Radio Frequency Interference (RFI) from cellular and other communication networks is commonly mitigated at the radio telescope without any active collaboration with the interfering sources. The expanding Universe and simultaneous proliferation of Earth-based and LEO communication infrastructure is causing unprecedented RFI that require collaborative strategies to maintain the scientific and societal goals of each. In this work, we develop deep learning based models that enable collaboration with minimal overhead while also providing accurate RFI characterization and simplified cancellation strategies. This multistage system design is adaptable to changing statistics of the RFI signals generated from cellular networks and allows single step RFI cancellation by signal processing chain modeling (e.g. filtering and digitization loss) at the Telescope. Through our analysis and simulation using real astronomical signals, we are able to remove RFI generated from cellular networks with comparable accuracy to the state of the art with only 25% of the communication overhead and overall reduced computation complexity from O(n3) to O(n2).

Direction of arrival (DoA) estimation has an important role in various applications and is widely used in modern communication, where signal power is higher than noise power for it to be decoded. Existing methods do not perform as accurately in low signal to noise ratio (SNR) as they do in higher SNR. However, passive sensing like radio astronomy or remote sensing operates at extremely low SNR, where the weakest radio frequency interference (RFI) from communication system impairs the scientific observations. Hence, it is essential to estimate the DoA of RFI at low SNR so that it can be removed at the telescopes or radiometers. In this paper, we propose a three stage algorithm that methodically exploits digital beamforming, creates virtual subarrays, inspects multiple options and introduces clustering to estimate the DoA in low SNRs. The proposed algorithm is simulated with sinusoidal as well as Automatic Dependent Surveillance-Broadcast (ADS-B) signals in Additive White Gaussian Noise (AWGN) and multipath fading channels. Experimental results show the proposed method outperforms well-known MUSIC (MUltiple SIgnal Classification) algorithm in low SNR.

Recently, there has been great interest in facilitating coexistence of active and passive users of the electromagnetic (EM) spectrum, with the primary objective of higher spectral utilization. The major challenge for passive users, such as Radio Astronomy Services (RAS), is the need for extremely quiet skies to make astronomical observations with maximum sensitivity of the radio telescope. This is increasingly difficult to guarantee because of densification of allocated spectrum, exponential growth of ubiquitous wireless communication and out-of-band astronomical observations required to observe fast radio bursts. This requires either bidirectional collaboration between active and passive users or innovative signal processing at the telescope site to cancel any incident Radio Frequency Interference (RFI). In this work, we show the feasibility of such a paradigm, where RFI from airborne sources, e.g., aircraft, LEO satellites, etc., is cancelled at the receiver of a Radio Telescope, by shaping the EM wavefront by an array of Reconfigurable Intelligent Surfaces (RIS). In contrast to conventional beam-nulling applications for RIS, this method requires precise calculation of the phase and the amplitude of the reflected signal by the RIS in order to guarantee complete cancellation of the incident RFI. We simulate this approach in a practical setting to study its error performance and boundary conditions of the system parameters, that will lead to a demonstrable prototype in near future. Our results indicate that an RIS array with 364 elements can fully cancel RFI for ADS-B systems at an elevation of$60^{\circ}$and an altitude of 10000 m.

Ultra-broadband communication in emerging spectrum, like Terahertz (THz) band, is the frontier to meet the data rate requirements of future wireless communication systems. Existing signal processing based methods are developed for sub-6 GHz band, which cannot capture the intricacies in ultra-broad THz bandwidth and non-linearities arising from hardware. To overcome these limitations, we develop neural network (NN) models for OFDM receiver, where expert knowledge of wireless communication is infused in different stages and parameters of the model to create a practical receiver that can adapt to different wireless environments. The parameters of the NN are derived from underlying theory and can be adapted to different wireless environments. Our model is designed to capture the correlation between real and imaginary components of wireless signals, that can be trained with limited data. The models are trained with over-the-air captured OFDM signals, transmitted in THz band with 10 GHz bandwidth. Our results show significant improvement in bit error rate (BER) for different modulation orders (upto 6 dB in BPSK and 1.2 dB in QAM 64) compared to existing signal processing based receiver designs.

