Massive MIMO Satellite Mobile Communications

Satellite communication is considered as one of the key technologies to achieve seamless global network coverage.In traditional multi-beam satellites,the beamformer on the satellite side is usually fixed(or changing at a very slow pace),which makes it difficult to adapt to real-time link variations in the network.In recent years,massive multiple-input multiple-output(MIMO,multiple-input multiple-output)has become one of the supporting technologies in terrestrial cellular systems.In a massive MIMO system,the base station can generate a large number of dynamic beams with a massive antenna array,which significantly improves the spectral efficiency and energy efficiency of the system.Extending massive MIMO to satellite communication systems enables satellites to have the ability to use a large number of dynamic beams for communications,which can greatly improve the performance of satellite communication systems.In view of this,this thesis conducts research on the theory and method of massive MIMO satellite mobile communication.The main work and contributions of this thesis are as follows:Firstly,this thesis investigates the downlink(DL)transmit design for massive MIMO satellite communication systems,where only the slow-varying statistical channel state information is exploited at the transmitter.The channel model for the DL massive MIMO satellite system is established,in which both the satellite and the user terminals(UTs)are equipped with uniform planar arrays(UPAs).Observing the rank-one property of the channel matrices,we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate.This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality.An efficient algorithm is devised to compute the precoding vectors.Furthermore,we formulate an approximate transmit design based on the upper bound on the ergodic sum rate,for which the optimality of single-stream precoding still holds.We show that,in this case,the design of precoding vectors can be simplified into that of scalar variables,for which an effective algorithm is developed.In addition,a low-complexity learning framework is proposed for optimizing the scalar variables.Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.Secondly,this thesis investigates the uplink(UL)transmit design for massive MIMO satellite communication(SATCOM)systems,where the long-term statistical channel state information is utilized at the UTs.We consider the UPAs are deployed at both the satellite and UTs and derive the UL massive MIMO satellite channel model.With the aim to achieve the ergodic sum rate capacity,we show that the rank of each UT’s optimal transmit covariance matrix does not exceed that of its channel correlation matrix at the UT sides.This reveals the maximum number of independent data streams that can be transmitted from each UT to the satellite.We further show that the design of the transmit covariance matrices can be reduced into that of lower-dimensional matrices,for which a stochastic programming based algorithm is developed by exploiting the optimal lower-dimensional matrices’ structure.To reduce the computational complexity,we invoke the asymptotic programming and develop a computationally efficient algorithm to compute the transmit covariance matrices.Simulations show that the proposed UL transmit strategies are superior to the conventional schemes,and the low-complexity asymptotic programming based UL transmit design can attain near-optimal performance in massive MIMO SATCOM.Finally,this thesis investigates the massive MIMO orthogonal frequency division multiplexing(OFDM)channel estimation for LEO SATCOM systems.We use the angle-delay domain channel to characterize the space-frequency domain channel for LEO satellite massive MIMO OFDM communications.We show that the asymptotic minimum mean square error of the channel estimation can be minimized if the array response vectors of the UTs that use the same pilot are orthogonal.Inspired by this,we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance.To reduce the computational complexity,we devise a novel two-stage channel estimation approach,in which the received signals at the satellite are manipulated with the per-subcarrier space domain processing followed by the per-user frequency domain processing.Moreover,the space domain processing of each UT is shown to be identical for all the subcarriers,and an asymptotically optimal vector for the persubcarrier space domain linear processing is derived.The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver.Simulation results show that the proposed channel estimation approach can achieve near-optimal performance with much lower complexity.