Smart Communication Satellite (SCS) Project Overview

Smart Communication Satellite (SCS) is the first low earth orbit (LEO) mobile communication test satellite of China, whose core mission is to conduct technical exploration and verification for building satellite internet. Launched in September, 2014, SCS completed its whole on-orbit experiments in October, 2014. In order to resolve the contradiction between the coverage area and communication rate of LEO communication satellites, SCS adopts on- board smart antenna, whereby dynamically changing spot beams can be formed. Moreover, SCS has developed payload-centered satellite design technique as well as internet-oriented software satellite technique, and finally accomplished the design for application-oriented micro-satellite which is 100Kg-class weighted and applicable to communication and navigation services. The innovative techniques of SCS verify the new direction of development in the future satellite internet

Expectation propagation approach to joint channel estimation and decoding for OFDM systems

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2 K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the knownchannel bound

Message-passing receiver for joint channel estimation and decoding in 3D massive MIMO-OFDM systems

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity

Low-complexity iterative detection for large-scale multiuser MIMO-OFDM systems using approximate message passing

One of the challenges in the design of large-scale multiuser MIMO-OFDM systems is developing low-complexity detection algorithms. To achieve this goal, we leverage message passing algorithms over the factor graph that represents the multiuser MIMO-OFDM systems and approximate the original discrete messages with continuous Gaussian messages through the use of the minimum Kullback-Leibler (KL) divergence criterion. Several signal processing techniques are then proposed to achieve near-optimal performance at low complexity. First, the principle of expectation propagation is employed to compute the approximate Gaussian messages, where the symbol belief is approximated by a Gaussian distribution and then the approximate message is calculated from the Gaussian approximate belief. In addition, the approximate symbol belief can be computed by the a posteriori probabilities fed back from channel decoders, which reduces the complexity dramatically. Second, the first-order approximation of the message is utilized to further simplify the message updating, leading to an algorithm that is equivalent to the AMP algorithm proposed by Donoho et al. Finally, the message updating is further simplified using the central-limit theorem. Compared with the single tree search sphere decoder (STS-SD) and the iterative (turbo) minimum mean-square error based soft interference cancellation (MMSE-SIC) in the literature through extensive simulations, the proposed message passing algorithms can achieve a near-optimal performance while the complexity is decreased by tens of times for a 64 64 MIMO system. In addition, it is shown that the proposed message passing algorithms exhibit desirable tradeoffs between performance and complexity for a low-dimensional MIMO system

Message-Passing Receiver for Joint Channel Estimation and Decoding in 3D Massive MIMO-OFDM Systems

In this paper, we address the message-passing receiver design for the 3D massive MIMO-OFDM systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. Specially, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response (CIR) between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization (EM)-based method is derived to learn the channel power-delay-profile (PDP). The proposed joint algorithm is assessed in 3D massive MIMO systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.Comment: submitted to IEEE Trans. Wireless Commu

Expectation propagation based iterative group wise detection for large-scale multiuser MIMO-OFDM systems

For the spatially correlated multiuser MIMOOFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms