Thesis

Simple Iterative Joint Channel Estimation and Detection for 5G OFDM Uplink Transmission

Due to the scarcity and increasing demand for bandwidth, Orthogonal Frequency Division Multiplexing, also known as OFDM has become a great candidate as a transmission scheme capable of supporting high data rate applications such as LTE, 4G, and 5G communication systems as well as recent technological advances such as 5G machine type communication systems (MTC) with high efficiency. OFDM offers better immunity against frequency selective fading channels and reputes combating multipath fading effectively. Channel estimation is fundamentally tasked in OFDM due to the absence of channel knowledge at the receiver, thus it is imperative to have coherent detection at the receiver. This fundamental task becomes more of a challenge with the existence of interference but is accomplished with the use of the least squares estimation (LSE) in conjunction with the expectation maximization (EM) algorithm, and the use of known pilot sub-carrier insertion in the OFDM frame. This thesis aims to utilize channel estimation using MAP-EM iterative receiver, LSE, to analyze SNR (db) at different EM iterations, multipath tap values, number of pilot insertion, and different number of antennas to explore system configuration for better throughput and analyze system performance and complexity trade-off at such parameters. This paper also evaluates feasibility of applying Massive MIMO to 5G Massive Machine-to-Machine Type Communications (MMTC).

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