As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.

Non-orthogonal multiple access (NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifthgeneration (5G) communication. Successive interference cancellation (SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper, we propose a convolutional neural networks (CNNs) approach to restore the desired signal impaired by the multiple input multiple output (MIMO) channel. Especially in the uplink NOMA scenario, the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.

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In recent years, B scanners have been widely used clinically. Obviously, it is of particular importance to use the commercial B scanners to characterize tissue by estimating its ultrasonic attenuation in vivo. However, there are a lot of difficulties in doing so because the output of a B scanner is affected by many unknown factors. In this paper, a time domain method named the difference ratio correction (DRC) method is proposed to estimate the ultrasonic attenuation of tissue in vivo. In this method, three tissue-mimicking phantoms with known acoustical properties were employed to eliminate the instrumentation errors of the B scanner and the measuring system and to correct beam diffraction for correct attenuation estimation. Other advantages of this method are that it is very convenient to apply this method clinically and there is no need to change the inner construction of the B scanner because this method only utilizes the video output. Experimental and clinical results have proved the validity of this method.