MArena is an open-access underwater wireless testing platform based on the deployment of software-defined underwater modems at a semi-permanent marina location. Each reprogrammable and reconfigurable software-defined underwater modem is connected to a network switch and a mini-PC to enable easy remote access and edge computing capabilities. This setup provides the foundation for conducting experiments and evaluating various communication and networking protocols and algorithms. Additionally, through LTE and Ethernet connectivity, computational power can be further extended over the cloud server for real-time signal processing, where the underwater software-defined modems are synchronized at the symbol level. This paper introduces the architecture, capabilities, and system design choices of MArena while providing in-depth information regarding the software and hardware implementation of its various testbed components. Additionally, the article demonstrates the diverse functionalities of MArena, including its ability to serve as a testbed for synchronized distributed MIMO transmission schemes, real-time wireless underwater video streaming, and PHY/MAC layer optimization.
Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) - a compressed representation of the CFR - from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package.
Rural broadband is critical to industries and community services such as precision agriculture, renewable energy, and rural education. Yet 39% of rural US and over 4 billion people around the world still lack broadband access. Addressing the challenge requires rural-focused technology research and innovation that provide high-capacity, long-range connectivity to sparsely populated areas at an affordable cost. To this end, we design, implement, and deploy AraMIMO, the first-of-its-kind TV White Space (TVWS) many-antenna MIMO (mMIMO) living lab for rural wireless research, education, and innovation. As a key part of the ARA PAWR infrastructure, AraMIMO features the field-deployed multiuser MIMO (MU-MIMO) system, Skylark Faros V2, exploiting the TVWS spectrum to provide high-capacity connectivity over large geographical areas of radius up to 10 km. With rich APIs to monitor and control the behavior of production-grade mMIMO systems, AraMIMO integrates programmability with high performance, and, with its user-friendly experimentation workflow and remote access support, AraMIMO serves as an invaluable living lab for rural wireless and applications. Measurement studies have demonstrated great promise, for instance, having achieved a single-sector spectral efficiency of 16 b/s/Hz with a 6 MHz TVWS channel and 6 clients in real-world rural settings. AraMIMO also enables interesting wireless experiments such as characterizing the impact of multi-user beamforming on network capacity, as well as application experiments such as agriculture automation.
The availability of datasets has been instrumental to drive advances in several disciplines like computer vision, image processing, and natural language processing. However, in the context of mobile traffic, data is often not available because of diverse reasons including data sensitivity, legal considerations and business competition. The lack of dataset availability restrains the research advance at large. In this paper, we make a twofold contribution. On the one hand, we make available a large dataset of mobile traffic from multiple Base Stations (BSs). The key distinct feature of the dataset is in the nature of the data, which is based on real LTE traffic information decoded from control channel information at the millisecond level. On the other hand, we carry out an in-depth characterization of user traffic and study how widely adopted probability distributions for mobile traffic do apply at short-term scales. Our analysis shows that mobile data traffic exhibits self-similarity and the number of Radio Resource Control (RRC) connected users exhibits a bi-modal distribution. Overall, our contribution key to verify and reproduce research outcomes as well as driving advances of Artificial Intelligence (AI)/Machine Learning (ML) applied to mobile networks.
As communications service providers ponder ways to cater to the diverse traffic requirements of mobile applications that range from the classic telephony to modern augmented reality (AR)-related use cases, the traditional quality of service (QoS)-based radio resource management (RRM) techniques for RAN slicing that are agnostic to the intrinsic workings of applications can result in a poor quality of experience (QoE) for the end-user. We argue that in addition to QoS, RAN slicing strategies should also consider QoE for efficient resource utilization. However, without comprehensively understanding the interplay between QoS, QoE and how various RRM techniques can potentially influence them, it is impossible to incorporate QoE-driven feedback for resource allocation. Consequently, in this work, we conduct a first-of-its-kind in-depth experimental campaign on an O-RAN compliant 5G cellular testbed to evaluate the performance of the QoE metrics of three varied applications---video-enabled voice calling, cloud gaming, and AR---under various RAN slice configurations. We discuss the key findings of this elaborate study, and motivate the need for a QoE-aware RRM framework for RAN slicing.
