Distributed Cooperative Communications and Wireless Power Transfer

In telecommunications, distributed cooperative communications refer to techniques which allow different users in a wireless network to share or combine their information in order to increase diversity gain or power gain. Unlike conventional point-to-point communications maximizing the performance of the individual link, distributed cooperative communications enable multiple users to collaborate with each other to achieve an overall improvement in performance, e.g., improved range and data rates. The first part of this dissertation focuses the problem of jointly decoding binary messages from a single distant transmitter to a cooperative receive cluster. The outage probability of distributed reception with binary hard decision exchanges is compared with the outage probability of ideal receive beamforming with unquantized observation exchanges. Low- dimensional analysis and numerical results show, via two simple but surprisingly good approximations, that the outage probability performance of distributed reception with hard decision exchanges is well-predicted by the SNR of ideal receive beamforming after subtracting a hard decision penalty of slightly less than 2 dB. These results, developed in non-asymptotic regimes, are consistent with prior asymptotic results (for a large number of nodes and low per-node SNR) on hard decisions in binary communication systems. We next consider the problem of estimating and tracking channels in a distributed transmission system with multiple transmitters and multiple receivers. In order to track and predict the effective channel between each transmit node and each receive node to facilitate coherent transmission, a linear time-invariant state- space model is developed and is shown to be observable but nonstabilizable. To quantify the steady-state performance of a Kalman filter channel tracker, two methods are developed to efficiently compute the steady-state prediction covariance. An asymptotic analysis is also presented for the homogenous oscillator case for systems with a large number of transmit and receive nodes with closed-form results for all of the elements in the asymptotic prediction covariance as a function of the carrier frequency, oscillator parameters, and channel measurement period. Numeric results confirm the analysis and demonstrate the effect of the oscillator parameters on the ability of the distributed transmission system to achieve coherent transmission. In recent years, the development of efficient radio frequency (RF) radiation wireless power transfer (WPT) systems has become an active research area, motivated by the widespread use of low-power devices that can be charged wirelessly. In this dissertation, we next consider a time division multiple access scenario where a wireless access point transmits to a group of users which harvest the energy and then use this energy to transmit back to the access point. Past approaches have found the optimal time allocation to maximize sum throughput under the assumption that the users must use all of their harvested power in each block of the harvest-then-transmit protocol. This dissertation considers optimal time and energy allocation to maximize the sum throughput for the case when the nodes can save energy for later blocks. To maximize the sum throughput over a finite horizon, the initial optimization problem is separated into two sub-problems and finally can be formulated into a standard box- constrained optimization problem, which can be solved efficiently. A tight upper bound is derived by relaxing the energy harvesting causality. A disadvantage of RF-radiation based WPT is that path loss effects can significantly reduce the amount of power received by energy harvesting devices. To overcome this problem, recent investigations have considered the use of distributed transmit beamforming (DTB) in wireless communication systems where two or more individual transmit nodes pool their antenna resources to emulate a virtual antenna array. In order to take the advantages of the DTB in the WPT, in this dissertation, we study the optimization of the feedback rate to maximize the energy efficiency in the WPT system. Since periodic feedback improves the beamforming gain but requires the receivers to expend energy, there is a fundamental tradeoff between the feedback period and the efficiency of the WPT system. We develop a new model to combine WPT and DTB and explicitly account for independent oscillator dynamics and the cost of feedback energy from the receive nodes. We then formulate a Normalized Weighted Mean Energy Harvesting Rate (NWMEHR) maximization problem to select the feedback period to maximize the weighted averaged amount of net energy harvested by the receive nodes per unit of time as a function of the oscillator parameters. We develop an explicit method to numerically calculate the globally optimal feedback period

Architectures and synchronization techniques for distributed satellite systems: a survey

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.This work was supported by the Luxembourg National Research Fund (FNR), through the CORE Project COHEsive SATellite (COHESAT): Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications, under Grant FNR11689919.Award-winningPostprint (published version

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field

Control Theory-Inspired Acceleration of the Gradient-Descent Method: Centralized and Distributed

