Ubiquitous connectivity is essential for emerging edge-assisted machine-type communication (MTC) applications for net-worked robotics, intelligent transportation, smart surveillance, etc., which demand ultra-low latency, high data rate, reliability, and energy efficiency. These applications generate significant uplink traffic, often consisting of raw sensor data, where the utility of data is closely tied to its timeliness. The traffic patterns in these scenarios are event-driven, characterized by high variability and bursts, making them difficult to predict. While 5G networks provide Radio Resource Control (RRC) states-Idle, Inactive, and Connected-designed to manage latency and user equipment (UE) power consumption based on deterministic traffic patterns like video streaming, they may not be optimized for the unpredictable nature of MTC traffic. In this study, we evaluate the performance of legacy 5G systems in handling MTC traffic using the open-source 3GPP-compliant testbed, Open Air Interface (OAI). Our analysis reveals that current 5G UE connection setup mechanisms can adversely affect control latency and UE power consumption due to the irregular and event-driven nature of MTC traffic. To mitigate these issues, we propose an enhanced connection setup process that allows UEs to remain in idle mode during inactivity, thereby conserving power, and facilitating a rapid transition to connected mode with reduced signaling overhead and power consumption. We implement and validate this framework on the OAI testbed, demonstrating its compatibility with the 3GPP standard. Empirical results indicate that our proposed method can reduce UE control latency and power consumption by up to$4\times$and$3\times$respectively, compared to legacy 5G connection setup approaches.

Recent advancements in artificial intelligence (AI) and edge computing have accelerated the development of machine-centric applications (MCAs), such as smart surveillance systems. In these applications, video cameras and sensors offload inference tasks like license plate recognition and vehicle tracking to remote servers due to local computing and energy constraints. However, legacy network solutions, designed primarily for human-centric applications, struggle to reliably support these MCAs, which demand heterogeneous and fluctuating quality of service (QoS) (due to diverse application inference tasks), further challenged by dynamic wireless network conditions and limited spectrum resources. To tackle these challenges, we propose an Application Context-aware Cross-layer Optimization and Resource Design (ACCORD) framework. This innovative framework anticipates the evolving demands of MCAs in real time, quickly adapting to provide customized QoS and optimal performance, even for the most dynamic and unpredictable MCAs. This also leads to improved network resource management and spectrum utilization. ACCORD operates as a closed feedback-loop system between the application client and network and consists of two key components: (1) Building Application Context: It focuses on understanding the specific context of MCA requirements. Contextual factors include device capabilities, user behavior (e.g., mobility speed), and network channel conditions, and (2) Cross-layer Network Parameter Configuration: Utilizing a deep reinforcement learning (DRL) approach, this component leverages the contextual information to optimize network configuration parameters across various layers, including physical (PHY), medium access control (MAC), and radio link control (RLC), as well as the application layer, to meet the desired QoS requirement in realtime. Extensive evaluation with the 3GPP-compliant MATLAB 5G toolbox demonstrates the practicality and effectiveness of our proposed ACCORD framework.

The ubiquitous deployment of 4G/5G technology has made it a critical infrastructure for society that will facilitate the delivery and adoption of emerging applications and use cases (extended reality, automation, robotics, to name but a few). These new applications require high throughput and low latency in both uplink and downlink for optimal performance, while coexisting with traditional downlink-heavy consumer applications. Successfully supporting these new use cases hinges on the network being able to allocate resources as efficiently as possible. In this paper, we utilize a 3GPP-compliant 5G testbed to analyze the limitations of legacy network resource allocation methods, which are based on instantaneous channel measurements, and examine the effect on throughput – a key performance indicator. We then propose a framework that allows resource allocation decisions to leverage predictions of network quality (computed at the connected devices), and study two different prediction methods that provide different degrees of reliability. We further validate our framework with real-world cellular data and demonstrate that with accurate channel metric forecast knowledge, the mean network throughput can improve by a factor of $\sim 1.8$ over the baseline reactive approach based on best CQI policy, for the considered scenario.

Ubiquitous connectivity is vital for emerging applications like extended reality, factory automation, and robotics, necessitating low latency, high data rates, and reliability in both downlink and uplink. From the network protocol perspective, successfully supporting these new use cases hinges on the network being resilient enough to address the heterogeneous demand in dynamic channel conditions. To assess the performance of legacy 5G networks for these applications, we focus on the physical (PHY) layer and analyze the existing 5G time division duplexing (TDD) method in terms of throughput. Our preliminary experiments with 3rd Generation Partnership Protocol (3GPP) compliant Matlab 5G toolbox reveal limitations of the fixed configuration of the PHY frames, that are typically used by commercial 5G networks, hindering adaptability to heterogeneous demands and compromising quality of service (QoS). To overcome this, we propose a machine learning-enabled optimization framework facilitating proactive PHY frame reconfiguration based on realtime prediction of wireless channel metrics computed at User Equipment (UE). Implementation and validation of our approach on the 3GPP-compliant Open Air Interface (OAI) 5G testbed demonstrate the practicality of our solution and its adherence to 3GPP standards. Overall, our dynamic PHY frame configuration approach consistently meets overall traffic demands better than any fixed configuration across various scenarios, while also having the lowest percentage of un-transmitted bytes in each scenario.

