The next generation of 6G networks aims to utilize ultra-wideband spectrum and massive antenna arrays to serve multiple users with both control and data channels at low latency and high efficiency. However, phased arrays at mmWave and mid-bands are fundamentally constrained to a single beam or suffer sharp beamforming loss when split across directions, limiting simultaneous control-data support. In FlexLink, we introduce and prototype a novel delay-phased array architecture that overcomes this limitation by redistributing energy jointly across frequency and space, enabling multiple narrow beams without sacrificing per-beam gain or requiring additional power. We design and prototype FlexLink on a custom 4–7 GHz hardware testbed, demonstrating for the first time that control and data beams can be decoupled in practice, achieving nearly double spectral efficiency compared to conventional phased arrays.

We introduce SIGAR, a Sensor Integration Gateway using Augmented Reality, which combines RFID-based passive sensing with AR for real-time visualization. Using batteryless, wireless RFID sensors, SIGAR eliminates the need for power sources, enabling sustainable and cost-effective monitoring. A mobile app automatically detects sensors within the camera's field of view and overlays realtime sensory data onto the physical environment. Demonstrated through applications like force, soil moisture and light sensing, SIGAR provides intuitive, context-aware insights for environmental monitoring, inventory management, and more. This fusion of AR and passive sensing bridges digital and physical worlds, offering scalable, low-power IoT solutions.

The advancement of self-driving technology is driven by the need for robust and efficient perception systems along with frameworks for End-to-End testing, enabled by the CARLA simulator. We introduce C-Shenron, a novel integration of a realistic radar sensor model within CARLA, enabling researchers to develop and test navigation algorithms using radar data. It is the first realistic radar simulator which utilizes LiDAR and camera sensors to generate high-fidelity radar ADC measurements from physics based modeling of the environment. Utilizing this radar sensor and showcasing its capabilities in simulation, we demonstrate improved performance in end-to-end driving scenarios. Our setup aims to rekindle the interest in radar-based self-driving research and promote the development of algorithms that leverages its strengths.

We demonstrate mmSubarray, a novel millimeter-wave sub-band phased array system designed to combat interference and maximize spectrum utilization in mmWave networks. Traditional approaches, such as frequency separation and beam nulling, often lead to spectrum under-utilization or coverage gaps. mmSubarray overcomes these limitations by splitting the spectrum into overlapping and non-overlapping sub-bands, assigning non-overlapping bands to interference-prone directions while using overlapping bands to support other directions. It dynamically allocates sub-bands across multiple beams and employs interference nulling to suppress interference below the noise floor. We built a prototype of mmSubarray using commercially available phased arrays and software-defined radio (SDR). Our experimental results show that the system significantly enhances network efficiency, ensuring robust and reliable communication links without sacrificing spectrum or coverage.

Practitioners face substantial challenges in building multi-modal platforms that are essential for autonomous systems' safe decision-making. Those complications, including synchronization, calibration, and tedious sensor validation, hinder user adoption for real-world applications. We present CMS, a Cooperative Multi-modal Sensing Platform. CMS provides one consistent interface, integrating LiDAR, camera, RaDAR, and GNSS/IMU, streamlines these processes and makes the intricacies transparent to users and applications. Our demonstration shows that CMS can obtain high-quality multi-modal sensor data, paving the way toward real-world prototypes of cooperative autonomous systems.

The advancement of self-driving technology is driven by the need for robust perception and navigation systems. Simulators for autonomous driving facilitate the rapid development and testing of navigation algorithms; however, a key issue for most is their inaccurate modeling of the radar sensor. This is a significant drawback as radars offer robust sensing capabilities in adverse weather conditions and occlusions. CARLA, a widely adopted open-source simulator, provides a simplistic radar model that fails to capture the complex physical and material-dependent behavior of real-world radar. To address these limitations, we present CShenron, a radar simulation framework integrated into CARLA, which generates realistic radar measurements by fusing LiDAR and camera data. C-Shenron also supports configurable radar parameters, multiple sensor placements, and scalable dataset generation. Our evaluations demonstrate that radar-camera fusion models, trained with C-Shenron’s generated data, achieve performance equivalent to traditional LiDAR-camera baselines on key metrics from the CARLA leaderboard.

Internet-of-things (IoT) devices (e.g., micro camera and microphone) are usually small form factor, low-cost, and low-power, which makes them easy to conceal and deploy in the indoor environment to spy on people for human private information such as location and indoor activities. As a result, these IoT devices introduce a great privacy and ethical threat. Therefore, it is important to reveal these concealed IoT devices in the indoor environment for human privacy protection. This paper presents RFScan, a system that can passively detect, fingerprint, and localize diverse concealed IoT devices in the indoor environment by sensing their unintentional electromagnetic emanations. However, sensing these emanations is challenging due to the weak emanation strength and the interference from the ambient wireless communication signals. To this end, we boost the emanation strength through the non-coherent averaging based on the emanation signal's characteristics and design a novel suppression algorithm to mitigate interference from the wireless communication signals. We further profile emanations across frequency and time that act as the emanation source's unique signature and customize a deep neural network architecture to fingerprint the emanation sources. Furthermore, we can localize the emanation source with an angle-of-arrival (AoA) based triangulation approach. Our experimental results demonstrate the efficiency of the IoT devices' detection, fingerprinting, and localization across different indoor environments.

