Novel Estimation and Detection Techniques for 5G Networks

The thesis presents several detection and estimation techniques that can be incorporated into the fifth-generation (5G) networks. First, the thesis presents a novel system for orthogonal frequency division multiplexing (OFDM) to estimate the channel blindly. The system is based on modulating particular pairs of subcarriers using amplitude shift keying (ASK) and phase-shift keying (PSK) adjacent in the frequency domain, which enables the realization of a decision-directed (DD) one-shot blind channel estimator (OSBCE). The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The system has also extended to exploits the channel correlation over consecutive OFDM symbols to estimate the channel parameters blindly. Furthermore, a reliable and accurate approach has been introduced to evaluate the spectral efficiency of various communications systems. The metric takes into consideration the system dynamics, QoS requirements, and design constraints. Next, a novel efficient receiver design for wireless communication systems that incorporate OFDM transmission has been proposed. The proposed receiver does not require channel estimation or equalization to perform coherent data detection. Instead, channel estimation, equalization, and data detection are combined into a single operation, and hence, the detector performs a direct data detector (D3). The performance of the proposed D3 is thoroughly analyzed theoretically in terms of bit error rate (BER), where closed-form accurate approximations are derived for several cases of interest, and validated by Monte Carlo simulations. The computational complexity of D3 depends on the length of the sequence to be detected. Nevertheless, a significant complexity reduction can be achieved using the Viterbi algorithm (VA). Finally, the thesis proposes a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation. The algorithm utilizes the vibration measurements collected from several built-in sensors to compute the temporal correlation using the autocorrelation function (ACF). Furthermore, the proposed model parameters are tuned by solving multi-objective optimization using a genetic algorithm (GA). The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant