cuMAC Features

• Reference scheduling algorithms - Includes clean, well-documented CUDA implementations of:

  • Proportional-fairness (PF)-based UE selection algorithms.

  • PF-based UE/logical channel sorting per QoS type.

  • PF-based PRB allocation algorithms.

  • ILLA + OLLA based link adaptation.

  • Link adaptation based on deep reinforcement learning (DRL).

  • MU-MIMO user grouping/pairing for 64T64R.

  • DL beamforming weights computation (zero-forcing).

• Real-time performance - The CUDA implementations of cuMAC algorithms are designed to meet the real-time performance requirements for the L2 stack.

• Interfacing between L2 stack and GPU - cuMAC-CP serves as the real-time interface for offloading MAC scheduling functions to the GPU.

• Joint scheduling across multiple cells - The CUDA implementations are designed for the joint scheduling across multiple cells within a coordinated cell group.

• Modular design - Built as a library of modular scheduling algorithms that can be integrated into existing L2 stacks, simulators, or custom testbeds to offload individual MAC scheduling functions to the GPU.

• Benchmarking suite - Includes tools to profile, test, and compare the performance of CUDA algorithm implementations against CPU-based equivalents.

• Extensible framework - Provides a C++/CUDA framework and API for researchers and engineers to implement and test their own novel MAC scheduling algorithms on the GPU.