This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput under limited transmission power. The corresponding optimization problem is first formulated as a mixed integer non-linear programming (MINP). To adequately address this challenging problem, an efficient LP and DFT-based beamforming algorithm are developed by utilizing several optimization tools, such as the weighted minimum mean square error transformation, duality method, and Hungarian algorithm. In addition, a greedy algorithm is proposed for benchmarking. A complexity analysis of these solutions is provided along with a comprehensive set of Monte Carlo simulations demonstrating the efficiency of our proposed algorithms.
To adjust for the non-uniform spatiotemporal nature of traffic patterns, next-generation high throughput satellite (HTS) systems can benefit from recent technological advancements in the space-segment in order to dynamically design traffic-adaptive beam layout plans (ABLPs). In this work, we propose a framework for dynamic beamforming (DBF) optimization and adaptation in dynamic environments. Given realistic traffic patterns and a limited power budget, we propose a feasible DBF operation for a geostationary multibeam HTS network. The goal is to minimize the mismatch between the traffic demand and the offered capacity under practical constraints. These constraints are dictated by the traffic-aware design requirements, the on-board antenna system limitations, and the signaling considerations in the K-band. Noting that the ABLP is agnostic about the inherent inter-beam interference (IBI), we construct an interference simulation environment using irregularly shaped beams for a large-scale multibeam HTS system. To cope with IBI, the combination of on-board DBF and on-ground precoding is considered. For precoded and non-precoded HTS configurations, the proposed design shows better traffic-matching capabilities in comparison to a regular beam layout plan. Lastly, we provide trade-off analyses between system-level key performance indicators for different realistic non-uniform traffic patterns.
For many wireless communication applications, traffic pattern modeling of radio signals combined with channel effects is much needed. While analytical models are used to capture these phenomena, real world non-linear effects (e.g. device responses, interferences, distortions, noise) and especially the combination of such effects can be difficult to capture by these models. This is simply due to their complexity and degrees of freedom which can be hard to explicitize in compact expressions. In this paper, we propose a more model-free approach to jointly approximate an end-to-end black-boxed wireless communication scenario using software-defined radio platforms and optimize for an efficient synthesis of subsequently similar "pseudo-radiosignals". More precisely, we implement a generative adversarial network based solution that automatically learns radio properties from recorded prototypes in specific scenarios. This allows for a high degree of expressive freedom. Numerical results show that the prototypes' traffic patterns jointly with channel effects are learned without the introduction of assumptions about the scenario or the simplification to a parametric model.
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