The Internet of nano things (IoNTs) envisions thousands of nanomachine nodes that sense, compute, actuate and communicate at the nanoscale. With the development of nanotechnology, DNA technology, a promising way to achieve precise manipulation in the microscopic world, have shown their potential as multiple nanomachine nodes within IoNTs, such as sensing and actuation. However, current DNA nanomachine research mostly focuses on solutions for specific diseases and lacks a generalizable and universal design. This will not only undoubtedly hinder the realization of DNA-based IoNTs but also limit the development of DNA nanomachine. In recent years, researchers has begun to realize this problem and proposed some more integrated and versatile DNA nanomachine designs, incorporating with logic and communication functions. Among these, the integration of molecular communication gives DNA nanomachine the potential to realize IoNTs, making it more promising for future applications. Therefore, this review comprehensively sorts out recent researches of DNA nanomachine over the past decade and discusses them from the perspective of IoNTs systems. In detail, we review and analyze existing DNA nanomachine researches from communication fundamental to various nodes within the network (computing, sensing, actuation, etc.), introduce the technology behind and evaluate the performance of DNA nanomachines. Finally, we present the development prospects of DNA-based IoNTs and discuss the challenges. We hope that this review will help inspire the design of more integrable DNA nanomachines and promote the progress of both DNA nanomachine and IoNTs research.

Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

Next generation cellular deployments are expected to exploit the 6–24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies make the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models no longer valid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. The near-field and SNS channel modeling approaches are validated against ray-tracing simulations. Finally, a simulation framework for near-field and SNS is developed based on the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compablack to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar–communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy, especially when multiple geographically distributed full-duplex (FD) access points (APs) are jointly coordinated. In this paper, we consider a wideband FD CF-DFRC system where each AP is equipped with movable antennas (MAs). This setting is fundamentally different from existing DFRC or MA-aided designs that typically assume fixed-position antennas, half-duplex operation, or perfect synchronization. First, we develop a field-response-based channel model and derive a worst-case weighted communication–sensing rate (WCSR) that explicitly captures the impact of inter-AP CFO on both the uplink communication signal-to-interference-plus-noise ratio (SINR) and the radar echo SINR. Our analysis reveals that CFO increases the Cramér–Rao lower bound (CRLB) of target position estimation, thereby degrading sensing accuracy. Based on this characterization, we formulate a robust worst-case WCSR maximization problem that jointly optimizes MA positions, transmit beamforming vectors, receive filters, and CFO-related parameters under transmit power and MA-position constraints. To tackle the resulting highly non-convex problem, we propose a two-stage robust optimization framework. In the first stage, we employ fractional programming together with manifold optimization (MO) and penalty dual decomposition (PDD) to solve the worst-case CFO subproblem on the complex unit-modulus manifold, thus obtaining a CFO-robust closed-form structure for the WCSR. In the second stage, we design a meta–reinforcement learning (MRL) based resource allocation scheme that jointly optimizes the MA positions and beamforming vectors in a data-driven manner for dynamic wireless environments. Unlike conventional deep reinforcement learning (DRL) methods, the proposed MRL framework learns a meta-policy that can rapidly adapt to varying channel and CFO realizations, substantially improving convergence speed and scalability. Simulation results show that the proposed robust MO–PDD–MRL framework significantly outperforms existing DRL-based and non-robust CF-DFRC schemes in terms of both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antenna (FPA) architectures, the MA-aided CF-DFRC system exhibits markedly enhanced robustness and adaptability to CFO effects and target mobility.

In future 6G networks, mobile edge computing (MEC) is envisioned to offer integrated computing, communication, and storage services at the network edge, enhancing both computational efficiency and communication quality. However, most existing MEC designs neglect the impact of jamming attacks, especially those from intelligent and adaptive jammers. To fill this important research gap, this paper investigates a jamming-resilient MEC framework that aims to improve communication reliability and reduce system delay under adversarial interference. Leveraging the emerging movable antenna (MA) technology, which allows dynamic adjustment of antenna positions and orientations, we propose a novel MA-assisted anti-jamming MEC architecture. Unlike existing works, our model explicitly considers the movement delay caused by MA, which is critical for practical deployment. We jointly optimize the MA positions at both the user equipment (UE) and the base station (BS), BS transmit beamforming, and task offloading ratios to minimize the total system delay. The resulting optimization problem is non-convex and highly coupled. Thus, we develop an efficient algorithm based on penalty dual decomposition (PDD) and successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed-position antenna (FPA) baselines in terms of jamming resilience and delay minimization, offering new insights into robust MEC system design for 6G networks.

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges by employing movable antennas (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, base station (BS) selection factor and MA position to minimize power consumption while ensuring accuracy. We then propose a constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. To the best of our knowledge, this is the first work that jointly integrates TS errors and imperfect CSI into a unified MA-aided cooperative ISAC framework, providing a physically consistent model for both communication and sensing under dual uncertainties. Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions. These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising technology for future 6G networks. However, to fully capitalize on the potential of CF-ISAC, accurate time synchronization (TS) between access points (APs) is critical. Due to the limitations of current synchronization technologies, TS errors have become a significant challenge in the development of the CF-ISAC system. In this paper, we propose a novel CF-ISAC architecture based on movable antennas (MAs), which exploits spatial diversity to enhance communication rates, maintain sensing accuracy, and reduce the impact of TS errors. We formulate a worst-case sensing accuracy optimization problem for TS errors to address this challenge, deriving the worst-case Cramér-Rao lower bound (CRLB). Subsequently, we develop a joint optimization framework for AP beamforming and MA positions to satisfy communication rate constraints while improving sensing accuracy. A robust optimization framework is designed for the highly complex and non-convex problem. Specifically, we employ manifold optimization (MO) to solve the worst-case sensing accuracy optimization problem. Then, we propose an MA-enabled meta-reinforcement learning (MA-MetaRL) to design optimization variables while satisfying constraints on MA positions, communication rate, and transmit power, thereby improving sensing accuracy. The simulation results demonstrate that the proposed robust optimization algorithm significantly improves the accuracy of the detection and is strong against TS errors. Moreover, compared to conventional fixed position antenna (FPA) technologies, the proposed MA-aided CF-ISAC architecture achieves higher system capacity, thus validating its effectiveness.

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Conventional far-field stationary models with fixed path parameters fail to capture these variations. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation of different types of non-line-of-sight (NLoS) paths. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

The rapid advancement in large foundation models is propelling the paradigm shifts across various industries. One significant change is that agents, instead of traditional machines or humans, will be the primary participants in the future production process, which consequently requires a novel AI-native communication system tailored for agent communications. Integrating the ability of large language models (LLMs) with task-oriented semantic communication is a potential approach. However, the output of existing LLM is human language, which is highly constrained and sub-optimal for agent-type communication. In this paper, we innovatively propose a task-oriented agent communication system. Specifically, we leverage the original LLM to learn a specialized machine language represented by token embeddings. Simultaneously, a multi-modal LLM is trained to comprehend the application task and to extract essential implicit information from multi-modal inputs, subsequently expressing it using machine language tokens. This representation is significantly more efficient for transmission over the air interface. Furthermore, to reduce transmission overhead, we introduce a joint token and channel coding (JTCC) scheme that compresses the token sequence by exploiting its sparsity while enhancing robustness against channel noise. Extensive experiments demonstrate that our approach reduces transmission overhead for downstream tasks while enhancing accuracy relative to the SOTA methods.

Toward the sixth generation (6G) Internet of vehicles (IoV) networks, key challenges such as massive connectivity, high mobility and superior energy efficiency have driven the development of advanced wireless technologies. Native artificial intelligence (AI) is expected to support diverse vertical industries and offer numerous emerging AI services for 6G. However, how to efficiently process AI services and improve resource utilization while ensuring quality of service is still a challenging problem. In this paper, non-orthogonal multiple access (NOMA) is applied in the designed three-layer IoV network architecture. Then, an energy efficiency maximization problem is formulated by jointly optimizing NOMA transmission power and AI task deployment decisions. Third, a two-level iterative algorithm is proposed using the Dinkelbac’s method. Simulation results verify that our proposed algorithm outperforms benchmarks in terms of energy efficiency.

This paper introduces a new multiple-input multiple-output (MIMO) communication system, referred to as "pseudo MIMO". Unlike conventional MIMO systems, the pseudo MIMO technology enables the transmission of a greater number of parallel data streams than the available radio frequency (RF) chains at the receiver. This is achieved by connecting multiple antenna elements to each receiving RF chain and applying a sequence of analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling interval. The entire signal processing procedures are formulated using matrix representations, leading to a compact expression of the equivalent channel matrix for each OFDM subcarrier. This matrix is further decomposed into the product of the frequency domain wireless channel matrix and the analog combining matrix. Numerical results and prototype testing demonstrate the benefits of pseudo MIMO in terms of both spectral and energy efficiency.

Presently, the emergence of large-scale AI models, particularly those powering AI-generated content (AIGC) systems such as ChatGPT, marks a new frontier in intelligent applications. As we move toward the era of 6G networks—characterized by ultra-low latency, ubiquitous intelligence, and massive connectivity—the integration of such models into real-world network systems becomes increasingly critical. However, two pressing challenges remain unresolved. First, task diversity across dynamic network environments necessitates models that can rapidly adapt to heterogeneous and evolving requirements. Second, the intensive computational and energy demands associated with the training and inference of large-scale models—especially under online learning scenarios—pose serious scalability concerns for network deployment.To address these dual challenges, we propose Multi-Adaptation Hubs (MAHs), a novel and scalable network architecture designed to synergize large AI models with intelligent network operations under 6G paradigms. MAHs operate by decomposing complex tasks into modular sub-domains, each handled by a lightweight sub-model constructed as a pair of low-rank matrices. This factorization strategy substantially reduces the number of trainable parameters, enabling faster adaptation and improved computational efficiency. The system then aggregates the outputs of these specialized sub-models to dynamically generate task-specific parameters, allowing for fine-grained control, modular reuse, and enhanced generalization across diverse tasks and network contexts.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Molecular communication (MC) leverages molecules as information carriers, offering advantages such as biocompatibility and low energy consumption. Currently, MC’s research focuses on signal detection using chemical sensors, nanoparticles or biomolecules. However, challenges remain in accurately demodulating sequences of bits, particularly due to the influence of system parameters such as channel length, background flow rate, and transmitter-side actuation settings, including injection volume and valve control timing. To address these challenges, this paper introduces a MC testbed based on particle speed detection, which transforms molecular signals into particle speed signals for communication. Using hydrogen peroxide (H2O2) as the information carrier, the signal is demodulated by mixing the solution at the receiving end with specially prepared active particles and detecting the particle movement speed. Sequential transmission experiments were conducted to analyze the effects of various parameters on system performance. Experimental results demonstrate that the system accurately transmits information within a tested range, validating the theoretical model and highlighting its potential for microscopic communication applications.

The massive scale of deployment, hundreds of parameters, differentiated scenarios and interworking with existing mobile networks leads to high complexity and high cost of optimization, operation and maintenance of the 5th generation mobile network (5G), which inspires that 6th generation mobile network (6G) should support high level autonomy at the beginning of deployment. Digital twin network (DTN) technology, with its advantages of intelligent decision making, low-cost experimentation, and preverification, has emerged as a key enabling technology for autonomous network. To address the need for flexibility to fulfill more diverse scenarios and high-level autonomy toward 2030, this article discusses the typical usage cases of DTN, and proposes an innovative and native design for 6G DTN, encompassing logical framework, architecture, functions, and deployment modes. Furthermore, the efficient DTN Model Construction and Intelligent Orchestration and Management are introduced to enable fully automated and high-performance DTN tasks. Finally, the future direction for DTN research is presented.

Multiple-input-multiple-output (MIMO) has been a promising technology in wireless communication systems. Channel models are of great importance for the development and assessment of system. With the increase of carrier frequency and MIMO size, the channel model needs to consider near-field spherical wave and spatial non-stationary characteristics, which is different from conventional far-field planar-wave-based geometry-based stochastic model (GBSM) in the 3rd Generation Partnership Project (3GPP). This paper focuses on comparing the channel characteristics and modeling in the far- and near-field region. In this work, we design the measurement campaign in the 6 GHz band (5.9-6.1 GHz) involving the unlicensed spectrum. The uniform planar array (UPA) is adopted from far-field to near-field, where the communication distance is decreasing from 21 m to 6 m (Rayleigh distance is about 14.8 m). Compared to the far-field, the spatial non-stationary phenomenon of channel parameters can be more clearly observed along the array in the near-field region. Then, we propose the extension channel model based on the channel modeling of 3GPP TR 38.901. The array domain is introduced to characterize the spatial non-stationarity of channel parameters (e.g., power, delay, angle). Subsequently, the channel characteristic parameters along the array are analyzed in the near-field range, and the non-stationary model related to the antenna array is established, including power, path loss, delay spread, angular spread, and Ricean K-factor. Finally, the model validation and parametrization are presented in detail with the actual indoor near-field MIMO channel measurements in the 6 GHz band, such as power, angle, and so on. The design and scheme of antenna array spacing are given under the influence of spatial non-stationary characteristics. These work will be helpful for the development and operation of MIMO technology in unlicensed spectra for wireless communication systems.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

Broadcast beam design and power control are essential for enhancing the network coverage, improving the quality of service (QoS) and reducing the energy consumption in massive multiple-input-multiple-output (MIMO) communications. To improve the broadcasting performance and decrease the power consumption in dynamic scenarios with varying user numbers and distributions, we leverage deep reinforcement learning (DRL) to jointly optimize the beam selection and power control policy, and propose a multi-agent DRL (MA-DRL) framework to address the extremely high action dimension brought by the non-convex combinational multi-objective optimization problem. To reduce the cost of performance fluctuations during the exploration of DRL, we construct a data-driven surrogate model (SM) as a virtual environment for the initial training phase, while using an empirical baseline scheme to ensure acceptable real-time performance. Simulation results demonstrate that the SM-enabled MA-DRL approach not only enhances the coverage and reduces the power consumption, but also enables safe exploration and rapid adaptation to varying user numbers and distributions. Moreover, since the optimization algorithm can interact with the SM much more quickly than the real network, a faster convergence speed can be achieved with the help of the SM.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm. In the pre-training stage, we introduce a multi-modal two-tower model and a corresponding contrastive learning method to integrate the explicit description of the scattering environment and implicit channel state information (CSI) into a universal representation, which encapsulates rich high-level knowledge and can be leveraged for all downstream tasks in different scenarios. Additionally, we present two specially designed model structures to enhance the interaction of communication modalities. In the second stage, based on the frozen pre-trained model, we propose a direct method and a pluggable method for flexible and low-cost task adaptation. Experimental results demonstrate that our proposed approach significantly outperforms benchmarks in both task performance and tuning parameter size for exemplary sub-tasks in unseen scenarios.

Traditional path loss methods typically employ statistical or empirical models, without fully considering the dynamic propagation environment. In this paper, we introduce a method called Visual Sensing-Based Path Loss Prediction (VSB-PLM), which predicts path loss using visual data obtained from multi-view sensing cameras. Specifically, we deploy multi-view cameras in real-world scenarios. Then, a Convolutional Neural Network (CNN) is designed to integrate environmental image features, the existence of the Line-Of-Sight (LOS) path, and the distance between the Transmitter (Tx) and Receiver (Rx) for path loss prediction. Finally, optimal path loss prediction results are obtained utilizing a multi-view selection algorithm. Simulation results demonstrate that the proposed algorithm has successfully improved path loss prediction accuracy by 9% compared to single-view sensing, achieving a Root Mean Squared Error (RMSE) of 3.66 dB.

Over the past two decades, advances in materials science and electronics have greatly supported the progress of the communication industry, and the emergence of new materials has made it possible to design higher performance communication modules. As a potential key technology for future 6G networks, the standardisation and industrialisation of visible light communication (VLC) cannot be separated from the improvement of the communication devices. In this paper, we first analyse and summarise the performance enhancement of new materials and designs of VLC devices. Then the support of improved capabilities of novel VLC devices for 6G new services is prospected. As device performance improves, VLC is expected to support more scenarios in 6G.

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a potential enabling technology for 6th-generation (6G) communication. The XL-MIMO channel research will be important for XL-MIMO system development. In this paper, the measurement of XL-MIMO channels from 32 to 512 transmitting antenna elements in the 6 GHz band is carried out in an indoor corridor scenario. The delay spread, angular spread, and channel capacity are investigated. The results are compared with the indoor channel model in the Third Generation Partnership Project (3GPP) TR 38.901. We find that the number of antenna elements has a small impact on the delay spread and angular spread. So the spatial non-stationary effect does not need to be considered specifically in the far-field range in this scenario. In addition, the special structure of the corridor leads to a difference in the comparison of the angular spread with the 3GPP model in each dimension. The closed environment of the corridor also results in a significant gap in channel capacity performance from the i.i.d. channel. This work can provide insights into XL-MIMO applications in the 6G era.

Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) communication, is recognized as a potential enabling technology for sixth-generation (6G) communication. Due to the large antenna aperture of XL-MIMO, spatial non-stationary (SnS) phenomena may occur on the array domain during deployment. This paper, relying on Ray-tracing (RT) simulations, analyzes the SnS phenomena from various perspectives of channel characteristics. Meanwhile, to accurately model the SnS phenomenon, this paper proposes a method for stationary sub-interval partitioning based on channel characteristics. It is assumed that the channel is stationary within each sub-interval, while it is non-stationary across different intervals. The method comprehensively considers factors such as channel correlation, delay spread (DS), azimuth angle spread of departure (ASD), and multipath components (MPCs) birth-death for sub-interval partitioning. By analyzing the independence of sub-intervals, this paper demonstrates that the proposed method performs better in sub-interval partitioning compared to the traditional averaging approach.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection is the premise of achieving the URLLC. In this paper, we propose a multi-view images assisted proactive BS selection scheme that can predict the optimal BS for the user in the next frame. The proposed scheme utilizes vision sensing and thus does not require the entire pilot resources, such that the latency caused by seeding and receiving pilots reduces. In addition, we design a multitask learning strategy and a prior knowledge based fine tuning method to ensure the accuracy and reliability of BS selection. Simulation results in an outdoor environment demonstrate the superior performance of the proposed scheme in terms of both the accuracy and the robustness.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

The 6th generation mobile communication system (6G) will provide everything as a service (XaaS), where X includes communication, sensing, computing, artificial intelligence (AI), big data and security and more. Novel features such as sensing as a Service (SaaS) will contribute to further realising Internet of Everything (IoE). Integrated Sensing and Communication (ISAC) is identified as one of the six usage scenarios for 6G by the International Telecommunication Union Radiocommunication Sector (ITU-R), and the corresponding studies on the detailed technical performance requirements and evaluation methodologies have begun in 2024. Although ISAC has become a popular topic, there are no systematic performance requirements metrics and corresponding evaluation methodologies defined for SaaS in a mobile communication system, while conventional key performance indicators (KPI) for radar systems have been borrowed currently. Therefore, to fill this gap, this paper proposes SensCAP, a systematic CAPability performance metric composed of Sensing Capacity, Accuracy and Probability. The sensing capacity reflects the comprehensive sensing performance, which can be expressed as the number of targets that can be detected per unit area within unit time, given Sensing Quality of Service (QoS) requirements consisting of sensing accuracy and probability. Furthermore, the performance evaluation of the SensCAP is conducted through system simulation using proposed evaluation methodologies, and the KPI values are suggested as the guidelines for further study in ITU-R.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.

Extremely-large-scale multiple-input multiple-output (XL-MIMO) technology, offering vast spatial degrees of freedom by deploying a huge number of antennas, is a promising enabling technology to empower sixth-generation mobile networks (6G). The XL-MIMO channel model is a prerequisite of XL-MIMO technology optimization, system design, and performance evaluation. In this paper, we provide an overview of challenges and ongoing research in XL-MIMO channel measurement, characterization, and modeling. In particular, characterizing and modeling near-field effects and spatial non-stationarity (SnS) are discussed. Also, the channel modeling methods that can describe these new channel characteristics are surveyed. Furthermore, open issues in XL-MIMO channel measurement, characterization, and modeling are presented to give insights into future XL-MIMO channel research.

Human-oriented image communication should take the quality of experience (QoE) as an optimization goal, which requires effective image perceptual quality metrics. However, traditional user-based assessment metrics are limited by the deviation caused by human high-level cognitive activities. To tackle this issue, in this paper, we construct a brain response-based image perceptual quality metric and develop a brain-inspired network to assess the image perceptual quality based on it. Our method aims to establish the relationship between image quality changes and underlying brain responses in image compression scenarios using the electroencephalography (EEG) approach. We first establish EEG datasets by collecting the corresponding EEG signals when subjects watch distorted images. Then, we design a measurement model to extract EEG features that reflect human perception to establish a new image perceptual quality metric: EEG perceptual score (EPS). To use this metric in practical scenarios, we embed the brain perception process into a prediction model to generate the EPS directly from the input images. Experimental results show that our proposed measurement model and prediction model can achieve better performance. The proposed brain response-based image perceptual quality metric can measure the human brain's perceptual state more accurately, thus performing a better assessment of image perceptual quality.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection and beam searching are the premises of achieving the URLLC. In this paper, we consider the mmWave communications systems where mobile users are served by the roadside unit (RSU). We propose a multi-camera view based proactive RSU selection and beam searching scheme that can predict the optimal RSU for the user in the next frame and search the corresponding beam pair. The proposed scheme utilizes vision sensing and reduces training resources. In addition, the visual information of multiple views makes the selection of the optimal RSU more accurate and reliable compared to the existing single view technologies. Simulation results in an outdoor environment show the superior performance of the proposed scheme in terms of predicting accuracy and achievable rate.

Scene graph generation (SGG) has been developed to detect objects and their relationships from the visual data and has attracted increasing attention in recent years. Existing works have focused on extracting object context for SGG. However, very few works have attempted to exploit implicit contextual correlations among relationships of the objects. Furthermore, most existing SGG schemes rely on high-level features to predict the predicates while overlooking the potential inherent association of low-level features with the object relationships. We present in this article a novel scheme to capture enhanced contextual information for both objects and relationships. We design a Dual-branch Context Analysis Transformer (DCAT) architecture to extract both object context and relationship context from the visual data with dual transformer branches and then effectively fuse both high-level and low-level features by an adaptive approach to facilitate relationship prediction. Specifically, we first conduct feature representation learning to enrich relation representations by the visual, spatial, and linguistic feature extractors. Next, two transformer branches are designed to leverage the modeling of global associative interaction and mine the hidden association among objects and relationships. Then, we devise a novel feature disentangling method to decouple contextualized high-level features with guidance from the visual semantics. Finally, we develop a refined attention module to perform low-level feature recalibration for the refinement of the final predicate prediction. Experiments on Visual Genome and Action Genome datasets demonstrate the effectiveness of DCAT for both image and video SGG settings. Moreover, we also test the quality of the generated image scene graphs to verify the generalizability on downstream tasks like sentence-to-graph retrieval and image retrieval.

To reduce the downlink beam sweep overhead of mmWave systems, we propose a deep reinforcement learning based dynamic beam selection (DRL-DBS) method. A new learning motivation is presented by analyzing the dynamic change laws of high- and low-frequency channels in the spatial domain: to learn the index offset between the optimal beam of mmWave and sub-6 GHz spatial spectrum. In the DRL-DBS method, we propose a novel action space where actions can dynamically adjust the size of the beam sweep subset according to the high-and low-frequency channel propagation laws. Hence, the DRL-DBS method can predict a mmWave downlink beam sweep subset with dynamic size, and the optimal beamforming index is from beam sweep results on the subset. A dual-input dueling Q-network with noisy networks and prioritized experience replay is designed to select the optimal action. The DRL-DBS method can achieve a dynamic trade-off between mmWave beam selection quality and beam sweep overhead based on the reward function. Simulation results demonstrate the superior performance of the DRL-DBS method compared with the existing strategies. Especially, the DRL-DBS method outperforms the exhaustive search algorithm in achievable rate because the overhead of mmWave beam sweep is considered.

Molecular communication and the Internet of Nanothings (IoNTs) have been extensively studied as potential in-body communication technologies. Communication between the inside and outside of the body, specifically the exchange of data between IoNTs and the external environment, has gradually become a research hotspot. Neural communication theory has been proposed as a promising method for transmitting information between the body's interior and exterior. However, there is currently limited theoretical research on neural communication and even fewer related experimental platform studies. To address this gap, we have constructed a digital communication system based on the sciatic nerve of a bullfrog. This platform is capable of stimulating the nerve trunk to produce electrical signals and receiving signals at the receiving end. This experiment investigates the fundamental characteristics of neural trunk channels, measuring the impulse response and the signal conduction rate. The experimental results demonstrate that signal transmission via the nerve trunk as a channel could be achieved using our experimental platform, thereby demonstrating the possibility of information transmission through the nervous system. This paper paves the way for the implementation of experiments connecting IoNTs within the body to external networks.

