hold the whole model: loop length grows with model size, slack is fixed when the ring is built, and the layouts that serve modern Mixture of Experts (MoE) models efficiently do not fit in it. We propose Optical Broadcast Memory (OBM), an open-ended hierarchical Tap-and-Amplify fiber architecture that generalizes and replaces the optical ring paradigm. A centralized memory server continuously broadcasts weights to distributed inference nodes that passively tap the data stream using receive-only co-packaged optics, delivering parameters directly to compute units without intermediate buffering. To overcome the limitations of continuous weight streaming, we use dynamically-sized slack to accommodate large and variable context lengths. We show how removing the restrictions of the optical ring allows OBM to efficiently serve MoE models. Evaluated at a datacenter scale of 80,000 accelerators, OBM eliminates petabytes of HBM while being competitive or better in throughput and energy per token than an HBM-based system.

This paper studies all-to-all broadcast scheduling for underwater swarms with collision-resilient physical layers that can decode asynchronously overlapping packets. Propagation-aware collision-free schedulers coordinate packet arrivals but do not account for this reception capability. We first design a data-driven neural reception model that captures per-interferer power and timing effects. The model improves prediction accuracy over SINR baselines while admitting an exact mixed-integer linear programming (MILP) formulation. The resulting neural-network-embedded MILP (NN-MILP) optimizes transmission times while explicitly encoding schedule-dependent overlaps and the predictor's inference, enabling globally optimal scheduling for small-scale instances. For larger networks, a two-stage ant colony optimization (ACO) heuristic combines low-cost transmission-order search with neural-aware timing refinement. Simulation results show that ACO shortens broadcast rounds compared with existing schedulers. It approaches the NN-MILP optimum on small-scale instances while supporting larger networks at substantially lower computational cost.

AI-RAN distributes network control across multiple autonomous agents, namely xApps and rApps operating over shared radio resources. When an alert or degradation appears, operators currently lack a structured way to trace which decision caused it, what context triggered that decision, and how other agents interacted. Existing explainability tools focus on single-model feature attribution or linear event logging, neither of which captures cross-agent decision chains. We propose the Context Graph, a heterogeneous directed graph that organizes policies, measurements, decisions, and alerts into a queryable explanation layer. Its foundational primitive is the Decision-Event node acting as a causal junction, where incoming edges capture triggering context and outgoing edges track downstream consequences that are progressively upgraded through counterfactual verification. Instantiated over existing E2, A1, and O1 data flows, our proof-of-concept demonstrates that compliance and diagnosis queries are resolved at sub-millisecond latency over graphs of tens of thousands of nodes, while reliably distinguishing true causal impacts from spurious correlations.

Low Earth orbit (LEO) satellites are expected to play a key role in sixth-generation (6G) non-terrestrial networks. The high mobility of LEO satellites necessitates frequent handovers (HOs), posing challenges to service continuity and availability for user equipment. Conventional methods based on handover margin (HOM) and time-to-trigger (TTT) have limited adaptability to time-varying channel conditions and do not explicitly account for the impact of feeder-link and ground station (GS) connectivity on HO interruption time. To address these limitations, this paper develops a topology-aware HO framework based on proximal policy optimization (PPO). The framework uses reference signal received power (RSRP), satellite--GS associations, remaining visibility time, and elevation angles to determine when to initiate an HO and which satellite to select. Simulation results demonstrate that the proposed policy achieves a service availability of 99.32% at a minimum elevation angle of 40 degrees, exceeding that of optimized HOM--TTT (O-HT) and least-delay (LD) satellite selection by 1.67 and 1.41 percentage points, respectively. The policy maintains service availability above 97.52% as the minimum elevation angle increases to 55 degrees, demonstrating its ability to sustain high service availability under stricter elevation constraints.

Federated Learning (FL) with Parameter-Efficient Fine-Tuning (PEFT) enables efficient adaptation of Large Language Models (LLMs), yet its energy and carbon costs across hardware remain poorly understood. We evaluated federated Low-Rank Adaptation (LoRA) fine-tuning on NVIDIA A40, RTX 6000 Ada, and A100 GPUs using a $2^2$ full factorial design with 135M/360M models and 10/100 local steps. CodeCarbon tracked energy and estimated CO$_2$-equivalent emissions. Descriptive allocation of variation assigned 84.8-91.2% of energy and emissions variation to local steps within these ranges. On A40 and RTX 6000 Ada, configurations with fewer steps achieved comparable accuracy with roughly 6-8-fold lower energy consumption and higher accuracy per kWh. These findings support smaller local budgets for energy-efficient federated fine-tuning within the evaluated setting.

IoT-Edge-Cloud infrastructures are increasingly heterogeneous, and experimenting across the continuum is difficult and risks disrupting live systems. Network Digital Twins (NDT) are a pragmatic answer, yet practitioners lack a structured account of three things: how to build an NDT from open tools, how to model wireless impairments, and how to understand whether the twin is faithful to the physical network, or not. This tutorial introduces NDT through the IRTF reference model and walks through a lightweight open-source blueprint built on Containerlab, Open vSwitch, \textit{Open Network Operating System (ONOS)}, and Prometheus/Grafana. It then explains how to define wireless impairment profiles (static, model-based, trace-driven, AI-generated, and live), and what each approach can and cannot reproduce. Finally, it presents a workflow to validate the twin against the physical network. A proof-of-concept Edge case study illustrates the procedure, including the pitfalls encountered and the lessons learned. Directions for future research are outlined.

The escalating complexity of deep neural networks hinders the deployment of federated learning (FL) on resource-constrained devices, especially for multimedia applications such as on-device image classification and speech recognition. Parallel split learning (PSL) has emerged as a promising solution by offloading a substantial portion of the computational workload to the server through layer-wise model partitioning, making it particularly suitable for resource-limited visual and speech intelligence scenarios. However, the absence of client-side aggregation renders PSL sus ceptible to severe training divergence and potential convergence failure under heterogeneous data distributions. To address this challenge, we propose PSL-RGHA, a gradient-aligned PSL framework that employs reference gradient half-space alignment mechanism to mitigate training divergence among client devices. PSL-RGHA comprises two novel components: reference gradient progressive construction (RGPC) and gradient half-space alignment (GHA). Specifically, RGPC evaluates the directional consistency of client gradients and progressively synthesizes a robust reference gradient via Weiszfeld updates, effectively approximating the global descent direction. Then, GHA constrains client gradients within the reference half-space defined by RGPC to mitigate gradient conflicts and rectify local updates. Extensive experiments demonstrate that our PSL-RGHA framework outperforms existing baselines in various settings.