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This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a licensed channel of a primary system. A deep Q-learning (DQL) algorithm, which combines non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) techniques, is proposed to maximize the long-term throughput of the system. By using our scheme, the agent (i.e. the SBS) can obtain the optimal decision by interacting with the environment to learn about system dynamics. Simulation results validate the superiority of the performance under the proposed scheme, compared with traditional schemes.

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.

Physical layer wireless communications are more and more essential, and are vulnerable to malicious users owing to the nature of broadcast channels. Herein, we consider a centralized multi-channel cognitive radio network in the presence of eavesdroppers (EVEs). In the network, the secondary base station (SBS) shares currently free primary channels to simultaneously communicate with secondary users (SUs), while passive eavesdroppers attempt to overhear data in the secondary communications. Each limited-battery SU is equipped with two antennas (one for transmitting signals, and other for receiving signals) and is powered by a solar energy harvester. Meanwhile, the SBS equipped with multiple antennas can operate in full-duplex (FD) transmission mode (simultaneously transmit and receive signals) or in half-duplex (HD) transmission mode (transmit and receive signals in turn during each half of a time slot) with the SUs. In this paper, we propose a novel scheme to maximize the secondary system security of the multi-channel cognitive system in the presence of multiple passive EVEs, in which the EVEs are able to overhear the data of the SBS−SU transmissions on all the primary channels. The problem of decision making is formulated as the framework of a partially observable Markov decision process (POMDP), and an optimal solution is achieved by adopting value iteration−based dynamic programming. Specifically, in each time slot, the SBS allocates optimal channel and optimal action (i.e. either stay silent or employ HD/FD transmission modes with optimal transmission power) for each SU in order to obtain maximum long-term secrecy rate for the secondary system.

The cognitive radio network (CRN) is not only considered a useful medium for users, but it is also an environment vulnerable to proactive attackers. This paper studies an attack strategy for a legitimate energy-constrained eavesdropper (e.g., a government agency) to efficiently capture the suspicious wireless communications (i.e., an adversary communications link) in the physical layer of a CRN in tactical wireless networks. Since it is powered by an energy harvesting device, a full-duplex active eavesdropper constrained by a limited energy budget can simultaneously capture data and interfere with the suspicious cognitive transmissions to maximize the achievable wiretap rate while minimizing the suspicious transmission rate over a Rayleigh fading channel. The cognitive user operation is modeled in a time-slotted fashion. In this paper, we formulate the problem of maximizing a legitimate attack performance by adopting the framework of a partially observable Markov decision process. The decision is determined based on the remaining energy and a belief regarding the licensed channel activity in each time slot. Particularly, in each time slot, the eavesdropper can perform an optimal action based on two functional modes: (1) passive eavesdropping (overhearing data without jamming) or (2) active eavesdropping (overhearing data with the optimal amount of jamming energy) to maximize the long-term benefit. We illustrate the optimal policy and compare the performance of the proposed scheme with that of conventional schemes where the decision for the current time slot is only considered to maximize its immediate reward.

Non-orthogonal multiple access (NOMA) is a promising technique to satisfy a host of access demand and provide higher throughput. It enables that multiple users are multiplexed with different power levels by using superposition coding at the transmitter side and successive interference cancellation at the receiver side. In this paper, we deal with the long-term throughput maximization of an uplink NOMA in the cognitive radio network (CRN). The secondary users (SUs) have limited capacity battery, hence, SUs equipped with an energy harvester can harvest energy from solar sources to prolong their operations. Particularly, a combination of NOMA and time division multiple access (TDMA) is proposed in order to reduce the complexity of massive wireless communication systems. By taking into account the practical applications, a deep Q learning algorithm is employed to maximize the long-term throughput of the system, where the agent (i.e secondary base station (SBS)) can interact with an environment to learn about system dynamics. As a result, the SBS learns how to allocate optimal transmission energy to SUs in each time slot. Simulation results demonstrate that the proposed scheme can achieve better performance than conventional schemes.

Cognitive radio networks (CRNs) have a shortcoming in that attackers can increase their ability to disturb secondary users (SUs). This paper focuses on jamming attacks in the physical layer, in which several attackers try to interrupt SUs by injecting the interference into their communications. Once a jammer transmits interfering signals on the channel during the defined time, all ongoing transmissions on this channel will be corrupted. It is quite difficult for SUs to protect a single-hop data transmission from jammers. So, obtaining a solution for secure multi-hop data transmission in the presence of jammers becomes a more challenging task in CRNs. This paper investigates a strategy to find the optimal route and channels for transmission between cognitive transmitters and receivers in the presence of jammers in CRNs. In this scenario, the jammers are located randomly and their jamming behavior is assumed to follow a Gaussian distribution. We provide an optimal link–channel pair allocation scheme in which the secondary transmitter (the source) selects the best relay and a suitable channel for each hop in the source-to-destination route to protect the information intended to the secondary receiver (the destination) from the jammers. Simulation results prove the efficiency of the proposed scheme in a CR network.

We study jamming attacks in the physical layer of multihop cognitive radio networks (MHCRNs) where energy-constrained relays forward information from the source to the destination. Meanwhile, a jammer can transmit interfering signals on a channel such that all ongoing transmissions on this channel will be corrupted. In this paper, all jammers can attack only one of the predefined channels in each time slot. Moreover, they can randomly switch channels to start jamming another channel at the beginning of every time slot. The switching behavior is assumed to follow a Gaussian distribution. Due to limited battery capacity in the relays, energy harvesting is utilized to solve the energy-constrained problem in the cognitive radio network. Subsequently, relays are able to harvest energy from non-radio frequency (non-RF) signals such as solar, wind, or temperature. In this paper, we determine the throughput/delay ratio as a key metric to evaluate the performance in MHCRNs. Owing to the limited battery capacity in the relays and the jamming problem, the source needs to select proper relays and channels for each data transmission frame to optimize overall network performance in terms of end-to-end delay, throughput, and energy efficiency. Therefore, we provide two novel multihop allocation schemes to maximize achievable end-to-end throughput while minimizing delay in the presence of jammers. Through simulation results, we validate the effectiveness of the proposed schemes under multiple jamming attacks in MHCRNs.