Li, Geoffrey YeAlbagami, Khalid2023-11-272023-11-272023-11-25https://hdl.handle.net/20.500.14154/69871Recently, applying deep learning (DL) in physical layer communications for channel estimation and signal detection has extensively been studied in the literature. The majority of the existing studies investigating the use of DL techniques to address the channel estimation and signal detection problem focus on analysing channel impulse response (CIR) that are generated from only one channel type distribution such as additive white Gaussian channel Noise (AWGN) and Rayleigh channel. Although this field is well-researched in the literature, DL models are yet to be widely adopted in practice for channel estimation and signal detection. The main challenge is that a pre-trained DL model exhibits sub-optimal performance after transitioning to a different wireless channel environment. Moreover, with the limited resources and processing units available at the base station, it is a cumbersome task to re-collect data and re-train the model whenever the wireless environment changes. In this thesis, we study and investigate the feasibility of applying universal deep neural network (Uni-DNN) model consisting of two cascaded DL models where the first one works as a wireless channel classifier and the second one as a signal detector. The channel classifier DL model works on identifying the wireless channel distribution that impaired the transmitted signal. Then, the signal detector DL model utilises this information along with the received signal to recover the transmitted signal. Three different Uni-DNN architectures were developed namely architectures A, B and C utilising a combination of deep neural network (DNN) and convolutional neural network (CNN) models. The proposed architectures are trained and tested on multiple frequency selective and frequency flat fast fading wireless channels and their bit error rate (BER) performance is compared to both conventional DL models and popular channel estimation techniques such as minimum mean square error (MMSE) and least square (LS) for orthogonal frequency division multiplexing (OFDM) multiplexing scheme under AWGN with signal-to-noise ratio (SNR) ranging from 0 to 20dB. The Uni-DNN architecture C with cascaded DNN-CNN has shown the most promising results out of the three in adapting to multiple wireless channels, outperforming conventional DL models, LS and nonperfect-CSI MMSE conventional channel estimators in low pilot frequency density. In addition, the theoretical and practical Uni-DNN model inference time complexity analysis shows that such a model is faster to compute compared to MMSE and can be deployed in real-time applications.116enChannel estimation and signal detectionDeep learningUniversal deep neural networkConvolutional neural networkThesis