Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks
Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks
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DOI：
10.1109/tccn.2023.3235738
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发表时间：
2023-04
影响因子：
8.6
通讯作者：
Zhenyu Tan;Danyang Wang;Qi Liu;Zan Li;Ning Zhang;E. Abdel-Raheem
中科院分区：
文献类型：
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作者：
Zhenyu Tan;Danyang Wang;Qi Liu;Zan Li;Ning Zhang;E. Abdel-Raheem
根据认知无线电网络中混合接入策略的规定，次用户（SU）需要识别主用户（PU）的特定发射功率电平，以避免对PU造成不可接受的干扰。然而，传统的发射功率识别方法不能在低信噪比、衰落信道和存在噪声不确定性的条件下准确地识别发射功率，因为这些方法是基于固定的统计理论来对动态电磁环境进行数学建模。为了解决这些问题，本文提出了一种基于ResNet的多级传输功率识别（MTPR）架构。此外，所提出的架构在两种情况下实现不同的观察数据。在第一种情况下，接收信号的协方差矩阵（CM）包含丰富的能量信息被用作CM-MTPR方案的观测数据。为了进一步提高识别精度，在第二种情况下，从保留更多原始信息的接收信号采样的同相和正交相位（IQ）数据被配置为IQ-MTPR方案的观测数据。然而，IQ-MTPR方案消耗额外的计算资源，这与CM-MTPR方案形成识别性能和计算消耗之间的权衡。仿真结果验证了所提方案的辨识性能。
According to the regulations of the hybrid access strategy in cognitive radio network, the secondary user (SU) needs to identify the primary user’s (PU) specific transmit power level to avoid unacceptable interference with the PU. However, the conventional transmit power recognition methods cannot accurately identify the transmit power in conditions with low signal-to-noise ratio, fading channels and the existence of noise uncertainty, since those methods are based on a fixed statistical theory to model the dynamic electromagnetic environment mathematically. To address these issues, a ResNet-based multi-level transmission power recognition (MTPR) architecture is presented in this paper. Furthermore, the proposed architecture is implemented in two cases with different observation data. In the first case, the received signal’s covariance matrix (CM) containing rich energy information is used as the observation data of CM-MTPR scheme. To further improve the identification accuracy, in the second case, the in-phase and quadrature-phase (IQ) data sampled from the received signal that preserves more original information is configured as the observation data of IQ-MTPR scheme. The IQ-MTPR scheme, however, consumes additional computing resources which forms a trade-off between identification performance and computational consumption with the CM-MTPR scheme. Simulation results demonstrate the identification performance of the proposed schemes.