Mathematical optimization problems are prevalent across various disciplines in science and engineering. Particularly in electrical engineering, convex and non-convex optimization problems are well-known in signal processing, estimation, control, and machine learning research. In many of these contemporary applications, the data points are dispersed over several sources. Restrictions such as industrial competition, administrative regulations, and user privacy have motivated significant research on distributed optimization algorithms for solving such data-driven modeling problems. The traditional gradient-descent method can solve optimization problems with differentiable cost functions. However, the speed of convergence of the gradient-descent method and its accelerated variants is highly influenced by the conditioning of the optimization problem being solved. Specifically, when the cost is ill-conditioned, these methods (i) require many iterations to converge and (ii) are highly unstable against process noise. In this dissertation, we propose novel optimization algorithms, inspired by control-theoretic tools, that can significantly attenuate the influence of the problem's conditioning. First, we consider solving the linear regression problem in a distributed server-agent network. We propose the Iteratively Pre-conditioned Gradient-Descent (IPG) algorithm to mitigate the deleterious impact of the data points' conditioning on the convergence rate. We show that the IPG algorithm has an improved rate of convergence in comparison to both the classical and the accelerated gradient-descent methods. We further study the robustness of IPG against system noise and extend the idea of iterative pre-conditioning to stochastic settings, where the server updates the estimate based on a randomly selected data point at every iteration. In the same distributed environment, we present theoretical results on the local convergence of IPG for solving convex optimization problems. Next, we consider solving a system of linear equations in peer-to-peer multi-agent networks and propose a decentralized pre-conditioning technique. The proposed algorithm converges linearly, with an improved convergence rate than the decentralized gradient-descent. Considering the practical scenario where the computations performed by the agents are corrupted, or a communication delay exists between them, we study the robustness guarantee of the proposed algorithm and a variant of it. We apply the proposed algorithm for solving decentralized state estimation problems. Further, we develop a generic framework for adaptive gradient methods that solve non-convex optimization problems. Here, we model the adaptive gradient methods in a state-space framework, which allows us to exploit control-theoretic methodology in analyzing Adam and its prominent variants. We then utilize the classical transfer function paradigm to propose new variants of a few existing adaptive gradient methods. Applications on benchmark machine learning tasks demonstrate our proposed algorithms' efficiency. Our findings suggest further exploration of the existing tools from control theory in complex machine learning problems. The dissertation is concluded by showing that the potential in the previously mentioned idea of IPG goes beyond solving generic optimization problems through the development of a novel distributed beamforming algorithm and a novel observer for nonlinear dynamical systems, where IPG's robustness serves as a foundation in our designs. The proposed IPG for distributed beamforming (IPG-DB) facilitates a rapid establishment of communication links with far-field targets while jamming potential adversaries without assuming any feedback from the receivers, subject to unknown multipath fading in realistic environments. The proposed IPG observer utilizes a non-symmetric pre-conditioner, like IPG, as an approximation of the observability mapping's inverse Jacobian such that it asymptotically replicates the Newton observer with an additional advantage of enhanced robustness against measurement noise. Empirical results are presented, demonstrating both of these methods' efficiency compared to the existing methodologies

Characterization of UAV-based Wireless Channels With Diverse Antenna Configurations

In the next wave of swarm-based applications, unmanned aerial vehicles (UAVs) need to communicate with peer drones in any direction of a three-dimensional (3D) space. On a given drone and across drones, various antenna positions and orientations are possible. We know that, in free space, high levels of signal loss are expected if the transmitting and receiving antennas are cross polarized. However, increasing the reflective and scattering objects in the channel between a transmitter and receiver can cause the received polarization to become completely independent from the transmitted polarization, making the cross-polarization of antennas insignificant. Usually, these effects are studied in the context of cellular and terrestrial networks and have not been analyzed when those objects are the actual bodies of the communicating drones that can take different relative directions or move at various elevations. In this work, we show that the body of the drone can affect the received power across various antenna orientations and positions and act as a local scatterer that increases channel depolarization, reducing the cross-polarization discrimination (XPD). In addition to communicating with other UAVs in a swarm, UAVs can also serve users on the ground. For example, at ultra-low altitudes, an unmanned aerial vehicle (UAV) can act as a personal base station where it communicates only with one or two users on the ground. The communication device used by a user can be in their pocket, held by hand, or attached to their bodies. In these scenarios, the wireless channel can go through different fading levels, depending on the UAV’s location, user orientation, the location of the UE near the user’s body, and the frequency of the transmitted signal. The extent to which these factors can affect Air-to-Ground channels at ultra-low altitudes is studied in this work. We answer questions regarding how the human body and different use-cases of holding a communication device on the ground can affect the quality of the wireless channel and the optimal UAV hovering location. Furthermore, we demonstrate how the observed effects can be leveraged to our advantage and increase the physical layer security of UAV-assisted networks relying on the human-induced effects. Finally, in situations where a UAV swarm needs to communicate with a target that is far or surrounded by undesired receivers, beamforming can be an attractive solution. With beamforming, the transmitted signal becomes shaped towards a certain direction confining its spatial signature and increasing the received signal-to-noise-ratio (SNR) at the receiver. However, phase synchronization across the swarm is difficult to achieve and there will always exist some degree of phase incoherency across the transmitted signals from the distributed UAVs. Phase differences between the distributed nodes would result in signals arriving at different times and their phases might not align with each other resulting in reductions in beamforming gain. Hence, a method to increase phase coherency at the receiver with limited channel overhead is desired. To this end, we propose a UAV rotation-based method through which the UAV, relying on its heterogeneous body structure, can alter the phase of the incoming signals and increase the beamformed signal level

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity