cellular network engineers

Abstract: Emerging intelligent wireless networks increasingly rely on autonomous AI agents to adapt spectrum usage to dynamic and heterogeneous operating conditions. However, collective learning is challenging when agents experience different local interference environments and cannot exchange raw observations due to communication-overhead and privacy constraints. We propose Federated Agentic Spectrum Coordination with Interference Awareness (FASCIA), a federated multi-agent deep reinforcement learning framework for adaptive spectrum coordination. In FASCIA, each network manager acts as an autonomous AI agent that perceives its local interference environment, accumulates experience in a persistent local memory, and learns an adaptive spectrum coordination policy through local interactions. Coordination among agents is achieved through periodic federated aggregation of locally trained models, without exchanging raw sensing data or experience. Since spectrum decisions made by one agent alter the environments observed by others, local learning is inherently heterogeneous and coupled. FASCIA employs proximal regularization to mitigate the resulting model drift and explicitly accounts for inter-carrier interference arising from partially overlapping channels. FASCIA is the first federated agentic AI framework to address this challenging interference-coupled spectrum coordination setting. Extensive simulations demonstrate that FASCIA achieves performance close to the centralized benchmark while avoiding raw-experience exchange and maintaining effectiveness across heterogeneous network configurations.

Abstract: Inferring directional handover traffic from aggregate mobility observations remains challenging because population density evolution does not fully describe the movement of individual users across cell boundaries. This paper develops a kinetic framework for geometry-induced serving-region crossings in nonstationary cellular networks, connecting boundary-normal velocity statistics with fluid spatiotemporal stochastic geometry (F-STSG) for density-based transport reconstruction. The framework distinguishes signed net population transfer from gross bidirectional crossings and reveals two information limits: density evolution does not uniquely determine physical pairwise transport, while first-order transport alone cannot resolve counter-moving crossings. We establish exact recovery conditions for potential transport and develop coherent and Gaussian mobility closures for estimating directional crossing rates. Sharp moment-constrained bounds further characterize the remaining uncertainty under bounded normal velocities without assuming a specific velocity distribution. An error bound links directional estimation accuracy to transport reconstruction and unresolved mobility information. The formulation also recovers the classical stationary Poisson--Voronoi handover law. Numerical experiments examine the information limits, reconstruction sensitivity, and closure accuracy. On 47 held-out vehicular simulation windows, F-STSG achieves lower net-flow reconstruction errors than three baselines, while auxiliary velocity statistics improve directional estimates under matched Gaussian mobility.

Abstract: Open Radio Access Network (O-RAN) enables mobility-management xApps, but a learned controller is not necessarily the most effective or efficient handover policy. This paper compares standard Event A3, a resource- and mobility-aware heuristic, and a Double Deep Q-Network (DDQN) through a common xApp-oriented interface and paired system-level simulation. The heuristic combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II, while DDQN-Multi uses temporal radio and resource observations. All controllers are evaluated on identical UE trajectories, traffic assignments, and ray-traced radio inputs under Base, High-Mobility, and High-UE scenarios. Performance is assessed using service, handover, resource, energy, and fairness KPIs, complemented by CRITIC--TOPSIS ranking and complexity analysis. Results show that the heuristic achieves the lowest outage in all three scenarios and ranks first in the CRITIC--TOPSIS comparison, while retaining linear online complexity and interpretable decisions. DDQN-Multi improves target feasibility under congestion but requires offline training and produces more handovers and ping-pong events. The results indicate that lightweight resource-aware control can provide a favourable performance-complexity trade-off relative to DDQN under the considered scenarios.

Abstract: AI models, and time-series models in particular are typically ranked by accuracy and deployed under constraints absent from that ranking. In this paper, we study a real setting where these constraints are explicit and measurable: handover pre- diction in cellular networks, where inference runs on the User Equipment once per 40 ms measurement period and must complete before the network acts. The timing bound is hard: a late prediction is superseded by the measurement it was predicting, while energy is a cumulative budget spent for the life of the device. Neither appears in the objective these models are selected on. We evaluate eight architectures under a protocol that screens for deadline feasibility at the tail rather than the mean, then ranks the feasible models on measured inference energy, count- ing the feature computation that precedes the forward pass and is usually omitted. Gradient-boosted trees on handcrafted temporal features are the most accurate model (F1=0.974) and among the cheapest to run, while the neural architecture an accuracy-only benchmark would rank first (CNN-1D, F1=0.914) costs over 3 times more energy per inference under a realistic duty-cycled deployment pattern. We give three conditions under which this outcome should be expected, so the finding applies beyond telecommunications. Accuracy-only benchmarking does not merely under-report deployment cost. It can rank first a model that cannot be deployed.

Abstract: We study the SNR, aperture, and port count needed to meet a target outage in a single-user downlink with a fixed transmit antenna and a single-RF fluid antenna receiver. For known spatially correlated Rayleigh channels and ideal port selection, a sampling-free residual certificate controls reduced-rank outage error over a continuous SNR interval. Positive Bessel features support large arrays, and bounded importance sampling converts the certificate into simultaneous design intervals. At 1 bit/s/Hz and outage $10^{-5}$, a four-wavelength, ten-port receiver requires 4.40--4.55 dB. Reducing its port count from 256 to ten costs 0.70--1.25 dB under a joint 95\% probability statement. At 3.5 wavelengths, ten ports require 4.90--5.05 dB. Integer-port screening across 3--8 dB shows how aperture and port requirements change with available SNR; an effective-SNR transformation extends these decisions to other rates. The reported intervals include model and sampling uncertainty under checked Gaussian covariance inputs. A constructive slow-growth rank bound and a 16--1024-port study support compact evaluation as port count grows.

Abstract: Recent 3GPP releases have extended 5G New Radio (NR) Sidelink (SL) to support device-to-device (D2D) relay and multi-hop capabilities. This paper presents SL-RFSIM, a component-based experimentation framework extending OpenAirInterface (OAI) with scalable multi-hop NR SL capabilities. SL-RFSIM replaces the legacy OAI RF simulator with a broker-based publish/subscribe architecture enabling arbitrary peer-to-peer connectivity while preserving compatibility with the OAI protocol stack. The framework further integrates pluggable mobility, propagation, reception, and monitoring services, and supports Layer-2 mesh networking through BATMAN-adv. Experimental evaluation on the SLICES-RI research infrastructure validates the proposed architecture through representative mesh networking scenarios and identifies the current software bottlenecks limiting scalability. SL-RFSIM provides an open-source foundation for reproducible experimental research on 5G NR SL and future multi-hop cellular mesh networks.

Abstract: Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconstructing the channel, the UE transmits a learned \emph{semantic embedding} optimized end-to-end for beam selection at the gNB. Comparing reconstruction-oriented feedback (CsiNet) against task-aware semantic feedback across two input domains and three observation scenarios, we show that a semantic embedding of just $d=8$ real values from only 43 NR CSI-RS pilots in the angular-delay domain achieves the highest beam prediction accuracy, outperforming every method with access to the full 512-subcarrier channel. The key insight is that beam-relevant information is intrinsically low-dimensional: the semantic encoder learns to discard reconstruction-irrelevant structure and retain only a compact representation that is relevant to beam selection, realizing the core principle of semantic communication: transmit the intent, not the signal.

Abstract: Distributed applications (dApps) bring AI to the microsecond-to-millisecond band beside the 5G distributed unit (DU), but every public dApp framework realizes them the same way: an external process that receives an indication and returns a control message. That boundary is right for sensing and advisory workloads. It cannot express a neural receiver that must finish inside a slot, and it cannot hold a scheduling decision the MAC is waiting on. This paper asks what a dApp interface must deliver, in latency and bandwidth, for the AI-RAN use cases now filed under that name to be realized as dApps at all. It treats the coupling between a dApp and the DU as a design axis with two legitimate ends, a C ABI inside the DU process and a protocol-standard E3AP association over SCTP, and places the three OCUDU dApp classes on it. An audited corpus of 39 runtime AI-RAN use cases, sized by 5G NR timing, shows that more than half cannot cross the observer boundary: inline PHY work because an indication has no return path into the same slot, and bounded control because of the tail under load. Measuring the mechanisms each framework actually uses, on a quiet host and with a live cell on the air, shows that every carrier meets a 100 us control deadline on an idle host and that only the in-process paths still do once the DU is running. The asynchronous use cases remain feasible at every position, so the protocol-standard end is kept as a first-class option; what in-process placement adds for them is accountable staleness and an observation-to-decision path with no message on it. These measurements derive the released ABI, a stream for inline work, a validated call for bounded control, and a choice of lease, supervised ring, or portable E3 client for observation, and four dApps of all three classes are validated together on one over-the-air cell.