Sensing allows us to interact with and quantify the natural world. Despite the advancements in sensor versatility, sensing systems still suffer from limited adoption due to their dependence on batteries, complex interfaces, energy-harvesting modules, and readout latency. To address these challenges, we present ZenseTag --- a miniaturized, sticker-like platform that can interface commercial sensors directly with COTS RFID tags. ZenseTag exploits the impedance response of COTS sensors to the measured stimulus at Radio Frequencies, tuned to the UHF RFID band. It combines reliable hardware realization of differential analog sensing with robust software for accurate, low-latency sensor readouts, even in the presence of multipath effects.

This work presents Windex, a novel light weight whittle index network-driven realtime scheduler for scalable service guarantees in NextG cellular networks. Windex addresses the resource allocation challenge in NextG cellular radio access networks (RAN), where resources must be shared among diverse user applications, each requiring guarantees on throughput and service regularity, taking into account service guarantees, channel quality, and system load. Implemented in a real time intelligent controller (RIC), and evaluating across standardized 3GPP service classes, we demonstrate the least service violations compared to state-of-the-art systems using over-the-air channel traces on a 5G testbed.

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a μApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%.

The 5G network promised transformative services across various industries, yet its integration has mostly been limited to existing 4G services like IMS-based multimedia and IoT. This paper identifies two key reasons for this underutilization: first, the stringent, multi-dimensional requirements of next-generation verticals like AR/VR, mobile gaming, and robotics, and the limitations of traditional Quality of Service (QoS) approaches in meeting these needs. We argue for a shift towards Quality of Experience (QoE), which better captures user perception and instantaneous application state. To meet stringent QoE demands and optimize network utilization, we emphasize the importance of application state and context awareness within the network. As a solution, we propose AppNet, a novel framework that integrates application awareness into the networking stack via RAN Intelligent Controllers (RICs) of the Open-RAN platform, enabling dynamic QoS adjustments based on application context. This paper highlights 5G private networks as an ideal testing ground, focusing on multi-user scenarios to deliver optimal real-time interactive services at scale.

With 6.5 billion smartphones in use worldwide, each relying on a battery for key subsystems like display, compute, and cellular connectivity, previous studies on power consumption often used invalidated indirect estimates that failed to isolate specific hardware usage. We address this by utilizing Google's On Device Power Rails Monitor (ODPM) tool for precise power measurements of individual components. Our findings indicate that connectivity (Wi-Fi, 4G/5G) and screen display are the primary power consumers, as shown with the Google Pixel 7A. We also confirmed similar power consumption trends using an energy estimation method on the Samsung S23+. Given the prevalence of smartphones, we discuss the challenges and opportunities for optimizing power usage.

We demonstrate FlexLink, a novel frequency-dependent multi-beamforming system designed to optimize spectral efficiency in millimeter-wave networks. Traditional phased arrays offer limited support for simultaneous control and data signals, leading to inefficiencies. FlexLink addresses this by using a delay-phase antenna array to produce high-gain beams for both control and data, nearly doubling spectral efficiency for data signals. Experiments demonstrate FlexLink's ability to utilize the entire frequency spectrum effectively, by radiating different frequencies corresponding to control and data signals to different directions concurrently.

The rapid expansion of wireless technologies demands effective management of complex RAN for MIMO in 5G systems. Current architectures and RAN Intelligent Controllers (RICs) lack critical real-time data processing and physical layer functionalities, hindering MIMO advancements. We introduce BeamArmor5G, an open-source MIMO App development framework that addresses these gaps with comprehensive PHY layer capabilities, enabling detailed wireless channel measurement streaming from RAN to the controller. BeamArmor5G is implemented with srsRAN to empower open-source community researchers to advance beamforming, localization, and interference management, significantly enhancing network performance and enabling new 5G applications.

The adoption of MIMO technology in wireless networks enhances spectral efficiency and enables novel functionalities such as wireless sensing and localization. These functionalities can be enabled by Open-RAN architecture to provide high computation, memory, and data-driven inference through a RAN Intelligent Controller (RIC). However, existing RICs focus mainly on higher network layers and lack essential PHY layer functionalities for MIMO. We present MIMO-RIC, an open-source RAN intelligent controller tailored for MIMO applications. We enable the streaming of extensive 3D wireless channel measurements across antennas, subcarriers, and time, from RAN to MIMO-RIC to develop various MIMO apps like beamforming and localization. We implemented MIMO-RIC on the srsRAN open-source platform using ZeroMQ messaging system for efficient, low-latency communication. Our over-the-air experiment setup consisting of USRP radios and commercial user equipment demonstrates effective jammer monitoring, nulling, and user localization applications.

This demo showcases the mm-O-RAN testbed, integrating mmWave arrays with USRP and the OpenAirInterface (OAI) 5G stack. Our system integrates the mmWave array with synchronized Time Division Duplex (TDD) switching and fast beamforming capabilities, using GPIO triggers from the OAI stack. As the first full integration of a mmWave testbed with an OAI 5G stack, mm-O-RAN addresses key challenges in design and implementation, providing a versatile platform for research and experimentation for AI and signal processing algorithms to optimize end-end network performance.

The smart buildings of the future need to understand the movement and occupancy of the people in the environment. Using cameras to provide this context can be privacy-invasive. Alternatively, installing dedicated hardware to sense the environment can be cost-prohibitive and limit ubiquitous adoption. WiFi-based sensing has hence been championed to provide this building-scale sensing, as it allows for both privacy and is ubiquitously deployed in most buildings. However, industry-translatable research in this space has been challenging as no building-scale systems can provide WiFi sensing data. Consequently, many real-world challenges of deploying these sensing systems remain a mystery. To overcome this veil of mystery, we develop and open-source WiSenseHub, a building-scale WiFi-sensing system. We build our system on commercially available WiFi radios, deploy our backend services to collect data on infinitely scalable AWS cloud or a local server desktop, and build a front-end phone-based interface to collect diverse WiFi sensing data. We deployed multiple WiFi radios in our building and collected data for user devices for over 38 hours.

The proliferation of transmissions in the RF spectrum demands robust and responsive signal analysis techniques to detect and label malicious activity. Traditional methods struggle to balance sensitivity and accuracy in real-time. This paper introduces Nomad, a modular system that combines global spectral pattern recognition with localized energy detection and classic signal processing methods for efficient and accurate RF analysis. Nomad's innovative architecture facilitates rapid development and deployment, while its intuitive visualizations provide actionable insights into even weak signals within complex RF environments.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

Interest in autonomous navigation and exploration for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use camera and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. Visual (LiDAR/camera-based) SLAM systems deploy compute and memory-intensive search algorithms to detect 'Loop Closures' to combat this drift, making the trajectory estimate globally consistent. Instead, WAIS (WiFi Assisted Indoor SLAM) demonstrates using WiFi-based sensing can reduce this resource intensiveness drastically. By covering over 1500 m in realistic indoor environments and WiFi deployments, we showcase 4.3× and 4× reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems. Incorporating WiFi into the sensor stack improves the resiliency of the Visual-SLAM system. We find the 90th percentile translation errors improve by ~ 40% and orientation errors by ~ 60% compared with purely camera-based systems. Additionally, we open-source a toolbox, WiROS, to furnish online and compute efficient WiFi measurements.

The accurate location of objects and people is central to providing contextual information for various AR/VR applications. We developed XRLoc [2], a compact localization module, sized less than 1 m, which can be integrated with television screens, soundbars or independently deployed in rooms to provide accurate locations of these assets. In this demo, we showcase the capability of XRLoc to localize and track UWB tags with cm-level accuracy in realistic room-level scenarios. We will additionally compare the location accuracy of XRLoc with visual-based HTC Vive trackers.

Sensors enable us to digitally capture stimuli like moisture, light, and force. Despite their low cost, reliability, and scalability, the lack of widespread adoption of IoT has hindered the realization of true ubiquitous sensing. A likely reason is that the current sensor platforms are bulky due to the batteries and complex electronics needed to interface sensors communication systems. In this work, we present a fully-passive, miniaturized, flexible form factor sensor interface titled ZenseTag that uses minimal electronics to read and communicate analog sensor data, directly at radio frequencies (RF). We exploit the fundamental principle of resonance, where a sensor's terminal impedance becomes most sensitive to the measured stimulus at its resonant frequency. This enables ZenseTag to read out the sensor variation using only energy harvested from wireless signals. We demonstrate its implementation with a 15x10mm flexible PCB that connects sensors to a printed antenna and passive RFID ICs, enabling near real-time readout through a performant GUI-enabled software.

Millimeter-wave (mmWave) technology is pivotal for next-generation wireless networks, enabling high-data-rate and low-latency applications such as autonomous vehicles and XR streaming. However, maintaining directional mmWave links in dynamic mobile environments is challenging due to mobility-induced disruptions and blockage. While effective, the current 5G NR beam training methods incur significant overhead and scalability issues in multi-user scenarios. To address this, we introduce CommRad, a sensing-driven solution incorporating a radar sensor at the base station to track mobile users and maintain directional beams even under blockages. While radar provides high-resolution object tracking, it suffers from a fundamental challenge of lack of context, i.e., it cannot discern which objects in the environment represent active users, reflectors, or blockers. To obtain this contextual awareness, CommRad unites wireless sensing capabilities of bi-static radio communication with the mono-static radar sensor, allowing radios to provide initial context to radar sensors. Subsequently, the radar aids in user tracking and sustains mobile links even in obstructed scenarios, resulting in robust and high-throughput directional connections for all mobile users at all times. We evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed integrated with a radar sensor in various indoor and outdoor scenarios, demonstrating a 2.5x improvement in median throughput and an 8x improvement in 20th percentile throughput compared to a non-collaborative baseline.

Mobile devices continuously beacon Bluetooth Low Energy (BLE) advertisement packets. This has created the threat of attackers identifying and tracking a device by sniffing its BLE signals. To mitigate this threat, MAC address randomization has been deployed at the link-layer in most BLE transmitters. However, attackers can bypass MAC address randomization using lower-level physical-layer fingerprints resulting from manufacturing imperfections of radios. In this work, we demonstrate a practical and effective method of obfuscating physical-layer hardware imperfection fingerprints. Through theoretical analysis, simulations, and field evaluations, we design and evaluate our approach to hardware imperfection obfuscation. By analyzing data from thousands of BLE devices, we demonstrate obfuscation significantly reduces the accuracy of identifying a target device. This makes an attack impractical, even if a target is continuously observed for 24 hours. Furthermore, we demonstrate the practicality of this defense by implementing it by making firmware changes to commodity BLE chipsets.

In this article, we study reconfigurable intelligent surface (RIS)-assisted networks to support Internet of Things (IoT) devices. We propose RIS beamforming strategies to maximize sum rate and fairness and analyze the RIS location. We derive a theoretical lower bound of the minimum number of RIS elements needed to guarantee specific network performance metrics and validate our results via simulations. We present two RIS scenarios in this study, both with the same total number of RIS elements: 1) centralized RIS, where a single RIS assists the network and 2) distributed RIS, where each transmitter has its own dedicated RIS. We study addressing two practical challenges related to RIS elements and channel state information (CSI) assumptions. First, we consider hardware limitations by assuming that each RIS element is equipped with a discrete phase shifter (PS). Second, we investigate the impact of CSI perfectness and availability in the network; therefore, we evaluate the performance of the RIS-assisted network under two scenarios: 1) centralized RIS with imperfect global CSI and 2) distributed RIS, where imperfect local CSI is available at each transmitter.

