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    Power efficient designs for 5G wireless networks

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    Kiani_Sharareh.pdf (1.407Mb)
    Date
    2021-01-01
    Author
    Kiani, Sharareh
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    Abstract
    In this dissertation, to step forward towards green communication, we study power efficient solutions in three potential 5G wireless networks, namely an asynchronous multicarrier two-way Amplify-and-Forward (AF) relay network, a multi-carrier two-way Filter-and-Forward (FF) network, and a massive Multiple Input Multiple Output (MIMO) network using the Non-Orthogonal Multiple Access (NOMA) scheme. In the first network, two transceivers using the Orthogonal Frequency Division Multiplexing (OFDM) scheme communicate through multiple relays in an asynchronous manner. As an attempt to design a simple solution, we assume the AF protocol at the relays. We jointly design the power allocation and distributed beamforming coefficients to minimize the total transmission power subject to sum-rate constraints. We propose an optimal semi-closed form solution to this problem and we show that at the optimum, the end-to-end channel has only one non-zero tap. To extend the first work to high data-rate scenarios, we consider a second relaying-based network which consists of two OFDM-based transceivers and multiple FF relays. We propose two approaches to tackle a total transmission power minimization problem: a gradient steepest descent-based technique, and a low-complexity method enforcing a frequency-flat Channel Impulse Response (CIR) response at the optimum. As the last network, we consider a massive MIMO-NOMA network with both co-located and distributed structures. We study the joint problem of power allocation and user clustering to minimize the total transmit power subject to QoS constraints. We propose a novel clustering algorithm which groups the correlated users into the same cluster and has an unique ability to automatically switch between using the spatial-domain-MIMO and the power-domain-NOMA. We show that our proposed method can substantially improve the feasibility probability and power consumption performance compared to existing methods.
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    https://hdl.handle.net/10155/1278
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    • Doctoral Dissertations [133]
    • Electronic Theses and Dissertations [1369]

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