Multimedia computational communications towards 6G can improve the transmission efficiency significantly by introducing intelligent computation in the communication process. This intelligent/smart communication architecture includes multimedia representation, coding, transmission and other parts from the perspective of semantics, where multimedia semantic representation is the core part and is mainly utilized to reduce the amount of multimedia data. In this paper, sketch graph is proposed as an effective representation of images to describe the pixel variations, geometric feature distribution and structural information and has potential applications in multimedia computational communications. Specifically, we developed a learning-based method to extract sketch graphs with edge detection, sketch point detection and sketch line detection by deep neural networks (DNNs). Moreover, we designed an end-to-end extraction method and achieved real-time processing. The experimental results on several datasets demonstrated the advanced performance in terms of classification and generation tasks. Image classification results on the HumanSketch, ImageNet, and Caltech datasets showed that sketch graphs extracted by our method had better describing ability than those extracted by traditional methods and other traditional image compression methods. On the other hand, image generation results on the Cityscapes dataset indicated the potential of the sketch-graph-based image compression codec.

Scene graph generation (SGG) is designed to represent images by objects and their relationships. Existing works mainly attempt to strengthen object pair representations for SGG. However, most methods ignore the significant semantic information implied in union regions, which refers to the surrounding area of object pairs. In this paper, we propose a new union message based architecture, named as USGG, to profoundly exploit the relational semantics of unions to facilitate SGG. Concretely, we employ sufficient feature extraction to enhance the features of objects and unions. Next, we devise the Union Embedding Network to model the relational representations through two symmetric encoder-decoder branches. Moreover, the Union Fusion Network is designed to integrate the refined semantics by two-stage feature fusion. Extensive experiments are conducted on Visual Genome dataset, which demonstrates that the proposed approach achieves competitive performance against state-of-the-art methods on Recall, mean Recall and Zero Shot Recall metrics.

Service based architecture enables network function virtualization and flourishes 6G network with ubiquitous services and infrastructures. Multiple virtual network functions can be connected in sequence to create service function chains (SFCs), which implements automated configuration processes to reduce system delay and improve resource utilization. Due to the limitation of user transmit power, time-varying wireless channels, and the dynamics of resources, a relay is introduced to assist transmission and act as an orchestrator to collect global information of users and manage SFCs. However, the multi-level service architecture brings additional complexity to efficient resource allocation and SFC placement. This paper investigates a relay-assisted system with the goal of long-term delay minimization. Transmit power, bandwidth resource and SFC placement are jointly optimized under the constraint of network stability. Utilizing Lyapunov technique, the formulated long-term non-convex optimization problem is transferred into one-slot sub-problems including resource allocation and SFC placement, which are further solved by introducing auxiliary variables and Hungarian algorithm, respectively. Simulation results demonstrate that the proposed algorithm can significantly reduce system delay and achieve a $[\mathcal{O}(1/V),\mathcal{O}(V)]$ trade-off between system delay and queue stability.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) based integrated localization and communication (ILAC) signal transmission scheme, where communication and localization signals of different user equipments (UEs) are superimposed respectively. We analyze the performance of localization and communication in terms of position error bound (PEB) and effective data rate (EDR) theoretically. We further compare the proposed NOMA-ILAC method with current NOMA-orthogonal multiple access (OMA) method where different UEs’ communication signals are superposed while their localization signals are transmitted orthogonally, both from theoretical analysis and simulations. Performance trade-off is then carried out w.r.t. the time-domain resource allocation. Numerical results demonstrate that by adapting the time allocation ratio, the proposed method is able to improve the communication performance by up to 33%, when the PEB of the two methods are equal.

The Internet of Bio-Nanothings (IoBNT) is one of the potential application scenarios in the 6th generation (6G) mobile network, which envisions the interaction between biological cells or nanodevices and the Internet. Molecular communication (MC) may offer an appropriate communication method for the nanodevices using chemical molecules as the information carriers. However, due to the complexity of experiments and insufficient interdisciplinary cooperation, MC study mainly focuses on theoretical research, which seriously hinders the MC’s advancement and further applications. Therefore, it is crucial to explore methods for constructing transmitter and receiver nanomachines to realize practical MC systems. Based on the research progress of micro/nanomachines (MNMs) in recent years, this article summarizes the possible technologies for implementing MNMs used for MC systems within 6G application scenarios.

In the 5G-Advanced and beyond systems, multi-frequency cooperative networking will become an inevitable development trend. However, the channels have not been fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the sub-6 GHz to millimeter-wave (mmWave) bands. In this paper, we carry out channel measurements at four frequency bands (3.3, 6.5, 15, and 28 GHz) in two scenarios including Urban Micro (UMi) and Outdoor-to-Indoor (O2I) with the same channel sounder. The channel characteristics are extracted and modeled, including path loss (PL), shadow fading, frequency dependence of cluster features, root mean square (RMS) delay spread (DS), Ricean K-factor, and the correlation properties. We mainly focus on the analysis of large-scale parameters and more consideration of the link budget coverage problem. We present the frequency dependence model of the channel characteristics. Among them, in the non-line-of-sight (NLoS) condition, it is found that except for theoretical value brought by higher frequency, additional path loss increment will be generated. Based on these channel characteristics, the impact on performance of the wireless system is analyzed including cell coverage radius, data rate and bit error rate (BER). The results can give an insight into the spectrum selection and optimization in 5G-Advanced and beyond multi-frequency communication systems.

In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aidednetwork architecturefor mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network. With images taken from street cameras and user’s identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy.

