In the Large-scale Satellite Network (LSN), inter-beam interference significantly hinders the transmission performance of Low Earth Orbit (LEO) satellite downlinks. This interference exhibits notable time-varying characteristics due to the relatively rapid movements between LEO satellites and ground users, posing a substantial challenge to existing transmission resource allocation techniques. To tackle this issue, we introduce the Hypergraph Neural Network (HGNN)-enabled Resource Allocation (HGNNRA) algorithm for the downlinks of LSN. This algorithm aims to solve a transmission-rate maximization problem, effectively handling the time-varying interference among multiple beams in the downlink through a well-tailored Dynamic Hypergraph Neural Network (DynHGNN). Specifically, considering the coupling complexity of satellite beam coverage and user participation, we have developed a Dynamic Hypergraph-based interference model, along with a customized construction algorithm, to describe their time-varying relationships precisely. Simulation results indicate that our proposed HGNNRA outperforms both Graph Convolutional Network (GCN) [14], HGNN [15], GNN-DDQN [48], and GCNRA in terms of transmission rate and the satisfaction degree of user transmission requirement metrics.

In this paper, we propose a novel composition aided generalized quadrature spatial modulation (C-GQSM) scheme to improve the spectral efficiency (SE) of the GQSM systems by exploiting the power domain degree of freedom. The C-GQSM scheme constitutes a hybridization of GQSM and composition modulation (CM) principles, allowing the information bits to encompass not only the antenna activation patterns (AAPs) and amplitude/phase modulated (APM) constellation symbols, but also the energy allocation patterns (EAPs). In addition, we present two low-complexity detection techniques for the proposed C-GQSM system. The first one is based on the ordered successive interference cancellation (OSIC) technique, while the other based on the weighted coordinate descent (WCD) algorithm. Moreover, the upper bound of the average bit error probability (ABEP) of the proposed C-GQSM scheme is derived under both uncorrelated and correlated channel conditions. Simulation results show that the proposed C-GQSM outperforms both the conventional CM and GQSM systems in terms of SE without sacrificing the bit error rate (BER) performance.

In contrast to most of the existing equalization methods, blind equalization (BE) can eliminate the effect of multipath fading without any known sequences. As a result, BE is a promising technique for wireless intelligence and non-cooperative communication. However, the conventional BE methods require long sequences or large-scale training to recover the received signals. In this paper, a deep learning-based neighborhood-assisted symbol-based decision (DL-NA-SBD) method is proposed to tackle this problem. Specifically, we replace the commonly used linear approaches with neural network to optimize the traditional SBD function and the mean square error (MSE) loss function in two stages to generate the equalizer coefficients. To avoid collecting large numbers of signals, we train the network using the online training strategy. Simulation results demonstrate that the proposed method achieves better inter-symbol interference (ISI) elimination and bit error rate (BER) performance compared with the conventional methods while requiring only a short-length signal sequence.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

Recent methods of cooperative multiagent rein-forcement learning built upon the individual global max value decomposition principle show promising results using variants of deep mixing networks, and credit assignment plays a crucial role in it. However, each agent in a multiagent system requires not only credit assignment but also credit feedback which tells each agent how many rewards it should obtain to maximize expected cumulative global rewards. In this work, we propose TLMIX, a novel Twin Leader Mixing Network for multiagent cooperation reinforcement learning while maintaining the centralized training and decentralized execution paradigm. TLMIX introduces a leader network to address the credit feedback issue by utilizing global information to provide reasonable objectives for agent networks. TLMIX also introduces a twin mixing network to find a more accurate target function from the Q-value functions, which avoids the rapid increase in parameter scale caused by introducing individual agents' twin networks and effectively mitigates the accumulation of high overestimation errors caused by temporal difference updates. Extensive results on SMAC experimental scenarios and the Predator-Prey environment demonstrate that TLMIX significantly outperforms comparable benchmark algorithms on convergence speed and performance.

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically, it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multiplexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.

Visual kinship verification aims to detect blood relatives in facial images. Its practical application have motivated many researchers to focus on the topic as of recent. In this paper, we focus on a new view of visual kinship technology: kin-based face generation. Specifically, we propose a two-stage kin-face generation model to predict the appearance of a child given a pair of parents. The first stage includes a deep generative adversarial auto-encoder conditioned on ages and genders to map between facial appearance and high-level features. The second stage is our proposed DNA-Net, which serves as a transformation between the deep and genetic features based on a random selection process to fuse genes of a parent pair to form the genes of a child. We demonstrate the effectiveness of the proposed method quantitatively and qualitatively. Experiments validate that the proposed model synthesizes convincing kin-faces using both subjective and objective standards.

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.