Cognitive aerial-terrestrial networks (CATN) hold promise in addressing the spectrum shortage challenges posed by thriving aerial networks, where aerial users (AUs) requiring high-quality downlink communications suffer severe interference from numerous terrestrial base stations (BSs). To alleviate such interference, we propose jointly optimizing the user association and coordinated beamforming (CBF) of the terrestrial network, thereby maximizing the sum rate of the secondary terrestrial users (TUs) under the interference temperature constraints of the primary AUs. Traditional iterative optimization schemes are impractical for this problem due to their high computational complexity and information exchange overhead. Although deep reinforcement learning (DRL)-based schemes offer a viable alternative, their performance is sensitive to the weight of the constraint violation penalty in the reward. To overcome these limitations, we propose a safe DRL-based user association and CBF scheme for CATN, which avoids multiple training attempts to find the optimal penalty weight before actual deployment and reduces expenses. Specifically, the studied system is modeled as a networked constrained partially observable Markov game, where each TU agent chooses its associated BS, and each BS agent decides its beamforming vectors, aiming to maximize the reward while satisfying the safety constraints to protect the AUs. Simulation results show that the proposed scheme can achieve a higher sum rate of TUs than a two-stage optimization scheme while the average received interference power of the AUs is generally below the threshold.

By integrating Cognitive Radio (CR) functionality into the Internet of Things (IoT), the CR-based IoT network offers a promising solution to the spectrum scarcity problem faced by traditional IoT systems. However, accurate and fair Cooperative Spectrum Sensing (CSS) faces many challenges, such as malicious nodes’ presence, sensor behaviour reliability, and IoT devices’ limited energy. In this paper, we propose a lightweight blockchain-enabled CSS system that enhances transparency and reliability in sensing report exchange and fusion. Specifically, we introduce a comprehensive trust evaluation algorithm and a fair sensor selection method to assess the reliability of sensors and fairly assign spectrum sensing tasks. To ensure the decentralization and security of IoT networks, we propose a lightweight consensus mechanism which utilizes trust values and deposits to determine voting weights during producer elections while removing malicious nodes by blocklist mechanism. A smart contract is also designed to automate the entire CSS process, including spectrum sensing, deposit management, and trust management. Finally, security analysis and numerical simulations are conducted to demonstrate the effectiveness and robustness of the proposed blockchain-enabled CSS system.

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to meet the rapidly increasing spectrum demand of various applications, because it can significantly improve the spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes, where the beamforming vectors are obtained by optimizing the normalized beamforming vectors and power control. In addition, we consider the case where only statistical channel state information is available and the case where channel estimation errors exist, and propose the corresponding beamforming schemes. Finally, we provide extensive numerical simulations to evaluate the performance of the proposed beamforming schemes and demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.

Cognitive aerial-terrestrial networks (CATNs), which enable aerial and terrestrial networks to share spectrum resources, have been identified as a promising solution to address the spectrum utilization challenges brought by thriving aerial networks. However, during spectrum sharing, aerial users, such as airplanes and flying cars, suffer severe interference from numerous terrestrial base stations (BSs). To alleviate such inter-ference, in this paper, we investigate a coordinated beamforming (CBF) problem, which aims to maximize the sum rate of the secondary terrestrial users while keeping the interference to the primary aerial users below a pre-defined threshold. Since aerial users usually move fast, obtaining real-time global channel state information is challenging. Also, frequently calculating the beamformers with high-complexity algorithms is unaffordable. These issues make traditional iterative optimization algorithms impractical in solving this problem. To address these issues, deep reinforcement learning (DRL) based algorithms can be applied. However, the performance of DRL-based algorithms is sensitive to the penalty weight of a weighted penalty term for violating constraints in the reward function. In this paper, we propose a safe DRL-based distributed dynamic CBF scheme to avoid the tricky adjustment of the penalty weight, where the studied CATN is described as a constrained Markov decision process (CMDP) with cost sets to decouple the objective and constraints. The CMDP is solved by a safe DRL algorithm, which maximizes the reward while satisfying the safety constraints. Simulation results show that the proposed algorithm can achieve a high sum rate of terrestrial users while interference power constraints are generally well satisfied.

Cognitive spectrum sharing has been regarded as a promising solution to improve spectrum utilization efficiency for space-air-ground integrated networks (SAGINs). However, in SAGIN, different networks may have different quality of service (QoS) requirements, which pose challenges to the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which may not be met when it acts as a secondary network. To address this issue, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGIN, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, in SAGIN, an aerial network and a terrestrial network share the spectrum of a satellite network. HCSSA gives higher priority to the aerial network by a QoS constraint, while the terrestrial network is the ordinary secondary network without protection. Besides, the satellite terminal requires the received interference to be below a threshold. Subject to these two constraints and the maximum transmit power constraints, we aim to maximize the sum rate of the terrestrial network by optimizing the transmit beamforming vectors of the aerial base station (BS) and the terrestrial BSs. To solve this non-convex problem, we propose an iterative beamforming scheme by exploiting the penalty method and the successive convex approximation scheme. Simulation results show the performance of the proposed beamforming scheme and illustrate the advantages of HCSSA compared with the traditional cognitive spectrum sharing architecture.