Research

• SPARC: Multi-site Spectrum Management

  

  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 μApp 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%.

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• Windex: A 5G scheduler for scalable service guarantees

  

  We address the resource allocation challenges in NextG cellular radio access networks (RAN), where heterogeneous user applications demand guarantees on throughput and service regularity. We leverage the Whittle indexability property to decompose the resource allocation problem, enabling the independent computation of relative priorities for each user. By simply allocating resources in decreasing order of these indices, we transform the combinatorial complexity of resource allocation into a linear one. We propose Windex, a lightweight approach for training neural networks to compute Whittle indices, considering constraint violation, channel quality, and system load. Implemented on a real-time RAN intelligent controller (RIC), our approach enables resource allocation decision times of less than 20μs per user and efficiently allocates resources in each 1ms scheduling time slot. Evaluation across standardized 3GPP service classes demonstrates significant improvements in service guarantees compared to existing schedulers, validated through simulations and emulations with over-the-air channel traces on a 5G testbed.

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• EdgeRIC: Delivering Realtime RAN Intelligence

  

  Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we introduce EdgeRIC, a real-time RIC co-located with the Distributed Unit (DU). It is decoupled from the RAN stack, and operates at the RAN timescale. EdgeRIC serves as the seat of real-time AI-in-the-loop for decision and control. It can access RAN and application-level information to execute AI- optimized and other policies in real-time (sub-millisecond). We demonstrate that EdgeRIC operates as if embedded within the RAN stack. We showcase RT applications called μApps over EdgeRIC that significantly outperforms a cloud-based near real-time RIC (> 15 ms latency) in terms of attained system throughput. Further, our over-the-air experiments with AI-based policies showcase their resilience to channel dynamics. Remarkable, these AI policies outperform model-based strategies by 5% to 25% in both system throughput and end user application-level benchmarks across diverse mobile scenarios.

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