Asynchronous bi-directional relay-assisted communication networks

We consider an asynchronous bi-directional relay network, consisting of two singleantenna transceivers and multiple single-antenna relays, where the transceiver-relay paths are subject to different relaying and/or propagation delays. Such a network can be viewed as a multipath channel which can cause inter-symbol-interference (ISI) in the signals received by the two transceivers. Hence, we model such a communication scheme as a frequency selective multipath channel which produces ISI at the two transceivers, when the data rates are relatively high. We study both multi- and single-carrier communication schemes in such networks. In a multi-carrier communication scheme, to tackle ISI, the transceivers employ an orthogonal frequency division multiplexing (OFDM) scheme to diagonalize the end-to-end channel. The relays use simple amplify-and-forward relaying, thereby materializing a distributed beamformer. For such a scheme, we propose two different algorithms, based on the max-min fair design approach, to calculate the subcarrier power loading at the transceivers as well as the relay beamforming weights. In a single-carrier communication, assuming a block transmission/reception scheme, block channel equalization is used at the both transceivers to combat the inter-blockinterference (IBI). Assuming a limited total transmit power budget, we minimize the total mean squared error (MSE) of the estimated received signals at the both transceivers by optimally obtaining the transceivers??? powers and the relay beamforming weight vector as well as the block channel equalizers at the two transceivers

Asynchronous bi-directional relay-assisted communication networks

We consider an asynchronous bi-directional relay network, consisting of two singleantenna transceivers and multiple single-antenna relays, where the transceiver-relay paths are subject to different relaying and/or propagation delays. Such a network can be viewed as a multipath channel which can cause inter-symbol-interference (ISI) in the signals received by the two transceivers. Hence, we model such a communication scheme as a frequency selective multipath channel which produces ISI at the two transceivers, when the data rates are relatively high. We study both multi- and single-carrier communication schemes in such networks. In a multi-carrier communication scheme, to tackle ISI, the transceivers employ an orthogonal frequency division multiplexing (OFDM) scheme to diagonalize the end-to-end channel. The relays use simple amplify-and-forward relaying, thereby materializing a distributed beamformer. For such a scheme, we propose two different algorithms, based on the max-min fair design approach, to calculate the subcarrier power loading at the transceivers as well as the relay beamforming weights. In a single-carrier communication, assuming a block transmission/reception scheme, block channel equalization is used at the both transceivers to combat the inter-blockinterference (IBI). Assuming a limited total transmit power budget, we minimize the total mean squared error (MSE) of the estimated received signals at the both transceivers by optimally obtaining the transceivers??? powers and the relay beamforming weight vector as well as the block channel equalizers at the two transceivers

Simultaneous Wireless Information and Power Transfer for Decode-and-Forward Multi-Hop Relay Systems in Energy-Constrained IoT Networks

This paper studies a multi-hop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) system where a source sends data to a destination with the aid of multi-hop relays which do not depend on an external energy source. To this end, we apply power splitting (PS) based SWIPT relaying protocol so that the relays can harvest energy from the received signals from the previous hop to reliably forward the information of the source to the destination. We aim to solve two optimization problems relevant to our system model. First, we minimize the transmit power at the source under the individual quality-of-service (QoS) threshold constraints of the relays and the destination nodes by optimizing PS ratios at the relays. The second is to maximize the minimum system achievable rate by optimizing the PS ratio at each relay. Based on convex optimization techniques, the globally optimal PS ratio solution is obtained in closed-form for both problems. By setting the QoS threshold constraint the same for each node for the source transmit power problem, we discovered that either the minimum source transmit power or the maximum system throughput can be found using the same approach. Numerical results demonstrate the superiority of the proposed optimal SWIPT PS design over conventional fixed PS ratio schemes.Comment: 14 pages, 14 figures, Accepted for Publication in IEEE Internet of Things Journa

Deep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints

Deep neural networks (DNNs) are currently emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints or base station quota, guaranteeing constraint satisfaction becomes a fundamental challenge. In this thesis, I propose a novel unsupervised learning framework to solve the classical power control and user assignment problem in a multi-user interference channel, where the objective is to maximize the network sum-rate with QoS, power budget, and base station quota constraints. The proposed method utilizes a differentiable projection function, defined both implicitly and explicitly, to project the output of the DNN to the feasible set of the problem. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate, but also achieve zero constraint violation probability, compared to the existing DNNs, and also outperform the optimization-based benchmarks in computation time

Efficient Resource Allocation Schemes for Search.

