We propose and demonstrate a wireless communications system wherein multiple Unmanned Aerial Vehicles (UAVs) collaborate for Distributed Transmit Beamforming (DTBF) without feedback from the target ...receiver. This system can extend the collaborative downlink communications range without a single point of failure and allow the target to be agnostic to aspects of beamforming. However, it faces considerable challenges due to channel variations from the UAVs hovering and the need for timely and accurate synchronization. We devise an intra-network protocol using Gold codes for simultaneous channel sounding and fast frequency offset compensation among the UAVs. The received signal model is developed and compared to the case where receiver feedback is available. Statistical channel models for UAV hovering and oscillator stability are experimentally derived and used to evaluate the coherence time and beamforming performance at 915, 2550, and 5900 MHz. A prototype was implemented using software-defined radios and used to conduct the first demonstrations of DTBF without feedback in a mobile environment. Our experiments with two DJI M100 UAVs achieved convergence in 200 ms with beamforming gains over 90% of the theoretical maximum and within 10% of our modeling predictions, validating the proposed design.
Next generation network access technologies and Internet applications have increased the challenge of providing satisfactory quality of experience for users with traditional congestion control ...protocols. Efforts on optimizing the performance of TCP by modifying the core congestion control method depending on specific network architectures or apps do not generalize well under a wide range of network scenarios. This limitation arises from the rule-based design principle, where the performance is linked to a pre-decided mapping between the observed state of the network to the corresponding actions. Therefore, these protocols are unable to adapt their behavior in new environments or learn from experience for better performance. We address this problem by integrating a reinforcement-based Q-learning framework with TCP design in our approach called QTCP. QTCP enables senders to gradually learn the optimal congestion control policy in an on-line manner. QTCP does not need hard-coded rules, and can therefore generalize to a variety of different networking scenarios. Moreover, we develop a generalized Kanerva coding function approximation algorithm, which reduces the computation complexity of value functions and the searchable size of the state space. We show that QTCP outperforms the traditional rule-based TCP by providing 59.5 percent higher throughput while maintaining low transmission latency.
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.
This article presents a design and systems level implementation of a magnetic resonance-based wireless power transfer system with a novel metasurface layer. This layer shapes the magnetic field ...through it that results in "MetaResonance." This phenomenon is key in transforming an existing surface into an intelligent wireless charger for the following: 1) reconfigurable and on-demand energy shaping that can customizable energy hologram; and 2) beamforming to charge multiple devices. The advantages of MetaResonance over conventional methods such as inductive and magnetic resonance charging, distributed RF and magnetic beamforming, and energy hopping lie in its ability to provide high-power delivery with safety guarantees, high end-to-end efficiency, and customized power distribution profile in three dimensions over the surface. From a systems implementation viewpoint, we achieve this through a power distribution layer at the bottom and the MetaResonance cell array layer at top. We have simulated, fabricated, and built an experimental setup of the proposed MetaResonance wireless power transfer system. Performance results demonstrate the reconfigurability in the power and energy fields over the whole surface with fine granularity. Specifically, the magnetic field can be blocked within 2 cm with more than 95% efficiency while the power transfer efficiency can be improved up to 92.8% by beamforming. We have demonstrated various real-world charging applications concerning consumer electronics, industrial tools, battery packs, and medical device wireless charging.
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.
Syntax is usually studied in the realm of linguistics and refers to the arrangement of words in a sentence. Similarly, an image can be considered as a visual ‘sentence’, with the semantic parts of ...the image acting as ‘words’. While visual syntactic understanding occurs naturally to humans, it is interesting to explore whether deep neural networks (DNNs) are equipped with such reasoning. To that end, we alter the syntax of natural images (e.g. swapping the eye and nose of a face), referred to as ‘incorrect’ images, to investigate the sensitivity of DNNs to such syntactic anomaly. Through our experiments, we discover an intriguing property of DNNs where we observe that state-of-the-art convolutional neural networks, as well as vision transformers, fail to discriminate between syntactically correct and incorrect images, when trained on only correct ones. To counter this issue and enable visual syntactic understanding with DNNs, we propose a three-stage framework- (i) the ‘words’ (or the sub-features) in the image are detected, (ii) the detected words are sequentially masked and reconstructed using an autoencoder, (iii) the original and reconstructed parts are compared at each location to determine syntactic correctness. The reconstruction module is trained with BERT-like masked autoencoding for images, with the motivation to leverage language model inspired training to better capture the syntax. Note, our proposed approach is unsupervised in the sense that the incorrect images are only used during testing and the correct versus incorrect labels are never used for training. We perform experiments on CelebA, and AFHQ datasets and obtain classification accuracy of 92.10%, and 90.89%, respectively. Notably, the approach generalizes well to ImageNet samples which share common classes with CelebA and AFHQ without explicitly training on them.