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering$\sim$1 MB of sensor data per robot.

This paper adopts a systems approach to study how millimeter wave (mmWave) radio transmitters on UAVs provide high throughput links under typical hovering conditions. With Terragraph channel sounder units, we experimentally study the impact of signal fluctuations and sub-optimal beam selection on a testbed involving DJI M600 UAVs. From the hovering-related insights and the measured antenna radiation patterns, we develop and validate the first stochastic UAV-to-Ground mmWave channel model with UAVs as transmitters. Our UAV-centric analytical model complements the classical fading with additional losses expected in the mmWave channel during hovering, considering 3-D antenna configuration and beamforming training parameters. We specifically consider lateral displacement, roll, pitch, and yaw, whose magnitude vary depending on the availability of specialized hardware such as real-time kinematic GPS. We then leverage this model to mitigate the hovering impact on the UAV-to-Ground link by selecting a near-to-optimum pair of beams. Importantly, our work does not change the wireless standard nor require any cross-layer information, making it compatible with current mmWave devices. Results demonstrate that our channel model drops estimation error to$\approx$0.2 percent, i.e., 18x lower, and improves the average PHY bit-rate by$\approx$10 percent when compared to existing state-of-the-art channel models and beamforming methods for UAVs.

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world’s largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.

Emerging applications like distributed coordinated beamforming (DCB), intelligent reflector arrays, and networked robotic devices will transform wireless applications. However, for systems-centric work on these topics, the research community must first overcome the hurdle of implementing fine-grained, over-the-air timing synchronization, which is critical for any coordinated operation. To address this gap, this paper presents an open-source design and implementation of 'RFClock' that provides timing, frequency and phase synchronization for software defined radios (SDRs). It shows how RFClock can be used for a practical, 5-node DCB application without modifying existing physical/link layer protocols. By utilizing a leader-follower architecture, RFClock-leader allows follower clocks to synchronize with mean offset under 0.107Hz, and then corrects the time/phase alignment to be within a 5ns deviation. RFClock is designed to operate in generalized environments: as standalone unit, it generates a 10MHz/1PPS signal reference suitable for most commercial-off-the-shelf (COTS) SDRs today; it does not require custom protocol-specific headers or messaging; and it is robust to interference through a frequency-agile operation. Using RFClock for DCB, we verify significant increase in channel gain and low BER in a range of [0 -- 10--3] for different modulation schemes. We also demonstrate performance that is similar to a popular wired solution and significant improvement over a GPS-based solution, while delivering this functionality at a fractional price/power point.

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting `signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in `train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10-4for the evaluated cases.

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel®Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

Wireless RF energy transfer for indoor sensors is an emerging paradigm that ensures continuous operation without battery limitations. However, high power radiation within the ISM band interferes with the packet reception for existing WiFi devices. The paper proposes the first effort in merging the RF energy transfer functions within a standards compliant 802.11 protocol to realize practical and WiFi-friendly Energy Delivery (WiFED). The WiFED architecture is composed of a centralized controller that coordinates the actions of multiple distributed energy transmitters (ETs), and a number of deployed sensors that periodically request energy from the ETs. The paper first describes the specific 802.11 supported protocol features that can be exploited by sensors to request energy and for the ETs to participate in the energy delivery process. Second, it devises a controller-driven bipartite matching-based algorithmic solution that assigns the appropriate number of ETs to energy requesting sensors for an efficient energy transfer process. The proposed in-band and protocol supported coexistence in WiFED is validated via simulations and partly in a software defined radio testbed, showing 15% improvement in network lifetime and 31% reduction in the charging delay compared to the classical nearest distance-based charging schemes that do not anticipate future energy needs of the sensors and are not designed to co-exist with WiFi systems.

Smart homes will compose of multiple sensors that will sense, compute and transmit information to a central cloud, all of which are energy consuming tasks. We propose to demonstrate a software-defined solution for wirelessly charging these sensors using RF energy, thereby extending their lifetimes. In our demo, the actions of more than one energy transmitter (ET) are synchronized in phase and frequency in real time using periodic feedback from the target sensor, but without any common clock reference. The controller selects the optimal subset of ETs to satisfy the energy request from a given sensor, which cooperatively beamform RF energy towards that sensor. Our software-defined framework, implemented in Python, allows the central controller to automatically discover the installed sensors, obtain energy needs, and schedule charging tasks in an asynchronous and non-blocking manner that allows the network to scale. The demonstration includes advancements in design and fabrication of RF energy harvesting circuits that interface with the TI EZ430 sensors, implementation of a software-defined control and data plane, as well as a real-time distributed beamforming algorithm on USRP radios that results in a battery-free network of sensors.