Communication Capacity Research Articles

A Sub-Channel Spatial Homogeneity-Based Channel Estimation Method for Underwater Optical Densely Arrayed MIMO Systems

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing Nt sub-channels of K-sparse to 2Nt irrespective of CIR residual compensation.

Green Computation Offloading With DRL in Multi‐Access Edge Computing

ABSTRACTIn multi‐access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers; (2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process; (3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS‐DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS‐DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous time slots to upgrade both the actor and critic networks, followed by a buffer reset. Comprehensive experiments validate the superior performance, including stability and convergence, of our algorithm when juxtaposed with prior studies.

Maximizing transmission capacity in optical communication systems utilizing a microresonator comb laser source with adaptive modulation and bandwidth allocation strategies

Traditional optical communication systems employ bulky laser arrays that lack coherence and are prone to severe frequency drift. Dissipative Kerr soliton microcombs offer numerous evenly spaced optical carriers with a high optical signal-to-noise ratio (OSNR) and coherence in chip-scale packages, potentially addressing the limitations of traditional wavelength division multiplexing (WDM) sources. However, soliton microcombs exhibit inhomogeneous OSNR and linewidth distributions across the spectra, leading to variable communication performance under uniform modulation schemes. Here, we demonstrate, for the first time, to our knowledge, the application of adaptive modulation and bandwidth allocation strategies in optical frequency comb (OFC) communication systems to optimize modulation schemes based on OSNR, linewidth, and channel bandwidth, thereby maximizing capacity. Experimental verification demonstrates that the method enhances spectral efficiency from 1.6 to 2.31 bit ⋅ s−1 ⋅ Hz−1, signifying a 44.58% augmentation. Using a single-soliton microcomb as the light source, we achieve a maximum communication capacity of 10.68 Tbps after 40 km of transmission in the C-band, with the maximum single-channel capacity reaching 432 Gbps. The projected combined transmission capacity for the C- and L-bands could surpass 20 Tbps. The proposed strategies demonstrate promising potential of utilizing soliton microcombs as future light sources in next-generation optical communication.

Sign language recognition using modified deep learning network and hybrid optimization: a hybrid optimizer (HO) based optimized CNNSa-LSTM approach.

Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

Enhanced Photodetection Performance of InBiSe3/ReS2 Polarization-Sensitive Heterostructure Photodetectors.

Individual anisotropic two-dimensional (2D) materials have been widely applied for developing polarization-sensitive photodetectors, but they often suffer from limitations in photoresponsivity, detection range, etc. To overcome these challenges, van der Waals (vdW) heterostructures created by stacking different 2D materials provide a promising solution to enhance the performance of the photoelectronic device. In this work, a novel polarization-sensitive photodetector is developed by leveraging a heterojunction formed by InBiSe3 and anisotropic ReS2 nanoflakes. The InBiSe3/ReS2 vdW heterostructure devices exhibit excellent photodetection performance with a high photoresponsivity (R)of 7.68 AW-1 and a specific detectivity (D*)up to 1.26×1011 Jones as well as an external quantum efficiency (EQE) of 1790% under 532 nm laser irradiation. Additionally, benefiting from the broadband light absorption of InBiSe3 crystals together with the pronounced anisotropic electronic and optical characteristics of ReS2 flakes, the devices demonstrate a broad spectral response range from 402 to 1006 nm with a distinct polarization sensitivity of 1.24. Moreover, the devices exhibit extraordinary optical communication and high contrast polarimetric imaging capacity. This work demonstrates the enhanced photodetection performance with the InBiSe3/ReS2 vdW heterostructures operating in a photoconductive mode and illustrates promising application of these heterostructures in integrated optoelectronic systems.

