What is NeoRadium?
NeoRadium is a Python library designed to simplify the simulation of physical layer communication pipelines based on the latest 3GPP 5G NR standard. Its object-oriented architecture effectively abstracts the complexities inherent in different stages of the communication pipeline, enabling rapid development and execution of end-to-end simulations on any standard computer.
Wireless communication research often focuses on specific blocks or modules within the end-to-end communication pipeline, such as equalization. However, implementing an entire 3GPP-compliant pipeline solely for the purpose of testing a single block can be both time-consuming and cumbersome. This is where NeoRadium proves invaluable. It provides comprehensive end-to-end communication pipeline functionality based on 3GPP standards, empowering researchers to effortlessly customize, study, and evaluate the performance of their unique implementations. All these capabilities are delivered without requiring high-end hardware, complex setups, or costly GPUs. As long as your computer operates with Python 3.8+ and a basic configuration, you are ready to begin.
NeoRadium includes a comprehensive Playground, offering numerous examples for experimentation. These examples are presented as Jupyter Notebooks and provide detailed explanations of API functionalities and their application in practical contexts.
Key Features
NeoRadium offers a versatile suite of functionalities engineered to streamline 5G NR physical layer research and development. Below is a summary of the capabilities available in the current version. New features are continually being added to further expand its scope.
Channel Coding: Efficient transport block encoding and decoding utilizing Polar and LDPC coding algorithms, in accordance with TS 38.212 specifications.
Carriers and Bandwidth Parts: Precise timing calculations for Cyclic Prefix, OFDM symbols, slots, subframes, and frames.
Reference Signal Generation: Includes DMRS, PT-RS, and CSI-RS generation as specified in TS 38.211, TS 38.212, and TS 38.214.
Resource Grid Functionality: Encompasses resource mapping, OFDM modulation/demodulation, and precoding, aligned with TS 38.101, TS 38.104, and TS 38.211.
Channel Estimation, Noise Estimation, and Equalization.
Resource Grid Visualization: Provides valuable insights through visualized resource grid contents.
PDSCH Communication Pipeline: Simulates the complete PDSCH end-to-end communication pipeline, including modulation/demodulation, mapping/de-mapping, interleaving/de-interleaving, scrambling/descrambling, and transport block size calculations, as specified in TS 38.211 and TS 38.214.
Antenna Array Implementation and Simulation: Based on TR 38.901.
Antenna Field Analysis: Calculates antenna field power and directivity, offering 2D/3D visualization.
Channel Modeling: Applies CDL or TDL channel models to time-domain or frequency-domain signals.
DeepMIMO Integration (New): Enables the import of DeepMIMO ray-tracing scenarios for creating user trajectories.
Trajectory-based Channel Models (New): Facilitates the creation of spatially and temporally consistent sequences of channels based on user trajectories, for use in end-to-end communication pipelines.
Channel Datasets (New): Introduces new functionality to generate datasets of channel matrices based on CDL channel models or temporally and spatially consistent sequences of channel matrices derived from DeepMIMO trajectories.