Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity, resource saving, and strong anti-interference ability. Although the compression sensing theory solves the problems brought by the traditional signal processing methods to a certain extent, it also encounters some new problems: the reconstruction time is long and the algorithm complexity is high. In order to solve these problems and further improve the quality of image processing, a new convolutional neural network structure CombNet is proposed, which uses the measured value of compression sensing as the input of the convolutional neural network, and connects a complete connection layer to get the final Output. Experiments show that CombNet has lower complexity and better recovery performance. At the same sampling rate, the peak signal-to-noise ratio (PSNR) is 12.79-52.67 higher than Tval3 PSNR, 16.31%-158.37% higher than DAMP , 1.00%-3.79% higher than DR²-Net, and 0.06%-2.60% higher than FCMN. It still has good visual appeal when the sampling rate is very low (0.01).

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The paper studies the cooperative spectrum sharing among multiple secondary users (SUs) in a clustering cognitive ad hoc network. The problem is formulated as a repeated game with the aim of maximizing the total transmission rate of SUs. Firstly, a clustering formation procedure is proposed to reduce the overhead and delay of game process in cognitive radio network (CRN). Then the repeated game-inspired model for SUs is introduced. With the model, the convergence condition of the proposed spectrum-sharing algorithm is conducted, and the convergence performance is investigated by considering the effects of three key factors: transmission power, discount factor, and convergence coefficient. Furthermore, the fairness of spectrum sharing is analyzed, and numerical results show a significant performance improvement of the proposed strategy when compared to other similar spectrum-sharing algorithms.

Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time-sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay-aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade-off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 10 6 to 10 7 , the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm.