Integrated Communication and Computation Resource Allocation for the Compressive Sensing Based Image Transmission

The data compression based transmission has been envisioned as a promising solution to improve the data transmission efficiency with the limited radio resources in the future sixth-generation (6G) wireless networks. In this paper, we propose an integrated communication and computation resource allocation system for image transmission based on compressive sensing (CS), which consists of several camera devices and a base station (BS). The device side first compresses the images, after which the compressed images are transmitted using non-orthogonal multiple access (NOMA) transmission, and finally the BS restores the received compressed images. Due to the limited energy supply, the total system energy consumption is minimized by jointly optimizing the image sampling rate, the image data transmission power, the number of floating point operations per second (FLOPS), the time of image compression and the time of data transmission under the constraints of latency and the peak signal-to-noise ratio (PSNR). Due to the non-convexity of the proposed problem, after a series of equal substitutions we convexify the problem. Then, the Karush–Kuhn–Tucker (KKT) condition and the gradient descent method are used to obtain the optimal solution of the target problem. After simulation experiments, it is concluded that the proposed CS-based image transmission scheme effectively reduces the total energy consumption by a factor of 2.7 compared with frequency division multiple access (FDMA), and the total latency by 180% compared with the original image transmission.