Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic environment of FAS, while data-driven deep learning approaches demand extensive training and are vulnerable to noise and hardware imperfections. To address these challenges, this paper introduces a novel self-supervised learning network (SSNet) designed for efficient and adaptive channel extrapolation in FAS. We formulate the problem of channel extrapolation in FAS as an image reconstruction task. Here, a limited number of unmasked pixels (representing the known CSI of the selected ports) are used to extrapolate the masked pixels (the CSI of unselected ports). SSNet capitalizes on the intrinsic structure of FAS channels, learning generalized representations from raw CSI data, thus reducing dependency on large labeled datasets. For enhanced feature extraction and noise resilience, we propose a mix-of-expert (MoE) module. In this setup, multiple feedforward neural networks (FFNs) operate in parallel. The outputs of the MoE module are combined using a weighted sum, determined by a gating function that computes the weights of each FFN using a softmax function. Extensive simulations validate the superiority of the proposed model. Results indicate that SSNet significantly outperforms benchmark models, such as AGMAE and long short-term memory (LSTM) networks by using a much smaller labeled dataset. A key observation is that the proposed model is more effectively trained using a small unmasked ratio of known CSI. Specifically, the proposed SSNet trained using CSI of 10 % total ports outperforms that trained using CSI of 25 % and 50 % total ports. This is because using a smaller number of known CSIs during training, the proposed model is forced to learn more effective channel correlation for channel extrapolation at the expense of higher training complexities. Ablation experiments reveal substantial performance gains from the MoE module’s integration. Furthermore, zero-shot learning experiments show a moderate performance degradation of about 3-5 dB, underscoring the model’s robust generalization ability. Finally, the inference speed experiments illustrate that the proposed model outperforms the benchmark models dramatically at the expense of a slightly longer execution time of 1.13 ms, 2.9 ms, and 3.12 ms on NVIDIA RTX 4090, 4060, and 3060 graphics processing units (GPU)s, respectively.

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation, thereby neglecting the synergies among various tasks and leading to extract CSI features inadequately for subsequent analysis. To address these limitations, this paper introduces a novel Multi-Task Channel Analysis framework named MTCA, aimed at improving the performance of wireless communication even sensing. MTCA is designed to handle four critical tasks, including channel prediction, antenna-domain channel extrapolation, channel identification, and scenario classification. Experiments conducted on a multi-scenario, multi-antenna dataset tailored for UAV-based communications demonstrate that the proposed MTCA exhibits superior comprehension of CSI, achieving enhanced performance across all evaluated tasks. Notably, MTCA reached 100% prediction accuracy in channel identification and scenario classification. Compared to the previous state-of-the-art methods, MTCA improved channel prediction performance by 20.1% and antenna-domain extrapolation performance by 54.5%.

In this study, we propose a mobile edge computing (MEC)-enabled vehicular Metaverse system designed for augmented reality (AR) services, where vehicles on the road can access the Metaverse service through nearby Metaverse service providers (MSPs) equipped with MEC servers. In this system, vehicles are charged for their use of computational and communication resources. Due to varying positions and viewing angles, vehicles may have different content preferences. To minimize cost while ensuring optimal quality of experience (QoE), we formulate an optimization problem that adjusts content resolution and resource allocation to match individual vehicle needs. To address this optimization problem, we introduce a deep reinforcement learning (DRL) algorithm integrated with active inference theory to solve the decision-making problem with the performance of low latency and high efficiency. Simulation results demonstrate that our proposed scheme outperforms comparative algorithms in comprehensive performance, providing an effective solution for optimizing AR-enabled vehicular Metaverse systems.

In the dynamic and demanding 6 G IoT environments, UAV-enabled Mobile Edge Computing (MEC) systems face significant challenges such as varying communication conditions, energy constraints, and high task demands. To address these challenges effectively, we propose a novel optimization framework that integrates Reconfigurable Intelligent Surfaces (RIS) and employs the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The primary goals of this framework are to minimize latency, maximize throughput, and ensure energy efficiency by dynamically coordinating UAV trajectories, task offloading decisions, and RIS phase shifts. Through extensive simulations, we demonstrate that the proposed framework significantly reduces delay and improves throughput compared to traditional methods, highlighting the substantial benefits of combining RIS with UAV-MEC systems in dynamic IoT scenarios. These findings suggest that the integration of RIS can effectively enhance system performance and adaptability in next-generation wireless networks, providing a promising approach to tackle the complex challenges of 6G IoT environments.

For the Internet of things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this paper, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyse the communication coverage and localization coverage probability with respect to the network density, time-frequency-power domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental trade-off between localization and communication performance attributed to the time-frequency-power domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

Orthogonal Time Frequency Space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this paper, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.