We introduceRFEye, a generalized technique to locate signals independent of the waveform, using a single Unmanned Aerial Vehicle (UAV) equipped with only one omnidirectional antenna. This is achieved by acquiring signals from uncoordinated positions within a sphere of 1-meter radius at two nearby locations and formulating an asynchronous, distributed receiver beamforming at the UAV to compute the Direction of Arrival (DoA) from the unknown transmitter. The proposed method includes four steps: 1) Blind detection and extraction of unique signature in the signal to be localized, 2) Asynchronous signal acquisition and conditioning, 3) DoA calculation by creating a virtual distributed antenna array at UAV and 4) Obtaining position fix of emitter using DoA from two locations. These steps are analyzed for various sources of error, computational complexity and compared with widely used signal subspace-based DoA estimation algorithms.RFEyeis implemented using an Intel-Aero UAV, equipped with a USRP B205 software-defined radio to acquire signals from a ground emitter. Practical outdoor experiments show thatRFEyeachieves a median accuracy of 1.03m in 2D and 2.5m in 3D for Wi-Fi, and 1.15m in 2D and 2.7m in 3D for LoRa (Long Range) waveforms, and is robust to external factors like wind and UAV position errors.

Channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) transmission is well investigated with model based approaches. Recent effort also explores the data driven approaches to exploit the capabilities of Neural Networks (NNs) to estimate the channel. These models are mostly being developed as black box without any anchor to the theory of wireless signal propagation. We propose a NN model, where the structure and parameters are derived from the domain knowledge of wireless signal and channel characteristics. Our model is developed in two stages: the first stage handles the noise reduction, while the second stage extracts the channel characteristics to reduce error caused by multipath. We have used the knowledge of signal to noise ratio and subcarrier correlation due to channel delay spread to empower the two stages of the proposed model. Our results also show that induction of domain knowledge results in reduction of data dependency by 60%. Our model outperforms the practical model based methods as well as blind data driven approaches. It achieves ∼10 dB improvement over Least Square channel estimation.

Spectrum access in the next generation congested cognitive wireless networks will be vulnerable to malicious intents of strong adversaries. One of the ways to obscure an adversary is to hide a signal to avoid the threats of being intercepted or jammed. In this paper, we present such a wireless steganography technique, where we hide the secret message as a form of noise. We use adversarial learning model to transform the domain of secret message, from bits to complex signal that is statistically identical to hardware noise of a transmitter. A three-node neural network, an encoder, a decoder, and a critic (steganalyzer), is optimized jointly to encode and decode a secret message while adhering to statistically identical properties of the encoded covert signal and hardware generated noise. Once the domain transfer is complete, this covert signal can be carried by any cover signal, independent of its waveform or modulation order. We provide an information-theoretic analysis to evaluate the embedding capability of the learning model. Our results show that the covert signal achieves a throughput of 12Mbps at 12dB SNR and is resilient to different levels of hardware noise.

Growing interest in utilizing the wireless spectrum by heterogeneous devices compels us to rethink the physical layer security to protect the transmitted waveform from an eavesdropper. We propose an end-to-end symmetric key neural encryption and decryption algorithm with a modulation technique, which remains undeciphered by an eavesdropper, equipped with the same neural network and trained on the same dataset as the intended users. We solve encryption and modulation as a joint problem for which we map the bits to complex analog signals, without adhering to any particular encryption algorithm or modulation technique. We train to cooperatively learn encryption and decryption algorithms between our trusted pair of neural networks, while eavesdropper’s model is trained adversarially on the same data to minimize the error. We introduce a discrete activation layer with a defined gradient to combat noise in a lossy channel. Our results show that a trusted pair of users can exchange data bits in both clean and noisy channels, where a trained adversary cannot decipher the data.

There is a growing interest towards enabling practical, dynamic and agile wireless applications by systems of independent or cooperative mobile agents such as Unmanned Aerial Vehicles (UAVs). Such mobile UAVs are often constrained on resources like storage, power and radio capabilities and require accurate position information to facilitate many of these wireless applications. In this paper, we introduce HiPER-V, which is a generalized UAV prototype platform to enable a broad range of applications in wireless communications using a single UAV or can be extended to a swarm of UAVs. We implement HiPER-V by using an UAV, equipped with resource constrained radio devices, and high precision position information available via RTK-GPS modules, achieving a median position accuracy of 3.8 cm. The details of implementation of HiPER-V and its applicability to a wide variety of applications in wireless communications are presented in this paper. With minimal payload and simple software modification, our solution can be ported to any UAV platform and extended to multiple UAV testbeds that enable an array of research in wireless applications using UAVs.

