cluster data aggregation. The network lifetime was improved using AI-based modeling with intra-network to enhance green communication. Unlike the artificial bee colony and genetic algorithm, the throughput, device lifetime, and jamming prediction were promoted using the proposed MOACO. A Wideband Autonomous CR (WACR) anti-jamming method presented in [123] evaded a jammer that swept across the whole wideband spectrum range. The WACR equipped 11 Secure Spectrum Sharing with Machine Learning: Methodologies spectrum knowledge acquisition ability to detect and identify the location of the sweeping jammer. A Q-learning-based method was proposed to allow the anti-jamming operation to cover over several hundred MHz of a wide spectrum in real-time. An anti-jamming-based secure communication protocol was then developed that selected a spectrum position with enough contiguous idle spectrum to resist interference by both deliberate jammers and inadvertent disruptions. The communication then switched to this position until the jam- mer arrived. When the jammer began to interfere with the CR's transmission, it switched to a new spectrum band that led to the longest possible uninterrupted transmission as learned through Q-learning. By including more agents in the system, the authors in [11] further proposed an advanced RL-based anti-jamming approach. The considered system model allowed multiple WACRs to operate over the same spectrum band simultaneously. Each radio attempted to evade other WACRs' transmissions and avoid jammer signals that swept across the whole spectrum band of interest. The WACR first detected and identified the frequency location of this sweeping jammer and the signals of other WACRs. A sub-band selection policy was then given by the RL-based approach based on the detection results to avoid both the jammer signal and interference from other radios. To enable network devices to detect and predict jamming signals in a system with multi- ple jamming modes and noises, it is critical to develop a rapid jamming de