arXiv subjects

Jithin Jagannath

Publications and source records attributed to Jithin Jagannath.

27 records · Page 2Linked to original sources

arXiv2021

Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

cs.LG↗

arXiv2020

Jam-Guard: Low-Cost, Hand-held Device for First Responders to Detect and Localize Jammers

Intentional and unintentional interferences collectively referred to as Radio Frequency Interference (RFI) result in severe security threat to the public safety, first responder emergency rescue and military missions. Such RFI if not detected and localized can disrupt the wireless communication which forms the backbone of first responder and military operations. The prime objective of this work was to design and prototype a RFI detection and localization device, Jam-Guard that significantly outperforms traditional approaches in real-life deployment yet be computationally feasible to be developed as a low SWaC (size, weight, and cost) device. The proposed device employs a unique combination of robust parallel detection algorithms based on Kurtosis and FRactional Fourier Transform (FRFT) with Golden Section Search algorithm to rapidly detect RFI that affects critical communication signals. The localization scheme is designed to leverage the FRFT output from the detection phase to ease the computational load. We give a detailed account of the device prototyping and evaluation. To demonstrate the efficacy of the proposed detection approach as opposed to conventional energy detection (employed in several commercial interference detectors), we compare the two schemes on test and target platforms. The proposed scheme depicted significant improvement (~40 dB) in detection probability both in preliminary implementation and target prototype experiments.

eess.SP↗

arXiv2020

Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment

Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Enabling autonomy and intelligence to the UAS will help overcome this hurdle and expand its use improving safety and efficiency. The exponential increase in computing resources and the availability of large amount of data in this digital era has led to the resurgence of machine learning from its last winter. Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter. A key area of focus that will be essential to enable autonomy to UAS is computer vision. Accordingly, we discuss how deep learning approaches have been used to accomplish some of the basic tasks that contribute to providing UAS autonomy. Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS. Next, we provide the reader with directions to choose appropriate simulation suites and hardware platforms that will help to rapidly prototype novel machine learning based solutions for UAS. We additionally discuss the open problems and challenges pertaining to each aspect of developing autonomous UAS solutions to shine light on potential research areas.

cs.LG↗

arXiv2020

Breaking the Bound: Rate-2, Full Diversity, Orthogonal MIMO-STBC Transceiver Design

Space Time Block Codes (STBCs) from orthogonal designs have attracted significant interest in recent years. However, with the growing demand for higher capacity schemes, the multiantenna transmission techniques must support and achieve higher symbol transmission rates. In this article, we focus on three and four transmit antenna schemes. For over two decades, STBC schemes for three and four transmit antennas that achieve a very high symbol transmission rate while being orthogonal, fully diverse, delay-efficient, and with very low decoding complexity have not been achieved. The schemes proposed so far trades off orthogonality, delay, diversity, or decoding complexity while achieving rate or vice-versa. This work is first of its kind to solve this problem while fulfilling all the desired properties. In this work, we carefully study the various aspects that must be considered in designing higher symbol transmission rate STBCs. The proposed designs, hereby referred to as, Jagannath schemes - for 4 x 3 and 4 x 4 configurations are orthogonal, achieve full diversity, and support a symbol transmission rate of 2 symbols/s/Hz. The design methodology follows a coding gain maximization approach. The low decoding complexity procedure to decode the proposed transmission schemes are proposed as well as extensively evaluated in simulations under diverse settings. The performance of proposed schemes are empirically compared to two state-of-the-art schemes; ACIOD and Jafarkhani. Jagannath 4 x 3 was shown to outperform ACIOD by ~12 dB while Jagannath 4 x 4 exhibited a ~7 dB superior performance in contrast to Jafarkhani at 4 bpcu. This motivates the adoption of Jagannath schemes in tactical and commercial communication systems to effectively double the throughput or to extend the

cs.NI↗

arXiv2020

High Rate-Reliability Beamformer Design for 2x2 MIMO-OFDM System Under Hostile Jamming

Multiple-input multiple-output (MIMO) systems find immense potential and applicability in the long term evolution (LTE), 5G, Internet of Things (IoT), vehicular ad hoc networks (VANETs), and tactical communication systems. Jamming poses significant communication hindrance as well as security risks to the wireless communication systems. The achievable rate and reliability are the two most compromised aspects of a wireless link under such severe jamming. Owing to the high capacity and reliability of MIMO systems, they are increasingly used in tactical and critical applications. Therefore, it becomes essential to assess and enhance their sustenance under hostile jamming scenarios. To this end, we address the rate and reliability requirements of a MIMO OFDM system and propose a novel rate-reliability beamformer transceiver design for highly reliable and spectrally efficient operation under the most detrimental jamming attacks. We consider the disguised all band and multiband jamming where the jammer continuously attempts to mimic the legit transmissions. Additionally, we evaluate the rate and reliability performance under barrage jamming. The significant contributions of the proposed rate-reliability beamformer scheme are: (i) achieves a minimum of 2 orders of magnitude better reliability in contrast to the state-of-the-art, (ii) outperforms the state-of-the-art scheme by 1.4x with regards to achievable spectral efficiency, (iii) a very low complexity (O(|Q|)) decoder is presented, and (iv) first work to evaluate the performance of state-of-the-art transmit diversity scheme under hostile jamming attacks.