This paper proposes a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction (NTP) models. The primary objective is to accumulate and leverage the acquired hyper-parameter optimization experience, enabling the rapid determination of optimal hyper-parameters for new tasks. In this paper, an attention-based deep neural network (ADNN) is employed as the base-learner to address specific NTP tasks. The meta-learner is an innovative framework that integrates meta-learning with the k-nearest neighbor algorithm (KNN), genetic algorithm (GA), and gated residual network (GRN). Specifically, KNN is utilized to identify a set of candidate hyper-parameter selection strategies for a new task, which then serves as the initial population for GA, while a GRN-based chromosome screening module accelerates the validation of offspring chromosomes, ultimately determining the optimal hyper-parameters. Experimental results demonstrate that, compared to traditional methods such as Bayesian optimization (BO), GA, and particle swarm optimization (PSO), the proposed framework determines optimal hyper-parameters more rapidly, significantly reduces optimization time, and enhances the performance of the base-learner. It achieves an optimal balance between optimization efficiency and prediction accuracy.

In the coexisting new radio-based access to unlicensed spectrum (NR-U) and WiGig networks (CNWNs), directional-sensing-based listen-before-talk (LBT) mechanisms, i.e., directional LBT (dirLBT) and paired LBT (pairLBT), have been proposed to address the exposed node problem caused by traditional omnidirectional LBT (omniLBT) mechanism. In this paper, we are the first to leverage the stochastic geometry to analyze the large-scale CNWN performance when NR-U base stations (NBSs) adopt the directional-sensing-based LBT mechanisms. The analytical expressions for the downlink successful transmission probabilities (STPs) of CNWNs are derived and validated by Monte Carlo simulations. Based on these STPs, the area spectral efficiency (ASE) of CNWNs is derived. Equipped with these results, the effect of NBS sensing threshold, density and sensing beamwidth on the STP and ASE performance are analyzed numerically. Moreover, the STP and ASE performance are compared when NBSs adopt dirLBT, pairLBT and omniLBT mechanisms. Furthermore, the asymptotic ASE of CNWNs when NBS density approaches infinity is derived and validated. The results show that directional-sensing-based LBT mechanisms outperform the omniLBT mechanism in terms of ASE in the CNWNs, especially in ultra-densely deployed scenarios. Under our simulation environment, the dirLBT mechanism can improve the ASE by up to 82.5% as compared with the omniLBT mechanism. Additionally, the NBS sensing threshold for directional-sensing-based LBT should be higher than −73 dBm to achieve a better STP and ASE as compared with that without adopting LBT in NBSs. Besides, there exists an optimal NBS density to maximize the STP and ASE of CNWNs, and when NBS density becomes larger than$200,000$NBSs per$\text {km}^{2}$, deploying more NBS has limited enhancement on the ASE. These results indicate that directional-sensing-based LBT mechanisms should be employed in the ultra-densely deployed CNWNs, and the NBS sensing threshold and sensing beamwidth should be carefully chosen to ensure the superiority of directional-sensing-based LBT mechanisms.

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To address these challenges, we present LinFormer, an innovative channel prediction framework based on a scalable, all-linear, encoder-only Transformer model. Our approach, inspired by natural language processing (NLP) models such as BERT, adapts an encoder-only architecture specifically for channel prediction tasks. We propose replacing the computationally intensive attention mechanism commonly used in Transformers with a time-aware multi-layer perceptron (TMLP), significantly reducing computational demands. The inherent time awareness of TMLP module makes it particularly suitable for channel prediction tasks. We enhance LinFormer’s training process by employing a weighted mean squared error loss (WMSELoss) function and data augmentation techniques, leveraging larger, readily available communication datasets. Our approach achieves a substantial reduction in computational complexity while maintaining high prediction accuracy, making it more suitable for deployment in cost-effective base stations (BS). Comprehensive experiments using both simulated and measured data demonstrate that LinFormer outperforms existing methods across various mobility scenarios, offering a promising solution for future wireless communication systems.

The orthogonal time frequency space (OTFS) modulation has garnered significant attention due to its potential to combat the frequency Doppler effect in high-mobility scenarios, especially in the low earth orbit (LEO) satellite communication. However, facilitating the OTFS in multiple-input and multipleoutput (MIMO) satellite communication requires accurate channel state information, which is a challenging task. To this end, we propose a novel deep-learning-based framework to enhance the channel estimation accuracy in a MIMO satellite communication network by exploiting the channel correlation of the OTFS-MIMO channels. Through extensive simulations, our proposed framework shows an impressive capability to predict MIMO-OTFS channels with remarkable accuracy enhancement. The effect of dataset and data distribution on the channel estimation accuracy and generalization are also revealed.

Next-generation mobile networks are poised to leverage Integrated Sensing and Communication (ISAC) as a pivotal technology, offering unprecedented support for various sectors including industrial Internet of Things (IIoT), extended reality (XR), and smart home applications. A critical challenge in implementing ISAC lies in extracting sensing parameters from radio signals, a task that has proven difficult using conventional methods. This paper, for the first time, introduces a novel approach to this challenge by proposing the extrapolation of sensing channel information, specifically the power delay profile (PDP), from readily available communication channel state information (CSI). We present a transformer-based framework designed to accomplish this task efficiently. Our extensive simulations demonstrate the framework's effectiveness in CSI-to-PDP extrapolation, achieving high accuracy in predicting both the delay and signal strength of individual paths. This innovative method has the potential to significantly enhance sensing capabilities in future mobile networks, paving the way for more robust and versatile ISAC applications.

In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficiency in smart city scenarios. The proposed framework leverages LLMs for intelligent DT problem analysis and radio resource management (RRM) in fully autonomous way without any manual intervention. Our proposed framework — LINKs, builds up a lazy loading strategy which can minimize transmission delay by selectively retrieving the relevant data. Based on the data retrieval plan, LLMs transform the retrieval task into an numerical optimization problem and utilizing solvers to build an optimal RRM, ensuring efficient communication across the network. Simulation results demonstrate the performance improvements in data planning and network management, highlighting the potential of LLMs to enhance the integration of DT and 6G technologies.

Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density.

The Long Term Evolution-Unlicensed (LTE-U) scheme has been proposed to exploit the unlicensed spectrum, especially the 5 GHz band, for cellular networks to further increase capacity. Since the 5 GHz band has already been used by WiFi networks, the carrier sense adaptive transmission (CSAT) scheme has been proposed LTE-U access points (LAPs) to harmoniously coexist with WiFi access points (WAPs), where duty cycles are used by LAPs to leave certain time slots that only allow WAPs to access the unlicensed band. However, the performance of the CSAT scheme has not been sufficiently analyzed for multiple unlicensed channels (UCs) in a large-scale network. In this work, we derive the explicit expressions of downlink successful transmission probabilities (STPs) of LAP users and WAP users for a large-scale multi-UC network using stochastic geometry tools. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum throughput of LTE-U and WiFi users, are analysed versus the duty cycle and the LAP density. Furthermore, the optimal duty cycle is obtained based on the derived STPs in the duty-cycle and non-duty-cycle durations. Our results show that for a given number of UCs and WAP density, and with the optimal duty cycle, the optimal LAP density increases with the increasing number of UCs for maximizing the fairness, but leads to a poorer minimum throughput performance.

Licensed-assisted access (LAA) is a promising technology to meet the exponential increase of traffic demand by exploiting the 5GHz unlicensed spectrum. However, without proper coexistence schemes, the performance of WiFi in coexistence with LAA will be degraded significantly. Equipped with listen-before-talk (LBT) schemes, LAA applies a channel access mechanism similar to distributed coordination function (DCF) in WiFi, which is not optimal in terms of spectrum efficiency. In this paper, we propose an joint optimization scheme to find the optimal combination of WiFi and LAA contention windows (CWs) that maximizes LAA throughput, while guarantees WiFi throughput above a predefined threshold. The accuracy and efficiency of the proposed scheme is evaluated by comparing with the exhaustive search: almost the same combination of CWs are achieved with much lower complexity by using the proposed scheme than the exhaustive search. Numeric results show that the proposed adaptive scheme is more effective in dense scenario, where high system (up to 40%) and LAA throughput gain (up to 100%) are achieved. The trade-off between LAA (system) throughput and WiFi throughput is also revealed.

License-assisted access (LAA) is a candidate feature in 3GPP Rel-13 to meet the explosive growth of traffic demand. The main idea of LAA is to deploy LTE in the unlicensed band (mainly the 5GHz band), which is abundant with available spectrum. However, the major concern is the coexistence between WiFi and LAA in the same band. This paper presents a new framework to evaluate the downlink performance of coexisting LAA and WiFi networks. By using Markov chain, analytical models are established based on WiFi distributed coordination function (DCF) and two listen-before- talk (LBT) schemes. These two LBT schemes are Cat 3 and Cat 4 LBT, which mainly differ in medium access schemes in terms of backoff procedure. Unlike most existing works, which focus on the impact on WiFi performance posed by LAA, the performance of LAA is also evaluated. Our analysis shows that throughput of a WiFi network can be enhanced by adding or replacing WiFi access points (APs) with LAA E-UTRAN Node Bs (eNBs), at the expense of different levels of WiFi performance degradation. A trade-off between WiFi protection and LAA-WiFi system performance enhancement is observed. WiFi throughput and delay are less affected by Cat 4 LBT scheme, while Cat 3 LBT scheme provides higher LAA-WiFi system throughput. The choice of LBT schemes relies on the network planning priority, WiFi performance protection and LAA system performance requirements.