The Federal Communications Commission (FCC) has allocated the 6 GHz band (5.925 - 7.125 GHz) for unlicensed, shared use in the US. Incumbents in the band are protected via Low Power Indoor (LPI) rules that do not require the use of an Automatic Frequency Control (AFC) mechanism and Standard Power (SP) rules which do. As the deployment of Wi-Fi 6E APs implementing LPI rules have been increasing, there is limited research examining the real-world interference potential of dense LPI deployments to fixed links, which remains a concern for incumbents. We have conducted a first-of-its-kind extensive measurement campaign of a dense indoor Wi-Fi 6E network at the University of Michigan, which includes walking, driving, and drone measurements to assess outdoor beacon Received Signal Strength Indicator (RSSI), building entry loss (BEL), channel utilization, and appropriate enabling signal level for a proposed client-to-client (C2C) mode in 6 GHz. Our detailed measurements under various conditions show median outdoor RSSI between -75 dBm and -85 dBm, BEL between 12 dB and 16 dB through double-pane low-emission windows, and only 5% of indoor Basic Service Set Identifiers (BSSIDs) observed outdoors. Our overall conclusion is that the probability of interference to incumbent fixed links is low, but more research is required to determine the appropriate signal level for the C2C enabling signal.
The adoption of Next-Generation cellular networks is rapidly increasing, together with their achievable throughput and their latency demands. Optimizing existing transport protocols for such networks is challenging, as the wireless channel becomes critical for performance and reliability studies. The performance assessment of transport protocols for wireless networks has mostly relied on simulation-based environments. While providing valuable insights, such studies are influenced by the simulator's specific settings. Employing more advanced and flexible methods for collecting and analyzing end-to-end transport layer datasets in realistic wireless environments is crucial to the design, implementation and evaluation of transport protocols that are effective when employed in real-world 5G networks. We present Hercules, a containerized 5G standalone framework that collects data employing the OpenAirInterface 5G protocol stack. We illustrate its potential with an initial transport layer and 5G stack measurement campaign on the Colosseum wireless network testbed. In addition, we present preliminary post-processing results from testing various TCP Congestion Control techniques over multiple wireless channels.
The D11 is Broadcom's proprietary IEEE 802.11 MAC implementation and an essential part of their WiFi chips. It is a microcontroller that orchestrates the Physical Layer and Radio Front-end implementation and is programmable through a custom microcode. We provide a new emulation framework for the D11 microcode and a corresponding firmware patch for the WiFi chip that enables essential debugging methods, including microcode breakpoints and D11 state extraction. This toolset allows researchers to analyze the D11 microcode in a controlled environment dynamically. To facilitate research on the D11, we provide an overview of state-of-the-art knowledge of the chip and publish all presented tools to the open-source community. We encourage everyone interested to enter the game, roll the D11, and provide new insights on Broadcom's MAC implementation.
Efficient spectrum management is crucial for the Citizen Broadband Radio Service (CBRS), promoting shared radio frequency spectrum use. The US Federal Communications Commission (FCC) designated the frequency range of 3550 MHz to 3700 MHz for three types of users: incumbent users, Priority Access License (PAL) users, and General Authorized Access (GAA) users. The Spectrum Access System (SAS) coordinates spectrum sharing among user tiers by using Environment Sensing Capabilities (ESC) sensors to detect incumbent users and prioritize their access while operating in uninformed incumbent detection mode. Machine Learning (ML) based techniques can be used for incumbent detection and enforce incumbent protection through SAS. Virginia Tech researchers have developed an open-source SAS for spectrum-sharing experimentation. In this work, we leverage the open-source SAS by enabling and testing Wireless Innovation Forum (WInnForum) standards (.i.e CBRS Base Station Device (CBSD) interface), implementing an ML-based incumbent radar detection mechanism using a feedforward neural network in an Software-defined Radio (SDR)-based experimental CBRS network deployed at CCI xG testbed and naming it "OpenSAS". If the presence of the incumbent is determined with at least a certainty of 85%, the incumbent protection is triggered. In 500 trials, the model achieves 95.83% validation accuracy in detecting the radar signal, and in Over the air (OTA) tests, it achieves 85.35% accuracy.
In the never-ending pursuit of bandwidth, mid-bands have been recently reconsidered as the primary spectrum for 5G NR and its evolution. Laying between the crowded lower frequency bands and the propagation-unfriendly higher bands (i.e., millimeter wave), the upper 6 GHz band (6425-7125 MHz) presents a valuable compromise between capacity and coverage. Recognizing its potential, the research community has expressed interest in this spectrum and conducted several studies. Despite this enthusiasm, the deployment of upper 6 GHz testbeds remains elusive. This paper aims to address this gap by presenting the results of a comprehensive measurement campaign conducted on a 5G NR cellular system operating in the upper 6 GHz band (6425-7125 MHz), specifically deployed within the Politecnico di Milano campus. Our objective was to evaluate the system's performance in a realistic environment and provide insights supported by empirical measurements. The measurement campaign yielded positive results, showcasing a remarkable channel capacity in urban areas, which remained consistently high even at the cell edge and in challenging non-line-of-sight and outdoor-to-indoor scenarios.
