Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy across various domains, often surpassing human performance. However, this accuracy necessitates significant computational and storage overhead, complicating their deployment on edge devices. To address these challenges, research has increasingly focused on optimizing hardware design constraints, including power efficiency, silicon area, and system scalability.

In recent years, the radio access network (RAN) domain has seen significant changes with increased virtualization and softwarization, driven by the Open RAN (O-RAN) movement. However, the fundamental building block of the cellular network, i.e., the base station, remains unchanged and ill-equipped to handle this architectural evolution. In particular, there exists a general lack of programmability and composability along with a protocol stack that grapples with the intricacies of the 3GPP and O-RAN specifications. Recognizing the need for an “O-RAN-native” approach to base station design, this paper introduces HexRAN– a novel base station architecture characterized by key features relating to RAN disaggregation and composability, 3GPP and O-RAN protocol integration and programmability, robust controller interactions, and customizable RAN slicing. Furthermore, the paper also includes a concrete systems-level prototype and comprehensive experimental evaluation of HexRAN on an over-the-air testbed. The results demonstrate that HexRAN uses only 8% more computing resources compared to the baseline, while managing twice the user plane traffic, delivering control plane processing latency of under 120μs, and achieving 100% processing reliability. This underscores the scalability and performance advantages of the proposed architecture.

With the open radio access network (O-RAN) movement driving the evolution of cellular networks towards 6G, real time (RT) RAN control and assurance has emerged as the next frontier in RAN programmability, with in-base station (BS) machine learning (ML) at its core. However, scalability demands associated with production-grade networks necessitate the need for a robust, yet lightweight in-BS ML operations framework to support automated ML workflows. To that end, this paper introduces TinyRIC-ML, a novel lightweight and high-performance ML platform for RT operations within the RAN. Key highlights include a comprehensive system architecture design, a concrete systems-level implementation, and a preliminary over-the-air experimental evaluation to demonstrate the system's performance and feasibility.

The increasing complexity of cellular networks has resulted in dynamic network performance optimization (NPO) playing a critical role in streamlining network operations. While the success of NPO techniques primarily depends upon the quality and quantity of telemetry data available from the underlying network, up until now, third-party access to such data has been largely limited due to the prevalence of proprietary interfaces throughout the access network. However, the upcoming open radio access network (RAN) architecture is set to change this trend. While a significant first step, the open RAN architecture lacks a mechanism for the generation, collection, and exposure of RAN-level statistics in a programmable manner, thereby limiting the effectiveness of dynamic NPO. To that end, this paper introduces RANSight, a system for programmable telemetry within next-generation open RANs. Key highlights include the establishment of novel primitives for programmability within RAN telemetry, a detailed component-level description of the RANSight architecture, and the design and implementation of RANSight in support of a RAN slice monitoring use case. Furthermore, the paper also includes a comprehensive experimental evaluation on an over-the-air testbed which demonstrates that not only does RANSight fulfill its stated objectives of programmable telemetry, it also significantly improves system scalability by providing a staggering 65% reduction in compute resource utilization compared to systems without RANSight.

The increasing popularity of programmable wireless networks has led to efforts by both academia as well as industry to redesign access networks based on the disaggregated Open Radio Access Network (O-RAN) concept. However, the absence of a development platform for prototyping O-RAN use cases and the perceived complexity of the O-RAN specification have led to a stagnation of systems research efforts in this domain with a disproportionate focus on monolithic RAN architectures. With a view to overcoming these challenges, this paper introduces the ProSLICE platform, a full-scale realization of the complete O-RAN specification based on purely open source components along with several enhancements and extensions for fine-grained network control and ease of use. Key contributions include a fully disaggregated O-RAN-compliant RAN, a custom O-RAN service model in support of network slicing, statistics and configuration applications for the O-RAN RAN Intelligent Controller (RIC), and a comprehensive use case-based performance evaluation.

While Open RAN harbors the potential to revolutionize cellular access networks, the absence of an open platform for use case prototyping and a near-exclusive focus on 5G SA have tempered its commercial appeal. With a view to addressing these limitations, this paper introduces a disaggregated O-RAN platform with a novel multi-RAT RAN slicing framework across LTE, 5G NSA, and 5G SA. A preliminary performance evaluation showcases promising results addressing key features related to end-user service quality and use case-specific adaptability.