Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Machine Learning Based Resource Allocation for Massive Mimo Systems(01. Izmir Institute of Technology, 2023) Sevgi, Hüseyin Can; Özbek, BernaCell-free massive MIMO communication systems is a promising technology that uses access-points(APs) deployed throughout the coverage area instead of usual cellular systems with centralized BS to serve multiple users simultaneously. By exploiting the large number of antennas and adopting advanced signal processing techniques, cell-free massive MIMO can mitigate inter-user interference and enhance the overall system performance. Optimal power allocation plays a crucial role in maximizing the spectral and energy efficiency of wireless networks. By intelligently allocating transmit power to different users, a balance between maximizing the system throughput and minimizing the total energy consumption can be achieved. In addition, user-centric clustering(UCC) is also a key technique to improve the performance of cell-free massive MIMO systems. This technique aims to pair user equipments (UEs) with appropriate APs to facilitate efficient resource allocation and interference management. In this thesis, cell-free mMIMO communication system is investigated through user-centric clustering and power allocation. The power allocation optimization problem is formulated to maximize energy efficiency of cell-free mMIMO systems and solved by using interior-point algorithm. User-centric clustering algorithm is proposed by disabling the non-master APs that are serving only one user. This additional feature aims to reduce total power consumption of the system without sacrificing the advantages of the cell-free mMIMO communication systems. Additionally, we propose a machine learning(ML) approach to reduce the computation time required for power allocation optimization. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithms in achieving significant gains in spectral and energy efficiency in cell-free massive MIMO systems. The results highlight the importance of optimal power allocation and user-centric clustering to design an efficient cell-free mMIMO systems through machine learning approach.Master Thesis Wireless Physical Layer Network Coding for Multiple Antenna Systems(01. Izmir Institute of Technology, 2020) İlgüy, Mert; Özbek, BernaWireless networks are prone to interference due to their broadcast nature. In the design of most of the traditional networks, this broadcast nature is perceived as a performance-degrading factor. However, physical layer network coding (PNC) exploits this broadcast nature by enabling simultaneous transmissions from different sources and facilitates an increase in the spectral efficiency of the wireless networks. Besides, the massive multiple input multiple output (MIMO) is considered as one of key technologies to improve the spectral efficiency for wireless communication systems. The combination of PNC and multi-user massive MIMO in the sixth generation (6G) networks can increase further the spectral efficiency. In this thesis, PNC based systems are examined via bit error rate (BER) and coverage probability by focusing on the BER of the network coded symbol (NCS). Hence, PNC based systems are compared with network coding (NC) and conventional schemes. The influence of the signal-to-noise ratio (SNR) differences of the users are examined on the BER performances. Thereby, an alternative method to estimate NCS is proposed for the MIMO-PNC systems without using log likelihood ratio (LLR). We derive a closed form expression for the coverage probability in PNC based multi-user massive MIMO systems by employing zero forcing (ZF) equalization. The non-orthogonal multiple access (NOMA) based PNC system is proposed. We show the applicability of the PNC in the NOMA based MIMO systems by giving the the BER performance results.