Communication over large-bandwidth millimeter wave (mmWave) spectrum bands can provide high data rate, through utilizing high-gain beamforming vectors (briefly, beams). Real-time tracking of such beams, which is needed for supporting mobile users, can be accomplished through developing machine learning (ML) models. While computer simulations were used to show the success of such ML models, experimental results are still limited. Consequently in this paper, we verify the effectiveness of mmWave beam tracking over the open-source COSMOS testbed. We particularly utilize a multi-armed bandit (MAB) scheme, which follows reinforcement learning (RL) approach. In our MAB-based beam tracking model, the beam selection is modeled as an action, while the reward of the algorithm is modeled through the link throughput. Experimental results, conducted over the 60-GHz COSMOS-based mobile platform, show that the MAB-based beam tracking learning model can achieve almost 92% throughput compared to the Genie-aided beams after a few learning samples.
Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry. In this paper, we present the first large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two Mobile Network Operators (MNOs) in Rome, Italy, to first, study network deployment and radio coverage aspects, and second, explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). Results show that 5G NSA can provide higher downlink throughput and slightly lower latency compared to Fourth Generation (4G) systems. However, performance is influenced by several factors, including propagation conditions, system configurations, and handover events, ultimately highlighting the need for further system optimization.
In this paper, we present SDR-LoRa, a full-fledged SDR implementation of a LoRa transmitter and receiver. First, we reverse-engineer the LoRa physical layer (PHY) functionalities, including the procedures of packet modulation, demodulation, and preamble detection. Based on this analysis, we develop the first Software Defined Radio (SDR) implementation of the LoRa PHY. Furthermore, we integrate LoRa with an Automatic Repeat Request (ARQ) error detection protocol. SDR-LoRa has been validated on (i) the Colosseum wireless channel emulator; and (ii) a real testbed with USRP radios and commercial-off-the-shelf (COTS) devices. Our experimental results demonstrate that the performance of SDR-LoRa is in line with commercial LoRa systems. We pledge to share the entirety of the SDR-LoRa code.
Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video at client's end requires significant computational power which is challenging especially for mobile devices. One of the ways to mitigate this is to offload the rendering at the edge cloud and stream the video and audio to the thin client. Remote edge-cloud rendering may increase the end-to-end delay of the system due to the added network and processing latency which is greater than local rendering system. We investigate network latency in edge-based remote rendering over NextG networks and identify the bottleneck deteriorating the application performance. Further, we delve into the current state of the art and challenges of performing rendering remotely at the edge cloud and study the associated problems that need to be addressed in order to realize remote augmented reality(AR)/virtual reality (VR). Our prototype implementation shows effectiveness of maintaining the application QoE by prioritizing data at the level of a sub-flow and reducing the motion-to-photon latency.
WiFi's fine time measurement (FTM) based ranging protocol has set the stage for mass adoption of location-aware applications and services in WiFi-pervading enterprise and consumer ecosystems. However, the lack of deployment of such commercial-scale localization solutions has motivated us to conduct a comprehensive experimental study that aims to verify whether WiFi's FTM is indeed ready for prime-time localization. With heterogeneity in operation (devices, environments, and spectrum) being the fundamental essence of commercial deployments, our study focuses on FTM's ability to deliver useable localization under such practical conditions. Being a first of its kind, our study reveals several interesting insights for practical operation of FTM, with the most critical of them being its inability to eliminate substantial offsets in estimated ranges between heterogeneous devices and configurations that degrade performance significantly (up to 20 m error). Albeit a negative result for FTM's readiness, we also propose a simple but promising remedy - an over-the-top auto-calibration solution that allows every WiFi device, when it enters an enterprise environment, to self-calibrate its offsets on-demand, thereby salvaging FTM to render it useful (median error of 2 m) for localization.
ARA is a first-of-its-kind wireless living lab for advanced wireless in rural regions. In ARA, users can reserve programmable wireless resources, for instance, Software Defined Radios (SDRs), and wireless spectrum to perform a wide range of of experiments. Given ARA-enabled open access to programmable wireless resources, it is important to enforce proper usage of the available spectrum, thereby ensuring no user (benign or malicious) creates any harmful interference to other experimenters or any incumbent. Therefore, we develop Wireless Guard (WG), a mechanism for wireless spectrum usage monitoring. For WG, we use two approaches for enforcing the spectrum policy: (i) reactive approach and (ii) proactive approach. In this paper, we present the hardware and software architectures of the ARA WG along with the end-to-end pipeline for managing experiments in case of deviation from the spectrum usage policy. Initial evaluations show the effectiveness of WG in enforcing spectrum usage policies in ARA.
Mobility management is the key feature of cellular networks. When integrating unmanned aerial vehicles (UAVs) into cellular networks, their cell association needs to be carefully managed for coexistence with other cellular users. UAVs move in three dimensions and may traverse several cells on their flight path, and so may be subject to several handovers. In order to enable research on mobility management with UAV users, this paper describes the design, implementation, and testing methodology for handover experiments with aerial users. We leverage software-defined radios (SDRs) and implement a series of tools for preparing the experiment in the laboratory and for taking it outdoors for field testing. We use solely commercial off-the-shelf hardware, open-source software, and an experimental license to enable reproducible and scalable experiments. Our initial outdoor results with two SDR base stations connected to an open-source software core network, implementing the 4G long-term evolution protocol, and one low altitude UAV user equipment demonstrate the handover process.
The rural US includes 72% of the nation's land and 46 million people, and it serves as major sources of food and energy for the nation. Thus rural prosperity is essential to US wellbeing. As a foundation for next-generation rural economy and communities, broadband connectivity is a key driver of rural prosperity. Yet 39% of the rural US lacks broadband access, and most agriculture (ag) farms are not connected at all. To address the rural broadband challenge, we will develop the ARA rural wireless living lab. ARA will not only serve as a first-of-its-kind, real-world wireless experimental infrastructure for smart and connected rural communities, it will also provide the living lab processes, activities, and organizations to engage the broad wireless and application communities in the research, education, innovation, and pilot of affordable, high-capacity rural broadband solutions. Through this visioning article, we illustrate the community, application, economic, and operational contexts of rural wireless, the design of ARA, ARA-enabled research, and how ARA is expected to make rural broadband as affordable as urban broadband today. This article is also a call-to-action for the broad wireless and application communities to participate in the ARA living lab activities and to join the ARA Consortium of public-private partners in shaping the future of advanced wireless, rural broadband, and rural communities in general.
Much like earlier "network softwarization" efforts, the Open RAN concept is poised to have a transformative impact on the manner in which radio access networks (RANs) are realized and operated. The inherent complexity of the RAN ecosystem and the fact that it is rapidly evolving makes Open RAN a rich area of research into use cases, system realization, security, and more. This same complexity, however, hampers research efforts. Specifically, there is a lack of end-to-end open source software and fully-developed use cases associated with the Open RAN ecosystem. Further, to truly advance the state of the art will require use cases to be explored in realistic wireless environments. This paper describes our efforts to address these shortcomings by realizing NexRAN, a top-to-bottom, open-source Open RAN use case in the POWDER mobile and wireless research platform. Specifically, NexRAN allows closed-loop control of a RAN slicing realization in an O-RAN ecosystem. RAN slicing is implemented in the srsRAN open source mobility stack and is exposed through a custom service model to the NexRAN xApp, which executes on a RAN intelligent controller (RIC) from the O-RAN Alliance. The NexRAN xApp realizes policy driven closed-loop control of RAN slices by reading the current state of RAN elements (using the O-RAN key performance measurements (KPM) service model) and controlling slice behavior via the custom slicing service model. We demonstrate and evaluate NexRAN in the POWDER platform and have open sourced all aspects of our realization to enable research into this domain.
Wireless Unmanned Aerial Vehicles (UAVs) were introduced in the world of 4th generation networks (4G) as cellular users, and have attracted the interest of the wireless community ever since. In~5G, UAVs operate also as flying Base Stations providing service to ground users. They can also implement independent off-the-grid UAV networks. In~6G networks, wireless UAVs will connect ground users to in-orbit wireless infrastructure. As the design and prototyping of wireless UAVs are on the rise, the time is ripe for introducing a more precise definition of what is a wireless UAV. In doing so, we revise the major design challenges in the prototyping of wireless UAVs for future 6G spectrum research. We then introduce a new wireless UAV prototype that addresses these challenges. The design of our wireless UAV prototype will be made public and freely available to other researchers.
