Aref Defends PhD Friday

Aref Defends PhD Friday

Mr. Mohamed Attia Abdelrahman Aref will defend his PhD Dissertation, "Cognitive Radios for Self-aware and Spectrum-agile Communications over Wideband Spectrum" in a Zoom Meeting chaired by Dr. Sudharman Jayaweera on Friday, Aug. 7 at 10 am.

Please refer to the abstract below to learn more about Mr Aref Dissertation.


In this dissertation, we introduce a novel cognitive engine design for intelligent, selfaware and spectrum-agile communications. The targeted cognitive radio (CR) has sensing and learning abilities that incorporate advanced signal processing and machine/deep learning (ML/DL) techniques. It can operate over a heterogeneous wideband spectrum that is much wider than the ones proposed in the literature, most likely covering several hundreds of MHz. It has the ability to detect existing signals in the surrounding RF environment and identify their origins. Furthermore, it cognitively uses channel selection, transmit power control and interference cancellation to adapt its communications mode inresponse to observed RF conditions. It can find the best transmission opportunities and maintain a certain level of quality of service (QoS). The proposed cognitive techniques can facilitate the use of intelligent spectrum management where operating licenses are issued based on the radio’s intelligence level. It also can be used in a wide range of applications including RF spectrum awareness, signals intelligence, anti-jamming and interference avoidance.

To this end, we implemented replicated Q-learning sub-band selection method for wideband spectrum based on partially-observable Markov decision process. Simulation results showed that the proposed algorithm can provide a substantial improvement over the random sub-band selection policy with only 16% below the optimal sub-band selection policy that requires complete state observability. A DL classification framework is introduced to distinguish between radar and communications signals based on their cyclostationary features. The proposed approach can reach classification accuracy around 99% at signal-to-noise ratio (SNR) of −4 dB. A multi-agent Q-learning technique is provided for anti-jamming and interference avoidance communications. An optimal energy detector is used based on the Neyman-Pearson (NP) criterion which maximizes the detection probability of existing signals subject to a certain false alarm level. Compared with existing schemes, the proposed approach uses two parallel communications operations: sensing and transmission, that can enable it to predict the locations of the jamming/interference signals accurately and hence avoid them efficiently.

A multi-task transfer deep reinforcement learning (DRL) approach is developed to provide spectrum-agile communications over wideband spectrum where communications over each sub-band represents a single task. The proposed approach attempts to learn an optimal policy for channel and transmit power level selection. The proposed approach has proved that it is suitable for real-time applications with its ability to adapt to sudden changes in the spectrum of interest. It outperforms existing schemes found in literature such as single-task DRL and Q-learning. A DL-aided successive interference cancellation (SIC) framework is proposed for massive multiple-input-multiple-output nonorthogonal multiple access (massive MIMO-NOMA) systems. A better realistic scenario, compared with similar approaches in the literature, that includes correlated channel fading and user mobility based on random waypoint (RWP) model is considered. Simulation results showed that the proposed approach provides a significant reduction in error propagation and computational complexity that might be encountered in traditional SIC schemes including those based on DL.