Danijela Cabric - US grants

This project aims to develop a comprehensive approach for characterization of RF environment in terms of spatial and temporal features including: i) the number of active transmitters, ii) their power, direction of arrival and location, and iii) modulation class. Spatio-temporal spectrum sensing requires novel multi-dimensional parameter estimation algorithms as opposed to conventional spectrum sensing that uses hypothesis testing based detection algorithms. In this work, the Bayesian estimation approach using angle-of-arrival measurements is applied to create the probabilistic map of the transmitter presence in a given region taking into account measurement noise and uncertainties. The tools from random matrix theory are applied to perform joint detection and parameter estimation, and analytically derive performance bounds. The methods for modulation classification are based on goodness-of-fit statistical tests with reduced sampling complexity, and provide the unified classification framework for a wide range of modulation classes. The results of this research will impact the design of novel medium access and routing protocols that manage the interference through awareness of the location and link quality of other transmitters in the region. The developed technologies could potentially apply for monitoring of RF transmissions within wireless infrastructure, and for the defense and national security applications.

Efficient spectrum sharing among disparate wireless systems without inter-system communication is the central problem in expanding existing and developing future wireless technologies. This proposal explores an integrated physical and network layer approach for spectrum sharing by developing algorithms and protocols that will allow heterogeneous networks to co-exist and maintain required interference constraints. The proposed framework is based on the novel idea of incorporating detailed real-time measurements and prediction of spectrum usage, including traffic parameters and network topology, into the design of cognitive protocols that respond to the actual spectrum occupancy in time, frequency, and space. The following critical enabling technologies of this framework are being developed: (i) identification of non-cooperative spectrally-overlapped transmitters based on location and modulation parameters; (ii) analysis and tracking of spectrum usage based on traffic estimation and prediction; (iii) cognitive co-existence protocols for spectrum sharing with combined traffic and location awareness. The objective of this research is to comprehensively analyze performance gains achieved by exploiting traffic and location awareness while taking into account estimation and prediction errors, physical limitations, and protocol overhead. The research approach is based on a closed loop between theoretical analysis, implementation, and experimental verification on the reconfigurable wireless testbed and network simulation tools. The algorithms, protocols and tools developed by this research will have practical impact on a broad range of wireless systems that share same spectrum resources including: current and future unlicensed bands, vehicular and safety networks, cellular infrastructure and femto cells, emergency and defense networks.

Cognitive radio techniques will be developed that enable rapid, wideband spectrum sensing in the presence of strong interference. Cognitive radios must detect available spectrum quickly in real time with high-probability of success, which requires extremely high dynamic range in the presence of powerful interference signals. Furthermore, future cognitive radio will operate over multiple frequency bands spread out over a wide frequency range. This compounds the challenges to the receiver design since all filtering must be capable of tuning over wide frequency ranges. Finally, the radio must agilely hop among frequency bands. The time required to detect the power in any channel is an overhead that limits the network throughput.

Most prior cognitive radio research applies digital baseband algorithms to conventional RF and analog radio circuitry, which is not designed to address the spectrum sensing application. This limits the achievable spectrum sensing bandwidth and agility and results in high power consumption for the RF, analog, and digital signal processing blocks. This work is fundamentally different in that it will customize the entire receive chain from the RF circuitry through the digital signal processing (DSP) to incorporate new techniques specifically targeted to address spectrum sensing. The techniques involve the injection of pseudonoise-modulated RF signals into the receiver to identify the blockers via correlation algorithms in the DSP as well as to calibrate and cancel the nonlinear characteristics of the receiver. We will develop and refine the proposed algorithms and demonstrate their utility in a 28-nm CMOS receiver integrated circuit.

Primary user networks provide wireless coverage over a large area by using multiple geographically separated transmitters operating on licensed frequency bands. On the other hand, cognitive radios are opportunistic users of unoccupied spectrum. In order to coexist with the transmitters in its vicinity, they need to sense the spectrum, i.e., detect the primary users' transmissions, and avoid causing them interference. This project focuses on the dynamic spectrum access in the presence of primary networks that are cellular, have anisotropic antennas, or employ frequency reuse, and aims to significantly increase the range of spectrum that cognitive radios can use. Through higher-layer radio scene analysis we aim to maximize the cognitive radio network throughput by increasing the detected spatial resolution and utilization of spectrum holes. This combination of increased spectrum and geographical spread makes large-scale cognitive radio networks a viable candidate for implementing smart grids, environmental networks, and traffic sensors. For defense purposes, the learned primary network topology provides unprecedented information about commonly deployed communication networks.

