Emerging services such as augmented reality (AR) and virtual reality (VR) have increased the volume of data transmitted in wireless communication systems, revealing the limitations of traditional Shannon theory. To address these limitations, semantic communication has been proposed as a solution that prioritizes the meaning of messages over the exact transmission of bits. This paper explores semantic communication for text data transmission in end-to-end (E2E) systems through a novel approach called KG-LLM semantic communication, which integrates knowledge graph (KG) extraction and large language model (LLM) coding. In this method, the transmitter first utilizes a KG to extract key entities and relationships from sentences. The extracted information is then encoded using an LLM to obtain the semantic meaning. On the receiver side, messages are decoded using another LLM, while a bidirectional encoder representations from transformers (i.e., BERT) model further refines the reconstructed sentences for improved semantic similarity. The KG-LLM semantic communication method reduces the transmitted text data volume by $30 \%$ through KG-based compression and achieves $84 \%$ semantic similarity between the original and received messages. This demonstrates the KG-LLM methods efficiency and robustness in semantic communication systems, outperforming the deep learning-based semantic communication model (DeepSC), which achieves only $63 \%$.

In 5G networks, network slicing has emerged as a pivotal paradigm to address diverse user demands and service requirements. To meet the requirements, reinforcement learning (RL) algorithms have been utilized widely, but this method has the problem of overestimation and exploration-exploitation trade-offs. To tackle these problems, this paper explores the application of self-play ensemble Q-learning, an extended version of the RL-based technique. Self-play ensemble Q-learning utilizes multiple Q-tables with various exploration-exploitation rates leading to different observations for choosing the most suitable action for each state. Moreover, through self-play, each model endeavors to enhance its performance compared to its previous iterations, boosting system efficiency, and decreasing the effect of overestimation. For performance evaluation, we consider three RL-based algorithms; self-play ensemble Q-learning, double Q-learning, and Q-learning, and compare their performance under different network traffic. Through simulations, we demonstrate the effectiveness of self-play ensemble Q-learning in meeting the diverse demands within 21.92% in latency, 24.22% in throughput, and 23.63% in packet drop rate in comparison with the baseline methods. Furthermore, we evaluate the robustness of self-play ensemble Q-learning and double Q-learning in situations where one of the Q-tables is affected by a malicious user. Our results depicted that the self-play ensemble Q-learning method is more robust against adversarial users and prevents a noticeable drop in system performance, mitigating the impact of users manipulating policies.

Radio access technology is crucial in both 5G and 6G cellular networks, providing differentiated services that demand reliability, low latency, and high throughput. To meet these requirements, machine learning (ML) has demonstrated considerable progress by facilitating resource allocation. However, these ML techniques can be susceptible to attacks, and the jamming attack is one of the most considered attacks in the literature, disrupting network functionality by sending interference signals. This paper, to the best of our knowledge for the first time, examines the vulnerability of radio access networks (RANs) to jamming attacks on resource allocation of a transfer reinforcement learning (TRL) based system and provides a mitigation approach to such attacks. A system model is presented for RAN slicing, followed by an introduction of the TRL algorithm for resource allocation. Afterward, we investigate covert patterned jamming attack (CPJA) on the TRL algorithm in downlink communication which decreases system throughput by 17% and 38.14% in the expert and learner agents and increases latency by 7.36% and 9.37% respectively. In addition, we propose a neural network (NN) solution to mitigate the CPJA trained on the network side and provide the trained NN model to the users' equipment (UEs) to eliminate interference from the signal by the filter. The trained NN is applied to predict the future activity of the interference generated by the attacker. Attack mitigation reduces the impact of the attack while the system's throughput suffers a 6% and 1.8% degradation, and its latency increases by 6.5% and 3.83% compared to the original system for expert and learner agents, respectively.

As wireless networks continue to evolve, machine learning (ML) algorithms are used to address communication challenges and meet various service requirements. While ML methods are promising, they can be prone to malicious attacks, which may degrade user experience and network performance. Specifically, the security challenges of radio access networks (RANs) are highlighted due to frequent interactions with a large number of users, and evaluating these attacks is critical for securing wireless communications. In this paper, for the first time, we investigate the vulnerability of transfer reinforcement learning (TRL) algorithms for resource allocation in 5G RAN slicing. In particular, we first present the system model for RAN slicing, and then the TRL algorithm is introduced for resource allocation. Afterward, we investigate three types of attack methods on the TRL algorithm. The simulations indicate that the attack on an expert agent can affect the performance of a learner agent since the expert shares knowledge with the learner. We show that the effect of the black-box policy poisoning attack on the learner increases latency by 18.31% and reduces throughput by 13.80%, while white-box policy poisoning attacks result in a 48.96% increase in latency and an 87.02% reduction in throughput.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the advantages of UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q- Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm.