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The device-to-device (D2D) communication is viewed as an attractive technique to increase the spectrum efficiency and the data transmission rate in the wireless network. In this paper, we investigate the joint resource allocation and power control problem for cooperative D2D users (DUs) which multiplex cellular users (CUs) in downlink cooperative D2D heterogeneous networks. The studied resource allocation problem contains the spectrum resource block allocation and the selection of an idle user which works as a relay to assist the D2D links communication, while the power control aims to reduce the interference between users and improve the communication quality of service (QoS). To efficiently maximize the total throughput of all the DU links and the CU links on the premise of guaranteeing the communication QoS for CUs, we propose a quantum coral reefs optimization algorithm (QCROA) to obtain the optimal joint resource allocation and power control scheme. The simulation results demonstrate that the proposed QCROA achieves an excellent performance for different network communication scenarios.

The recent appreciation of deep reinforcement learning (DRL) arises from its successes in many domains, but the applications of DRL in practical engineering are still unsatisfactory, including optimizing control strategies in cognitive electronic warfare (CEW). CEW is a massive and challenging project, and due to the sensitivity of the data sources, there are few open studies that have investigated CEW. Moreover, the spatial sparsity, continuous action, and partially observable environment that exist in CEW have greatly limited the abilities of DRL algorithms, which strongly depend on state-value and action-value functions. In this paper, we use Python to build a 3-D space game named Explorer to simulate various CEW environments in which the electronic attacker is an unmanned combat air vehicle (UCAV) and the defender is an observation station, both of which are equipped with radar as the observation sensor. In our game, the UCAV needs to accomplish the task of detecting the target as early as possible to perform follow-up tracking and guidance tasks. To allow an "infant" UCAV to understand what "target searching" is, we train the UCAV's maneuvering strategies by means of a well-designed reward shaping, a simplified constant accelerated motion control, and a deep deterministic policy gradient (DDPG) algorithm based on a generative model and variational Bayesian estimation. The experimental results show that when the operating cycle is 0.2 s, the search success rate of the trained UCAV in 10000 episodes is improved by 33.36% compared with the benchmark, and the target destruction rate is similarly improved by 57.84%.

The wireless energy harvesting (EH) technique is regarded as a new way to provide an energy supply for energy-constrained cognitive relay networks (CRNs). A novel wireless EH cognitive multi-user relay network (CMRN) for the underlay protocol is investigated in this paper. In this system, there are multiple primary users (PUs) and multiple secondary users (SUs). The SUs can share the licensed spectrum and harvest energy from ambient signals. The problems of multiple relay selection by the SUs and of finding the optimal EH ratio are considered. We analytically derive the exact expression of the throughput of a secondary network. In it, there are four constraints: for the permitted peak interference with each primary transmitter (PT); for the sum interference for each PT; that the transmit power of secondary source nodes (SSNs) and secondary relays (SRs) should be less than the energy harvested; and that each secondary source node/secondary destination node (SSN-SDN) pair can only choose one SR. To obtain the optimal performance of the secondary network's throughput, we should optimize the multiple relay selection scheme and the EH ratio. Actually, it is a classic integer optimization problem to design an optimal multiple relay selection scheme. However, the selection of the optimal EH ratio is a continuous optimization problem. The joint multiple relay selection and time slot allocation is a classical hybrid optimization problem. So, we propose a novel quantum sine cosine algorithm (QSCA) for resolving the difficulty with optimization of multiple relay selection and the EH ratio. Our simulation results verify our proposed solution by showing the influence of different parameters for the proposed model and by demonstrating good performance under the QSCA.

Cognitive radio technology is an important branch in the field of wireless communication, and automatic identification is a major part of cognitive radio technology. Convolutional neural network (CNN) is an advanced neural network, which is the forefront of application in the digital image recognition area. In this paper, we explore CNN in an automatic system to recognize the cognitive radio waveforms. Excitedly, it is a more effective model with high ratio of successful recognition (RSR) under high power background noise. The system can identify eight kinds of signals, including binary phase shift keying (Barker codes modulation) linear frequency modulation, Costas codes, Frank code, and polytime codes (T1, T2, T3, and T4). The recognition part includes a CNN classifier. First, we determine the appropriate architecture to make CNN effective for proposed system. Specifically, we focus on how many convolutional layers are needed, what appropriate number of hidden units is, and what the best pooling strategy is. Second, we research how to obtain the image features into CNN that based on Choi-Williams time-frequency distribution. Finally, by means of the simulations, the results of classification are demonstrated. Simulation results show the overall RSR is 93.7% when the signal-to-noise ratio is -2dB.