This project research is based on development of cooperative algorithms for the identification of spectrum holes and classification of primary network activity by geographically large-scale cognitive radio networks. A novelty of the proposed methods is that they will not rely on knowledge of the channel propagation models or the location of the radios. This blind nature of the algorithms allows for more diverse applications. Instead of geographical vicinity, correlations in the received signals will be used for distinguishing between primary transmitters and learning the shape of their footprints. For the case that footprints of two or more transmitters overlap, message passing based cooperative spectrum sensing methods will be formulated for the joint estimation of the multiple transmitters spectrum occupancy. The learned footprint and the detected spectrum occupancy will be used to analyze the primary user activity over time. Primary users that are part of the same infrastructure-based primary networks will be identified. Further, learning their traffic statistics will enable the classification of their channel access methods and protocols.

Ever increasing demands for higher mobile data rates have resulted in exploration of new wireless technologies such as millimeter wave (mmWave) cellular networks due to large bandwidth availability at mmWave frequencies. However, higher channel propagation loss and higher probability of signal blockage in the mmWave band, as compared to the microwave band, have introduced challenges in establishing and maintaining the link between base stations and user equipment. In order to compensate for the losses, these devices use a large number of antennas and narrow beams to achieve high beamforming gains needed to establish the link. Further, ultra-dense base station deployment is needed to provide better coverage in the region. As a result, the mmWave cellular networks face new design challenges: i) establishing the link during initial channel access, ii) coordinated association of a user equipment to base stations and allocation of base station resources (beams and time) to users, and iii) beam adaptation for mobile user equipment and managing handovers in ultra-dense mobile networks. The proposed technologies are of fundamental importance to realize future ultra-dense 5G cellular networks, support user mobility and enable various applications requiring high data rate including video streaming, cloud computing, virtual reality, and augmented reality.

This project will address these challenges and enable ultra-fast, low delay, and high throughput cellular networks in the mmWave band. First, it aims to develop an initial access procedure needed to establish the link between base stations and user equipment using compressive sensing measurements while considering practical hardware impairments. Next, it proposes to use the channel parameters obtained during the initial access to associate users with a specific base station and allocate resources accordingly. The novel association techniques will allow multiple base stations to simultaneously serve one user using independent RF chains, thereby increasing the data rate and reducing the probability of outage. Finally, a novel mobility management technique based on low-complexity beam-tracking and handover algorithms is proposed to enable seamless data transfer in ultra-dense mobile network. The proposed methodology will involve formulation of optimization framework for problems of coordinated beam association, resource allocation, and handovers and design of low complexity algorithms that solve the underlying optimization while enabling their practical realization.

Unmanned aerial vehicles (UAVs) have been recently proposed as a solution for mobile wireless infrastructure. Majority of research on UAV-based communications considers either a single UAV or multiple UAVs as independent entities. This project aims to explore the benefits of utilizing multiple coordinated UAVs, also known as the UAV Swarm, within the emerging fog radio access networks (Fog-RAN). By leveraging the mobility of UAVs with on-board radios and processor, and the distributed processing within the swarm, the proposed system aims to enable various on-demand applications of internet-of-things (IoT) and multiple-input multiple-output (MIMO) wireless access with improved energy efficiency and spectral efficiency. Due to its ease of deployment, the UAV swarm assisted Fog-RAN can serve underprivileged communities and accommodate fluctuations in capacity demands during emergency or other event-driven applications.

This project addresses a set of unique challenges in circuits and systems arising in UAV swarm communications: i) swarm array synchronization and backhaul, ii) distributed processing on the edge devices, and iii) UAV swarm placement for optimized link budget and distributed MIMO channel capacity. Two different swarm systems will be explored: 1) weakly coordinated swarm with light swarm synchronization and simple coordination protocol, and 2) strongly coordinated swarm that requires tight synchronization and distributed array processing. For weakly coordinated swarm, low-complexity synchronization and coordination algorithms, low-power RF circuits and baseband processing, with only a single antenna in each radio, will be designed. For strongly coordinated swarm, a hybrid RF and base-band processing for frequency, phase, and time synchronization, and novel array signal processing for distributed MIMO combining within UAV swarm, will be designed to enable coherent transmission. The framework for UAV swarm placement to achieve optimal capacity and range will be developed and analyzed for robustness against channel, location, and actuation uncertainties. A proof-of-concept prototype using off-the-shelf UAVs and a radio testbed with customized RF components will be built and tested in the air.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This award is a planning grant for the Spectrum Innovation Initiative: National Center for Wireless Spectrum Research (SII-Center). The focus of a spectrum research SII-Center goes beyond 5G, IoT, and other existing or forthcoming systems and technologies to chart out a trajectory to ensure United States leadership in future wireless technologies, systems, and applications in science and engineering through the efficient use and sharing of the radio spectrum. The radio spectrum should be utilized to the greatest public benefit at national and global scales. Spectrum shortages, both real and perceived, are leading to conflicts between existing users and anticipated new uses ? some of which were not imagined when existing spectrum allocations were made decades ago. Many stakeholders seek to protect and advance their interests, with aspects of these interests overlapping and conflicting with each other. Hence, the public debate over the optimal model for managing spectrum is a complex interplay of technology, economics, law and regulation, policy, and the history of past successes and failures.

This project is aimed at the development of a comprehensive plan for an SII-Center which would help maintain and extend US leadership in future wireless technologies, systems, and applications in science and engineering through the efficient use and sharing of radio spectrum. The project team is led by the University of Notre Dame, with partners from Northwestern University, Clemson University, University of California, Berkeley, University of California, Los Angeles, New York University, and Stanford University. The team has an extensive record of successful research, as well as significant and relevant industry and implementation experience. The project seeks to develop plans for a multi-disciplinary center that emphasizes instrumentation of the radio spectrum; collecting and sharing accurate regulatory, usage, and economic data; and developing data-rich system designs and regulatory policies for more efficient spectrum utilization.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

5G evolution and future 6G cellular networks are targeting operations at higher millimeter wave and sub-Tera Hertz (sub-THz) bands due to the availability of large channel bandwidths to further improve data rate, latency, quality-of-service, and reliability. However, the use of these bands for mobile radio access imposes substantial technical challenges, including the quality, cost- and energy-efficiency of the electronics, the extreme path loss and propagation characteristics, and the overall deployment costs to provide indoor and outdoor network coverage with mobility support. Considering these challenges, this project will investigate the utility of machine learning algorithms, that have been successful in solving complex problems in various domains, for designing physical layer technologies and network management procedures, involved in both user equipment and base stations, that aim to improve robustness and reliability of connectivity under mobility. The project’s expected contributions are at the forefront of emerging 6G standard and applications of modern machine learning tools in wireless communications at high frequency bands.<br/><br/>The research will address three key thrusts: i) Thrust 1 will develop machine learning assisted and data driven approaches for user equipment beam training and tracking using compressive sensing-based channel probing for low latency and robustness to phased antenna array impairments. In addition, it will accelerate beam training and tracking on the base station side by using deep reinforcement learning for optimizing beam probing strategies based on environment characteristics and user trajectories. The outcome of this thrust will be significant reduction in beam management overhead in the presence of mobility; ii) Thrust 2 will use a novel receiver processing architecture where the signal path exploits convolutional neural network layers in both time and frequency domains to compensate the effects of the power amplifier nonlinearity and phase noise in a wideband orthogonal frequency division multiplexing (OFDM) receiver, while accounting for frequency-selective multipath channel effects. The outcome of this thrust will be improvement in transmitter power efficiency, coverage and bit error probability. iii) Thrust 3 will develop intelligent handover algorithms to minimize disruptions in user connectivity by exploiting position estimates and beam-level reference signal power measurements with distributed deep reinforcement learning while considering user rate requirements and reducing measurement reporting and sharing overheard between base stations. The research methodology will rely on extensive measurements and data set generations using millimeter wave testbeds for the development and evaluation of the proposed solutions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.