## 4 Antennas for Beam Steering and Beamforming
### Beam-Steering/Beamforming Techniques and Their Possible Features for CubeSat Communications
Moreover, low-dimensional digital beamformers can be combined with analog phase shifters or switches, and sparse array structures can reduce the complexity while maintaining performance. Artificial intelligence (AI)-driven beam steering is emerging as a transformative approach in CubeSat communications, offering enhanced adaptability and efficiency in dynamic space environments. Traditional beam-steering methods, such as mechanical adjustments or predefined electronic configurations, often struggle to meet modern CubeSat missions' rapid and complex demands. By integrating AI, particularly machine learning algorithms, CubeSats can achieve real-time adaptive beamforming, optimizing signal directionality and strength in response to changing conditions. For instance, supervised learning algorithms have been proposed for real-time adaptive beamforming on multibeam satellites, enabling precise beam projection and dynamic adjustments to beamwidth and sidelobe levels, thereby enhancing communication performance. The application of AI to beam steering also addresses the limitations of traditional mechanical systems, which can be bulky and slow to respond. AI algorithms can process vast amounts of data from onboard sensors to predict and react to environmental changes, such as satellite movement or signal interference, allowing for swift electronic adjustments without mechanical components. This capability particularly benefits CubeSats, for which size, weight, and power constraints are critical considerations. Research has demonstrated that AI-driven beam steering can effectively manage these constraints while maintaining high communication performance. Moreover, AI-driven beam steering facilitates the implementation of advanced communication techniques, such as multiple-input, multiple-output (MIMO) systems, by efficiently managing the complex signal processing tasks required for these technologies. This integration enhances the data throughput and link reliability, which are essential for the increasing demands of CubeSat applications. Studies have shown that AI algorithms can optimize the performance of MIMO systems in satellite communications, leading to significant improvements in data rates and connection stability. In addition to improving performance, AI-driven beam steering contributes to the resilience of CubeSat communication systems. By continuously learning and adapting to the space environment, AI algorithms can anticipate potential issues, such as signal blockages or degradations, and proactively adjust beam patterns to maintain optimal communication links. This initiative-taking approach reduces the risk of communication disruptions and enhances the overall reliability of CubeSat missions. Research in this area has highlighted the potential of AI to significantly increase the robustness of satellite communication systems through adaptive beam steering [72; 73; 74]. When beam steering and beamforming are discussed, signal processing should be considered an inevitable part. Signal-processing techniques are fundamental to CubeSat communications, enabling efficient data transmission within limited power and bandwidth constraints. Digital modulation schemes, such as phase shift keying (PSK) and frequency shift keying (FSK), are commonly employed to optimize the spectral efficiency and resilience to noise. Additionally, error correction codes like Reed-Solomon and convolutional codes enhance the data integrity by mitigating the signal degradation effects during transmission. Integrating software-defined radios (SDRs) allows for flexible implementation of these techniques, facilitating adaptability to various communication protocols and frequency bands. This flexibility is particularly advantageous for CubeSats operating in the crowded VHF and UHF bands, as it enables dynamic spectrum access and efficient utilization of available frequencies [75]. The advent of 5G technology has introduced opportunities to enhance CubeSat communications through advanced signal-processing techniques.