太赫兹信道传播特性对太赫兹通信系统的设计、评估和优化至关重要。此外, 反射在信道传播中起着重要作用。本文基于大量的信道测量工作, 对太赫兹通道的反射系数进行研究。首先, 建立从220 GHz到320 GHz的太赫兹信道测深平台, 入射角范围从10°到80°。根据实测的传播损耗, 分别计算玻璃、瓷砖、木板、石膏板和铝合金五种建筑材料的频率和入射角的反射系数。研究发现, 由于缺乏与太赫兹相关的参数, 导致非金属材料的菲涅耳模型无法成功地拟合实测数据。因此, 通过改进菲涅耳模型与洛伦兹和德鲁德模型, 提出一个频角二维反射系数模型。该模型表征了反射系数的频率和入射角, 与实测数据的均方根误差较小。总的来说, 这些结果对于太赫兹通道的建模做出贡献。

The rapid development of communications industry has spawned more new services and applications. The sixth-generation wireless communication system (6G) network is faced with more stringent and diverse requirements. While ensuring performance requirements, such as high data rate and low latency, the problem of high energy consumption in the fifth-generation wireless communication system (5G) network has also become one of the problems to be solved in 6G. The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks, and has the advantages of reducing network energy consumption and improving resource utilization. In wide-area coverage signaling cells, on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users, and its effect will affect the efficiency of network resource utilization. This paper constructs a user-centric dynamic allocation model of wireless resources, and proposes a deep Q-network based dynamic resource allocation algorithm. The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users. According to the simulation results, the proposed algorithm can effectively improve the average user experience on a long time scale, and ensure network users a high data rate and low energy consumption.

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.

mmWave communications systems usually rely on analog or hybrid analog/digital architectures and thus need a predefined codebook to perform beamforming. Traditional codebooks are designed for universal environments, although in practice a particular BS will only serve a particular environment. In this paper, we propose novel site-specific codebook design methods by utilizing the visual information captured through cameras. Different from other site-specific codebook design methods that require a large amount of measured channel state information (CSI), the proposed ones need only a simple snapshot of the environment followed by efficient computer vision (CV) techniques. Thus the proposed CV-aided codebook design reduces the overhead of communications system, such as the cost of time, human resources, as well as the hardware installation and calibration. Specifically, we propose a CV-based approach that detects the LOS area around the BS and reconstructs the LOS channel vectors set (CVS). With this knowledge, we build a vision-based beam codebook using Lloyd algorithm. Further, we design a FusionNet to generate the codebook that can serve the non-line-of-sight (NLOS) users. The simulation results demonstrate the effectiveness of the proposed CV-aided codebook design methods and their superiority compared to the conventional methods.

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST.

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.

Multi-user multiple-input multiple-output (MU-MIMO) is one of the key techniques to meet high spectral efficiency requirements for the fifth generation (5G) and beyond 5G wireless network. To achieve the high capacity of uplink MU-MIMO, a generalized polar precoding scheme based on rotation and permutation (GP-RP) is proposed in this paper, which can enhance the reliability distinctions between users’ channels. Via the three-stage channel transformation, the binary polar coding, signal modulation and the original multi-user channel are successively partitioned into multiple bit polarized channels. To further amplify the polarization effect, an unitary precoding scheme is proposed for the uplink MU-MIMO system without changing the communication capacity. The simulation results show that the proposed precoding scheme can further improve the polarization between the synthesized user channels and achieve lower block error ratio (BLER).

As a visible light communications (VLC) modulation method, the layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is promising. The layers in LACO-OFDM provide a high flexibility for power allocation and modulation selection. In this paper, we analyze the impacts of LACO-OFDM layer selection, layer power allocation and modulation selection. Firstly, we compare the BER performance of different power allocation methods and investigate the impact of layers on BER and optical power. On this basis, we propose a joint layer selection and power allocation algorithm. Simulation results show that the proposed power allocation scheme has a better bit error ratio (BER) performance than the common schemes. Finally, we verify the layer modulation selection and propose a search method with low time complexity.

As the transmitter in visible light communications (VLC) system, when the electrical current signal exceeds the input range of light emitting diode (LED), it will cause a nonlinear distortion. Since the electrical current is proportional to the optical power, the nonlinear distortion of electrical current signal is equivalent to the nonlinear distortion of optical power signal. This paper studies the impacts of optical power signal nonlinear distortion on signal distribution and capacity. Firstly, we derive the maximum entropy distribution of the optical power signal and obtain the corresponding capacity lower bound using the entropy power inequality (EPI). Then the nonlinear distortion is modelled as the signal clipping noise, and we analyze the impacts of upper-clipping and lower-clipping on system performance. Numerical results show that a certain degree of signal clipping is acceptable at low optical signal-to-noise ratio (OSNR). However, the clipping should be minimum at high OSNR.

In visible light communications, adding direct current (DC) bias is the most common solution to the bipolar problem of signal, so that the precoding and optical orthogonal frequency division multiplexing (O-OFDM) can be performed. Moreover, it will be easier to achieve dimming control. In this paper, we analyze the impact of DC bias on optical power signal in the view of distribution, capacity and nonlinear distortion. Firstly, we classify the impacts of DC bias into two types depends on the total power, and derive their maximum entropy distributions. Then we investigate the capacity of two different channels with DC bias. Finally, we study the nonlinear distortion of power signal. Derivation and simulation results show that the capacity would decrease as the DC bias increases in most cases. The conclusions will help the consideration of DC bias in future system design.

Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.

As one of the potential candidate technologies for sixth generation (6G), visible light communication (VLC) can provide indoor communication services with high data rate when combined with multiple-input multiple-output (MIMO). In this paper, we first study the impact of precoding on zonotopes model of channel and then derive the capacity of VLC-MIMO with precoding when the number of transmitters is greater than receivers. Based on the zonotopes graph, two design principles for VLC-MIMO optimal precoding are obtained. Simulation results show that the commonly used radio frequency (RF)-MIMO precodings can hardly catch the capacity lower bound. The optimal precoding in RF may not be optimal in VLC.

With the development of mobile Internet and the Internet of Everything (IoE) for beyond the fifth generation (BSG)/sixth generation (6G) mobile network, the short-packet communication have attracted much attention. In order to improve the utilization of sporadic spectrum and to reduce the peak-to-average power ratio (PAPR) and out-of-band emission (OOBE), a filtering and windowing based orthogonal frequency division multiplexing (FW-OFDM) waveform is proposed in this paper. An optimization problem is formulated to minimize the power spectral density (PSD) in the stopband, and subject to the constraints of the PAPR of the FW-OFDM signal. Meanwhile, both windowing and filtering operation will not cause energy degradation of the transmitted signal. The suboptimal coefficient vectors of windowing and filtering are obtained by the genetic algorithm (GA). Simulation results show that FW-OFDM and cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) perform better than filter bank multi-carrier (FBMC) in terms of time efficiency. Besides, because of better OOBE and PAPR performance, the proposed FW-OFDM waveform is more suitable for the IoE scenario.