cs.NI↗

arXiv2019

Towards Higher Spectral Efficiency: Rate-2 Full-Diversity Complex Space-Time Block Codes

The upcoming 5G networks demand high-speed and high spectral-efficiency communications to keep up with the proliferating traffic demands. To this end, Massive multiple-input multiple-output (MIMO) techniques have gained significant traction owing to its ability to achieve these without increasing bandwidth or density of base stations. The preexisting space-time block code (STBC) designs cannot achieve a rate of more than 1 for more than two transmit antennas while preserving the orthogonality and full diversity conditions. In this paper, we present Jagannath codes - a novel complex modulation STBC, that achieves a very high rate of 2 for three and four transmit antennas. The presented designs achieve full diversity and overcome the previously achieved rates with the three and four antenna MIMO systems. We present a detailed account of the code construction of the proposed designs, orthogonality and full diversity analysis, transceiver model and conditional maximum likelihood (ML) decoding. In an effort to showcase the improvement achieved with the presented designs, we compare the rates and delays of some of the known STBCs with the proposed designs. The effective spectral efficiency and coding gain of the presented designs are compared to the Asymmetric Coordinate Interleaved design (ACIOD) and Jafarkhani code. We presented an effective spectral efficiency improvement by a factor of 2 with the proposed Jagannath codes. Owing to the full diversity of the presented designs, we demonstrate significant coding gains (6 dB and 12 dB) with the proposed designs.

cs.IT↗

arXiv2019

Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

cs.NI↗

arXiv2019

VL-ROUTE: A Cross-Layer Routing Protocol for Visible Light Ad Hoc Network

Visible Light Ad Hoc Networks (LANETs) is being perceived as an emerging technology to complement Radio Frequency (RF) based ad hoc networks to reduce congestion in the overloaded RF spectrum. LANET is intended to support scenarios requiring dense deployment and high data rates. In Visible Light Communication (VLC), most of the attention has been centered around physical layer with emphasis on point-to-point communication. In this work, we focus on designing a routing protocol specifically to overcome the unique challenges like blockage and deafness that render routes in LANETs highly unstable. Therefore, we propose a cross-layer optimized routing protocol (VL-ROUTE) that interacts closely with the Medium Access Control (MAC) layer to maximize the throughput of the network by taking into account the reliability of routes. To accomplish this in a distributed manner, we carefully formulate a Route Reliability Score (RRS) that can be computed by each node in the network using just the information gathered from its immediate neighbors. Each node computes an RRS for every known sink in the network. RRS of a given node can be considered as an estimate of the probability of reaching a given sink via that node. The RSS value is then integrated to the utility based three-way handshake process used by the MAC protocol (VL-MAC) to mitigate the effects of deafness, blockage, hidden node, and maximize the probability of establishing full-duplex links. All these factors contribute towards maximizing the network throughput. Extensive simulation of VL-ROUTE shows 124% improvement in network throughput over a network that uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) along with shortest path routing. Additionally, VL-ROUTE also showed up to 21% improvement in throughput over the network that uses VL-MAC along with a geographic routing.

cs.NI↗

arXiv2019

HELPER: Heterogeneous Efficient Low Power Radio for Enabling Ad Hoc Emergency Public Safety Networks

Natural and man-made disasters have been causing destruction and distress to humanity all over the world. In these scenarios, communication infrastructures are the most affected entities making emergency response operations extremely challenging. This invokes a need to equip the affected people and the emergency responders with the ability to rapidly set up and use independent means of communication. Therefore, in this work, we present a complete end-to-end solution that can connect survivors of a disaster with each other and the authorities using a completely self-sufficient ad hoc network that can be setup rapidly. Accordingly, we develop a Heterogeneous Efficient Low Power Radio (HELPER) that acts as an access point for end-users to connect using custom website application. These HELPERs then coordinate with each other to form a LoRa based ad hoc network. To this end, we propose a novel cross-layer optimized distributed energy-efficient routing (SEEK) algorithm that aims to maximize the network lifetime. The HELPER is prototyped using WiFi enabled Raspberry Pi and LoRa module that is configured to run using Li-ion batteries. We implement the required cross-layer protocol stack along with the SEEK routing algorithm. We have conducted demonstrations to establish the feasibility of exchanging of text messages over the HELPER network, live map updates, ability to send distress messages to authorities. Emergency responders can leverage this technology to remotely monitor the connectivity of the affected area and alert users of imminent dangers. SEEK algorithm was shown to outperform a greedy geographical routing algorithm implemented on HELPER testbed by up to 53 % in terms of network lifetime and up to 28 % in terms of throughput. Overall, we hope this technology will become instrumental in improving the efficiency and effectiveness of public safety activities.

cs.NI↗