Mobile Robots (MRs), typically equipped with single-antenna radios, face many challenges in maintaining reliable connectivity established by multiple wireless access points (APs). These challenges include the absence of direct line-of-sight (LoS), ineffective beam searching due to the time-varying channel, and interference constraints. This paper presents REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized bandit model directly in real-world settings of a factory floor. REMARKABLE employs reconfigurable intelligent surfaces (RISs) with passive reflective elements to create beamforming toward target robots, eliminating the need for multiple APs. We develop a method to create a beamforming codebook, reducing the search space complexity. We also develop a reconfigurable rotational mechanism to expand RIS coverage by rotating its projection plane. To address non-stationary conditions, we adopt the bandit over bandit idea that employs adaptive restarts, allowing the system to forget outdated observations and safely relearn the optimal interference-constrained beam. We show that our approach achieves a dynamic regret and the violation bound of Õ(T3/4B1/4) where T is the total time, and B is the total variation budget which captures the total changes in the environment without even assuming the knowledge of B. Finally, experimental validation with custom-designed RIS hardware and mobile robots demonstrates 46.8% faster beam selection and 94.2% accuracy, outperforming classical methods across diverse mobility settings.

Small form-factor single antenna devices, typically deployed within wireless sensor networks, lack many benefits of multi-antenna receivers like leveraging spatial diversity to enhance signal reception reliability. In this paper, we introduce the theory of achieving spatial diversity in such single-antenna systems by using reconfigurable intelligent surfaces (RIS). Our approach, called ‘RIS-STAR’, proposes a method of proactively perturbing the wireless propagation environment multiple times within the symbol time (that is less than the channel coherence time) through reconfiguring an RIS. By leveraging the stationarity of the channel, RIS-STAR ensures that the only source of perturbation is due to the chosen and controllable RIS configuration. We first formulate the problem to find the set of RIS configurations that maximizes channel hardening, which is a measure of link reliability. Our solution is independent of the transceiver’s relative location with respect to the RIS and does not require channel estimation, alleviating two key implementation concerns. We then evaluate the performance of RIS-STAR using a custom-simulator and an experimental testbed composed of PCB-fabricated RIS. Specifically, we demonstrate how a SISO link can be enhanced to perform similar to a SIMO link attaining an 84.6% channel hardening improvement in presence of strong multipath and non-line-of-sight conditions.

This paper proposes and experimentally validates a new paradigm for computing with wireless signals over-the-air (OTA). It demonstrates the first fully connected (FC) neural network (NN) constructed entirely using channel propagation and signal interference principles. Our design is based on architecting the desired linear operation of an FC layer through the superposition of signals emitted from multiple transmitters and received at a single receiver, similar to multiple input single output (MISO) systems. Our design takes into account several practical considerations, such as the impact of multiple subcarriers, the number of transmit antennas, and the changing wireless channel. The key outcome of our work is developing a principled methodology that transforms a given trained digital FC NN into its OTA equivalent. This novel computational paradigm, which we call AirFC, allows us to run NN tasks without compute-specific hardware during tests. We validate our design using 9 time-synchronized software-defined radios (SDRs) available on the ORBIT testbed, emulating a 16 antenna array. We use the MNIST dataset as input to our wireless FC NN and demonstrate classification with 92.61% accuracy, which proves that our NN with OTA FC layers performs similar to the conventional, all-digital version with an accuracy decrease of only 0.73%.

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.

Ambient radio frequency (RF) energy harvesting (RF-EH) allows powering low-power electronic devices without wires, batteries, and dedicated energy sources. Current RF-EH circuit designs for ambient RF harvesting are optimized and fabricated for a predetermined frequency band. Thus, a single circuit is tuned for a given band with simple extensions to multiple circuits operating individually in distinct bands. Our approach is different in the sense that it designs and implements a common circuit design that can operate on multiple different RF cellular and ISM bands. This paper makes two contributions. First, it presents a study of ambient RF signal strength distribution conducted in Boston, MA, USA, indicating locations and associated RF bands that can point toward the practicality of ambient RF-EH. Second, it demonstrates an adjustable circuit for harvesting from LTE 700-MHz, GSM 850MHz, and ISM 900-MHz bands with one single circuit. Our circuit design is fabricated on printed circuit board with comprehensive evaluations at each associated frequency to test the power conversion efficiency (PCE). In addition, we characterize the charging performance, and feasibility of powering sensors outdoors such as TI eZ430-RF2500. Results reveal more than 45% PCE for our prototype.

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.