In this paper, the robust transceiver design is investigated for "cell-free" massive MIMO system which is deemed as one of the promising technology for the future mobile communication network. When constructing the optimization problem, different access point (AP) sets are considered for different user equipment (UE) to simulate the true "UE-centric" system. Besides, the idea of interference leakage is used so that the overall optimization problem could be separated into several subproblems that can be carried out in parallel. To make the algorithm more practical, a low complexity robust design is proposed based on alternating direction method of multipliers (ADMM) algorithm. At the end of the paper, it is demonstrated by the simulation that the proposed robust algorithm has better performance than the non-robust design.

Three-dimensional (3D) multiple input and multiple output (3D-MIMO) with massive antennas is a key technology to achieve high spectral efficiency and user experienced data rate for the fifth generation (5G) mobile communication system. To implement 3D-MIMO in 5G system, practical constraints on the product design should be considered. This paper proposes a systematic design for the 3D-MIMO product by considering the restrictions of both base band and the hardware, including cost, size, weight, and heat dissipation. The design has been implemented for 2.6-GHz time-division duplex band, and field trials have been conducted for performance validation with practical intercell interference in commercial network. The trial results show that this 3D-MIMO design can meet the spectral efficiency requirement of the 5G enhanced mobile broadband services. The performance gain of 3D-MIMO varies with the traffic load. When the traffic load is heavy, 3D-MIMO can enhance the cell throughput by 4~6.7 times. When the traffic load is low, the performance gain of this 3D-MIMO design decreases. The results from field trial also show that the performance of 3D-MIMO degrades in mobility scenarios, where further enhancement on acquiring instant channel status information are necessary to improve the robustness of 3D-MIMO to mobility.

3D-MIMO, using two dimensional antenna array at base station, demonstrates promising throughput gain over conventional antenna system during recent academic and industry studies. To realize the performance gain, accurate channel state information (CSI) feedback is one essential aspect. One effective way to obtain this CSI is taking advantage of channel reciprocity between uplink and downlink in TDD system. This paper analyzes the uplink pilot contamination problem in practical TDD LTE system using a novel pilot contamination model, besides, pilot contamination elimination methods in terms of increasing pilot power and reducing pilot collision probability are evaluated. The simulation results show that system performance degradation is less than 21% considering the practical pilot contamination, which still outperform the traditional 2D-MIMO system. Moreover, about 15% performance gain could be benefit from pilot contamination reduction approaches.

In real environments, channel reciprocity cannot be directly exploited between uplink and downlink in time-division duplex (TDD) due to antenna gain mismatch. Previous work about antenna calibration mainly focused on single cell. This work proposes an adaptive scheme for antenna calibration between two cooperative BSs. The proposed scheme aims at achieving an equal ratio between antenna transmitter and receiver analog gains among cooperative BSs in flat fading channels. The essential idea is to relay calibration parameter through a calibration path. This procedure can be controlled by a certain BS dubbed primary BS. Evaluation of the proposed scheme is carried out by computer simulation.

We investigate linear detection and precoding for denoising-based physical network coding (D-PNC) in two-way multi-input multi-output relay channels. We propose an MMSE-based detector, which first gives a coarse detection to the two source messages using MMSE detector and then detects the product of the two coarse detected messages. The advantage of such detector is to randomize the interference. A simple precoder is also proposed for relay message transmission, which provides fairness. Simulation results show that D-PNC with the proposed detector and precoder has similar pair error rate performance to other schemes while keeps simple structure and lower complexity.

In this paper, measurement data from wideband multiple-input multiple-output (MIMO) channel measurements at 2.35 GHz are presented. Measurements were preformed in indoor hotspot scenario with no line of sight (NLOS). Space-alternating generalized expectation (SAGE) algorithm was utilized to estimate angular domain parameters and powers of multipath components (MPCs), which are then used to compute the circular angular spread (CAS) and to construct the power azimuth spectrum (PAS) of transmitter and receiver sides. It is found that PASs of both sides follow multi-cluster truncated Laplacian distribution. The spatial correlation, capacity and multiplexing gain are also presented. Due to rich scatters in the indoor hotspot environment, the correlation space is Q.2lambda and the multiplexing gain is 15, which indicate indoor hotspot MIMO channels facilitate multiplexing and could achieve high capacity. The results provide important basis for future channel modeling and technique evaluation.

In this paper, we propose a novel spatial autocorrelation model of the shadow fading process in urban macro environments. The proposed model is based on the empirical results obtained from extensive wideband radio channel measurement campaigns at 2.35 GHz in an urban area of a typical medium-sized Chinese city. The shadow fading component was extracted assuming a single-slope log-distance path loss model. The consistency with the level crossing theory of Gaussian processes is achieved by an implicit constraint on the parameters of the model. The proposed model gives a better fit to the empirical results in individual measurement routes than the widely reported exponential and double exponential models. An heuristic explanation of the proposed autocorrelation property is also presented.

The wideband channel measurements at 580 MHz, 2.35 GHz and 4.90 GHz have been performed in the urban micro- and macrocell scenarios in the cities of China with multiple-input multiple-output (MIMO) channel sounder. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis for both scenarios are presented in this paper, including path loss (PL), root mean square (rms) delay spread (DS) and maximum excess delay (maxED), and angular spread (AS). In NLOS case, the frequency dependent factor (FDF) is observed as 32.1 by fitting the PL model of 2.35 GHz and 4.90 GHz. Moreover, a larger rms DS in urban microcell and AS at both base station (BS) and mobile subscriber (MS) are found for the denser and higher buildings in the cities of China. As the frequency varying from 580 MHz to 4.90 GHz, the median rms DS is decreased from 330 ns to 130 ns for NLOS case.

