Communications

This MATLAB script demonstrates an approach to beamforming and interference suppression in scenarios with multiple users and multiple interferers. It constructs an N-element linear array, computes beamformer weights through a generalized eigen-decomposition of summed desired and interference correlation matrices, and then runs a Monte Carlo simulation to estimate the Signal-to-Interference-plus-Noise Ratio (SINR) for one of the users under random channel conditions.

The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.

To verify the tracking capability of a DP-IMDD system, a post-FEC experiment is performed by tuning on the KP4 FEC encoder and decoder on the line side. With random scrambling or with hand shaking, vibrating or twisting the fiber, there is no codeword error observed in 2 minutes and the reported KP4 FEC T-max did not exceed 4

 Here is a dataset for our paper  RED-Scenario: A Resource-Efficient Deployment Framework for Scenarios through Dependency Package Management

Dependency and Size Knowledge Graphs for 10979 Python packages with 597,049 versions, and 28,151 Node.js packages with 738,927 versions, each version containing size and dependency information.