Classification of MIMO wireless signals with no prior information of the number of transmitter antenna elements and channel(s) is of tremendous importance in both military and civilian applications. In this research, we present a novel generalized algorithm for classification of digital modulation combined with the recognition of the number of transmit antennas. Our algorithm is developed to rely on only one receiver equipped with a single antenna and does not require any prior channel knowledge. We have introduced a two- phase classifier system. In the first phase, we use a discrete-wavelet transform on the received complex samples to separate the different modulation classes. Following that, we use the K- nearest neighbor approach coupled with k-means clustering to further classify the signals, utilizing the symmetry and relative distances of the constellation points. The performance of the classifiers at different stages of the algorithm demonstrates a high accuracy in an SNR regime where the packets can be decoded.

With exponential growth of IoT devices [1], the 5G network will experience a variety of traffic patterns not prevalent in earlier applications. These, often will transmit short sporadic messages, which are not well suited to the connection-oriented modes associated with legacy 3GPP network resulting in high service latency and excessive control overhead. It is acknowledged that current 4G network could be overwhelmed by the surge in both traffic and control plane signaling load. For 5G, it is necessary to redesign the mobile network to provide a low delay, low control overhead IoT mode that will work efficiently for emerging application scenarios. The MAC layer has to be designed in such a way that IoT messages experience low access latency across both the radio access network and core network. The goal is to operate in the same band as current LTE, thus not requiring any separate bandwidth allocation and is backward compatible with the current 4G system. In this paper, we propose a cross-layer MAC and Physical layer solution for low power, low bitrate devices that require low access delay and long range for communication.

In recent years, we have experienced many initiatives in developing smart cities across the world. In these densely populated cities, video surveillance will play an integral role to ensure the safety of the citizens. The major challenge will be to transport high volumes of data generated from dense deployment of video cameras throughout the city to a central aggregation facility for analysis and storage. To address this problem, we propose a system solution using existing public bus transit system to collect data from the cameras and physically transport it to the bus terminus, to be uploaded to the data center. The system uses heterogeneous wireless network interfaces, where the camera nodes form a mesh network to route the data to the nearest bus stop for offloading. We utilize high capacity links using mmWave devices at bus stops to offload the data to the buses when they make routine stops. We also propose a novel routing protocol, R2H, to route the data generated from these cameras to a bus stop while minimizing the time to offload the video data to an incoming bus. The protocol is designed to be aware of the various challenges that are specific to this system like bus schedule and video payload. We have evaluated our system using wireless experiments and simulation using actual bus route maps and times from New York city area. Our analysis shows that R2H achieves 60% less end to end delay compared to OLSR under varying video payload and time of the day.

Recent work has examined techniques to estimate the “best” modulation rate for data networks such as 802.11a/g. While accurate rate estimation yields better rate-selection decisions and increased throughput, those methods must still choose between a handful of modulation rates. Each modulation rate is effective in a range of actual signal-to-noise ratios (SNRs) but the limited number of practical rates means that transmitters are often forced to “step down” to a lower data rate despite having a higher SNR than the minimum required for that lower rate. In this paper we describe, evaluate and implement a practical multiuser communication scheme that exploits these discrete “steps” in modulation rates to transmit two packets in the time normally needed to transmit a single packet, increasing aggregate throughput precisely when it is most needed—when the network is busy and suffers from rate unfairness. Because the method transmits a group of packets simultaneously, we call this scheme Group Rate Transmission with Intertwined Symbols, or GRaTIS. In addition to up to 120% improvement in network throughput achieved by GRaTIS, the technique is backward compatible with 802.11 and doesn’t require complex DSP algorithms as required by competing methods.

As we move forward towards the next generation of wireless protocols, the push for a better radio physical layer is ever increasing. Conventional radio architectures are limited to narrow operating regions and fails to adapt with changing technology. This is further strengthened with the advent of cognitive radio, which needs a more versatile and flexible framework that is programmable within the timing constraints of a protocol. In this paper we present an architecture for Software Defined Cognitive Radio that caters to the specific baseband processing requirements in a changing environment. We aim to provide more flexibility by de-constructing the radio pipeline into a framework of user controlled kernels that can be reconfigured at run-time. This architecture provides the bare-bones of a OFDM based radio physical layer that can adapt to perform a varied number of tasks in different radio networks. We also present a novel message based real-time reconfiguration method to transmit and receive a wide range of waveforms used in concurrent wireless protocols.

Vehicular safety systems for collisions or sensing rapid changes in traffic typically use two methods to communicate and disseminate traffic hazards. Many current systems use RADAR systems that transmit a radio wave and sense the reflective waves for angle-of-arrival or time-of-arrival information. Several proposed systems use vehicular networks to disseminate information about braking, emergencies or road conditions; when coupled with accelerometer or GPS information, these radio systems may also offer information on speed, traffic density or distance. In-vehicle RADAR systems are relatively expensive; vehicular radio based systems are less expensive. In this paper, we present a cooperative technology that combines these two techniques, seeking to adopt characteristics of both systems by employing a software defined radio for “cooperative RADAR” and vehicular networking. Our method uses multicarrier wireless communication to detect and disseminate. Using precise timing and synchronization, we can detect the distance of each of the vehicles, their current velocity and current acceleration or deceleration conditions. Using simultaneous, multi-party acknowledgments, we can rapidly disseminate or determine information about a number of vehicles in an efficient manner.

Network protocols have traditionally been designed using a layered method in part because it is easier to implement some portions of network protocols in software and other portions must be implemented in hardware for performance reasons. These different implementation techniques enforce layer boundaries. In this paper, we show that with the advent of software defined radios, it becomes possible to blur those layer boundaries and produce higher performance network protocols as a result. In this paper we exploit a programmable physical layer and simultaneous transmission to have clients signal whether they have packets to send. By detecting the high energy at the simultaneous transmission, the AP gets the following information: a) which stations have packets to send and b) whether the traffic load is high, medium or low. Again, using the programmable physical layer, the AP schedules clients efficiently while wasting little of the spectrum on signaling overhead. The proposed protocol is a) fast, since no packet transmission is required for polling responses and all clients respond concurrently; b) reliable, as the poll response is contention free and c) scalable. We demonstrate the feasibility of implementing such a system using a FPGA based prototype software defined radio platform. We then show how the MAC protocol can scale using the QualNet network simulator and compare the performance to a contention based protocol.

Network protocol designers, both at the physical and network level, have long considered interference and simultaneous transmission in wireless protocols as a problem to be avoided. This, coupled with a tendency to emulate wired network protocols in the wireless domain, has led to artificial limitations in wireless networks.

Abstract A fundamental problem that distinguishes wireless networks from wired networks is the mutual interference between routes within the proximity of each other. This phenomenon is known as route coupling and it restricts the possibility of occurrence of simultaneous communications along the coupled routes. In this context, the use of directional antenna, having smaller transmission beam‐width compared to omni‐directional antenna, helps to easily decouple interfering routes, and improves network performance through space division multiple access (SDMA). However, even if we have an efficient directional MAC protocol, it alone would not be able to guarantee good system performance, unless we have a proper routing strategy in place that exploits the advantages of directional antenna. So, in this paper, an adaptive routing strategy is proposed that exploits the advantages of directional antenna inad hocnetworks through the selection of maximally zone‐disjoint shortest routes. Zone‐disjoint routes would minimize the effect of route coupling and improve the overall network performance. The proposed strategy ensures effective load balancing and is applied to design and implement both single path and multipath routing protocols in ad hoc networks with directional antennas. Simulation results obtained on QualNet network simulator shows the effectiveness of the proposed routing protocols. Copyright © 2006 John Wiley & Sons, Ltd.

