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With the development of unmanned aerial vehicles (UAVs), currently it is a prominent problem to support large-scale UAV networking. Millimeter wave (mmWave) with directional communication offers the advantage of high-speed anti-jamming transmission, while the sub-6 GHz with omni-directional communication demonstrates better performance in maintaining and controlling the network. In this research, a Dual-frequency Cooperation MAC protocol (DC-MAC) is designed to integrate both advantages from sub-6 GHz and mmWave communication. In DC-MAC, a criterion of link concurrency and a beam tracking algorithm are proposed to reduce interference and enhance transmission efficiency of the network. To evaluate the performance of the protocol, we propose an analysis model and consequently derive the closed-form calculation for network throughput. Simulation experiments show that DC-MAC protocol achieves higher throughput and lower control overhead compared to the state-of-the-art protocols. Besides, several experiments are performed to validate the proposed model and analyze the influence of key parameters on throughput, providing a reference for protocol design.

The 6G wireless communication demands intelligent and versatile interaction between humans and machines that can deal with various intelligent tasks. Semantic communication that focuses on transmitting the meanings rather than the data is expected to be one of the promising technologies to achieve this goal. However, most existing semantic communication systems optimize the whole system under a single objective, lacking the scalabi-lity to dynamic scenarios. For a dynamic scenario with changing channel conditions and background knowledge, we propose a layered semantic communication system (LSCS), which takes advantage of layered coding architec-ture at the semantic and syntactic levels. In addition, a symbolic attention-based denoising network is designed at the receiver to recover transmitted meanings. Simulation results demonstrate that the proposed LSCS can adapt to dynamic scenarios and achieve superior performance over benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) region.