Modern mmWave systems have limited scalability due to inflexibility in performing frequency multiplexing. All the frequency components in the signal are beamformed to one direction via pencil beams and cannot be streamed to other user directions. We present a new flexible mmWave system called mmFlexible that enables flexible directional frequency multiplexing, where different frequency components of the mmWave signal are beamformed in multiple arbitrary directions with the same pencil beam. Our system makes two key contributions: (1) We propose a novel mmWave front-end architecture called a delay-phased array that uses a variable delay and variable phase element to create the desired frequency-direction response. (2) We propose a novel algorithm called FSDA (Frequency-space to delay-antenna) to estimate delay and phase values for the real-time operation of the delay-phased array. Through evaluations with mmWave channel traces, we show that mmFlexible provides a 60-150% reduction in worst-case latency compared to baselines1.

RF signals can be leveraged for many sensing and monitoring tasks in industrial, home, or robot applications. Despite the advantages of leveraging WiFi sensing modality, no versatile WiFi sensors are available. We develop WiROS to address this immediate need. We leverage the robot operating system (ROS) framework to expose real-time WiFi sensing information to an end-user. Specifically, we demonstrate a plug-and-play toolbox providing access to coarse-grained WiFi signal strength (RSSI), fine-grained WiFi channel state information (CSI), and other MAC-layer information (device address, packet id’s or frequency-channel information). Additionally, we opensource state-of-art algorithms to calibrate and process WiFi measurements to intuitively visualize/debug measurements and measure signal path parameters like the signal’s angles of arrival or departure.

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally Massive MIMO requires a digital beamforming architecture that needs a separate RF chain per antenna, so the power consumption scales with number of antennas. Instead, GreenMO creates a new Massive MIMO architecture with just a single physically laid RF chain, shared by all the antennas and introduces for the first time, the concept of virtualizing the RF chain hardware. That is, GreenMO creates an optimal number of virtual RF chains to serve a given number of spatial streams, depending on channel conditions and network load. Due to efficient, softwarized control over the number of virtual RF chains, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3× more power-efficient than traditional Massive MIMO and 4× more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 spatial streams, and can save upto 50% power in modern 5G NR base stations.

Millimeter-wave (mmWave) communications are crucial for unlocking the potential of future communication systems. Phased arrays play a vital role in generating directional beams, mitigating the high path-loss associated with these frequencies. Consequently, implementing beamforming within the radio is necessary to serve users in various directions. Applications such as AR/VR and vehicular communications demand rapid beam direction changes, typically within microseconds, to maintain stable connections when the user is mobile. In this demonstration, we showcase a mmWave setup capable of switching beams at microsecond intervals, along with a compact and portable user equipment setup for conducting experiments with high user mobility.

Spectrum sensing systems provide real-time feedback essential for spectrum sharing. However, the growth of spectrum sharing is limited by the capabilities of these spectrum sensors. Sharing a new frequency band is only possible if sensors can detect activity in that band with sufficient time granularity and signal fidelity to meet spectrum sharing policy requirements. In this work, we introduce Crescendo, a system design that shows we can achieve wideband, real-time, high-fidelity spectrum sensing using sweeping spectrum sensors. We first provide an analysis that demonstrates there are operating points of sweeping sensors that can sense multiple popular protocols. Then we demonstrate these sensors can be built in practice with an adaptive gain super-heterodyne RF frontend with high-fidelity LO generation, and evaluate a prototype built with COTS components. In our benchmarks, Crescendo outperforms prior wideband spectrum sensors, achieving a 30 dB increase in dynamic range and 10 dB increase in SNR.

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas that allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves 2.4 cm median accuracy and 5.3 cm 90th percentile accuracy in dynamic scenarios, performing at least 8× better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over 10 tags at update rates of 100 Hz, with a localization latency of ~1 ms. We have additionally open-sourced our system's codebase at https://github.com/ucsdwcsng/xrloc.git

NextG cellular networks must support diverse applications, such as interactive media streaming or robot control that have strict requirements on throughput, latency and reliability. These requirements must be met via optimizing wireless resources by utilizing application layer information, such as media streaming stall counts or robot pose estimates, along with network information, such as channel qualities and backlogs.

FMCW radars are integral to automotive driving for robust and weather-resistant sensing of surrounding objects. However, these radars are vulnerable to spoofing attacks that can cause sensor malfunction and potentially lead to accidents. Previous attempts at spoofing FMCW radars using an attacker device have not been very effective due to the need for synchronization between the attacker and the victim. We present a novel spoofing mechanism called mmSpoof that does not require synchronization and is resilient to various security features and countermeasures of the victim radar. Our spoofing mechanism uses a "reflect array" based attacker device that reflects the radar signal with appropriate modulation to spoof the victim’s radar. We provide insights and mechanisms to flexibly spoof any distance and velocity on the victim’s radar using a unique frequency shift at the mmSpoof’s reflect array. We design a novel algorithm to estimate this frequency shift without assuming prior information about the victim’s radar. We show the effectiveness of our spoofing using a compact and mobile setup with commercial-off-the-shelf components in realistic automotive driving scenarios with commercial radars.

