We propose an agile-beam non-orthogonal multiple access (NOMA) scheme for millimeter wave (mmWave) communication networks. The agile-beam NOMA scheme flexibly switches between the single/multi-beam NOMA depending on the user pairing results. In particular, a novel user pairing strategy employing both distance and angle information is designed to facilitate the agile-beam NOMA transmission. More specifically, the base station first selects one user from the far user group randomly and then pairs it with the user that has the minimum angle difference in the near user group. This pairing strategy efficiently exploits the channel sparsity in user domain while supporting flexible mmWave transmission with agilebeam NOMA. Then an in-depth performance analysis is carried out to reveal the interplay between the key system parameters and the coverage probability of the proposed agile-beam NOMA by using the tool of stochastic geometry. Moreover, numerical results demonstrate the superiority of the proposed agile-beam NOMA over the conventional NOMA and orthogonal multiple access (OMA) in mmWave communication networks. INDEX TERMS Millimeter wave (mmWave) communication, non-orthogonal multiple access (NOMA), user pairing.
Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.
Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.
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