We have proposed a scheme for supporting priority-based QoS in mobile ad hoc networks by classifying the traffic flows in the network into different priority classes, and giving different treatment to the flow-rates belonging to different classes. We have adopted a control-theoretic approach to adaptively control the low-priority flows so as to maintain the high priority flow-rates at their desired level, thus guaranteeing QoS to high-priority flow. At the same time, our objective is to adaptively maximize low priority flows while maintaining high priority flows at a desired level so that full utilization of wireless medium can be achieved through adaptive rate control. To provide this desired service guarantee to high priority flows, we need a distributed flow-control algorithm. Here, the low priority flows, causing interference to a high priority flow, detect and measure high priority flow-rate at each node on their routes and consequently adjust their flow-rates using a feedback control mechanism to maintain the high priority flow at its desired level. This detection and measurement is done at MAC layer of each node participating in routing from source to destination. We have proposed this protocol with a very nominal overhead using omni-directional antenna and modified the scheme to show the overall improvement in throughput using directional antenna. The performance has been evaluated using QualNet network simulator and the results indicate the effectiveness of our scheme.

Application of multipath routing techniques in mobile ad hoc networks has been explored earlier, as multipath routing may help to reduce end-to-end delay, perform load balancing and consequently improve throughput. However, it has also been shown that the success of multipath routing in ad hoc wireless network depends on network topology and channel characteristics can severely limit the gain offered by multipath routing strategies. The most significant challenge to making the use of multipath routing protocols effective in this environment involves considering the effects of route coupling. Route coupling in wireless medium occurs when two routes are located physically close enough to interfere with each other during data communication. As a result, the nodes in multiple routes are constantly contending for access to the medium they share and can end up performing worse than a single path protocol. In this paper, we propose a notion of zone-disjoint routes in wireless medium where paths are said to be zone-disjoint when data communication over one path will not interfere with data communication in other path. The notion of zone-disjointness is used as route selection criteria. However, zone-disjointness alone is not sufficient for performance improvement. If the path-length (number of hops) were large, that would increase the end-to-end delay even in the context of zone-disjointness. So, it is imperative to select maximally zone-disjoint shortest paths. However, getting zone-disjoint or even partially zone-disjoint routes in ad hoc network with omni-directional antenna is difficult, since the transmission zone of each node is larger compared to that with directional antenna. Hence, one way to reduce this transmission zone of a node is to use directional antenna. In this paper, we investigate the effect of directional antenna on zone-disjoint multipath routing and evaluated its effectiveness in QualNet network simulator.

It has been shown that use of directional antenna in the context of ad hoc wireless networks can largely reduce radio interference, thereby improving the utilization of wireless medium. However, that alone does not always guarantee improvement in overall system performance. In this paper, we have identified several criteria and investigated their interrelationships and impact on overall system performance in this context. Our methodology uses optimisation techniques using multicriteria decision analysis. We use analytic hierarchy process (AHP) to identify relative weights of different criteria under different application-specific scenario in order to solve the optimisation problem for each scenario by TOPSIS approach. The result shows that the parameter setting required to get optimum performance is application-specific; depending on the situation or application-scenario, several parameters need to be controlled to get better system performance.

Use of directional antenna in the context of ad hoc wireless networks can largely reduce radio interference, thereby improving the utilization of wireless medium. Our major contribution in this paper is to devise a routing strategy, along with a MAC protocol, that exploits the advantages of directional antenna in ad hoc networks for improved system performance. In this paper, we have illustrated a MAC and routing protocol for ad hoc networks using directional antenna with the objective of effective load balancing through the selection of maximally zone disjoint routes. Zone-disjoint routes would minimize the effect of route coupling by selecting routes in such a manner that data communication over one route will minimally interfere with data communication over the others. In our MAC protocol, each node keeps certain neighborhood status information dynamically in order that each node is aware of its neighborhood and communications going on in its neighborhood at that instant of time. This status information from each node is propagated periodically throughout the network. This would help each node to capture the approximate network status periodically that helps each node to become topology-aware and aware of communications going on in the network, although in an approximate manner. With this status information, each intermediate node adaptively computes routes towards destination. The performance of the proposed framework has been evaluated on QualNet Network Simulator with DSR (as in QualNet) as a benchmark. Our proposed mechanism shows four to five times performance improvement over DSR, thus demonstrating the effectiveness of this proposal.