To meet the increasing demand of wireless services associated with the scarcity of the radio spectrum and the trend to provide end to end quality of service (QoS), on the one hand advanced technologies that harness the available resource efficiently should be developed, on the other hand the collaboration of different layers such as physical (PHY) layer and medium access control (MAC) layer is needed. In this paper, we propose a joint space-frequency-power scheduling algorithm (JSFP) for real time service in multiuser cellular MIMO-OFDM system which jointly optimizes the subcarrier, bit and power allocation in the PHY layer along with the scheduling in the MAC layer to exploit the multiuser diversity. This algorithm considers both the user equipments (UE)' QoS requirements (such as packet delay and packet loss ratio) and the UEs' channel conditions and includes three parts: packet scheduling, subcarrier allocation and antenna selection, power allocation. Numerical results show that the proposed algorithm achieves significant system performance improvement compared with the conventional methods.

In this paper, a generic framework for validating wideband MIMO channel models based on channel measurement results is proposed. The framework is formulated as a series of continuous functions (metrics) and a definition of distance of continuous function space (degree of approximation). The metrics characterize the MIMO channel from different perspectives, and the distance provides a quantitative measure of the degree of approximation for the specified model. Several fundamental metrics which reflect the spatial multiplexing gain, diversity capability, time and frequency variability, are derived for exploring the frequency-selective fading property. Based on an extensive measurement campaign at 5.25 GHz, the propagation channel is reconstructed by a WINNER-like model. The metrics are calculated from both the model generated channel realizations and the measured impulse response as a demonstration. The proposed framework can be applied to compare different channel models and to evaluate the simplified version of channel models.

Radio frequency allocation is of great importance in the multihop cellular network, because some extra resources (frequency or time slot) seem to be allocated to the relay station. Generally, the tradeoff must be made between the frequency reuse factor and the inter-cell interferences during establishing frequency allocation scheme. A "pre-configured and fixed (PreF)" frequency allocation scheme has been proposed in [1]. In this paper, the concept of "soft frequency reuse (SFR)" in [2] is first borrowed, and applied to the multihop cellular network. After that, the improvement is made to the SFR scheme, and a more efficient "modified SFR (MSFR)" frequency allocation scheme is proposed. Through the simulations, it is demonstrated that, almost the same cell throughput can be achieved in the PreF and SFR schemes for the uniform traffic distribution, and the MSFR scheme can provide nearly twice cell throughput as large as that in the aforementioned two schemes. Moreover, the SFR and MSFR schemes both outperform the PreF scheme for the non-uniform traffic distribution because of the support to dynamic frequency resource allocation in the proposed schemes.

Through exploiting the multiuser diversity, the capacity of MIMO-OFDM system can increase dramatically. In multiuser singular value decomposition (MU-SVD) based MIMO-OFDM system, the scheduler can allow multiple users to simultaneously transmit independent data to base station (BS) on the same subcarrier and the data can be separated in space domain at the BS. In this paper the effect of the correlations between the singular vectors in a MU-SVD based system was investigated, and propose a greedy scheduling algorithm based on MU-SVD. Given a set of users, the algorithm finds the best and most orthogonal spatial subchannels, in order to exploit the multiuser spatial diversity. The simulation results show that if the user data are transmitted on the maximum singular mode (MSM), the amplified noises on the spatial subchannels may lead to the degradation of the system performance, however the proposed algorithm can greatly increase the system capacity.

As the evolution of 3G, long term evolution (LTE) standardization activity is issued in 3GPP and 3GPP2. In previous paper OFDMA is proposed for downlink of LTE. As the channel reciprocity can be obtained in TD-SCDMA LTE system, the channel status information (CSI) can be exploited at the transmitter to obtain the spatial-frequency multiuser diversity by joint spatial-frequency subcarrier and antenna assignment of MIMO OFDMA for the independent fading of different user in spatial and frequency domain. To guarantee the user fairness, a joint spatial-frequency proportional fairness (PF) scheduling is proposed in this paper. Further, no inter-cell interference mitigation capability can be observed from the current MIMO OFDMA schemes in downlink, the soft frequency reuse is adopted in this paper to avoid the inter-cell interference. Finally the downlink performance of the TD-SCDMA LTE with no-real time service is evaluated with spatial frequency PF scheduling and soft frequency reuse in multi-cell scenario

Orthogonal frequency division multiplexing access (OFDMA) which can make full use of frequency resources by using adaptive modulation and coding (AMC) and multi-user diversity, is a promising technology for the next generation wireless communication system. The flexibility of OFDMA also makes the radio resource management (RRM) more complicated. This paper proposes a modified largest weighted delay first (M-LWDF) packet scheduling algorithm with subcarrier allocation for the real time service in the multiuser OFDMA systems. The simulation result shows that it can maximize the system throughput and guarantee the QoS (quality of service) of different users.

In this paper we propose a multipath delay estimation scheme for MIMO-OFDM system with the code division preamble. The algorithm is composed of three steps: the delays of L1stronger paths are firstly estimated by coarse multipath delay estimation module. Then the channel coefficients of L1paths are estimated and fed back to interference reconstruction module, by which the interference from other antennas will be recovered. After interference cancellation, fine multipath delay estimation module is used to get the delays of L paths and accurate estimation of delays could be taken advantage by channel estimation module. It is verified by simulation that the proposed algorithm can correctly estimate all paths for 4 antennas once SNR is higher than 6 dB. As antenna number is increased to 16, the correct estimation probability of former 4 paths is still about 90% when SNR is higher than 3 dB. So the proposed multipath delay estimation scheme with interference cancellation can efficiently improve the accuracy of multipath delay estimation in MIMO-OFDM system.

In multiuser MIMO system, multiuser multiplexing and diversity gain can be achieved by spatial scheduling. However, the limited battery life and terminal size of the User Equipment (UE) in a cellular system put a constraint on MIMO when the tranmitter has more antenna than the receiver in downlink, the conventional MIMO schemes can only exploit partial multiplexing gain and diversity gain. In a multiuser MIMO OFDMA system, multiuser diversity gain can be achieved by exploiting the independent frequency and spatial selective fading one another by joint spatial and frequency scheduling. In this paper, different multiuser multiple access schemes with greedy scheduling of MIMO OFDMA are investigated, e.g. TDMA, FDMA/TDMA and SDMA/FDMA/TDMA. For full spatial-frequency multiuser diversity gain and spatial multiplexing gain can be achieved, SDMA/FDMA/TDMA obtains highest spectrum efficiency. Its gain exceeds that of TDMA and FDMA/TDMA at least 80% and 40% respectively. The more antennas configured, the more gain can be observed from SDMA/FDMA/TDMA.