Recently, 5G installation has been started globally. Different capabilities are in the consistent procedure, like ultrareliability, mass connectivity, and specific low latency. Though, 5G is insufficient to meet all the necessities of the future technology in 2030 and so on. Next generation information and communication technology is playing an important role in attraction of researchers, industries, and technical people. With respect to 5G networks, sixth‐generation (6G) CR networks are anticipated to familiarize innovative use cases and performance metrics, such as to offer worldwide coverage, cost efficiency, enhanced spectral, energy improved intelligence, and safety. To reach such requirements, upcoming 6G CRNs will trust novel empowering technologies. Innovative network architecture and transmission technologies and air interface are of excessive position, like multiple accesses, waveform design, multiantenna technologies, and channel coding schemes. (1) To content, the condition should be of worldwide coverage, there will be no limit on 6G to global CR communication networks that may require to be completed with broadcast networks, like satellite communication networks, therefore, attaining a sea integrated communication network. (2) The spectrums overall will be entirely travelled to the supplementary rise connection density data rates in optical frequency bands, millimeter wave (mmWave), sub‐6 GHz, and terahertz (THz). (3) To see big datasets created because of tremendously varied CR communication networks, antenna rush, diverse communication scenarios, new provision necessities, wide bandwidth, and 6G CRNs will allow an innovative variety of intelligent applications with the assistance of big data and AI technologies. (4) Need to improve network security when deploying 6G technology in CR networks. 6G is decentralized, intended, intelligent innovative, and distributed network. In this article, we studied a survey of current developments and upcoming trends. We studied the predicted applications, possible technologies, and security issues for 6G CR network communication. We also discussed predicted future key challenges in 6G.

Unlicensed cognitive-radio (CR)-assisted Internet of Vehicles (IoV) users can access licensed providers’ radio spectrum and concurrently utilize the dedicated channel for data transmission in vehicular communication. Optimizing channel access in cognitive IoV networks can help maximize available spectrum resources. This article proposes a novel sensing and communication integrated framework, dubbed as the CR-assisted IoV network (CRAV-Net), using a cluster-based hybrid optimization approach with adaptive congestion-aware modeling for dynamic high-mobility vehicular networks in an urban city context. In CRAV-Net, intelligent hybrid learning spectrum agents are introduced, which perform spectrum sensing (SS) using a deep learning (DL) model. It dynamically learns the multilevel spatial and temporal graphical features from input spectrograms through layer-by-layer propagation. It efficiently predicts the spectrum occupancy in the primary spectrum, without a priori knowledge of the radio environment. Then, to assign the vacant channels to the secondary vehicles, a support vector machine classifier is trained based on several learning features, including the vehicle stay time, vehicle density, and network capacity, to select the optimal resource route. The proposed framework achieves an overall accuracy of 99.74% in SS using the custom data set, outperforming state of the art by 12.60% at −25-dB signal-to-noise ratio. In addition, it brings a performance gain of 0.81% in SS accuracy when evaluated on real-world signals. Furthermore, in optimal network node allocation, the proposed framework achieves a mean accuracy of 98.45%, outperforming the existing methods by 0.63% and 18.32% in terms of accuracy and allocation time, respectively.

Computing tasks offloaded from user devices (UDs) can be carried out by one or more mobile edge computing (MEC) servers to alleviate the computing burden of UDs. The incentive is needed to encourage MEC servers to provide their computing services to other network nodes. In this article, inspired by the fact that bargaining games have the available features of incentive, self-enforcement, and satisfaction for all participants, we propose a two-level bargaining-based incentive mechanism for task offloading and collaborative computing in MEC-enabled networks. In the first-level bargaining between UDs and local MEC server (LMECS), both UDs and LMECS try to maximize their respective offloading utilities, which are all defined as a saved-cost function considering the time and energy consumption of task execution, and computing service fees. The task offloading decision, uplink transmitting power of UDs, computing resource allocation of LMECS, and the fees paid by UDs to LMECS are jointly optimized. When large computing tasks are offloaded to LMECS, which results in LMECS overload, the second-level bargaining is proposed to achieve a computing load balance of LMECS and maximize the respective collaboration utilities of LMECS and collaborative MEC server group (CMECG), in which the optimized normalized fees paid by LMECS to CMECG for additional computing resources are obtained. The first-level and the second-level bargainings are proved to be quasi-concave and concave, respectively, and each has a unique Nash bargaining solution (NBS). The simulation results show that the proposed method gets better performance than benchmark methods.

