SaiDhiraj Amuru

Bio: SaiDhiraj Amuru is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Jamming & Computer science. The author has an hindex of 14, co-authored 60 publications receiving 764 citations. Previous affiliations of SaiDhiraj Amuru include Indian Institute of Technology, Hyderabad & Virginia Tech.

Learning distributed caching strategies in small cell networks

Abstract: Caching has emerged as a vital tool in modern communication systems for reducing peak data rates by allowing popular files to be pre-fetched and stored locally at end users' devices. With the shift in paradigm from homogeneous cellular networks to the heterogeneous ones, the concept of data offloading to small cell base stations (sBS) has garnered significant attention. Caching at these small cell base stations has recently been proposed, where popular files are pre-fetched and stored locally in order to avoid bottlenecks in the limited capacity backhaul connection link to the core network. In this paper, we study distributed caching strategies in such a heterogeneous small cell wireless network from a reinforcement learning perspective. Using state of the art results, it can be shown that the optimal joint cache content placement in the sBSs turns out to be a NP-hard problem even when the sBS's are aware of the popularity profile of the files that are to be cached. To address this problem, we propose a coded caching framework, where the sBSs learn the popularity profile of the files (based on their demand history) via a combinatorial multi-armed bandit framework. The sBSs then pre-fetch segments of the Fountain-encoded versions of the popular files at regular intervals to serve users' requests. We show that the proposed coded caching framework can be modeled as a linear program that takes into account the network connectivity and thereby jointly designs the caching strategies. Numerical results are presented to show the benefits of the joint coded caching technique over naive decentralized cache placement strategies.

6G White Paper on Machine Learning in Wireless Communication Networks

Abstract: The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.

A Communications Jamming Taxonomy

Abstract: With the now widespread availability of software-defined radio technology for wireless networks, the distinction between jamming in the original electronic warfare sense and wireless cybersecurity attacks becomes hazy. A taxonomy delineates these concepts in the rapidly expanding field of wireless security, classifying communication jammers' theoretical behaviors and characteristics.

Jamming Bandits—A Novel Learning Method for Optimal Jamming

Abstract: Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter–receiver pair. We formalize the problem using a multiarmed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present online learning algorithms to maximize the jamming efficacy against static transmitter–receiver pairs and prove that these algorithms converge to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sublinear, i.e., the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter–receiver pairs.

Optimal Jamming Against Digital Modulation

Abstract: Jamming attacks can significantly impact the performance of wireless communication systems, and can lead to significant overhead in terms of re-transmissions and increased power consumption. This paper considers the problem of optimal jamming over an additive white Gaussian noise channel. We derive the optimal jamming signal for various digital amplitude-phase-modulated constellations and show that it is not always optimal to match the jammer’s signal to the victim signal in order to maximize the error probability at the victim receiver. Connections between the optimum jammer obtained in this analysis and the well-known pulsed jammer, popularly analyzed in the context of spread spectrum communication systems, are illustrated. The gains obtained by the jammer when it knows the victim’s modulation scheme and uses the optimal jamming signals obtained in this paper as opposed to conventional additive white Gaussian noise jamming are evaluated in terms of the additional signal power needed by the victim receiver to achieve the same error rates under these two jamming strategies. We then extend these findings to obtain the optimal jamming signal distribution: 1) when the victim uses an orthogonal frequency-division multiplexing (OFDM)-modulated signal and 2) when there is multiple jammer attacking a single victim transmitter–receiver pair. Numerical results are presented in all the above cases to validate the theoretical inferences presented.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

Abstract: This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers, without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical layer message authentication is also briefly introduced. The survey concludes with observations on potential research directions in this area.

An Introduction to Game Theory

Abstract: This chapter is devoted to a more detailed examination of game theory. Game theory is an important tool for analyzing strategic behavior, is concerned with how individuals make decisions when they recognize that their actions affect, and are affected by, the actions of other individuals or groups. Strategic behavior recognizes that the decision-making process is frequently mutually interdependent. Game theory is the study of the strategic behavior involving the interaction of two or more individuals, teams, or firms, usually referred to as players. Two game theoretic scenarios were examined in this chapter: Simultaneous-move and multi-stage games. In simultaneous-move games the players effectively move at the same time. A normal-form game summarizes the players, possible strategies and payoffs from alternative strategies in a simultaneous-move game. Simultaneous-move games may be either noncooperative or cooperative. In contrast to noncooperative games, players of cooperative games engage in collusive behavior. A Nash equilibrium, which is a solution to a problem in game theory, occurs when the players’ payoffs cannot be improved by changing strategies. Simultaneous-move games may be either one-shot or repeated games. One-shot games are played only once. Repeated games are games that are played more than once. Infinitely-repeated games are played over and over again without end. Finitely-repeated games are played a limited number of times. Finitely-repeated games have certain or uncertain ends.

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Abstract: As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.