Direct-to-device (D2D) satellite connectivity, an exemplar of non-terrestrial network (NTN) integration with 5G and forthcoming 6G mobile ecosystem, is enjoying rapidly growing prominence. Delivering space-enabled broadband connectivity to smartphones, with their low-power, omnidirectional antennas, requires multitude of technological advancements. One innovation exploits distributed satellite systems using swarms of small regenerative satellites in low-Earth orbit (LEO) that synthesize multiple steerable beams. Another advancement optimizes efficiency of the already scarce radio-frequency (RF) spectrum with full frequency reuse, unfortunately intensifying intrabeam interference. This paper develops novel methodology for unified interference mitigation, comprised of selective precoder and high-performance receiver. Precoder is based on maximizing signal-to-leakage-and-noise ratio (SLNR), made selective about which subset of interference to leave unmitigated. It has low complexity, implemented in two stages to reduce computational requirements and alleviate user-scheduling constraints. High-performance receiver jointly processes signal and dominant interference, building on divide-and-conquer (DAC) paradigm to synergize with selective precoding. It has low complexity with computational load that does not grow exponentially with the number of dominant interferers. Extensive simulations reveal that unified interference mitigation significantly surpasses state-of-the-art schemes in sum-rate throughput, outage probability, and block error rate, also under scenarios of synchronization errors and imperfect channel state information at the transmitter (CSIT).

The increasing deployment of massive active antenna arrays in low Earth orbit (LEO) satellites necessitates computationally efficient and adaptive precoding techniques to mitigate dynamic channel variations and enhance spectral efficiency. Regularized zero-forcing (RZF) precoding is widely used in multi-user MIMO systems; however, its real-time implementation is limited by the computationally intensive inversion of the Gram matrix. In this work, we develop a low-complexity framework that integrates the Woodbury (WB) formula with adaptive randomized singular value decomposition (arSVD) to efficiently update the Gram matrix inverse as the satellite moves along its orbit. By leveraging low-rank perturbations, the WB formula reduces inversion complexity, while arSVD dynamically extracts dominant singular components, further enhancing computational efficiency. Monte Carlo simulations demonstrate that the proposed method achieves computational savings of up to 61% compared to conventional RZF precoding with full matrix inversion, while incurring only a modest degradation in sum-rate performance. These results demonstrate that WB-arSVD offers a scalable and efficient solution for next-generation satellite communications, facilitating real-time deployment in power-constrained environments.

Ultra high-throughput satellite (UHTS) systems are expected to play an essential role in future beyond 5G and 6G networks. One of the main sources of degradation in such system is the phase noise. Satellite manufacturers and equipment suppliers have to resort to cumbersome and time-consuming transmission chain simulations to estimate the carrier-to-interference ratio (CIR) caused by the phase noise. Therefore, a novel mathematical CIR formula is proposed in this work for the characterization of phase noise in future UHTS systems. Not only does this expression enable an efficient and accurate assessment of the phase noise degradation, but it can be easily incorporated in the satellite link budget and simply requires a predefined phase noise mask as an input. In this context, the influence of the main phase noise variables and the behavior of typical phase noise masks are analyzed, thus, bringing valuable insights. Finally, the theoretical results are validated through numerical simulations.

To meet the target requirements of 5G and 6G systems in terms of throughput and global connectivity, the upcoming Low Earth Orbit (LEO) mega-constellations are set to seamlessly integrate into terrestrial communication networks. Although extensively utilized in terrestrial systems, multi-user multiple-input multiple-output (MU-MIMO) transmission has not yet been implemented in LEO mega-constellation communication systems. In this work, we compare the throughput performance of a MU-MIMO downlink transmission and full frequency reuse (FFR) with the throughput of a multibeam LEO system with conventional four-color frequency reuse (FR4). We employ a deterministic modeling approach of the spacecraft motion, the satellite channel and the pre-generation of the downlink channel state information (CSI). To mitigate co-channel interference in the MIMO case, zero-forcing precoding is applied. We study the effects of an outdated CSI on the MIMO downlink performance. Simulation results confirm that even under the effects of an outdated CSI at the precoder, the MIMO LEO satellite system with FFR substantially outperforms the multibeam scenario with the FR4.

In this paper, a geographical non-orthogonal multiple access based multiuser beamforming (NOMA-BF) scheme is investigated to improve spectral efficiency in multi-beam satellite-based Internet of Things (IoT), where the primary user per beam needs to be timely served with a small targeted data rate while other users per beam are opportunistically served by using the NOMA concept. We first design a novel user scheduling to enhance spectrum efficiency. Then, a multi-layer beamforming (BF) is developed to mitigate inter-beam interference, augment the difference of the effective channel gains of the users, and perform inter-beam power allocation in a separate manner. Additionally, a dynamic intra-beam power allocation policy is conceived to further improve spectrum efficiency, while meeting predefined target data rates of the primary NOMA users. Numerical results show the capability of our proposal.

The development of next-generation high throughput satellite systems raises major design challenges, especially for their uplink. To supply data to a large number of user beams, tens of feeder links in the Q/V-band must be deployed. Moreover, transmit diversity is required to cope with the strong rain fades experienced in this higher range of frequencies. To address these issues, a multiple-input-multiple-output (MIMO) feeder link architecture is introduced. By doubling the amount of supported data traffic per link, this solution enables a reduction of the number of spatially separated feeder beams required to operate the system. As a consequence, larger angular separations can be realized to lower the inter-beam interference. Besides, a better robustness against weather impairments is ensured. Using a carrier to interference plus noise ratio measure, an outage analysis of a MIMO-based smart gateway architecture with 15 active MIMO feeder links and 1 redundant link proves the superiority of this novel approach.

In communication satellites, high power amplifiers (HPAs) are generally driven into the nonlinear region to improve their power efficiency. Due to the emergence of the onboard processors (OBPs), digital predistortion (DPD) can now be directly implemented in the transponder to improve the power efficiency of the HPA while maintaining its linearity. However, in DPD implementation, the computational complexity of the DPD coefficients estimation is a key performance metric, especially since the contemporary OBPs have extremely limited processing capabilities. This paper presents and discusses a possible implementation of low-effort sample-based DPD solutions and compares their performance against the state-of-the-art blockbased computationally exhaustive DPD techniques. The presented simulation results show that the sample-based methods exhibit a similar carrier to noise ratio gain and intermodulation noise reduction at a much lower computational and hardware cost.