Towards low complexity matching theory for uplink wireless communication systems

Millimetre wave (mm-Wave) technology is considered a promising direction to achieve the high quality of services (QoSs) because it can provide high bandwidth, achieving a higher transmission rate due to its immunity to interference. However, there are several limitations to utilizing mm-Wave technology, such as more extraordinary precision hardware is manufactured at a higher cost because the size of its components is small. Consequently, mm-Wave technology is rarely applicable for long-distance applications due to its narrow beams width. Therefore, using cell-free massive multiple input multiple output (MIMO) with mm-Wave technology can solve these issues because this architecture of massive MIMO has better system performance, in terms of high achievable rate, high coverage, and handover-free, than conventional architectures, such as massive MIMO systems’ co-located and distributed (small cells). This technology necessitates a significant amount of power because each distributed access point (AP) has several antennas. Each AP has a few radio frequency (RF) chains in hybrid beamforming. Therefore more APs mean a large number of total RF chains in the cell-free network, which increases power consumption. To solve this problem, deactivating some antennas or RF chains at each AP can be utilized. However, the size of the cell-free network yields these two options as computationally demanding. On the other hand, a large number of users in the cell-free network causes pilot contamination issue due to the small length of the uplink training phase. This issue has been solved in the literature based on two options: pilot assignment and pilot power control. Still, these two solutions are complex due to the cell-free network size. Motivated by what was mentioned previously, this thesis proposes a novel technique with low computational complexity based on matching theory for antenna selection, RF chains activation, pilot assignment and pilot power control. The first part of this thesis provides an overview of matching theory and the conventional massive MIMO systems. Then, an overview of the cell-free massive MIMO systems and the related works of the signal processing techniques of the cell-free mm-Wave massive MIMO systems to maximize energy efficiency (EE), are provided. Based on the limitations of these techniques, the second part of this thesis presents a hybrid beamforming architecture with constant phase shifters (CPSs) for the distributed uplink cell-free mm-Wave massive MIMO systems based on exploiting antenna selection to reduce power consumption. The proposed scheme uses a matching technique to obtain the number of selected antennas which can contribute more to the desired signal power than the interference power for each RF chain at each AP. Therefore, the third part of this thesis solves the issue of the huge complexity of activating RF chains by presenting a low-complexity matching approach to activate a set of RF chains based on the Hungarian method to maximize the total EE in the centralized uplink of the cell-free mm-Wave massive MIMO systems when it is proposed hybrid beamforming with fully connected phase shifters network. The pilot contamination issue has been discussed in the last part of this thesis by utilizing matching theory in pilot assignment and pilot power control design for the uplink of cell-free massive MIMO systems to maximize SE. Firstly, an assignment optimization problem has been formulated to find the best possible pilot sequences to be inserted into a genetic algorithm (GA). Therefore, the GA will find the optimal solution. After that, a minimum-weighted assignment problem has been formulated regarding the power control design to assign pilot power control coefficients to the quality of the estimated channel. Then, the Hungarian method is utilized to solve this problem. The simulation results of the proposed matching theory for the mentioned issues reveal that the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes for antenna selection and RF chain activation. In addition, the proposed matching schemes outperform the state-of-the-art techniques concerning the pilot assignment and the pilot power control design. This means that network scalability can be guaranteed with low computational complexity

The Importance of a Pictorial Medical History in Assisting Medical Diagnosis of Individuals with Intellectual Disabilities: A Telemedicine Approach

When face-to-face physical medical exams are not possible, virtual physical exams, in the form of a pictorial medical exam/history, can be substituted, and telemedicine can be the means to deliver these virtual exams. The goal of this work was to determine if presence in the form of a visual and/or pictorial medical history can be of benefit to clinicians in the diagnosis of medical conditions of individuals with developmental disabilities (DDs) and/or intellectual disabilities (IDs), in particular those who cannot, because of their cognitive and/or physical disabilities, verbally relate their illness to a clinician. Virtual exams can also be useful in cases where clinicians may need additional advice from fellow experts, especially if those experts are not physically present. A web-based telemedicine application used for treating persons with DD/IDs was developed. This application includes a visual medical history component incorporated into an electronic medical records application. The purpose is to allow the clinician to use an environment that integrates a written and visual representation of a patient’s medical history and physical findings to aid the clinician in determining a medical diagnosis. Twenty-two clinicians and five direct service aids of a New York State Developmental Disabilities Services Office facility, who deliver healthcare to DD/ID patients on a daily basis, accessed the telemedicine application instead of their traditional hardcopy/paper medical history when examining patients. A comprehensive survey was distributed to the clinicians to determine the effectiveness of the application as well as help answer the primary questions proposed by this research. The results of this study showed that presence in the form of a video medical history is preferred by clinicians rather than having just a written medical history of the patient. Clinicians felt the visual medical history component of the telemedicine application was useful and informative for delivering healthcare to individuals with DD/ID and enabled them in diagnosing a patient as well as lessened the need to transfer patients to the emergency room, resulting in a significant cost savings

A fast non-decoupled algorithm to solve the load flow problem in meshed distribution networks

The purpose of this work is to compare the classical methods of power flow resolution (Newton–Raphson and Gauss–Seidel) with a more recent algorithm known as Alternating Search Direction (ASD), for which its equations, the steps to follow and the parameters to consider are described. In addition, a series of tests are carried out in different distribution networks where the reduction of execution time, accuracy, and robustness of the presented algorithm is demonstrated, taking as a reference the behavior of the well-known Newton–Raphson algorithm. Finally, the advantage of selecting certain parameters in the ASD algorithm is studied

A cloud-edge based data security architecture for sharing and analyzing cyber threat information

Cyber-attacks affect every aspect of our lives. These attacks have serious consequences, not only for cyber-security, but also for safety, as the cyber and physical worlds are increasingly linked. Providing effective cyber-security requires cooperation and collaboration among all the entities involved. Increasing the amount of cyber threat information (CTI) available for analysis allows better prediction, prevention and mitigation of cyber-attacks. However, organizations are deterred from sharing their CTI over concerns that sensitive and confidential information may be revealed to others. We address this concern by providing a flexible framework that allows the confidential sharing of CTI for analysis between collaborators. We propose a five-level trust model for a cloud-edge based data sharing infrastructure. The data owner can choose an appropriate trust level and CTI data sanitization approach, ranging from plain text, through anonymization/pseudonymization to homomorphic encryption, in order to manipulate the CTI data prior to sharing it for analysis. Furthermore, this sanitization can be performed by either an edge device or by the cloud service provider, depending upon the level of trust the organization has in the latter. We describe our trust model, our cloud-edge infrastructure, and its deployment model, which are designed to satisfy the broadest range of requirements for confidential CTI data sharing. Finally we briefly describe our implementation and the testing that has been carried out so far by four pilot projects that are validating our infrastructure

Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks