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Deep reinforced traffic-aware CPU allocation in centralized RAN.

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This study introduces Deep Reinforced CPU Allocation (DRCA) for dynamic CPU scheduling in Baseband Units (BBUs) to enhance Radio Access Network (RAN) performance. DRCA improves packet processing throughput and radio resource utilization in cellular networks.

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Area of Science:

  • Computer Science
  • Telecommunications Engineering

Background:

  • Centralization of Radio Access Networks (RAN) and increasing Quality of Experience (QoE) demands from next-generation services escalate computational needs in Baseband Units (BBUs).
  • Efficient CPU resource utilization is critical for boosting RAN performance.

Purpose of the Study:

  • To propose and evaluate a Deep Reinforced CPU Allocation (DRCA) framework for dynamic CPU resource scheduling in BBUs.
  • To address limitations of fixed CPU scheduling in improving RAN performance.

Main Methods:

  • Developed the DRCA framework within a RAN intelligent controller platform.
  • Implemented three DRCA schemes: Traffic-Aware (TA-DRCA), User-Aware (UA-DRCA), and Radio Resource-Aware (RA-DRCA).
  • Utilized RAN throughput as feedback and incorporated network state indicators for dynamic scheduling.

Main Results:

  • The TA-DRCA scheme demonstrated a 30% increase in BBU packet processing throughput on simulated datasets.
  • Achieved up to 18% improved radio resource utilization with TA-DRCA compared to static CPU allocation.
  • Evaluated DRCA performance using an industry-grade simulator and an open-source dataset.

Conclusions:

  • The DRCA framework effectively enables dynamic CPU resource scheduling in BBUs.
  • DRCA significantly enhances BBU packet processing throughput and radio resource utilization.
  • The proposed DRCA framework shows strong potential for future cellular networks.