Millimeter wave (mmWave) sensing has recently gained attention for its robustness in challenging environments. When visual sensors such as cameras fail to perform, mmWave radars can be used to provide reliable performance. However, the poor scattering performance and lack of texture in millimeter waves can make it difficult for radars to identify objects in some situations precisely. In this paper, we take insight from camera fiducials which are very easily identifiable by a camera, and present R-fiducial tags, which smartly augment the current infrastructure to enable myriad applications with mmwave radars. R-fiducial acts as fiducials for mmwave sensing, similar to camera fiducials, and can be reliably identified by a mmwave radar. We identify a set of requirements for millimeter wave fiducials and show how R-fiducial meets them all. R-fiducial uses a novel spread-spectrum modulation technique to provide low latency with high reliability. Our evaluations show that R-fiducial can be reliably detected with a 100% detection rate up to 25 meters with a 120-degree field of view and a few milliseconds of latency. We also conduct experiments and case studies in adverse and low visibility conditions to demonstrate the potential of R-fiducial in a variety of applications.

Digital and analog beamforming are well-known methods to suppress interference using multiple-antenna structures, but they have practical limitations: (i) Digital beamforming requires multiple analog-to-digital converters (ADCs) to enable digital conversion, which increases the cost and complexity; (ii) Although analog beamforming does not require expensive ADCs, it uses phase shifters, which cause quantization errors, insertion losses, and reduced power efficiency. In this paper, we consider a$K$-user uplink interference channel and propose a low-complexity algorithmic interference-suppression solution relying on simple switch-based reconfigurable antennas at the receivers. We utilize switches to enable/disable antennas to maximize each user’s signal-to-interference-plus-noise ratio (SINR). We present an optimization approach to approximate the optimal solution. To evaluate the results, we compare our method with relevant benchmarks. Moreover, we derive a lower bound on the minimum number of antenna elements per receiver to attain the desired SINR and verify the findings via simulations.

With its ability to communicate long distances, LoRa promises city-scale IoT deployments for smart city applications. This long-range, however, also increases contention as many thousands of devices are connected. Recently, CSMA has been proposed as a viable MAC for resolving contention in LoRa networks. In this paper, supported by measurements, we demonstrate that CSMA is ineffective in urban deployments. While gateways stationed at rooftops enjoy a long communication range, 70% of the devices placed at street level fail to sense each others' transmissions and remain hidden, owing to obstructions by tall structures. We present Busy Signal Multiple Access (BSMA), where the LoRa gateway transmits a downlink busy signal while receiving an uplink transmission. The IoT devices defer uplink transmissions while a busy signal is present. Practically viable BSMA requires a full-duplex LoRa gateway - i.e., a gateway that can simultaneously receive and transmit in the same channel. We develop the first full Duplex LoRa gateway in the 915 MHz ISM band, overcoming challenges that arise from a 9× greater delay spread and the need for 1000× greater self-interference cancellation. Our implementation works with COTS LoRa devices and improves network capacity by 100% compared to CSMA in bursty loads while being fair to all IoT devices near and far.

Interoperability among a vast number of heterogeneous IoT nodes is a key issue. However, the communication among IoT nodes does not fully interoperate to date. The underlying reason is the lack of a lightweight and unified network architecture for IoT nodes having different radio technologies. In this paper, we design and implement TinyNet, a lightweight, modular, and unified network architecture for representative low-power radio technologies including 802.15.4, BLE, and LoRa. The modular architecture of TinyNet allows us to simplify the creation of new protocols by selecting specific modules in TinyNet. We implement TinyNet on realistic IoT nodes including TI CC2650 and Heltec IoT LoRa nodes. We perform extensive evaluations. Results show that TinyNet (1) allows interoperability at or above the network layer; (2) allows code reuse for multi-protocol co-existence and simplifies new protocols design by module composition; (3) has a small code size and memory footprint.

Wide-scale adoption of VR/AR technologies in gaming, video conferencing, and for other remote telepresence applications demands limb tracking for a more immersive experience. In an attempt to bolster limb tracking, we present UWBTrac, a UWB + IMU based fusion tracker for VR applications. In this demo, and accompanying video1, we showcase this UWB tracker in comparison with HTC Vive VR trackers.

RAN Intelligent Control (RIC) has developed in parallel with Open Radio Access Networks (O-RAN) as a means of utilizing newly available interfaces. Focus has been largely on non-realtime (non-RT: > 1 sec) dealing with RAN management and offline training, and near-realtime (near-RT: 10 ms to 1 sec) dealing with UE load balancing and RAN configuration. We contend that the true power of RIC can be unleashed only with realtime (RT: < 100 μs) measurement, optimization, and control of RAN resources, corresponding to the cellular transmission time interval (TTI: 125 μs to 1 ms).

Software Defined Radios (SDRs) combine a universal radio frontend with flexible processing. The radio frontend can be tuned to capture various wireless signals, while software processing allows quick and scalable deployment for diverse applications. SDRs seem like a good fit for the ever-evolving needs of today's spectrum usage: SDRs can be deployed today, then managed and upgraded with software to support the needs of tomorrow. However, the prevailing architecture of SDRs prevent real-time observation of wideband RF signals due to backhaul and processing resource constraints.

Mobile devices increasingly function as wireless tracking beacons. Using the Bluetooth Low Energy (BLE) protocol, mobile devices such as smartphones and smartwatches continuously transmit beacons to inform passive listeners about device locations for applications such as digital contact tracing for COVID-19, and even finding lost devices. These applications use cryptographic anonymity that limit an adversary’s ability to use these beacons to stalk a user. However, attackers can bypass these defenses by fingerprinting the unique physical-layer imperfections in the transmissions of specific devices.We empirically demonstrate that there are several key challenges that can limit an attacker’s ability to find a stable physical layer identifier to uniquely identify mobile devices using BLE, including variations in the hardware design of BLE chipsets, transmission power levels, differences in thermal conditions, and limitations of inexpensive radios that can be widely deployed to capture raw physical-layer signals. We evaluated how much each of these factors limits accurate fingerprinting in a large-scale field study of hundreds of uncontrolled BLE devices, revealing that physical-layer identification is a viable, although sometimes unreliable, way for an attacker to track mobile devices.

