Determination of the Local Standard of Rest using the LSS-GAC DR1

We re-estimate the peculiar velocity of the Sun with respect to the local standard of rest using a sample of local stars within 600 pc of the Sun, selected from the LAMOST Spectroscopic Survey of the Galactic Anti-centre (LSS-GAC). The sample consists of 94332 FGK main-sequence stars with well-determined radial velocities and atmospheric parameters. To derive the LSR, two independent analyses are applied to the data. Firstly, we determine the solar motion by comparing the observed velocity distribution to that generated with the analytic formulism of Schonrich & Binney that has been demonstrated to show excellent agreement with rigorous torus-based dynamics modelling by Binney & McMillan. Secondly, we propose that cold populations of thin disc stars, selected by applying an orbital eccentricity cut, can be directly used to determine the LSR without the need of asymmetric drift corrections. Both approaches yield consistent results of solar motion in the direction of Galactic rotation, V_sun, that are much higher than the standard value adopted hitherto, derived from Stromgren's equation. The newly deduced values of V_sun are 1-2 km/s smaller than the more recent estimates derived from the Geneva-Copenhagen Survey sample of stars in the solar neighbourhood (within 100 pc). We attribute the small difference to the presence of several well-known moving groups in the GCS sample that, fortunately, hardly affect the LSS-GAC sample. The newly derived radial and vertical components of the solar motion agree well with the previous studies. In addition, for all components of the solar motion, the values yielded by stars of different spectral types in the LSS-GAC sample are consistent with each other, suggesting that the local disk is well relaxed and that the LSR reported in the current work is robust. Our final recommended LSR is, (U,V,W)_sun = (7.01+/-0.20, 10.13+/-0.12, 4.95+/-0.09) km/s.Comment: MNRAS accepted, 13 pages, 11 figures, 7 table

Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC

Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we explore the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging our recently developed unitary approximate message passing based matrix factorization (UAMP-MF) algorithm, we design a message passing based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme

Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms the conventional systems

Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface

This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneously reflect and transmit the signals, thereby providing full-dimensional rate enhancement. To tackle such a problem, we formulate it by jointly optimizing the IOS's phase shift and the UAV trajectory. Although it is difficult to solve it optimally due to its non-convexity, we propose an efficient iterative algorithm to obtain a high-quality suboptimal solution. Simulation results show that the IOS-assisted UAV communications can achieve more significant improvement in achievable rates than other benchmark schemes.Comment: 6 pages, 5 figure

Training Deeper Neural Machine Translation Models with Transparent Attention

While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications. In this work we attempt to train significantly (2-3x) deeper Transformer and Bi-RNN encoders for machine translation. We propose a simple modification to the attention mechanism that eases the optimization of deeper models, and results in consistent gains of 0.7-1.1 BLEU on the benchmark WMT'14 English-German and WMT'15 Czech-English tasks for both architectures.Comment: To appear in EMNLP 201