Research on Chinese Universities' Communication Work for Global Southern Z-Generation Youth under the Theory of "The Bias of Communication" —A Case Study Based on Z University's Communication with Africa

At present, the significant role of strengthening international communication efforts towards the Global South is increasingly highlighted, with communication work targeting Z-Generation youth being a top priority in promoting the construction of international communication capabilities. The rise and development of the Theory of The Bias of Communication holds certain contemporary value. In the new era, China's communication work towards the Global Southern Z-Generation youth should apply this theory critically, which is specifically manifested in facing China's modernization development strategy and the concept of a community with a shared future, emphasizing the status of communication media and the renewal of new media, and promoting the shift from media dominance to content as king. Faced with challenges such as China's international communication capacity does not match the level of its comprehensive national strength and the disadvantages of “time-biased” and “space-biased” media in a late-developing context, based on the practical experience of Z University's communication work with African Z-Generation youth, Chinese universities' international communication work for Global Southern Z-Generation youth should create a fluid channel for the transformation of time and space on the basis of balancing time and space, thereby actively responding to national strategic needs. It should undertake the mission of the times to tell Chinese stories and spread Chinese voices, and build an international communication discourse system with Chinese characteristics, becoming an important position for comprehensively improving international communication efficiency.

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework.

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment.

Ultra-broadband multimode fiber-to-chip edge coupler based on periodically segmented waveguides.

Mode-division multiplexing (MDM) technology demonstrates a bright outlook for enhancing the capacity of chip-scale or fiber-based optical communication. Nevertheless, the fiber-to-chip MDM optical interconnects are hindered by the considerable mode mismatch and inter-modal cross talk between the few-mode fiber (FMF) and on-chip few-mode waveguide (FMW). In this Letter, a new, to the best of our knowledge, multimode coupling solution based on periodically segmented waveguides for the MDM system is proposed, which achieves efficient conversion between LP01, LP11a, and LP11b modes in FMF and E11, E21, and E12 modes in FMW with low refractive index difference. The simulation results show that the coupling loss is less than 0.41, 0.27, and 0.90 dB for the three modes, over the wavelength range of 1100-1800 nm. The fabricated device based on a polymer platform shows low fiber-to-chip coupling losses of less than 1.8, 1.7, and 3.0 dB, respectively, over a 130 nm wavelength. The presented scheme provides a competitive solution for realizing the ultra-efficient integration of prospective fiber-chip optical interconnections and communications.

High gain distributed Raman amplifier for orbital angular momentum mode-division multiplexing and wavelength-division multiplexing in a 104-km ring-core fiber over the C + L band

Mode-division multiplexing (MDM) and wavelength-division multiplexing (WDM) are regarded as effective solutions for enhancing the communication capacity of fiber-optic transmission systems. To extend the transmission distance, a suitable amplification technique is required to amplify the signal light, with distributed Raman amplifier (DRA) offering a high-flat gain. In this paper, we experimentally demonstrate the transmission of 3 orbital angular momentum (OAM) mode-division multiplexing and 9 wavelength-division multiplexing ranging from 1530 nm to 1610 nm over a 104-km self-designed ring-core fiber (RCF) with the assistance of DRA. The homemade all-fiber mode selective couplers (MSCs) can (de)multiplex OAM modes (OAM01, OAM11, OAM21) efficiently in long-distance MDM + WDM fiber-optic transmission systems. The OAM-DRA provides high gain (with an on-off gain greater than 10 dB) as well as flat gain (with a differential wavelength gain of less than 0.88 dB and a differential mode gain of less than 0.28 dB) over the C + L band.

Introducing edge intelligence to smart meters via federated split learning

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers.

Research of developmental models and techniques in the function of improving communication capacities

Communication as a basic form of social interaction is based on transmission of experiential contents through signs, whether they are symbols, signals or their combinations. In addition to the numerous factors that contribute to intensification and more efficient communication, there are also a large number of negative factors that reduce the effect of unforced, easy and relaxed communication. We shall try by this work to open current discussions about the elements, aspects, types, forms, functions, models and techniques of improving communication in social interactions. Consistently to the results of the conducted research, we shall provide new interpretations on manner how to overcome situations when we know "what should we say" but we do not know "how to announce it". The increasing communication capacities, the increasing the probability of better and more successful social interaction occur, which causally initiates an increase in the quality of life.

1 × N All‐Logic Optical Switch Based on Polymer Platform Using Multimode Interferometer

The compact and broadband optical switch with a large port count is demanded with the increasing communication capacity. In this article, a universal method for modeling the 1 × N switch using multimode interferometer (MMI) through transmission matrixes is proposed. Herein, the reasons for the narrowing of the operating bandwidth switch are analyzed. As a proof of concept, a wide bandwidth 1 × 4 switch, which has an insertion loss lower than 23.7 dB, and a cross talk better than −10 dB at 1550 nm are simulated, designed, and fabricated. The cross talk throughout the C band is lower than −8.5 dB. According to the experimental result, the 1 × 4 switch with four‐equal‐length modulating arms shows a 32 nm bandwidth for −10 dB cross talk which is 13 times larger than traditional switch. The switch realizes a multi‐port logic optical switch by modulation. The 1 × N switch based on the generalized Mach–Zehnder interferometer (GMZI) structure reduce the footprint significantly compared with the 1 × N switch consisting of a 1 × 2 switch cascade. It is believed that 1 × N switch based on GMZI structures is a promising solution to increase integration density.

Measuring the Collective Community Capacity of a Network to Address Health Inequities during a Public Health Emergency: Findings from the National COVID-19 Resiliency Network.

This study assesses the collective community capacity of the National COVID-19 Resiliency Network (NCRN), a multisectoral network mitigating the disproportionate impact of COVID-19 on minoritized populations. From January to April 2022, we used two concurrent data collection methods: a Collective Community Capacity (C3) survey (n=65) and key informant interviews (KIIs) (n=26). The C3 assessed capacity for creation of a shared vision, engagement in community change, and distributive leadership. KIIs assessed perspectives on network formation and implementation. We used a convergent design and triangulation for interpretation. NCRN has growing collective community capacity. The C3 survey found high capacity for establishing a shared mission and evidence of mutual commitment, trust, and accountability. About three-quarters of respondents strongly agreed that partners addressed social, economic, and cultural barriers related to COVID-19. Interviewees valued NCRN leaders' openness, availability, and willingness to listen. Partners learned from one another, increased their health communication capacity, and supported sustainability. They sought greater opportunities to partner and support decision-making. NCRN developed a collaborative network with a shared vision of improving health equity during and beyond the COVID-19 pandemic, while identifying areas for improvement in distributive leadership. Findings can support other organizations seeking to build collective community capacity to address equity in public health emergencies.

Topological-charge-tunable and wavelength- switchable vortex laser enabled by a helically twisted high-absorption few-mode erbium-doped fiber.

Vortex beams carrying orbital angular momentum (OAM) offer a solution for enhancing spatial degrees of freedom, particularly in conjunction with wavelength division multiplexing, which can significantly boost data capacity for optical communication. Addressing the increasing demand for high information-carrying capacity, we present a dynamically tunable OAM laser source in this study. We demonstrate a ring-cavity vortex fiber laser employing intra-cavity mode conversion through a helically twisted high-absorption few-mode erbium-doped fiber (HA-FM-EDF). The constructed vortex fiber laser exhibits wavelength switchability via an integrated Sagnac loop, facilitated by a homemade ring-core fiber. Furthermore, topological-charge tunability is achieved through the utilization of twisted HA-FM-EDF with varying helical pitches. To our knowledge, this marks the first successful implementation of two-dimensional multiplexing of wavelength and OAM in a vortex fiber laser. The OAM laser serves as a versatile vortex source with high tunability and flexibility, holding significant potential for deployment in ultrahigh-speed/ultrahigh-capacity communications, ultrahigh-resolution imaging, and ultrahigh-sensitivity sensing applications.

Self-Adhesive Electronic Skin with Bio-Inspired 3D Architecture for Mechanical Stimuli Monitoring and Human-Machine Interactions.

Recent development of wearable devices is revolutionizing the way of artificial electronic skins (E-skin), physiological health monitoring and human-machine interactions (HMI). However, challenge remains to fit flexible electronic devices to the human skin with conformal deformation and identifiable electrical feedback according to the mechanical stimuli. Herein, an adhesive E-skin is developed that can firmly attach on the human skin for mechanical stimuli perception. The laser-induced adhesive layer serves as the essential component to ensure the conformal attachment of E-skin on curved surface, which ensures the accurate conversion from mechanical deformation to precise electrical readouts. Especially, the 3D architecture facilitates the non-overlapping outputs that bi-directional joint bending and distinguishes strain/pressure. The optimized E-skin with bio-inspired micro-cilia exhibited significantly improved sensing performances with sensitivity of 0.652 kPa-1 in 0-4kPa and gauge factor of 8.13 for strain (0-15%) with robustness. Furthermore, the adhesive E-skin can distinguish inward/outward joint bending in non-overlapping behaviors, allowing the establishment of ternary system to expand communication capacity for logic outputs such as effective Morse code and intelligent control. It expects that the adhesive E-skin can serve as a functional bridge between human and electrical terminals for applications from daily mechanical monitoring to efficient HMI.

FedADMM-InSa: An inexact and self-adaptive ADMM for federated learning

Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. Recently developed FedADMM methods show great resilience to both data and system heterogeneity. However, they still suffer from performance deterioration if the hyperparameters are not carefully tuned. To address this issue, we propose an inexact and self-adaptive FedADMM algorithm, termed FedADMM-InSa. First, we design an inexactness criterion for the clients’ local updates to eliminate the need for empirically setting the local training accuracy. This inexactness criterion can be assessed by each client independently based on its unique condition, thereby reducing the local computational cost and mitigating the undesirable straggle effect. The convergence of the resulting inexact ADMM is proved under the assumption of strongly convex loss functions. Additionally, we present a self-adaptive scheme that dynamically adjusts each client’s penalty parameter, enhancing algorithm robustness by mitigating the need for empirical penalty parameter choices for each client. Extensive numerical experiments on both synthetic and real-world datasets have been conducted. As validated by some tests, our FedADMM-InSa algorithm improves model accuracy by 7.8% while reducing clients’ local workloads by 55.7% compared to benchmark algorithms.

Investigation of dual-mode cloaked cylindrical slot antennas with a pulsed radar signal processing

In this paper, the cloaked slot antennas (CSAs) in a circular conducting cylinder have been proposed, numerically studied, and fundamentally characterized with respect to the pulsed radar aspects. The two cylindrically slotted antennas have been introduced, namely the axially slotted cloaked antenna and the circumferentially slotted cloaked antenna (CSCA). These two cloaked slot antennas have been parametrically studied to explore the antenna’s resonant characteristics together with their cloaking performance. The resonant frequency of the antennas is controlled by the parameters of the aperture of the slot such as width and length, while independently cloaking the CSAs at the cloaking frequency controlled by the parameters of the coated metasurface. The characteristics of the dual-mode operation at resonant and cloaking frequencies have been intensively studied by pulsed radar signal processing. The modulated Gaussian pulse with the modulation frequency being either resonant or cloaking frequency is used as an excitation source. Several scenarios have been proposed and studied to characterize the cloaking performance and communication capacity of the cloaked slot antennas. The radiating pulse from the CSAs has been numerically characterized by a pulse radar signal processing with an interval of 22.5° in terms of the theta and phi polarizations. These two CSAs have directive and omnidirectional characteristics in the theta- and phi-polarization, respectively.

Time Evolution of Orbital Angular Momentum Modes for Deep-Routing Multiplexing Channels

Optical orbital angular momentum (OAM) mode multiplexing has emerged as a promising technique for boosting communication capacity. However, most existing studies have concentrated on channel (de)multiplexing, overlooking the critical aspect of channel routing. This challenge involves the reallocation of multiplexed OAM modes across both spatial and temporal domains—a vital step for developing versatile communication networks. To address this gap, we introduce a novel approach based on the time evolution of OAM modes, utilizing the orthogonal conversion and diffractive modulation capabilities of unitary transformations. This approach facilitates high-dimensional orthogonal transformations of OAM mode vectors, altering both the propagation direction and the spatial location. Using Fresnel diffraction matrices as unitary operators, it manipulates the spatial locations of light beams during transmission, breaking the propagation invariance and enabling temporal evolution. As a demonstration, we have experimentally implemented the deep routing of four OAM modes within two distinct time sequences. Achieving an average diffraction efficiency above 78.31%, we have successfully deep-routed 4.69 Tbit·s−1 quadrature phase-shift keying (QPSK) signals carried by four multiplexed OAM channels, with a bit error rate below 10–6. These results underscore the efficacy of our routing strategy and its promising prospects for practical applications.

Vocal functional flexibility in a nonprimate vocal learning species

Abstract Can nonhuman animals use the same acoustic signal to transmit different illocutions on different occasions? This communicative capacity is known as vocal functional flexibility and occurs, for example, in speech, when a sentence serves different illocutionary forces or functions on different occasions based on changes to visual and intonational cues. Although common in human speech, there is a lack of clear evidence for this ability in other species. Here, we examined a likely candidate, the Brown-headed Cowbird (Molothrus ater), which is a vocal-learning songbird species that develops a repertoire of structurally distinct song types. Most of this species’ songs are directed towards conspecific males and females less than a meter away, making it unusually easy to determine the apparent target of songs, unlike the broadcast songs done by most songbirds. Songs directed to other males have clear aggressive/threatening intent, while those to females involve courtship/sexual intent. Extensive prior work shows that male cowbirds perform the visual display that accompanies singing differently in these two social settings and also modulate the intonation of song types differently. Because of these display and tonal modulations, constancy of song type usage across male- vs female-directed singing would provide evidence of vocal functional flexibility. Herein, we examined 4,828 songs in three captive flocks containing twenty-four males and thirty females during the breeding season. Males did not use their song types randomly and had strongly favored songs and less commonly used ones. Importantly, favored song types and less commonly used ones were the same whether directing courtship song to a female, aggressive song to another male or singing nonsocially with no receiver nearby. Results were consistent within and across the three flocks, providing strong evidence of vocal functional flexibility. These findings indicate that some species may evolve the ability to modulate and exaggerate visual display components and prosody more than vocal presentation per se because a learned phonological system in this and possibly other species is constrained by its vital role as an indicator trait.

The international dissemination of China’s English academic journals on Twitter: Structure, theme and impact

Objective/Significance: To measure the international communication capacity of China’s English academic journal papers based on social media platforms. Method/Process: Social network analysis, content analysis and emotion analysis were used to describe the diffusion structure of China’s English academic journal papers on Twitter from the aspects of network structure and identify important node users, explore the identity characteristics and emotion characteristics of users as well as diffusion capability of journals of different disciplines. Conclusion/Discovery: Journal articles in the fields of natural sciences and engineering as well as humanities and social sciences exhibit a strong positive correlation between their diffusion structure and network size. Networks with a mixed diffusion structure typically have larger network sizes. The users occupying key positions in the diffusion network typically have profile information that is academically relevant. The retweets and comments are mainly statements without emotional inclination, and the comments are mainly summary descriptions of the papers. The distribution of diffusion width, diffusion strength and diffusion speed of each discipline is significantly different. There is a significant positive correlation between the diffusion structure of papers and their diffusion capabilities. Innovation/Value: To reveal the diffusion structure and characteristics of China’s English academic journal papers on social media platforms, so as to provide reference for expanding the visibility of China’s English academic journals and improving the international dissemination capacity of China’s English journals.