Cooperative communication has used to be a hot topic and it has been studied extensively in the past 10 years, but in recent years, it becomes less likely to find substantial innovation in this field as before. In this paper, we propose a new hybrid decode-forward and amplify-forward with non-orthogonal multiple access (NOMA) (HDAF-NOMA) transmission scheme for a cellular system with multiple relays. To the best of our knowledge, this is the first work that attempts to integrate decode-forward (DF), amplifyforward, and NOMA into one strategy design to improve system performance. To verify the performance advantages, the proposed HDAF-NOMA scheme is compared with the other four traditional schemes in terms of channel capacity and average system throughput, and the optimal number of selected DF relays is also determined for the HDAF-NOMA scheme. Simulation results show that compared with the traditional schemes, the proposed HDAF-NOMA scheme can achieve larger sum channel capacity for the transmission of x 1 and x 2 , and it can also achieve larger average system throughput at high SNR region. INDEX TERMSNOMA, non-orthogonal multiple access, DF, decode-forward, AF, amplify-forward, relay. YANG LIU received the B.Eng. degree in automation from Tsinghua University, Beijing, China, in 1997, the M.Eng. degree in electrical engineering and electronics from Nanyang Technological University, Singapore, in 2002, and the Ph.D. degree in information and communication system from the Beijing University of Posts and Telecommunications (BUPT), China, in 2011. He is currently an Assistant Professor with the School of Electronic Engineering, BUPT. His research interests include 5G Communication technologies such as NOMA, MIMO, broadcast/multicast, cooperative communications, and interference management. GAOFENG PAN received the B.Sc. degree in communication engineering from Zhengzhou University, Zhengzhou, China, in 2005, and the Ph.D. degree in communication and informa-

Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy. INDEX TERMS Wind power prediction, salp swarm algorithms, extreme learning machine, optimization algorithm.

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