The performance of space-time block codes in providing transmit diversity is severely degraded when strong localized interference is present. This problem is addressed by investigating a recently proposed coherent space-time block code decoding algorithm for unknown interference suppression. The algorithm assumes a Gaussian noise and interference approximation and is based on a cyclic-based maximum-likelihood estimation technique (CML). In this thesis, simulations are done applying CML in a coherent system with unstructured interference to validate previous work. An extension of these results is obtained by examining factors that affect CML performance and modifying CML for use in a noncoherent system. To improve bit error rate performance, a turbo code for channel coding was added to both systems. This addition required the development of reliability metrics for soft-information transfer between the space-time block code detector and the turbo code decoder. Significant coding gains exceeding 8dB at a bit error rate of are achieved for the turbo-coded system when compared to that of an uncoded system.

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A mobile ad hoc network (MANET) is a collection of mobile nodes that form a wireless network without the use of a fixed infrastructure or centralized administration, and every node acts as a host as well as a router. The topology of an ad hoc network changes frequently and unpredictably. The mobile nature and dynamic topology of MANETs make it very difficult to provide Quality-of-Service (QoS) assurance in such networks. Considering the limited bandwidth and battery power, finding routes that satisfy the bandwidth constraint of applications is a significant challenge. Ad hoc on-demand distance vector routing (AODV) is an on-demand routing protocol that only provides best-effort routes. QS-AODV is proposed in this thesis. It is based on AODV and creates routes according to the QoS requirements of the applications. It is shown that QS-AODV provides performance comparable to AODV under light traffic. In heavy traffic, QS-AODV provides higher packet delivery ratios and lower routing overheads, at a cost of slightly longer end-to-end delays as the routes in QS-AODV are not always the shortest. The effects of network size and mobility on the performance of QSAODV are shown.

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Approved for public release; distribution is unlimited / Orthogonal frequency division multiplexing (OFDM) is being used by wireless local area network (WLAN) standards, such as IEEE 802.11a, and wireless metropolitan area network (MAN) standards, such as IEEE 802.16a. OFDM is a very efficient communications scheme for wireless ADHOC networks. However, the wireless environment causes inter-symbol interference (ISI) and inter-carrier interference (ICI). Estimating the starting point of an OFDM symbol must be handled efficiently and effectively to reduce the errors. OFDM must be time synchronized to prevent inter-symbol interference (ISI) and inter-carrier interference (ICI). Many techniques exist to realize timing synchronization in OFDM systems. In this thesis, the need for timing synchronization, the timing errors, and the performance of different techniques under a variety of mobile channel models (indoor and outdoor) are investigated, and simulation performance results for each technique under different channel models are presented. / First Lieutenant, Turkish Army

The pervasive application of Internet of Things (IoT) has been seen in many aspects in human daily life and industrial production. The concept of IoT originates from traditional machine-to-machine (M2M) communications which aimed at solving domain-specific and applicationspecific problems. Today, the rapid progress of communication technologies, the maturation of Internet infrastructures, the continuously reduced cost of sensors, and emergence of more open standards, have witnessed the approaching of the expected IoT era, which envisions full connectivity between the physical world and the digital world via the Internet protocol. The popularity of cloud computing technology has enhanced this IoT transform, benefiting from the superior computing capability and flexible data storage, let alone the security, reliability and scalability advantages. However, there are still a series of obstacles confronted by the industry in deployment of IoT services. First, due to the heterogeneity of hardware devices and application scenarios, the interoperability and compatibility between link-layer protocols, sub-systems and back-end services are significantly challenging. Second, the device management requires a uniform scheme to implement the commissioning, communication, authorization and identity management to guarantee security. Last, the heterogeneity of data format, speed and storage mechanism for different services pose a challenge to further data mining. This thesis aims to solve these aforementioned challenges by proposing a data-centric IoT framework based on public cloud platforms. It targets at providing a universal architecture to facilitate the deployment of IoT services in massive IoT and broadband IoT categories. The framework involves three representative communication protocols, namely WiFi, Thread and Lo-RaWAN, to enable support for local, personal, and wide area networks. A security assessment taxonomy for wireless communications in building automation networks is proposed as a tool to evaluate the security performance of adopted protocols, so as to mitigate potential network flaws and guarantee the security. Azure cloud platform is adopted in the framework to provide device management, data processing and storage, visualization, and intelligent services, thanks to the mature cloud infrastructure and the uniform device model and data model. We also exhibit the value of the study by applying the framework into the digitalization procedure of the green plant wall industry. Based on the framework, a remote monitoring and management system for green plant wall is developed as a showcase to validate the feasibility. Furthermore, three specialized visualization methods are proposed and a neuron network-based anomaly detection method is deployed in the project, showing the potential of the framework in terms of data analytics and intelligence.

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