Sixth-generation (6G) networks must deliver ultra-low latency and near-100 percent reliability to support massive-scale Internet of Things (IoT) deployments and Hyper-Reliable Low-Latency Communications (HRLLC). Grant-free access protocols permit devices to transmit without prior scheduling; nevertheless, this uncoordinated transmission introduces uncertainty at the receiver, necessitating Active User Detection (AUD) to ascertain which devices are active. Quantum search methods—most notably Grover’s algorithm—can accelerate AUD, yet they require knowing the optimal number of iterations, which depends on the (typically unknown and time-varying) number of valid solutions induced by the current activity pattern and channel/noise conditions. To overcome this, we formulate an optimization problem that optimizes the number of Grover iterations to maximize detection accuracy and minimize computational cost without any prior activity information. We then apply a Deep Q-Network (DQN) to learn, via deep reinforcement learning, an adaptive policy for selecting the iteration count. Simulation results verify that the DQN converges to an optimal strategy and outperforms two baseline schemes under varying fading conditions and active-user transmit powers.

The evolution towards 6G networks promises a massive increase in connected devices and demanding use cases, intensifying the challenge of managing limited spectrum resources efficiently. This paper addresses this challenge in an underlay cognitive radio network framework where secondary users (SUs) employ the code domain non-orthogonal multiple access (NOMA) mechanism for communication while incorporate energy harvesting (EH) to enhance their operational longevity and support green communication principles. Specifically, we assume SUs utilize EH via a wireless powered communication network (WPCN) process. A difficulty within this combined cognitive radio and WPCN scenario is the precise and efficient identification of active SUs for effective resource allocation and interference management. While traditional active user identification methods exist, they can face challenges, including computational complexity and experiencing limitations in accuracy under certain conditions. To address this issues we proposes the application of Grover’s quantum search technique. Furthermore, we investigate the impact of the number of users on the detection success probability and the trade-off between this probability and energy efficiency in this scenario. A comparison between the proposed approach and a non-quantum search technique is also provided.

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.

Anticipated advancements in 5G wireless networks and beyond would necessitate an increased emphasis on security measures to accommodate the projected rise in demand for connections and services. Therefore, this paper aims to investigate the physical layer security (PLS) to evaluate the privacy of authorized users in multi-cellular networks, which represent a fundamental architecture in next-generation networks. Each cell is assumed to include a base station (BS) that serves multiple users. This scenario also takes into account the presence of several eavesdroppers. Every BS functions as a reinforcement learning (RL) agent that must undergo training in order to optimize security. To enhance the safety and speed of training, a federated learning (FL) technique is utilized. In this approach, a central unit regularly receives the neural network (NN) weights from the agents, updates them, and then transfers the result back to the agents to update their model. We examine and compare two deep RL methodologies, specifically deep Q-network, and Reinforce deep policy gradient. The findings of our research demonstrate the influence of the number of eavesdroppers on security, as well as the impact of the number of cells and the aggregation frequency of neural network parameters.

Physical-layer security (PLS) is examined in this paper for an underlay cognitive radio network (CRN). Two secondary users (SUs) interact through a relay that is equipped with multiple antennas and harvests energy from the SU transmitter's messages via a power splitting (PS) approach. Communication between the relay and SU destination is being intercepted by several eavesdroppers. Therefore, to diminish the eavesdroppers' interception capabilities, the SU destination gathers energy from relayed messages and exploits it to generate and broadcast jamming signals intended to mislead the eavesdroppers. Colluding and non-colluding eavesdroppers are both considered and contrasted as possible strategies for intercepting private information. Additionally, for a more realistic assumption, the connection between the relay and the legitimate SU receiver is assumed to follow the cascaded Rayleigh fading model. PLS is assessed in terms of the probability of non-zero secrecy capacity and the intercept probability.

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs’ transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs’ messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

In this paper, physical-layer security (PLS) of an underlay cognitive radio network (CRN) operating over cascaded Rayleigh fading channels is examined. In this scenario, a secondary user (SU) transmitter communicates with a SU receiver through a cascaded Rayleigh fading channel while being exposed to eavesdroppers. By harvesting energy from the SU transmitter, a cooperating jammer attempts to ensure the privacy of the transmitted communications. That is, this harvested energy is utilized to generate and spread jamming signals to baffle the information interception at eavesdroppers. Additionally, two scenarios are examined depending on the manner in which eavesdroppers intercept messages; colluding and non-colluding eavesdroppers. These scenarios are compared to determine which poses the greatest risk to the network. Furthermore, the channel cascade effect on security is investigated. Distances between users and the density of non-colluding eavesdroppers are also investigated. Moreover, cooperative jamming-based energy harvesting effectiveness is demonstrated.

Physical-layer security (PLS) for an underlay cognitive radio network (CRN)-based simultaneous wireless information and power transfer (SWIPT) over cascaded κ-µ fading channels is investigated. The network is composed of a pair of secondary users (SUs), a primary user (PU) receiver, and an eavesdropper attempting to intercept the data shared by the SUs. To improve the SUs’ data transmission security, we assume a full-duplex (FD) SU destination, which employs energy harvesting (EH) to extract the power required for generating jamming signals to be emitted to confound the eavesdropper. Two scenarios are presented and compared; harvesting and non-harvesting eavesdropper. Moreover, a trade-off between the system’s secrecy and reliability is explored. PLS is studied in terms of the probability of non-zero secrecy capacity and the intercept probability, whereas the reliability is studied in terms of the outage probability. Results reveal the great impact of jamming over the improvement of the SUs’ secrecy. Additionally, our work indicates that studying the system’s secrecy over cascaded channels has an influence on the system’s PLS that cannot be neglected.

In this paper, physical-layer security (PLS) for an underlay cognitive radio network (CRN) over cascaded Rayleigh fading channels is studied. The underlying cognitive radio system consists of a secondary source transmitting to a destination over a cascaded Rayleigh fading channel. An eavesdropper is attempting to intercept the confidential information of the secondary users (SUs) pair. The secrecy is studied in terms of three main security metrics, which are the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (Prnzc), and the intercept probability (Pint). The effects of the path loss and the variation of the distances from the SU transmitter over the secrecy are also analyzed. Results reveal the great effect of the cascade level over the system secrecy. In addition, the effect of varying the interference threshold that the PU receiver can tolerate over the secrecy of the SUs pair is studied. The effect of the channel model parameters of both the main and the wiretap channels is investigated using both simulation and analytical results.