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.

Millimeter-wave communication with high throughput and high reliability is poised to be a gamechanger for V2X and VR applications. However, mmWave links are notorious for low reliability since they suffer from frequent outages due to blockage and user mobility. We build mmReliable, a reliable mmWave system that implements multi-beamforming and user tracking to handle environmental vulnerabilities. It creates constructive multi-beam patterns and optimizes their angle, phase, and amplitude to maximize the signal strength at the receiver. Multi-beam links are reliable since they are resilient to occasional blockages of few constituent beams compared to a single-beam system. We implement mmReliable on a 28 GHz testbed with 400 MHz bandwidth, and a 64 element phased array supporting 5G NR waveforms. Rigorous indoor and outdoor experiments demonstrate that mmReliable achieves close to 100\% reliability providing 2.3x improvement in the throughput-reliability product than single-beam systems.

Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in Wi-Fi-based localization by 80% (median & 90th percentile) across the two different spaces.

In the last decade, the bandwidth expansion and MIMO spatial multiplexing have promised to increase data throughput by orders of magnitude. However, we are yet to enjoy such improvement in real-world environments, as they lack rich scattering and preclude effective MIMO spatial multiplexing. In this paper, we present ScatterMIMO, which uses smart surface to increase the scattering in the environment, to provide MIMO spatial multiplexing gain. Specifically, smart surface pairs up with a wireless transmitter device say an active AP and re-radiates the same amount of power as any active access point (AP), thereby creating virtual passive APs. ScatterMIMO avoids the synchronization, interference, and power requirements of conventional distributed MIMO systems by leveraging virtual passive APs, allowing its smart surface to provide spatial multiplexing gain, which can be deployed at a very low cost. We show that with optimal placement, these virtual APs can provide signals to their clients with power comparable to real active APs, and can increase the coverage of an AP. Furthermore, we design algorithms to optimize ScatterMIMO's smart surface for each client with minimal measurement overhead and to overcome random per-packet phase offsets during the measurement. Our evaluations show that with commercial off-the-shelf MIMO WiFi (11ac) AP and unmodified clients, ScatterMIMO provides a median throughput improvement of 2 X over the active AP alone.

Social distancing has been the key factor which has helped control the COVID-19 pandemic spread. We present BluBLE, which utilizes Bluetooth Low Energy (BLE) based mobile sensing to help monitor these social distancing protocols. Specifically, we formulate the problem in two parts - spatial and temporal social distancing. The spatial distancing formulation aims to enforce the 6 feet distance recommended by various public health organization around the world. The temporal distancing formulation aims to inform and prevent users from entering high-occupancy regions (hotspots) in buildings. BluBLE achieved more than 80 % classification accuracy in both the tasks, that is, predicting if a user is within '6' feet of another user as well as characterizing the user's location within a particular hotspot.

Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions. Radars can potentially solve this problem as they are barely affected by adverse weather conditions. However, specular reflections of wireless signals cause poor performance of radar point clouds. We introduce Pointillism, a system that combines data from multiple spatially separated radars with an optimal separation to mitigate these problems. We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds. Furthermore, we present the design of RP-net, a novel deep learning architecture, designed explicitly for radar's sparse data distribution, to enable accurate 3D bounding box estimation. The spatial techniques designed and proposed in this paper are fundamental to radars point cloud distribution and would benefit other radar sensing applications

Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters.

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available at https://github.com/mengyuest/SIGNet

We present SparSDR, a resource-efficient architecture for softwaredefined radios whose backhaul bandwidth and compute power requirements scale in inverse proportion to the sparsity (in time and frequency) of the signals received. SparSDR requires dramatically fewer resources than existing approaches to process many popular protocols while retaining both flexibility and fidelity. We demonstrate that our approach has negligible impact on signal quality, receiver sensitivity, and processing latency. The SparSDR architecture makes it possible to capture signals across bandwidths far wider than the capacity of a radio's backhaul through the addition of lightweight frontend processing and corresponding backend reconstruction to restore the signals to their original sample rate. We employ SparSDR to develop two wideband applications running on a USRP N210 and a Raspberry Pi 3+: an IoT sniffer that scans 100 MHz of bandwidth and decodes received BLE packets, and a wideband Cloud SDR receiver that requires only residential-class Internet uplink capacity. We show that our SparSDR implementation fits in the constrained resources of popular low-cost SDR platforms, such as the AD Pluto.

Internet-of-things (IoT) devices are becoming widely adopted, but they increasingly suffer from limited power, as power cords cannot reach the billions and batteries do not last forever. Existing systems address the issue with ultra-low-power designs and energy scavenging, which inevitably limit functionality. To unlock the full potential of ubiquitous computing and connectivity, our solution uses capacitive power transfer (CPT) to provide battery-like wireless power delivery, henceforth referred to as "Capttery". Capttery presents the first room-level (~5 m) CPT system, which delivers continuous milliwatt-level wireless power to multiple IoT devices concurrently. Unlike conventional one-to-one CPT systems that target kilowatt power in a controlled and potentially hazardous setup, Capttery is designed to be human-safe and invariant in a practical and dynamic environment. Our evaluation shows that Capttery can power end-to-end IoT applications across a typical room, where new receivers can be easily added in a plug-and-play manner.

Bluetooth Low Energy (BLE) tags have become very prevalent over the last decade for tracking applications in homes as well as businesses. These tags are used to track objects, navigate people, and deliver contextual advertisements. However, in spite of the wide interest in tracking BLE tags, the primary methods of tracking them are based on signal strength (RSSI) measurements. Past work has shown that such methods are inaccurate, and prone to multipath and dynamic environments. As a result, localization using Wi-Fi has moved to Channel State Information (CSI, includes both signal strength and signal phase) based localization methods. In this paper, we seek to investigate what are the challenges that prevent BLE from adopting CSI based localization methods. We identify fundamental differences at the PHY layer between BLE and Wi-Fi, that make it challenging to extend CSI based localization to BLE. We present our system, BLoc, that incorporates novel, BLE-compatible algorithms to overcome these challenges and enable an accurate, multipath-resistant localization system. Our empirical evaluation shows that BLoc can achieve a localization accuracy of 86 cm with BLE tags, a 3X improvement over a state-of-the-art baseline.

Current VR headsets are tethered to computers which limits mobility and poses a tripping hazard. Delivering VR content over a wireless link is challenging due to the high data rates and stringent time delivery requirements. Millimeter wave communication at 60 GHz (WiGig) can meet these requirements, but, it is unreliable due to blockages and beam misalignments. In this poster, we present an idea of using both WiFi and WiGig interfaces to transmit the VR content. We divide a video frame into tiles and prioritize the tiles in the user's field of view. Based on the wireless link conditions, the tiles are encoded with varying qualities and transmitted over either WiFi or WiGig interface. We formulate an optimization framework to deliver the VR video with high reliability and low latency.

We introduce the design and implementation of FreeRider, the first system that enables backscatter communication with multiple commodity radios, such as 802.11g/n WiFi, ZigBee, and Bluetooth, while these radios are simultaneously used for productive data communication. Furthermore, we are, to our knowledge, the first to implement and evaluate a multi-tag system. The key technique used by FreeRider is codeword translation, where a tag can transform a codeword present in the original excitation signal into another valid codeword from the same codebook during backscattering. In other words, the backscattered signal is still a valid WiFi, ZigBee, or Bluetooth signal. Therefore, commodity radios decode the backscattered signal and extract the tag's embedded information. More importantly, FreeRider does codeword translation regardless of the data transmitted by these radios. Therefore, these radios can still do productive data communication. FreeRider accomplishes codeword translation by modifying one or more of the three dimensions of a wireless signal --- amplitude, phase and frequency. A tag ensures that the modified signal is still comprised of valid codewords that come the same codebook as the original excitation signal. We built a hardware prototype of FreeRider, and our empirical evaluations show a data rate of ~60kbps in single tag mode, 15kbps in multi-tag mode, and a backscatter communication distance up to 42m when operating on 802.11g/n WiFi.

Today's virtual reality (VR) headsets require a cable connection to a PC or game console. This cable significantly limits the player’s mobility and hence her/his VR experience. The high data rate requirement of this link (multiple Gbps) precludes its replacement by WiFi. Thus, in this paper, we focus on using mmWave technology to deliver multi Gbps wireless communication between VR headsets and their game consoles. The challenge, however, is that mmWave signals can be easily blocked by the player's hand or head motion. We describe novel algorithms and system design that allow such mmWave links to sustain high data rates even in the presence of a blockage, enabling a high quality untethered VR experience.

We present HitchHike, a low power backscatter system that can be deployed entirely using commodity WiFi infrastructure. With HitchHike, a low power tag reflects existing 802.11b transmissions from a commodity WiFi transmitter, and the backscattered signals can then be decoded as a standard WiFi packet by a commodity 802.11b receiver. Hitch-Hike's key invention is a novel technique called codeword translation, which allows a backscatter tag to embed its information on standard 802.11b packets by just translating the original transmitted 802.11b codeword to another valid 802.11b codeword. This allows any 802.11b receiver to decode the backscattered packet, thus opening the doors for widespread deployment of low-power backscatter communication using widely available WiFi infrastructure. We show experimentally that HitchHike can achieve an uplink throughput of up to 300Kbps at ranges of up to 34m and ranges of up to 54m where it achieves a throughput of around 200Kbps.

We present xFabric, a novel datacenter transport design that provides flexible and fast bandwidth allocation control. xFabric is flexible: it enables operators to specify how bandwidth is allocated amongst contending flows to optimize for different service-level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc. xFabric is also very fast, it converges to the specified allocation one-to-two order of magnitudes faster than prior schemes. Underlying xFabric, is a novel distributed algorithm that uses in-network packet scheduling to rapidly solve general network utility maximization problems for bandwidth allocation. We evaluate xFabric using realistic datacenter topologies and highly dynamic workloads and show that it is able to provide flexibility and fast convergence in such stressful environments.

We present the first low power backscatter system that can be deployed completely using commodity WiFi infrastructure. With this system, a low power tag reflects existing 802.11b transmissions from a commodity WiFi transmitter, and the backscattered signals can be decoded as a standard WiFi packet by a commodity 802.11b receiver. The key invention is a novel technique called \textbf{codeword translation}, which allows a backscatter tag to embed its information on standard 802.11b packets by just translating the original transmitted 802.11b codeword to another valid 802.11b codeword. This allows any 802.11b receiver to decode the backscattered packet, thus opening the doors for widespread deployment of low-power backscatter communication using widely available WiFi infrastructure. We show experimentally that we can achieve an uplink throughput of up to 1Mbps at ranges of up to 8m and ranges of up to 50m where it achieves a throughput of around 100Kbps, which is twice as better than the recently published passive WiFi system.

We present BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter devices and WiFi APs using ambient WiFi transmissions as the excitation signal. Specifically, we show that it is possible to design devices and WiFi APs such that the WiFi AP in the process of transmitting data to normal WiFi clients can decode backscatter signals which the devices generate by modulating information on to the ambient WiFi transmission. We show via prototypes and experiments that it is possible to achieve communication rates of up to 5 Mbps at a range of 1 m and 1 Mbps at a range of 5 meters. Such performance is an order to three orders of magnitude better than the best known prior WiFi backscatter system [27,25]. BackFi design is energy efficient, as it relies on backscattering alone and needs insignificant power, hence the energy consumed per bit is small.

This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.

This paper presents demonstration of a real-time full duplex point-to-point link, where transmission and reception occurs in the same spectrum band simultaneously between a pair of full-duplex radios. This demo first builds a full duplex radio by implementing self-interference cancellation technique on top of a traditional half duplex radio architecture. We then establish a point-to-point link using a pair of these radios that can transmit and receive OFDM packets. By changing the environmental conditions around the full-duplex radios we then demonstrate the robustness of the self-interference cancellation to adapt to the changing environment.

This paper presents, FastForward (FF), a novel full duplex relay that constructively forwards signals such that wireless network throughput and coverage is significantly enhanced. FF is a Layer 1 in-band full duplex device, it receives and transmits signals directly and simultaneously on the same frequency. It cleanly integrates into existing networks (both WiFi and LTE) as a separate device and does not require changes to the clients. FF's key invention is a constructive filtering algorithm that transforms the signal at the relay such that when it reaches the destination, it constructively combines with the direct signals from the source and provides a significant throughput gain. We prototype FF using off-the-shelf software radios running a stock WiFi PHY and show experimentally that it provides a 3× median throughput increase and nearly a 4× gain at the edge of the coverage area.

This paper asks the following question: could we transform the radios found in our personal gadgets into powerful multipurpose scanning devices that can detect and locate tumors, guns, buried human bodies, a la the Star Trek Tricoder? Our key insight is that if radios could measure the backscatter of their own transmissions (i.e. reflections from the environment of their transmissions), then Tricorder-style powerful object detection and localization algorithms could be realized. In this paper we focus specifically on backscatter measurement, we describe novel circuits and algorithms that can be added to existing radios to enable them to accurately and concurrently receive and disentangle their own transmissions' reflections and infer its properties.

This paper presents the design and implementation of the first in-band full duplex WiFi radios that can simultaneously transmit and receive on the same channel using standard WiFi 802.11ac PHYs and achieves close to the theoretical doubling of throughput in all practical deployment scenarios. Our design uses a single antenna for simultaneous TX/RX (i.e., the same resources as a standard half duplex system). We also propose novel analog and digital cancellation techniques that cancel the self interference to the receiver noise floor, and therefore ensure that there is no degradation to the received signal. We prototype our design by building our own analog circuit boards and integrating them with a fully WiFi-PHY compatible software radio implementation. We show experimentally that our design works robustly in noisy indoor environments, and provides close to the expected theoretical doubling of throughput in practice.

BACKGROUND: Next Generation Sequencing technologies have revolutionized many fields in biology by reducing the time and cost required for sequencing. As a result, large amounts of sequencing data are being generated. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. This data consists of both the nucleotide sequences and per-base quality scores that indicate the level of confidence in the readout of these sequences. Quality scores account for about half of the required disk space in the commonly used FASTQ format (before compression), and therefore the compression of the quality scores can significantly reduce storage requirements and speed up analysis and transmission of sequencing data. RESULTS: In this paper, we present a new scheme for the lossy compression of the quality scores, to address the problem of storage. Our framework allows the user to specify the rate (bits per quality score) prior to compression, independent of the data to be compressed. Our algorithm can work at any rate, unlike other lossy compression algorithms. We envisage our algorithm as being part of a more general compression scheme that works with the entire FASTQ file. Numerical experiments show that we can achieve a better mean squared error (MSE) for small rates (bits per quality score) than other lossy compression schemes. For the organism PhiX, whose assembled genome is known and assumed to be correct, we show that it is possible to achieve a significant reduction in size with little compromise in performance on downstream applications (e.g., alignment). CONCLUSIONS: QualComp is an open source software package, written in C and freely available for download at https://sourceforge.net/projects/qualcomp.

This paper presents a full duplex radio design using signal inversion and adaptive cancellation. Signal inversion uses a simple design based on a balanced/unbalanced (Balun) transformer. This new design, unlike prior work, supports wideband and high power systems. In theory, this new design has no limitation on bandwidth or power. In practice, we find that the signal inversion technique alone can cancel at least 45dB across a 40MHz bandwidth. Further, combining signal inversion cancellation with cancellation in the digital domain can reduce self-interference by up to 73dB for a 10MHz OFDM signal. This paper also presents a full duplex medium access control (MAC) design and evaluates it using a testbed of 5 prototype full duplex nodes. Full duplex reduces packet losses due to hidden terminals by up to 88%. Full duplex also mitigates unfair channel allocation in AP-based networks, increasing fairness from 0.85 to 0.98 while improving downlink throughput by 110% and uplink throughput by 15%. These experimental results show that a re- design of the wireless network stack to exploit full duplex capability can result in significant improvements in network performance.

In this letter, we investigate the relay and power allocation problem for OFDM-based cognitive radio (CR) systems with single antennae. We propose a method where the capacity of CR user employing relays is maximized while total transmission power is kept within a budget and the interference introduced to the primary user (PU) band is kept within a prescribed threshold. The optimization problem is a mixed-integer problem, which is NP-hard. Hence in this letter, we have proposed three sub-optimal schemes. The presented numerical results show that the performance of proposed suboptimal schemes is close to the optimal solution.