Spectrum Sensing of Cognitive Radio for CubeSat Swarm Network

Low earth orbit CubeSat swarms improve the spatial and temporal resolution of remote sensing, rural communication, and space exploration due to their innovative and economical satellite design. Unlike conventional large satellites, which demand high transmission power for data exchange, the CubeSat swarm communication system provides interoperability, a high data rate between networked nodes, and global coverage with real-time measurement. However, CubeSat swarms’ main challenges include inefficient usage of spectrum resources and delay of data exchange, and the issues become more severe with the increased number of on-orbit CubeSats. Spectrum sensing in cognitive radio is often proposed as a critical solution to efficient spectrum utilization and low delay of data exchange. Typically, the secondary user takes over the link while the primary user does not occupy the channel. This paper proposes blind source separation (BSS) and Least Mean Square (LMS) for primary user detection with MIMO antennas equipped in all CubeSats. Each antenna receives a mixture of radio signals, including primary and interfering signals. Practical implementation issues are studied through simulations, with main performance metrics including algorithm performance and MIMO antenna deployment. These are essential for the communication resource allocation, power budget calculation, and hardware design of the CubeSat platform on configuring LEO non-terrestrial networks.