The existing passive wake-up receivers (WuRxs) are radio frequency identification (RFID) tag based, which incur high cost and complexity. In this paper, we study cost-effective and long-range WuRx ...solutions for range-based wake-up (RW) as well as directed wake-up (DW). In particular, we consider the case of an RF energy harvesting wireless sensor node and investigate how a low-cost WuRx can be built using an RF energy harvester available at the node. Experimental results show that our developed prototype can achieve a wake-up range of 1.16 m with +13 dBm transmit power. Furthermore, our empirical study shows that at +30 dBm transmit power the wake-up distance of our developed RW module is >9 m. High accuracy of DW is demonstrated by sending a 5-bit ID from a transmitter at a bit rate up to 33.33 kbps. Finally, we present optimized WuRx designs for RW and DW using Agilent advanced design system, which offer up to 5.69 times higher wake-up range for RW and energy savings per bit of about 0.41 mJ and 21.40 nJ, respectively, at the transmitter and the sensor node in DW.
A new design for an energy harvesting device is proposed in this paper, which enables scavenging energy from radiofrequency (RF) electromagnetic waves. Compared to common alternative energy sources ...like solar and wind, RF harvesting has the least energy density. The existing state-of-the-art solutions are effective only over narrow frequency ranges, are limited in efficiency response, and require higher levels of input power. This paper has a twofold contribution. First, we propose a dual-stage energy harvesting circuit composed of a seven-stage and ten-stage design, the former being more receptive in the low input power regions, while the latter is more suitable for higher power range. Each stage here is a modified voltage multiplier, arranged in series and our design provides guidelines on component choice and precise selection of the crossover operational point for these two stages between the high (20 dBm) and low power (-20 dBm) extremities. Second, we fabricate our design on a printed circuit board to demonstrate how such a circuit can run a commercial Mica2 sensor mote, with accompanying simulations on both ideal and non-ideal conditions for identifying the upper bound on achievable efficiency. With a simple yet optimal dual-stage design, experiments and characterization plots reveal approximately 100% improvement over other existing designs in the power range of -20 to 7 dBm.
Unmanned aerial vehicle (UAV) mounted millimeter-wave (mmWave) base stations as well as aerial backhaul links will enable on-demand deployment of network resources. However, prior work has shown ...aerial links are prone to the frequent disruption caused by: 1) constant hovering due to GPS inaccuracies that impact narrow beamwidths; 2) blockages in the direct line of sight; and 3) suboptimal beam selection, especially if reduced angular sectors are searched in a highly dynamic environment. This article characterizes the impact of each of these phenomena for aerial mmWave links and proposes methods to distinctly identify when they occur in isolation or in combination during deployment. Furthermore, it also proposes corrective actions at the UAV, appropriate for the specific type(s) of impacting events: physical displacement from its earlier location, angular rotation around its vertical axis, or beamwidth adjustment. Our approach relies on exploiting the information contained in the angular domain of a large data set of experimentally collected beam-selection outcomes, under the above practical scenarios. We incorporate GPS accuracy models and antenna radiation patterns to create a robust model of potential outages. We then propose device-agnostic algorithms that jointly optimize UAVs' physical movement and the beamforming procedure. The experimental results obtained by mounting a pair of 60-GHz channel sounders on M600 DJI UAVs reveal loss reduction of up to 74.7%, translated into 260% physical layer bit-rate improvement compared to the classical 802.11ad standards-defined approach.
Cognitive radio ad hoc networks (CRAHNs) constitute a viable solution to solve the current problems of inefficiency in the spectrum allocation, and to deploy highly reconfigurable and self-organizing ...wireless networks. Cognitive radio (CR) devices are envisaged to utilize the spectrum in an opportunistic way by dynamically accessing different licensed portions of the spectrum. To this aim, most of the recent research has mainly focused on devising spectrum sensing and sharing algorithms at the link layer, so that CR devices can operate without interfering with the transmissions of other licensed users, also called primary users (PUs). However, it is also important to consider the impact of such schemes on the higher layers of the protocol stack, in order to provide efficient end-to-end data delivery. At present, routing and transport layer protocols constitute an important yet not deeply investigated area of research over CRAHNs. This paper provides three main contributions on the modeling and performance evaluation of end-to-end protocols (e.g. routing and transport layer protocols) for CRAHNs. First, we describe NS2-CRAHN, an extension of the NS-2 simulator, which is designed to support realistic simulation of CRAHNs. NS2-CRAHN contains an accurate yet flexible modeling of the activities of PUs and of the cognitive cycle implemented by each CR user. Second, we analyze the impact of CRAHNs characteristics over the route formation process, by considering different routing metrics and route discovery algorithms. Finally, we study TCP performance over CRAHNs, by considering the impact of three factors on different TCP variants: (i) spectrum sensing cycle, (ii) interference from PUs and (iii) channel heterogeneity. Simulation results highlight the differences of CRAHNs with traditional ad hoc networks and provide useful directions for the design of novel end-to-end protocols for CRAHNs.
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