Unmanned aerial vehicle (UAV) energy consumption and task completion time are two significant aspects in UAV-enabled Internet of Things (IoT) data acquisition. However, while energy consumption and task completion time are not conflicting constraints, optimizing the former often does not automatically lead to optimization of the latter, and vice-versa. In this paper, we aim to further decrease UAV energy consumption while minimizing UAV task completion time. We propose a novel UAV utility function to describe task completion time for data acquisition and energy consumption for propulsion and receiver's circuits. Then, we formulate a mixed-integer nonconvex utility maximization problem, involving joint optimization of UAV trajectory, velocity, acceleration, task completion time and scheduling of IoT devices for data uploading. To tackle this problem, we decompose the original problem into two sub-problems and propose a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm, which converges in polynomial time. Accordingly, we apply block coordinate descent (BCD) and successive convex approximation (SCA) algorithms to optimize UAV trajectory, and bisection search algorithm to minimize task completion time, respectively. Simulation results show that the proposed data acquisition scheme significantly decreases the UAV's energy consumption while minimizing task completion time, compared with the other time-minimization schemes.

Device‐to‐device (D2D) communication is an excellent technology for improving the system capacity through sharing the spectrum resources of cellular networks. Multicast service is considered an effective transmission mode for the future mobile social contact services. Therefore, multicast with D2D technology can improve the resource efficiency. In this paper, a resource allocation scheme based on cognitive radio (CR) for D2D underlay multicast communication (CR‐DUM) is proposed to improve system performance. In each D2D multicast group of the cognitive cellular system, the secondary users reuse the different orthogonal cellular resources to accomplish a multicast transmission. To maximise the total system capacity under the condition of interference and noise impairment, the authors formulate an optimal transmitting power allocation for the cellular and D2D multicast communications jointly. The proposed scheme includes two steps. First, two channel allocation rules are proposed to reduce the interference from cellular networks to receivers in D2D multicast group. Second, the optimal power allocation is formulated as a non‐linear programming problem and the optimal solution is achieved by searching from a finite set for the allocated channel. The simulation results show that the proposed method can ensure the quality of service (QoS) and improve the system capacity.

Satellite-terrestrial spectrum sharing is an effective method to alleviate the scarcity of spectrum resources. However, with the dense deployment of terrestrial systems, the satellite downlink beam covers multiple terrestrial base stations (BSs) and interacts with terrestrial downlinks during spectrum sharing. The spectrum sharing users encounter co-channel interference of inter-system, inter-cell and intra-cell, posing significant challenges for downlink spectrum sharing in converged system. In this paper, we propose a cooperative multichannel spectrum sharing framework where the multiple terrestrial BSs downlinks share multichannel spectrum resources of low earth orbit (LEO) satellite while guaranteeing the rate requirements of all shared users and aiming at improving spectrum efficiency of converged system. We formulate the cooperative satellite-terrestrial spectrum sharing strategy as a global spectrum efficiency maximization problem for the converged system, achieved through multichannel assignment and power allocation optimization for terrestrial shared users. To solve the non-convex optimization problem, we provide a dynamic and collaborative spectrum sharing (DCSS) algorithm based on multi-agent deep reinforcement learning (MADRL), which includes DDQN-based multichannel assignment and DDPG-based power allocation. DCSS transforms the problem to a decentralized partially observable Markov decision processes (Dec-POMDP) and adopts centralized-training-distributed-execution (CTDE) framework to address the challenge of acquiring global information in real-time. Specifically, each terrestrial BS acts as an agent with a globally trained two-layer joint deep neural network model that can adaptively allocate the multichannel and power based on observed system local information. Extensive simulation results demonstrate that DCSS algorithm outperforms the existing typical algorithms.