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Dan Kilper

Publications and source records attributed to Dan Kilper.

10 recordsLinked to original sources
arXiv2026

Energy-Latency Trade-offs in O-RAN with Distributed Baseband Processing and AI Inference

The Open Radio Access Network (O-RAN) architecture introduces flexible functional splits and open interfaces that enable distributed and centralized deployment of baseband processing. While this flexibility offers opportunities for improved resource utilization, it also introduces fundamental trade-offs between energy efficiency and latency. In this paper, we develop a throughput-based end-to-end energy consumption model for O-RAN and extend it by incorporating detailed latency modeling and application-specific Artificial Intelligence/Machine Learning inference costs. The proposed end-to-end modeling framework provides a general representation of processing, transport, and inference-related energy and delay across the access, metro, and long-haul network segments. Building on this general model, we formulate an optimization problem that selects the placement of baseband processing and AI inference tasks across candidate O-RAN configurations to analyze energy-latency tradeoffs under network load, server frequency, and energy-budget constraints. Using representative hardware platforms and realistic traffic assumptions, we evaluate multiple baseband processing placements corresponding to different O-RAN functional configurations. Our results reveal how user quality of service requirements and network load conditions jointly determine the optimal placement of baseband processing and AI inference tasks, highlighting the inherent trade-off between energy efficiency and latency. The analysis provides practical insights for latency-aware and energy-efficient O-RAN deployments supporting emerging AI-driven services.

cs.NI↗
arXiv2026

Spectrum Configuration Framework for Throughput Maximization in Open Systems with Roll-Off-Based QoT Optimization

We propose a spectrum-configuration framework for open and disaggregated optical systems that maximizes throughput while guaranteeing the quality of transmission (QoT) margins. The framework jointly optimizes transceiver parameters, including modulation format, symbol rate, pulse-shaping roll-off factor, and wavelength-selective switch (WSS) bandwidth, under fixed spectral allocation constraints. The impact of roll-off factor optimization is first experimentally evaluated in the presence of cascaded WSS filtering, demonstrating measurable QoT gains for both single- and multi-channel transmission. Building on these observations, a knapsack-based optimization is applied in the context of Optical Spectrum as a Service (OSaaS) to select service configurations that maximize aggregate throughput within a fixed spectrum width and limited transceiver resources. Experimental validation on a metro-scale open testbed confirms the effectiveness of the proposed approach in achieving efficient spectrum utilization and adaptive throughput-margin trade-offs.

cs.NI↗
arXiv2025

Design and Analysis of Power Consumption Models for Open-RAN Architectures

The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based on commercial hardware and considering the full end-to-end data path from the radio unit to the data center. We focus on recent fanout limitations and early baseband processing requirements related to current implementations of O-RAN and assess the power consumption impact when baseband processing is employed at different centralization points in the network. Additionally, we explore how greater fanout and sharing deeper into the network impact the balance of processing and transmission. Low processing fanout restrictions motivate greater centralization of the processing. At the same time, allowing for more open radio units per open distributed unit will quickly increase the transmission capacity requirements and related energy use.

cs.NI↗
arXiv2025

Heterogeneous Transmission of Analog Radio and Digital Coherent Signals Over Multi-Span Metro and PON for Bandwidth-Efficient Fronthaul in mmWave Centralized RAN Networks [Invited]

We experimentally investigate the transparent coexistence of heterogeneous Analog Radio-over-Fiber (ARoF) and Digital Coherent Optical (DCO) signals in a converged metro/PON network. Our streamlined setup employs RF generation via optical heterodyning, so that both carrier and modulated signals can be generated centrally and transmitted to the antenna site, across a metro network and Passive Optical Network (PON). The experiment includes the transmission of 8.8 Gbps mmWave signals and 400 Gbps coherent optical signals within a 68.75 GHz ROADM channel bandwidth and a 1:32 to 1:128 split PON. We also analyze the impact of varying ROADM channel bandwidth, PON split ratios, metro network distance and number of ROADMs traversed, on the performance of DCO and ARoF signals. The results reveal that the error vector magnitude (EVM) of the ARoF signal is significantly influenced by the allocated bandwidth, the number of ROADMs, and the overall network loss, providing insights into optimizations necessary to achieve target EVM levels.

physics.optics↗
arXiv2025

Transfer Learning for EDFA Gain Modeling: A Semi-Supervised Approach Using Internal Amplifier Features

The gain spectrum of an Erbium-Doped Fiber Amplifier (EDFA) has a complex dependence on channel loading, pump power, and operating mode, making accurate modeling difficult to achieve. Machine Learning (ML) based modeling methods can achieve high accuracy, but they require comprehensive data collection. We present a novel ML-based Semi-Supervised, Self-Normalizing Neural Network (SS-NN) framework to model the wavelength dependent gain of EDFAs using minimal data, which achieve a Mean Absolute Error (MAE) of 0.07/0.08 dB for booster/pre-amplifier gain prediction. We further perform Transfer Learning (TL) using a single additional measurement per target-gain setting to transfer this model among 22 EDFAs in Open Ireland and COSMOS testbeds, which achieves a MAE of less than 0.19 dB even when operated across different amplifier types. We show that the SS-NN model achieves high accuracy for gain spectrum prediction with minimal data requirement when compared with current benchmark methods.

cs.NI↗