SPARC: Spatio-Temporal Adaptive Resource Control for Multi-site Spectrum Management in NextG Cellular Networks

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a xApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks.

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Publication Details

Journal
Proceedings of the ACM on Networking
Published
2024-11-25
DOI
https://doi.org/10.1145/3696405
Citations
1
Primary Topic
Cognitive Radio Networks and Spectrum Sensing
Type
article
Field-Weighted Citation Impact
0.35
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article

SPARC: Spatio-Temporal Adaptive Resource Control for Multi-site Spectrum Management in NextG Cellular Networks

Srinivas Shakkottai, Nathan Stephenson, Dinesh Bharadia, Ushasi Ghosh et al.
1 citations
Proceedings of the ACM on Networking
Cognitive Radio Networks and Spectrum Sensing
0.35
article

SPARC: Spatio-Temporal Adaptive Resource Control for Multi-site Spectrum Management in NextG Cellular Networks

Srinivas Shakkottai, Nathan Stephenson, Dinesh Bharadia, Ushasi Ghosh, Vijay K. Shah, Azuka Chiejina
article en
1 citations

Abstract

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a xApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks.

Proceedings of the ACM on NetworkingVol. 2(CoNEXT4)
North Carolina State University (US), George Mason University (US), UC San Diego Health System (US), North Central State College (US), Texas A&M University (US)
Decent work and economic growth
Openalex Percentile: Top 36%
Cognitive Radio Networks and Spectrum Sensing
0.35
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SPARC: Spatio-Temporal Adaptive Resource Control for Multi-site Spectrum Management in NextG Cellular Networks — Srinivas Shakkottai, Nathan Stephenson, et al. · Proceedings of the ACM on Networking (2024) | TGRS Research Map | TGRS