Ultra-reliable and low-latency communication (URLLC) with statistical quality-of-service (QoS) guarantees has garnered increasing attention. Cell-free massive multiple-input multiple-output (CF-mMIMO) emerges as a promising network architecture for such applications. This paper analyzes the reliability and latency-constrained performance in CF-mMIMO uplink transmission. First, we derive the closed-form expression for the signal-to-plus-noise ratio (SNR) distribution with maximum ratio combining (MRC) under independent non-identically distributed (i.n.i.d.) Rayleigh fading channels, precisely characterizing large-scale fading effects. Subsequently, we develop closed-form approximations for both decoding error probability and effective capacity. Finally, the QoS exponent is obtained by solving the effective bandwidth-effective capacity equation under Poisson traffic arrivals, thereby providing the delay violation probability expression. Monte Carlo simulations validate the theoretical results and demonstrate that the derived results achieve accurate approximations for both decoding error probabilities and delay violation probabilities. Furthermore, they highlight significant enhancements in reliability and latency assurance enabled by CF-mMIMO architectures.

In this paper, we investigate the hybrid traffic scheduling problem in 5G-TSN integrated systems, to achieve the endogenous deterministic communication for 5G. A novel strategy namely time-slot orchestration is proposed, and a latencyand jitter-aware traffic scheduling problem with the objective of maximizing the resource utilization is further formulated. To the best of our knowledge, it is the first time for such a problem being studied in the context of 5G-TSN integration. To solve this complex combinatorial optimization problem, a low-complexity heuristic scheduling algorithm is elaborately designed and extensively evaluated. Experimental results show that, compared with the optimal method derived from the genetic algorithm (GA) and the method based on the classical earliest deadline first (EDF) scheduling, the proposed method can efficiently enhance the endogenous deterministic communication capability of 5G by orders of magnitude, especially under high loads.

In this paper, we consider real-time beamforming design for dynamic wireless environments with varying channels and different numbers of access points (APs) and users in cell-free systems. Specifically, a sum spectral efficiency (SE) maximization optimization problem is formulated for the beamforming design in dynamic wireless environments of cell-free systems. To efficiently solve it, a high-generalization network (HGNet) is proposed to adapt to the changing numbers of APs and users. Then, a high-generalization beamforming module is also designed in HGNet to extract the valuable features for the varying channels, and we theoretically prove that such a high-generalization beamforming module is able to reduce the upper bound of the generalization error. Subsequently, by online adaptively updating about 3% of the parameters of HGNet, an online adaptive updating (OAU) algorithm is proposed to enable the online adaptive real-time beamforming design for improving the sum SE. Numerical results demonstrate that the proposed HGNet with OAU algorithm achieves a higher sum SE with a lower computational cost on the order of milliseconds.

In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint and the discrete constraint of AP clustering. Through transformations, the intractable simultaneous optimization of continuous and discrete variables is reduced to optimizing only the sparsity of the continuous variables, facilitating a computationally efficient unsupervised deep learning algorithm. In addition, to further reduce the computational complexity, a computationally effective unsupervised deep learning algorithm is proposed to implement robust joint AP clustering and beamforming design with imperfect CSI in cell-free systems. Numerical results demonstrate that the proposed unsupervised deep learning algorithm achieves a higher worst-case sum rate under a smaller number of AP clustering with computational efficiency.

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relations among joints because the feature representations transformed only by global relations via the Transformer neglect information on the human skeleton. To deal with this problem, we propose a novel method in which the Transformer encoder and GCN blocks are alternately stacked, namely AMPose, to combine the global and physically connected relations among joints towards HPE. In the AMPose, the Transformer encoder is applied to connect each joint with all the other joints, while GCNs are applied to capture information on physically connected relations. The effectiveness of our proposed method is evaluated on the Human3.6M dataset. Our model also shows better generalization ability by testing on the MPI-INF-3DHP dataset. Code can be retrieved at https://github.com/erikervalid/AMPose.

Hybrid heterogeneous cellular networks (HCNets) with sub-6G and millimeter-wave (mmWave) base stations (BSs) achieves good performance-and-cost tradeoffs. Via noticing the negligible retrieving latency and the potential joint transmission (JT) gain of sub-6G BSs, we propose a novel cache-enabled user association scheme for hybrid HCNets with limited local storage, in which partial sub-6G local storage caches contents with relatively low popularity for higher overall content diversity. To sufficiently exploit the large bandwidth of mmWave BSs, they are endowed with higher association priority. Then, the success probability and meta distribution of achievable rate are studied for the proposed scheme, respectively. We further provide the optimization of caching parameters and the condition for superior performance over the MPC-based scheme. Analytical and numerical results show that the network performance can be improved for larger sub-6G BS cooperation size and the array size at mmWave BSs, while the mmWave BS density has two adverse effect for low-to-medium and high rate threshold. For the proposed scheme, the content diversity in combination with JT gain benefits the success probability. And its performance advantage increases for larger local storage and/or higher association probability with mmWave BSs.

This paper focuses on the outage performance of a downlink integrated satellite-terrestrial relay network (ISTRN), which includes a satellite (S) transmitting messages to a user (D) with the help of K relays ({Rk, k =1,⋯,K}) using a threshold-based decode-and-forward (DF) transmission protocol. In addition, we consider the case of relay selection based on out-dated channel state information (CSI) to better reflect the actual scenario. The shadowed-Rician (SR) fading and Nakagami-m fading are considered for the channel model of the S-Rklinks and Rk-D links, respectively. In order to reveal the impact of the parameters on the performance of the considered network, the outage probability is adopted as a performance criterion and the corresponding exact closed-form expressions are derived. Further, in order to investigate the influence of system parameters more intuitively, asymptotic outage probabilities are considered for the two cases: 1) high average SNR for S-Rklink and Rk-D link; 2) high average SNR for Rk-D link. The asymptotic analysis results exactly reveal the influence of the relevant system parameters on the decoding gain and the diversity gain. Finally, simulation and numerical results verify the correctness of the analysis.

In this paper, we propose to endow deep 3D models with rotation invariance by expressing the coordinates in an intrinsic frame determined by the object shape itself. Key to our approach is to find such an intrinsic frame which should be unique to the identical object shape and consistent across different instances of the same category. Interestingly, the principal component analysis exactly provides an effective way to define such a frame, i.e. setting the principal components as the frame axes. As the principal components have direction ambiguity, there exist several intrinsic frames for each object. To achieve absolute rotation invariance for a deep model, we adopt the coordinates expressed in all intrinsic frames as inputs to obtain multiple output features, which will be aggregated as a final feature via a self-attention module. Comprehensive experiments demonstrate that our approach can achieve state-of-the-art performance on rotated 3D object classification and retrieval tasks.

Deep models are capable of fitting complex high dimensional functions while usually yielding large computation load. There is no way to speed up the inference process by classical lookup tables due to the high-dimensional input and limited memory size. Recently, a novel architecture (PointNet) for point clouds has demonstrated that it is possible to obtain a complicated deep function from a set of 3-variable functions. In this paper, we exploit this property and apply a lookup table to encode these 3-variable functions. This method ensures that the inference time is only determined by the memory access no matter how complicated the deep function is. We conduct extensive experiments on ModelNet and ShapeNet datasets and demonstrate that we can complete the inference process in 1.5 ms on an Intel i7-8700 CPU (single core mode), 32× speedup over the PointNet architecture without any performance degradation.

This paper investigates secure cooperative transmissions in a dual-hop MIMO relay system using a combined transmit antenna selection (TAS) and maximal-ratio combining (MRC) scheme over the Nakagami-m fading channels, where an adaptive decode-and-forward relaying protocol and an multi-antenna eavesdropper are considered. Due to the feedback delay, channel state information (CSI) for TAS might be outdated at both the source and the relay. To evaluate the secrecy performance of the TAS/MRC scheme and the impacts of outdated CSI, the closed-form expressions for the metrics of exact ergodic secrecy rate and exact secrecy outage probability are derived under both perfect and outdated CSI conditions in a channel feedback error model. In order to explicitly reveal the behaviors of the secrecy performance in high signal-to-noise ratio regime, asymptotic expressions for both the metrics are further derived. As validated by simulation results, analytically numerical results demonstrate that outdated the CSI always results in a loss in the secrecy performance, but the loss can be recovered by increasing the number of antennas at legitimate receivers. Furthermore, the outdated CSI yields a reduced secrecy diversity order, whereas only the perfect CSI leads to the full secrecy diversity order.