Zhang, Yirun

English

The severe scarcity of spectrum resource inspires the development of new spectrum management techniques. Cognitive radio networks (CRNs), in which secondary users (SUs) are able to sense and access the spectrum white spaces by using dynamic spectrum access (DSA) technology, has become a promising approach to improve SU’s spectrum efficiency. On the other hand, full-duplex (FD) operation, which allows devices to transmit and receive over the same frequency band at the same time, has also emerged as a solution to alleviate the spectrum scarcity problem.Although both areas have been widely studied, there still exists various challenges for developing a high-quality and robust transmission system in the next-generation CRNs and FD networks, for example, the decision-making problem for FD-CRN, the security issues in the sensing result fusion strategy against malicious attacks at the fusion centre (FC), the privacy-preserving capability of the entire system and the multi-user resource allocation problem for FD Wi-Fi networks. With the help of rapid development of machine learning and deep learning techniques, multiple efficient and effective machine learning-based solutions for these problems have been proposed in the thesis.Specifically, we first propose a neural network (NN) predictor for multi-slot prediction and an adaptive mode selection scheme in FD-CRNs, with the goal of improving SUs throughput while alleviating collision to PU. Simulation results show that the proposed scheme achieves almost the same SUs average throughput as FD transmission-andreception mode when PU has a low tolerance for collision. Meanwhile, the collision probability can be reduced by up to 92% close to that of FD transmission-and-sensing mode.Besides the throughput-collision tradeoff, the real-world FD-CRNs are also facing security threats such as malicious attacks which influence SUs’ safe data transmission. We then propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in FD-CRNs, which is robust with accuracy against malicious attacks and interference. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of Spectrum Sensing Data Falsification (SSDF) SUs, only at the cost of slightly higher inference time.Moreover, the data privacy-preserving capability of the entire cooperative sensing has also become a crucial issue that prevents SUs from using a truly safe cooperative sensing service in modern CRNs. Therefore, we propose a novel vertical federated learning-based cooperative sensing (VFL-CS) scheme where sensing results are kept locally at each smart SU (SSU) and the model is trained in a decentralised collaborative learning setting, where the data privacy of each SSU is preserved. Simulation results show that our proposed VFL-CS scheme outperforms the conventional soft-fusion based cooperative sensing (SFCS) scheme in terms of a much higher area under the curve (AUC) score with high data privacy-preserving capability.Apart from spectrum sensing and sharing in CRNs, the resource allocation problem for Wi-Fi networks with FD Access Points (APs) brings another hot research topic. Under this topic, we propose a novel deep learning-based method to solve the user pairing problem in an FD Wi-Fi network which consists of one FD Access Point and multiple half-duplex users where the number of users is dynamic. With the objective of maximising the total sum rate of all uplink and downlink user pairs, a user pairing problem is formulated. Simulation results show that our proposed method can effectively and efficiently solve the user pairing problems for both fixed and dynamic numbers of users. Meanwhile, our proposed method outperforms the two benchmark methods in terms of optimal ratio, average bias ratio and average total sum rate.<br/

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Machine Learning-Based Full Duplex Communications and Cognitive Radio Networks

https://kclpure.kcl.ac.uk/portal/files/177718906/2022_Zhang_Yirun_1614134_ethesis.pdf

Machine Learning-Based Full Duplex Communications and Cognitive Radio Networks

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