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The Open Radio Access Network (RAN) and its embodiment through the O-RAN Alliance specifications are poised to revolutionize the telecom ecosystem. O-RAN promotes virtualized RANs where disaggregated components are connected via open interfaces and optimized by intelligent controllers. The result is a new paradigm for the RAN design, deployment, and operations: O-RAN networks can be built with multi-vendor, interoperable components, and can be programmatically optimized through a centralized abstraction layer and data-driven closed-loop control. Therefore, understanding O-RAN, its architecture, its interfaces, and workflows is key for researchers and practitioners in the wireless community. In this article, we present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early research results. We provide a deep dive of the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges of O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, security, and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development. 

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frameworks are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, allowing it to run optimization and machine learning-based frame- works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network- based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con- ditions and traffic patterns. QCell is prototyped on Colosseum, the world’s largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user’s throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency. 

Recent years have seen the introduction of large- scale platforms for experimental wireless research. These platforms, which include testbeds like those of the PAWR program and emulators like Colosseum, allow researchers to prototype and test their solutions in a sound yet realistic wireless environment before actual deployment. Emulators, in particular, enable wire- less experiments that are not site-specific as those on real testbeds. Researchers can choose among different radio frequency (RF) scenarios for real-time emulation of a vast variety of different situations, with different numbers of users, RF bandwidth, antenna counts, hardware requirements, etc. Although very powerful, in that they can emulate virtually any real-world deployment, emulated scenarios are only as useful as how accurately they can capture the targeted wireless channel and environment. Achieving emulation accuracy is particularly challenging, especially for experiments at scale for which emulators require considerable amounts of computational resources. In this paper we propose a framework to create RF scenarios for emulators like Colosseum from rich forms of inputs, like those obtained by measurements through radio equipment or via software (e.g., ray-tracers and electromagnetic field solvers). Our framework optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We showcase our method by generating wireless scenarios for Colosseum by using Remcom’s Wireless InSite, a commercial-grade ray-tracer that produces key characteristics of the wireless channel. Examples are provided for line-of-sight and non-line-of-sight scenarios on portions of the Northeastern University main campus.