With its wide applicability across a variety of enterprise verticals, the private wireless (PW) industry is growing at a rapid pace. However, existing hierarchical cellular network architectures are sub-optimal for most PW use cases due to their high computational overhead, lack of compatibility with on-premises enterprise applications, and complex management structure. To that end, this paper espouses the notion of RAN-Core Convergence for the PW user plane as an alternative to the traditional RAN-Core hierarchy through the introduction of LEVEL, a new converged approach to cellular network design. With a specific emphasis on the user plane, key highlights include a unified system architecture and protocol stack design, new primitives for interacting with the cellular control plane, and a programmable approach to QoS management. The paper also includes a high-performance systems-level prototype of the LEVEL User Plane (LEVEL-UP), deployed on an over-the-air experimental testbed. The resulting performance evaluation showcases that LEVEL-UP seamlessly handles traffic in excess of$\mathrm{\rm{20}~Gbps}$, while achieving a$60\%$reduction in compute utilization and$30\%$reduction in energy consumption when compared to state-of-the-art solutions. Furthermore, latency benchmarks reveal a delay reduction of at least$70\%$. Finally, in typical high mobility environments, LEVEL-UP helps cut bufferbloat and reduces delays from seconds to milliseconds, thereby serving as an ideal companion for the burgeoning PW industry.

Network slicing plays a crucial role in realizing 5G/6G advances, enabling diverse Service Level Agreement (SLA) requirements related to latency, throughput, and reliability. Since network slices are deployed end-to-end (E2E), across multiple domains including access, transport, and core networks, it is essential to efficiently decompose an E2E SLA into domain-level targets, so that each domain can provision adequate resources for the slice. However, decomposing SLAs is highly challenging due to the heterogeneity of domains, dynamic network conditions, and the fact that the SLA orchestrator is oblivious to the domain's resource optimization. In this work, we propose Odin, a Bayesian Optimization-based solution that leverages each domain's online feedback for provably-efficient SLA decomposition. Through theoretical analyses and rigorous evaluations, we demonstrate that Odin's E2E orchestrator can achieve up to 45% performance improvement in SLA satisfaction when compared with baseline solutions whilst reducing overall resource costs even in the presence of noisy feedback from the individual domains.

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.

Network slicing has been widely recognized as one of the flagship use cases for Open Radio Access Network (O-RAN), enabling the provisioning of isolated network services over a shared physical infrastructure. Each slice is characterized by a set of distinct service level agreements (SLAs) tailored to meet the needs of various industries and applications. At the same time, industry-critical applications often require strict adherence to the SLA even in the worst-case scenarios. However, existing network slicing strategies merely incorporate SLA violations as penalties within the reward function, thus failing to consistently ensure perpetual SLA compliance. To address these challenges, this paper introduces PSASlicing, an intelligent resource allocation system designed for RAN slice management across the access network. More specifically, PSASlicing introduces a new reinforcement learning algorithm for maximizing resource utilization while perpetually guaranteeing the diverse SLA requirements across slices. Furthermore, PSASlicing also incorporates a trace-driven network emulator that effectively replicates the dynamic behavior of cellular networks by integrating a transition model with real-world data from an over-the-air 5G Standalone testbed. A comprehensive experimental evaluation showcases that PSASlicing achieves an average resource savings of approximately 24.0% when compared to the state-of-the-art, while guaranteeing no SLA violations.

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 emergence of latency-critical use cases for cellular networks has necessitated the need for real-time (RT) network performance optimization. While solutions such as the O-RAN architecture provide primitives for non-RT and near-RT control, the notion of RT control is still missing. To that end, in this paper, we introduce TinyRIC, a first-of-its-kind RT control platform for O-RAN base stations. Key highlights include a comprehensive system architecture design, a systems-level implementation in support of flexible user scheduling, and a preliminary over-the-air experimental evaluation to demonstrate the system's performance and feasibility.

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.

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario.

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber–physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling. The pervasiveness of cyber–physical systems of this kind necessitates a robust constellation design, characterized by optimized coverage and consistent connectivity. Thus, optimal constellation design is of great importance to system architects. However, the constellation design frameworks prevalent today do not scale well beyond a few dozen satellites, adversely impacting the system’s operational abilities. To this end, the objective of this article is two fold. The primary objective is the development of a modular and highly customizable large-scale constellation design framework, with the secondary objective involving the use of the proposed framework for designing robust constellations for IoST, serving a wide variety of use cases ranging from global coverage to region-specific coverage scenarios. More specifically, the framework presented herein has been developed to optimize constellation design based on CubeSat density, as well as a rigorous mathematical characterization of coverage and connectivity parameters. Furthermore, an extensive set of performance comparisons with existing state-of-the-art constellations has been presented to validate the efficacy of the IoST constellations designed using the proposed framework. Finally, the impact of design parameter variations on the developed constellations has also been examined in great detail.

The traditional monolithic on-premises model of application deployment is fast being replaced by a cloud-based microservices paradigm, driven in part by the rise of numerous cloud infrastructure providers providing seamless access to a variety of computing hardware, and the need for applications to serve an ever-increasing audience necessitating scalability. While container-based virtualization has been the preferred method of microservice deployment, Cloud Consumers have not had much opportunity for cost and resource optimization thus far. To this end, this paper introduces a resource allocation framework for the containerized deployment of microservices, called Adaptive Containerization for Microservices in Distributed Cloud Systems, which helps reduce operating costs while ensuring a minimum guaranteed level of service. Further, a variety of performance evaluation metrics have been provided to reinforce the validity of the proposed framework.

The increasing popularity of CubeSats has given rise to the possibility of ubiquitous cyber-physical systems serving a wide variety of applications that range from monitoring and reconnaissance to in-space backhauling. Expectedly, such systems are characterized by the need for a robust data routing framework that is purpose-built for resource-constrained CubeSats. To this end, a software-defined networking (SDN) based segment routing (SR) framework for CubeSats has been introduced in this paper. More specifically, a robust analytical characterization of the SR problem has been presented, along with an online algorithm for near-optimal route computation characterized by a provable performance bound. Furthermore, a comprehensive performance evaluation has also been provided, with the results obtained being benchmarked against classical SDN systems. The results demonstrate that the proposed framework not only ensures a higher level of demand satisfaction but also results in a significant reduction in control traffic, along with load balancing.