This thesis concerns the problem of efficient resource allocation under constraints. In many applications a finite budget is used and allocating it efficiently can improve performance. In the context of medical imaging the constraint is exposure to ionizing radiation, e.g., computed tomography (CT). In radar and target tracking time spent searching a particular region before pointing the radar to another location or transmitted energy level may be limited. In airport security screening the constraint is screeners' time. This work addresses both static and dynamic resource allocation policies where the question is: How a budget should be allocated to maximize a certain performance criterion. In addition, many of the above examples correspond to a needle-in-a-haystack scenario. The goal is to find a small number of details, namely `targets', spread out in a far greater domain. The set of `targets' is named a region of interest (ROI). For example, in airport security screening perhaps one in a hundred travelers carry prohibited item and maybe one in several millions is a terrorist or a real threat. Nevertheless, in most aforementioned applications the common resource allocation policy is exhaustive: all possible locations are searched with equal effort allocation to spread sensitivity. A novel framework to deal with the problem of efficient resource allocation is introduced. The framework consists of a cost function trading the proportion of efforts allocated to the ROI and to its complement. Optimal resource allocation policies minimizing the cost are derived. These policies result in superior estimation and detection performance compared to an exhaustive resource allocation policy. Moreover, minimizing the cost has a strong connection to minimizing both probability of error and the CR bound on estimation mean square error. Furthermore, it is shown that the allocation policies asymptotically converge to the omniscient allocation policy that knows the location of the ROI in advance. Finally, a multi-scale allocation policy suitable for scenarios where targets tend to cluster is introduced. For a sparse scenario exhibiting good contrast between targets and background this method achieves significant performance gain yet tremendously reduces the number of samples required compared to an exhaustive search.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60698/1/bashan_1.pd

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Interferometric Methods

Future radio telescopes promise great advances in resolution and sensitivity. These include the Square Kilometer Array, a two array instrument, in South Africa and Australia. Similarly, the next generation Very Large Array (ngVLA) is being designed for construction in North America. These arrays all promise exceptional advances in sensitivity, angular resolution, and survey speed. The SKA and ngVLA are both specified to have sensitivities at the level of $\mu$Jy's. The SKA-Low instrument will consist of a huge number of dipoles antennas in Australia which is pushing the bounds of current FX correlator technology with $\mathbb{O}(n^2)$ scaling, where $n$ is the number of antennas. The design proposals for these instruments include a dense core of antennas, necessitating advances in imaging methods for these very dense cores versus more traditionally sparse instruments. Another ambitious experiment is the Hydrogen Epoch of Reionisation Array (HERA) in South Africa which hopes to make the first direct detection of the Epoch of Reionisation through the red-shifted H{\sc i} signal which is a factor of $10^{5}$ smaller than the thermal-like noise. In this thesis, these problems are tackled by re-examining the underlying principles of interferometry. The first working example of a direct imaging correlator is presented which allows images to be formed directly from the voltages off each antenna in a dense array, without an expensive cross-correlation operation as is typically required. A detailed discussion is given of how standard steps in interferometric imaging differ in this new scheme, including calibration. Additionally the first wide field direct imaging correlator is presented, which allows the problems of non-coplanarity to be dealt with for both sparse and dense arrays in a very efficient manner on modern GPU compute hardware. These are, to the best of the authors knowledge, the only working implementations of a direct imaging correlator for generic arrays with no restrictions on the geometry of the array or homogeneity of constituent receiver elements. These new approaches have been published in the scientific literature as discussed in the Declaration. Moving on from this, the closure phase bispectrum is presented as a way of uncovering the cosmological Epoch of Reionisation signal from the H{\sc i} line. This is using the HERA telescope, which consists of a dense core of parabolic antennas in a highly redundant layout. A data reduction and processing pipeline for the HERA telescope is constructed and presented, for use with the bispectrum. Initial results towards a cosmologial limit are reported. The HERA telescope relies on redundancy in its antenna elements for its calibration and measurement strategy. The bispectrum with its unique mathematical propeties, in combination with forward modelling, is shown to be a potent tool for probing departures from the assumed reudundancy. It is shown, through this method, that HERA suffers significant direction-dependent non-redundancies in the dataset used for our analysis, which are extremely difficult to calibrate out. Finally, the problem of wide-field imaging in next generation arrays is tackled through the development and implementation of a new scheme of wide field imaging. This uses a new method of parallelising the problem of wide-field imaging, and is intended for use with the very large datasets that will be produced by upcoming instruments. Two schemes are introduced: $w$-towers, and Improved $w$-towers. The latter generalises the former in combination with advances in optimal convolution theory for the radio astronomy ``gridding'' problem. The theory behind this approach is explored, and a high performance implementation is presented for $w$-towers and Improved $w$-stacking within Improved $w$-towers.ARM Ltd iCase Sponsorshi

