Multibeam Satellite Communications with Massive MIMO: Asymptotic Performance Analysis and Design Insights

TL;DR

The paper addresses CSI-free multibeam satellite downlinks with massive MIMO by using fixed-beam precoding and location-based user selection, modeling ground-user locations as a PPP. It derives concise asymptotic rate-scaling laws that reveal how user density, the number of antennas $M^2$, and the number of beams $K$ interact, showing that a density scaling $\lambda \sim M^q$ with $q>1$ yields non-vanishing rates in the single-beam case, and that an additional scaling factor $\ell$ is required in the multi-beam case to manage inter-beam interference, with $q=2$ achieving asymptotically optimal performance. The results provide design insights for beam spacing, user density, and CSI-free operation, and quantify the trade-offs between multiplexing gains and interference under fixed-beam precoding. Overall, the work demonstrates that fixed-beam MIMO in multibeam GEO satellites can approach optimal performance in the massive-antenna regime when user density scales appropriately, without requiring instantaneous CSI from ground users.

Abstract

To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then select a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing corresponding beam width. By modeling the ground users' locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales in power with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality.

Figures (6)

• Figure 1: Illustration of the downlink multibeam satellite communication.
• Figure 2: Beam gain comparisons between the parabolic reflector array versus the phased array.
• Figure 3: In a single beam case, $\mathcal{R}_1$ versus $M^2$ with radius $R_1 = 250$km for different $\lambda$.
• Figure 4: In a multiple beam case, $\mathcal{R}_\Sigma$ versus $M^2$ for different $\lambda$. $K$ is determined as the number of beams that completely fill the whole coverage area with the beam configuration in \ref{['eq:beam position']} with $\ell=1$.
• Figure 5: In a multiple-beam case, the simulation results of \ref{['eq:result interf']} as increasing $q$ under different sets of $\ell$ and $s$.

Theorems & Definitions (20)

• Remark 1: Rain attenuation
• Remark 2: Satellite array model
• Corollary 1
• proof
• Theorem 1
• proof
• Theorem 2
• proof
• Corollary 2
• proof