Optimal Linear Precoding Under Realistic Satellite Communications Scenarios
In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16X16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB- S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications
Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications. Space-Division Multiple Access precoding is a technique that reduces interference among satellite beams…
Deep Reinforcement LearningDownlink Transmit Design for Massive MIMO LEO Satellite Communications
This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state inf…
User-Centric Beam Selection and Precoding Design for Coordinated Multiple-Satellite Systems
This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT)…
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning
Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boostin…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Energy-Efficient Precoding and Feeder-Link-Beam Matching Design for Bent-Pipe SATCOM Systems
This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jo…
compressed sensing