Towards naturalistic scanning environments for wearable magnetoencephalography

Magnetoencephalography (MEG) is a neuroimaging technique that probes human brain function, by measuring the magnetic fields generated at the scalp by current flow in assemblies of neurons. A direct measure of neural activity, MEG offers high spatiotemporal resolution, but limitations imposed by superconducting sensor technologies impede its clinical utility. Specifically, neuromagnetic fields are up to a billion times smaller than that of the Earth, meaning MEG must be performed inside a magnetically shielded room (MSR), which is typically expensive, heavy, and difficult to site. Furthermore, current MEG systems employ superconducting quantum interference devices (SQUIDs) to detect these tiny magnetic fields, however, these sensors require cryogenic cooling with liquid helium. Consequently, scanners are bulky, expensive, and the SQUIDs must be arranged in a fixed, one-size-fits-all array. Any movement relative to the fixed sensors impacts data quality, meaning participant movement in MEG is severely restricted. The development of technology to enable a wearable MEG system allowing free participant movement would generate a step change for the field. Optically-pumped magnetometers (OPMs) are an alternative magnetic field detector recently developed with sufficient sensitivity for MEG measurements. Operating at body temperature, in a small and lightweight sensor package, OPMs offer the potential for a wearable MEG scanner that allows participant movement, with sensors mounted on the scalp in a helmet or cap. However, OPMs operate around a zero-field resonance, resulting in a narrow dynamic range that may be easily exceeded by movement of the sensor within a background magnetic field. Enabling a full range of participant motion during an OPM-MEG scan therefore presents a significant challenge, requiring precise control of the background magnetic field. This thesis describes the development of techniques to better control the magnetic environment for OPM-MEG. This includes greater reduction of background magnetic fields over a fixed region to minimise motion artefacts and facilitate larger movements, and the application of novel, multi-coil active magnetic shielding systems to enable flexibility in participant positioning within the MSR. We outline a new approach to map background magnetic fields more accurately, reducing the remnant magnetic field to <300 pT and yielding a five-fold reduction in motion artefact, to allow detection of a visual steady-state evoked response during continuous head motion. Employing state-of-the-art, triaxial OPMs alongside this precision magnetic field control technique, we map motor function during a handwriting task involving naturalistic head movements and investigate the advantages of triaxial sensitivity for MEG data analysis. Using multi-coil active magnetic shielding, we map motor function consistently in the same participant when seated and standing, and demonstrate the first OPM-MEG hyperscanning experiments. Finally, we outline how the integration of a multi-coil system into the walls of a lightweight MSR, when coupled with field control over a larger volume, provides an open scanning environment. In sum, these developments represent a significant step towards realising the full potential of OPM-MEG as a wearable functional neuroimaging technology