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This paper focuses on an intelligent reflecting surface (IRS)-assisted lossy communication system with correlated Rayleigh fading. We analyze the correlated channel model and derive the outage probability of the system. Then, we design a deep reinforce learning (DRL) method to optimize the phase shift of IRS, in order to maximize the received signal power. Moreover, this paper presents results of the simulations conducted to evaluate the performance of the DRL-based method. The simulation results indicate that the outage probability of the considered system increases significantly with more correlated channel coefficients. Moreover, the performance gap between DRL and theoretical limit increases with higher transmit power and/or larger distortion requirement.

Semantic Communication (SC) is an emerging technology aiming to surpass the Shannon limit. Traditional SC strategies often minimize signal distortion between the original and reconstructed data, neglecting perceptual quality, especially in low Signal-to-Noise Ratio (SNR) environments. To address this issue, we introduce a novel Generative AI Semantic Communication (GSC) system for single-user scenarios. This system leverages deep generative models to establish a new paradigm in SC. Specifically, At the transmitter end, it employs a joint source-channel coding mechanism based on the Swin Transformer for efficient semantic feature extraction and compression. At the receiver end, an advanced Diffusion Model (DM) reconstructs high-quality images from degraded signals, enhancing perceptual details. Additionally, we present a Multi-User Generative Semantic Communication (MU-GSC) system utilizing an asynchronous processing model. This model effectively manages multiple user requests and optimally utilizes system resources for parallel processing. Simulation results on public datasets demonstrate that our generative AI semantic communication systems achieve superior transmission efficiency and enhanced communication content quality across various channel conditions. Compared to CNN-based DeepJSCC, our methods improve the Peak Signal-to-Noise Ratio (PSNR) by 17.75% in Additive White Gaussian Noise (AWGN) channels and by 20.86% in Rayleigh channels.

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

This letter proposes a novel anti-interference technique, semantic interference cancellation (SemantIC), for enhancing information quality towards the sixth-generation (6G) wireless networks. SemantIC only requires the receiver to concatenate the channel decoder with a semantic auto-encoder. This constructs a turbo loop which iteratively and alternately eliminates noise in the signal domain and the semantic domain. From the viewpoint of network information theory, the neural network of the semantic auto-encoder stores side information by training, and provides side information in iterative decoding, as an implementation of the Wyner-Ziv theorem. Simulation results verify the performance improvement by SemantIC without extra channel resource cost.

This letter proposes a novel relaying framework, semantic-forward (SF), for cooperative communications towards the sixth-generation (6G) wireless networks. The SF relay extracts and transmits the semantic features, which reduces forwarding payload, and also improves the network robustness against intra-link errors. Based on the theoretical basis for cooperative communications with side information and the turbo principle, we design a joint source-channel coding algorithm to iteratively exchange the extrinsic information for enhancing the decoding gains at the destination. Surprisingly, simulation results indicate that even in bad channel conditions, SF relaying can still effectively improve the recovered information quality.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst-case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst-case security provisioning as compared with the baselines., Comment: 14 pages, 10 figures, accepted at IEEE IoTJ

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated is the performance superiority of our proposal as compared with the baselines., Comment: 14 pages, 10 figures, submitted to IEEE Journal

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test., Comment: accepted for publication in Experimental Astronomy

The use of drone small cells (DSCs) has recently drawn significant attentions as one key enabler for providing air-to-ground communication services in various situations. This paper investigates the co-channel deployment of dense DSCs, which are mounted on captive unmanned aerial vehicles (UAVs). As the altitude of a DSC has a huge impact on the performance of downlink, the downlink interference control problem is mapped to an altitude control problem in this paper. All DSCs adjust their altitude to improve the available signal-to-interference-plus-noise ratio (SINR). The control problem is modeled as a mean-field game (MFG), where the cost function is designed to combine the available SINR with the cost of altitude controling. The interference introduced from a big amount of DSCs is derived through a mean-field approximation approach. Within the proposed MFG framework, the related Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations are deduced to describe and explain the control policy. The optimal altitude control policy is obtained by solving the partial differential equations with a proposed finite difference algorithm based on the upwind scheme. The simulations illustrate the optimal power controls and corresponding mean field distribution of DSCs. The numerical results also validate that the proposed control policy achieves better SINR performance of DSCs compared to the uniform control scheme.

The energy efficiency (EE) of a multi-user multi-relay system with the maximum diversity network coding (MDNC) is studied. We explicitly find the connection among the outage probability, energy consumption and EE and formulate the maximizing EE problem under the outage probability constraints. Relay scheduling (RS) and power allocation (PA) are applied to schedule the relay states (transmitting, sleeping, \emph{etc}) and optimize the transmitting power under the practical channel and power consumption models. Since the optimization problem is NP-hard, to reduce computational complexity, the outage probability is first tightly approximated to a log-convex form. Further, the EE is converted into a subtractive form based on the fractional programming. Then a convex mixed-integer nonlinear problem (MINLP) is eventually obtained. With a generalized outer approximation (GOA) algorithm, RS and PA are solved in an iterative manner. The Pareto-optimal curves between the EE and the target outage probability show the EE gains from PA and RS. Moreover, by comparing with the no network coding (NoNC) scenario, we conclude that with the same number of relays, MDNC can lead to EE gains. However, if RS is implemented, NoNC can outperform MDNC in terms of the EE when more relays are needed in the MDNC scheme., Comment: 13 pages, 7 figures, Part of this work was presented at the International Conference on Telecommunications (ICT), 2016

Context: We investigated the molecular gas associated with 6.7 GHz methanol masers throughout the Galaxy using a J=1-0 transition of the CO isotopologues. Methods:Using the 13.7-meter telescope at the Purple Mountain Observatory (PMO), we have obtained ^{12}CO and ^{13}CO (1-0) lines for 160 methanol masers sources from the first to the third Galactic quadrants. We made efforts to resolve the distance ambiguity by careful comparison with the radio continuum and HI 21 cm observations. Results: First, the maser sources show increased ^{13}CO line widths toward the Galactic center, suggesting that the molecular gas are more turbulent toward the Galactic center. This trend can be noticeably traced by the ^{13}CO line width. Second, the ^{12}CO excitation temperature shows a noticeable correlation with the H_2 column density. A possible explanation consistent with the collapse model is that the higher surface-density gas is more efficient to the stellar heating and/or has a higher formation rate of high-mass stars. Third, comparing the IRDCs, the maser sources on average have significantly lower H_2 column densities, moderately higher temperatures, and similar line widths. Fourth, in the mapped regions, 51 ^{13}CO cores have been revealed. Only 17 coincide with the radio continuum emission (F_{cm}>6 mJy), while a larger fraction (30 cores) coincide with the infrared emissions. The IR-bright and radio-bright sources exhibit significantly higher $^{12}$CO excitation temperatures than the IR-faint and radio-faint sources, respectively. Conclusions: The 6.7 GHz masers show a moderately low ionization rate but have a common-existing stellar heating that generates the IR emissions. The relevant properties can be characterized by the ^{12}CO and ^{13}CO (1-0) emissions in several aspects as described above., Comment: 38 pages, 13 figures, 4 tables, accepted to Astronomy and Astrophysics

A jet acceleration model for extracting energy from disk-corona surrounding a rotating black hole is proposed. In the disk-corona scenario, we obtain the ratio of the power dissipated in the corona to the total for such disk-corona system by solving the disk dynamics equations. The analytical expression of the jet power is derived based on the electronic circuit theory of the magnetosphere. It is shown that jet power increases with the increasing black hole (BH) spin, and concentrates in the inner region of the disk-corona. In addition, we use a sample consisting of 37 radio loud quasars to explore their jet production mechanism, and show that our jet formation mechanism can simulate almost all sources with high power jet, that fail to be explained by the Blandford-Znajek (BZ) process.

We propose a disc-corona model in which a geometrically thin, optically thick disc surrounds a Kerr black hole, and magnetic fields exert a time-steady torque on the inner edge of the accretion disc. The analytical expression of the total gravitational power is derived from the thin-disc dynamics equations by using this new boundary condition. It is shown that the magnetic torque can considerably enhance the amount of energy released in the disc-corona system. Furthermore, the global solutions of this disc-corona system are obtained numerically. We find that the fraction of the power dissipated into the corona in the total for such disc-corona system increases with the increasing dimensionless black hole spin parameter $a_\ast $, but is insensitive on the $\Delta \varepsilon $ which is the additional radiative efficiency parameter relevant to magnetic torque, for $\Delta\varepsilon > 1$. In addition, the emerged spectra from this disc-corona system are simulated by using Monte-Carlo method, and the effect of the different parameters on the output spectra is discussed.

We present a multi-wavelength observational study towards the high-mass young stellar object G8.68-0.37. A single massive gas-and-dust core is observed in the (sub)millimeter continuum and molecular line emissions. We fitted the spectral energy distribution (SED) from the dust continuum emission. The best-fit SED suggests the presence of two components with temperature of $T_{\rm d}=20$ K and 120 K, respectively. The core has a total mass of up to $1.5\times10^3$ $M_{\odot}$ and bolometric luminosity of $2.3\times10^4 L_{\odot}$. Both the mass and luminosity are dominated by the cold component ($T_{\rm d}=20$ K). The molecular lines of C$^{18}$O, C$^{34}$S, DCN, and thermally excited CH$_3$OH are detected in this core. Prominent infall signatures are observed in the $^{12}$CO $(1-0)$ and $(2-1)$. We estimated an infall velocity of 0.45 km s$^{-1}$ and mass infall rate of $7\times10^{-4} M_{\odot}$ year$^{-1}$. From the molecular lines, we have found a high DCN abundance and relative abundance ratio to HCN. The overabundant DCN may originate from a significant deuteration in the previous cold pre-protostellar phase. And the DCN should now be rapidly sublimated from the grain mantles to maintain the overabundance in the gas phase., Comment: 24 pages, 8 figures, accepted for publication in MNRAS

We present an observational study towards the young high-mass star forming region G23.44-0.18 using the Submillimeter Array. Two massive, radio-quiet dusty cores MM1 and MM2 are observed in 1.3 mm continuum emission and dense molecular gas tracers including thermal CH$_3$OH, CH$_3$CN, HNCO, SO, and OCS lines. The $^{12}$CO (2--1) line reveals a strong bipolar outflow originated from MM2. The outflow consists of a low-velocity component with wide-angle quasi-parabolic shape and a more compact and collimated high-velocity component. The overall geometry resembles the outflow system observed in the low-mass protostar which has a jet-driven fast flow and entrained gas shell. The outflow has a dynamical age of $6\times10^3$ years and a mass ejection rate $\sim10^{-3} M_{\odot}$ year$^{-1}$. A prominent shock emission in the outflow is observed in SO and OCS, and also detected in CH$_3$OH and HNCO. We investigated the chemistry of MM1, MM2 and the shocked region. The dense core MM2 have molecular abundances of 3 to 4 times higher than those in MM1. The abundance excess, we suggest, can be a net effect of the stellar evolution and embedded shocks in MM2 that calls for further inspection., Comment: 8 figures, 3 tables, accepted to MNRAS

The origin of the shallow decay segment in gamma-ray burst's (GRB) early light curves remains a mystery, especially those cases with a long-lived plateau followed by an abrupt falloff. In this paper, we propose a mechanism to understand the origin of the abrupt falloff after plateau by considering solidification of newborn quark stars with latent heat released as energy injection to GRB afterglow. We estimate the total latent heat released during the phase transition of quark stars from liquid to solid states, to be order of ~ 10^{51}ergs, which is comparable to the emission energy in the shallow decay segment. We also estimate the time scale of radiating the latent heat through thermal photon emission, and find that the time scale agrees with observations. Based on our estimation, we analyze the process of energy injection to GRB afterglow. We show that the steady latent heat of quark star phase transition would continuously inject into GRB afterglow in a form similar to that of a Poyntingflux- dominated outflow and naturally produce the shallow decay phase and the abrupt falloff after plateau. We conclude that the latent heat of quark star phase transition would be an important contribution to the shallow decay radiation in GRB afterglow, and would explain the general features of GRB light curves (including the plateau), if pulsar-like stars are really (solid) quark stars., Comment: 5 pages, 1 figures. Submitted to Science China Press