This paper presents a novel handover algorithm designed to address the challenges of ubiquitous connectivity in LEO-based Direct-to-Cell (D2C) satellite systems, specifically focusing on maximizing both coverage and link quality across the service area. Unlike existing approaches, our method jointly optimizes the service and feeder links for a more realistic solution. We propose a new bipartite graph structure, representing satellites and satellite ground cells aligned with earth-fixed beams, and utilize two bipartite graphs to enable simultaneous optimization of both links. The algorithm maximizes both coverage and link quality in the service link while determining optimal gateway locations and antenna numbers in the feeder link to support the maximization targets in service link. Simulations based on a Walker-Delta satellite constellation and real map-based scenarios demonstrate that the proposed algorithm outperforms random gateway placement, improving coverage by up to 4% and increasing the average SNR by approximately up to 3 dB across all cells.

This paper proposes a low-complexity decoding algorithm for complex low-density lattice codes (CLDLC). The key is a method to approximate an infinite complex Gaussian mixture that occurs at the variable node of the belief propagation (BP) decoder. We define the reliability of check-to-variable messages and a threshold function to determine the number of complex Gaussian functions to use in the approximation. This allows the number of Gaussians in the approximation to be adaptively selected depending upon its reliability. By using the minimum number of Gaussians needed for an accurate approximation, the complexity of the decoder can be significantly reduced. The reliability is based on a likelihood function, and we form an upper bound on the Kullback-Leibler (KL) divergence to find the threshold function via linear regression. The approximation based on reliability of each message can reduce the complexity to $O(n\cdot t\cdot 1.35^{d-1})$ at high volume-to-noise ratio (VNR), where n is the lattice dimension, d is the degree of the inverse generator matrix, and t is the number of iterations. This algorithm provides higher performance and lower complexity compared to previously proposed approximation algorithms.

This paper proposes reliability-based parametric decoding of low-density lattice codes (LDLC). We define the reliability of the check-to-variable messages for two purposes. The first one is to choose to approximate the infinite Gaussian mixtures by one or two Gaussians. The reliability of each check-to-variable message is calculated. If there is higher reliability than a fixed threshold value, one Gaussian will be selected; otherwise two Gaussians will be used. The other purpose is for the updating sequence of variable nodes of the parametric shuffled BP (SBP) decoding algorithm. The parametric SBP increases the convergence speed. The updating sequence of SBP follows the order of reliability of the check-to-variable messages from high to low. The numerical results show that the proposed algorithm gives superior performance and lower complexity compared to two or three Gaussian decoding algorithm. At a probability of symbol error equal 10-4and n = 100 and 1000, the proposed algorithm gains 0.25 and 0.2 dB, respectively. Moreover, the proposed algorithm provides lower decoding time, fewer number of iterations for convergence and lower memory requirement.