The proposed project, REGAL is a novel approach and a key enabling technology for SES Dynamic Resource Management (SDRM) systems for the next-generation satellites. The main objective of the project is to provide advanced traffic matching capabilities to the future satellite communication systems by implementing fast beam adaptation along with aggressive frequency reuse. Consequently, the research aims to propose a process for CSI collection and transmission design by jointly designing Precoding, Scheduling and ACM adaptation for the altering channel conditions with the changing multi-beam pattern. Furthermore, in order to improve the channel quality measurements, the research purpose is to study candidate pilot sequences such as Zadoff Chu sequence, pseudo-random sequence and Walsh-Hadamard for orthogonality tolerance by analysing the autocorrelation and cross-correlation properties and propose an optimum pilot sequence. The research also intends to investigate the feasibility to introduce adaptive pilot lengths to the future DVB superframe standard releases of satellite communication systems. Besides, with the help of filters and machine learning methodologies, the research also wishes to accomplish on improving the quality of channel conditions at the network through channel predicting and not relying on CSI for the estimation. Hence, by employing feedback (CSI) optimisation and channel prediction algorithms, the project intends to solve the present industrial problems precluding the progress of the SDRM systems. Also, the implementation of Dynamic Beamforming as a flexible, adaptive solution, will not only benefit from serving the unpredictable capacity demand but also focuses on making the satellite systems more cost-effective for the operators such as SES.