This paper considers the application of non-orthogonal multiple access (NOMA) into cooperative cognitive radio (CR) networks with simultaneous wireless information and power transfer (SWIPT). For NOMA in cooperative CR networks with SWIPT, the cognitive relay harvests the transmission power from the secondary transmitter with power splitting scheme, while the fixed power allocation scheme is used for the NOMA protocol. The closed-form analytical expression of the overall outage probability for the proposed networks is derived, as well as its diversity order at high signal-to-noise ratio (SNR) region is investigated. Furthermore, compared to OMA in cooperative CR networks with SWIPT, the proposed scheme can always achieve the same diversity order, but lower overall outage performance. Compared with NOMA in cooperative CR networks using its own battery for transmission, the SWIPT NOMA in cooperative CR networks will lead to losing a little of the overall outage performance, but without losing the diversity order. INDEX TERMS Non-orthogonal multiple access, cognitive radio network, decode-and-forward, simultaneous wireless information and power transfer, outage probability, diversity gain.

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Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.