6 research outputs found

    Transmitter Optimization Techniques for Physical Layer Security

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    Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm

    Resource Allocation Techniques for Non-Orthogonal Multiple Access in Beyond 5G

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    To support the wide range of envisioned applications, including autonomous vehicles, augmented reality, holographic communication, and Internet of Everything (IoE), future wireless networks must meet demanding requirements for higher spectral and energy efficiency, lower end-to-end latency and massive connectivity. This requires a vast upgrade in the technologies of the sixth-generation (6G) wireless networks. Non-orthogonal multiple access (NOMA) has been advocated as a prospective effective multiple access technique for future wireless networks due to the wide range of its potential benefits, including superior spectral efficiency (SE), energy efficiency (EE), compatibility, user fairness, and flexibility. To exploit additional degrees of freedom and address the computational complexity with massive connectivity, NOMA has been recently combined with different types of multiple access techniques and appropriate optimization designs. Hence, this thesis attempts to utilize the combination of NOMA with different key technologies, including multiple antenna techniques, conventional OMA techniques, and intelligent reflecting surface (IRS). In particular, different resource allocation techniques have been developed for such integrated NOMA systems, from the downlink (DL) single-input single-output (SISO)-NOMA system, to DL multiple-input single-output (MISO)-NOMA system, as well as the IRS-assisted NOMA system. Firstly, a hybrid time division multiple access (TDMA)-NOMA system is considered, where both the available time slots and the available transmit power are jointly allocated to maximize the global EE. To further exploit the promising advantages of this hybrid system, the SE-EE trade-off based design and max-min fairness based design are presented in this thesis. By utilizing different convex relaxation and approximation techniques, the non-convexity of the formulated optimization problems are transformed into convex problems. Finally, this thesis investigates a worst-case robust design for an IRS-assisted NOMA multi-user MISO system to maximize the EE with a set of quality of service (QoS) constraints. In particular, an iterative algorithm based on alternating optimization (AO) is proposed to design the transmit beamforming vectors at the base station (BS) and reflection coefficient matrix for IRS. The effectiveness advantages of all the proposed schemes are demonstrated through numerical simulation results
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