Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

Channel acquisition for precoding design in massive multiple-input multiple-output (MIMO) systems faces increasingly prominent challenges due to the huge overhead caused by channel feedback and transmission of pilot signals. In response, directly mapping location information to precoding without channel feedback has emerged as a feasible and efficient solution. However, existing deep learning-based methods often struggle to map low-dimensional positional data to high-dimensional precoding vectors accurately. To address this challenge, we propose a spatially adaptive mapping network (SAM-Net) that enhances feature extraction and representation by leveraging transposed convolution and incorporating spatial information for fine-grained adaptive adjustments. While SAM-Net improves mapping performance, its complexity also increases. Therefore, we introduce a lightweight spatially adaptive mapping network (LSAM-Net) that combines average pooling and max pooling to reduce the number of parameters and computational complexity while it maintains near-optimal performance. Evaluation results demonstrate that SAM-Net achieves superior and stable mapping performance, while LSAM-Net offers a more lightweight alternative with minimal loss in performance.

Downlink channel temporal prediction is a critical technology in massive multiple-input multiple-output (MIMO) systems. However, existing methods that rely on fixed-step historical sequences significantly limit the accuracy, practicality, and scalability of channel prediction. Recent advances have shown that large language models (LLMs) exhibit strong pattern recognition and reasoning abilities over complex sequences. The challenge lies in effectively aligning wireless communication data with the modalities used in natural language processing to fully harness these capabilities. In this work, we introduce Csi-LLM, a novel LLM-powered downlink channel prediction technique that models variable-step historical sequences. To ensure effective cross-modality application, we align the design and training of Csi-LLM with the processing of natural language tasks, leveraging the LLM's next-token generation capability for predicting the next step in channel state information (CSI). Simulation results demonstrate the effectiveness of this alignment strategy, with Csi-LLM consistently delivering stable performance improvements across various scenarios and showing significant potential in continuous multi-step prediction.

Deep learning (DL)-based channel state information (CSI) feedback has shown great potential in improving spectrum efficiency in massive MIMO systems. However, DL models optimized for specific environments often experience performance degradation in others due to model mismatch. To overcome this barrier in the practical deployment, we propose UniversalNet, an ID-photo-inspired universal CSI feedback framework that enhances model generalizability by standardizing the input format across diverse data distributions. Specifically, Uni-versalNet employs a standardized input format to mitigate the influence of environmental variability, coupled with a lightweight sparsity-aligning operation in the transformed sparse domain and marginal control bits for original format recovery. This enables seamless integration with existing CSI feedback models, requiring minimal modifications in preprocessing and postprocessing without updating neural network weights. Furthermore, we propose an efficient eigenvector joint optimization method to enhance the sparsity of the eigenvector matrix by projecting the spectrum channel correlation into the eigenspace, thus improving the implicit CSI compression efficiency. Test results demonstrate that UniversalNet effectively improves generalization performance and ensures precise CSI feed-back, even in scenarios with limited training diversity and previously unseen CSI environments.

We propose “MaskDSC”, a novel system designed to facilitate robust visual data transmission over unreliable wireless channels. MaskDSC effectively balances compression efficiency and transmission resilience by leveraging contextual modeling within the semantic latent space, complemented by unequal error protection mechanism at the physical layer, ensuring compatibility with existing digital communication systems. The novelty of our approach lies in a dual-functional masked Transformer architecture that exploits causal-order contextual dependencies among visual tokens. This architecture not only enhances compression efficiency through improved contextual entropy modeling but also provides robust error concealment capabilities to address diverse transmission error patterns inherent in volatile wireless channels. Our experimental evaluations conducted on image datasets demonstrate that MaskDSC outperforms state-of-the-art transmission systems, especially in terms of efficiency and resilience under dynamic wireless channel conditions.

Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However, existing methods typically optimize a uniform bandwidth-distortion trade-off over the entire image, potentially leading to the loss of crucial details and inconsistent content for tasks with diverse regions of interest. In this paper, we propose a flexible fine-grained bandwidth allocation method for deep JSCC that enables highly efficient, task-scalable image transmission across various semantic communication scenarios using a single codec. Our method optimizes the bandwidth-distortion trade-off by constraining image distortion through a 2D pixel-wise quality map. Guided by the pixel-wise quality map, we introduce a novel conditional affine transformation that generates dedicated semantic feature maps tailored to specific tasks. Additionally, we introduce a semantic guidance network to automatically generate task-aware quality maps via backpropagation without additional retraining. This approach leverages a pretrained variable-length neural JSCC codec and adjusts the transmission quality on a fine-grained level, eliminating the need to train separate models for different tasks. Experimental results demonstrate the effectiveness of our bandwidth allocation method, enhancing task-specific performance in various goal-oriented image communication scenarios without additional training.

The deep learning-based (DL-based) Channel State Information (CSI) feedback faces significant challenges in openworld wireless communication systems, where CSI data is characterized by multi-scenario diversity and high dynamics. These properties introduce distribution bias and distribution shift issues: the former leads to overfitting during model training, while the latter causes model degradation during deployment and catastrophic forgetting during updates. To address these challenges across the artificial intelligence (AI) lifecycle, this work proposes a unified scenarios-aware CSI feedback framework that operates throughout the training, monitoring, and updating phases. It includes: bias-resilient model training, which introduces CSI scenario awareness to enhance CSI reconstruction while mitigating overfitting; learnable-free outof-distribution (OOD) detection for model monitoring, which identifies distribution shifts and enables efficient updates via in-distribution (ID) samples filtering; and forgetting-resistant model updating via a hybrid domain adaptation (HDA) strategy, which retains knowledge of known scenarios while improving CSI reconstruction in unseen scenarios. Extensive experiments demonstrate the superiority of the scenario-aware CSI feedback framework: it achieves up to 4.65 dB normalized mean squared error (NMSE) improvement over its prototype in random bias setups dataset, enables responsive OOD detection using both Softmax and energy-based confidence functions with an average gain of 1 dB after updates on filtering ID samples, and facilitate adaptation to unseen scenarios (up to 0.30 similarity improvement) while preserving known knowledge (above 97.6% scenario-aware accuracy) even under significant scenario shifts. Codes are available on GitHub1.

With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates. However, the primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi. In this paper, we address the challenge by designing channel access mechanisms and power control strategies to mitigate interference and ensure fair coexistence. First, we propose a novel collaborative channel access (CCHA) mechanism that integrates channel access with resource allocation through collaborative interactions between base stations (BS) and SL-U users. This mechanism ensures fair coexistence with incumbent systems while improving resource utilization. Second, to further enhance the performance of the coexistence system, we develop a cooperative subgoal-based hierarchical deep reinforcement learning (C-GHDRL) algorithm framework. The framework enables SL-U users to make globally optimal decisions by leveraging cooperative operations between the BS and SL-U users, effectively overcoming the limitations of traditional optimization methods in solving joint optimization problems with nonlinear constraints. Finally, we mathematically model the joint channel access and power control problem and balance the trade-off between fairness and transmission rate in the coexistence system by defining a suitable reward function in the C-GHDRL algorithm. Simulation results demonstrate that the proposed scheme significantly enhances the performance of the coexistence system while ensuring fair coexistence between SL-U and Wi-Fi users.

Deep learning (DL)-based channel state information (CSI) feedback holds substantial promise for boosting spectrum efficiency in massive MIMO systems. However, prevailing studies often treat compressed CSI bits uniformly, assuming their accurate transmission over noisy channels. Such assumptions falter when confronted with bandwidth limitations or low signal-to-noise ratios (SNRs), significantly impairing CSI reconstruction quality. In this paper, we introduce a novel soft variational approach to implement deep joint source-channel coding for CSI feedback within noisy environments, conceptualizing the system as an end-to-end rate-distortion (RD) optimization framework. Specifically, our model utilizes a nonlinear transform to extract the latent representation of CSI and employs an entropy model as a prior to guide the variable-rate transmission of latent features across noisy channels. Experimental results demonstrate that our approach achieves substantial improvements over benchmark schemes, saving 40% in feedback bandwidth under normalized mean squared error (NMSE) and ensuring the robustness to varying wireless channels.

Channel State Information (CSI) feedback, powered by Deep Learning (DL) methodologies, exhibits significant promise in enhancing spectrum efficiency within massive MIMO systems. However, DL-based approaches typically necessitate substantial CSI datasets for each specific scenario, and managing multiple learned models demands considerable storage and updating bandwidth. To overcome this costly barrier, we develop a solution for efficient training and deployment enhancement of DL-based CSI feedback, which involves a lightweight translation model to cope with new CSI environments and introduces a novel dataset augmentation based on domain knowledge. Specifically, we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which incorporates spherical normalization to mitigate the challenge of path loss variation. Additionally, SPTM2-ISTANet+ integrates a trainable measurement matrix and residual CSI recovery blocks to enhance efficiency and accuracy. Employing SPTM2-ISTANet+ as a foundational feedback model, we introduce an adaptive CSI feedback architecture termed CSI-TransNet. CSI-TransNet features a scenario-adaptive plug-in module for CSI translation, composed of a sparsity aligning function and a compact DL module, facilitating the reuse of pretrained models in unencountered environments. To accommodate the small datasets, we propose a lightweight and general augmentation strategy based on domain knowledge. Test results demonstrate the efficacy and efficiency of the proposed solution for accurate CSI feedback given limited measurements for unseen CSI environments.

It is essential to locate the victims timely for efficient rescue after the disaster occurs in the global positioning system (GPS) denied forest area due to the influence of the tree shading. Existing works have studied optimizing the trajectory of the unmanned aerial vehicle (UAV) to exploit the wireless signal from the ground users for localization. However, current works mainly focus on optimizing the localization accuracy while paying limited attention to the localization task completion time, which makes it challenging to meet the timeliness requirements of emergency rescue missions. To provide accurate localization services for the ground victims quickly, we propose a multi-agent deep reinforcement learning (MA-DRL)-based UAV trajectory planning algorithm, which can provide high-efficiency cooperation between the multiple UAVs by exploiting the prior information and measurements from the partners. Specifically, a low-resolution trajectory planning algorithm is proposed to reduce redundant flight distances in the pre-fly stage to localize the victim’s quantity and dispersion. Furthermore, to provide high localization accuracy and energy-efficiency victims location services quickly, we exploit the coarse user information from the pre-fly stage, integrate an adaptive forest channel model and UAV energy consumption model, and propose an MA-DRL-based UAV trajectory planning algorithm which can perform decentralized execution for the high-efficiency cooperation localization. Simulation results show that our method can finish localization missions faster with less energy consumption while guaranteeing the localization accuracy compared to other benchmark algorithms.

In this paper, we propose a massive access control scheme in machine-type communications (MTC) aided smart grid, which prioritizes and clusters the devices based on the latency requirement and the distance between devices. The considered use case features a single cell in the massive connectivity smart grid and a large number of devices with different priority types. For the delay-sensitive devices, a dynamic random access channel (RACH) resource allocation scheme is proposed, where a back-off mechanism is used to defer the access requests of delay-tolerance devices. In addition, we propose a cluster-based congestion control algorithm, which clusters the devices to establish local collaboration. Simulation results show the proposed scheme reduces the average blocking probability, while significantly reducing access delay for delay-sensitive devices compared with state-of-the-art access control methods.

When signals propagate through forest areas, they will be affected by environmental factors such as vegetation. Different types of environments have different influences on signal attenuation. This paper analyzes the existing classical propagation path loss models and the model with excess loss caused by forest areas and then proposes a new short-range wireless channel propagation model, which can be applied to different types of forest environments. We conducted continuous-wave measurements at a center frequency of 605 MHz on predetermined routes in distinct types of forest areas and recorded the reference signal received power. Then, we use various path loss models to fit the measured data based on different vegetation types and distributions. Simulation results show that the proposed model has substantially smaller fitting errors with reasonable computational complexity, as compared with representative traditional counterparts.

The flexible and controllable mobility makes Un-manned Aerial Vehicle (UAV) useful in collecting data from distributed wireless sensor nodes, especially in emergency situations where infrastructures are destroyed or absent. However, due to the limited battery capacity, the mission duration of UAV is severely restricted, which makes the trajectory design become challenging. In this paper, a data-collection oriented multiple UAVs path planing algorithm is proposed to minimize the data collection time. Specifically, an enhanced particle swarm optimization (E-PSO) algorithm is first proposed to optimize the visiting sequence of sensors, which can shorten the flight time. Moreover, a novel hover-on-edge algorithm is designed by jointly considering wireless communication ranges and locations of sensors in traversal sequence. The proposed algorithms are compatible with UAV flying directly above the sensor (E-PSO) and UAV hovering on the communication range of sensor (HE-PSO) which enhance the robustness of the system. Simulation results demonstrate path distance and flight time reduction of our proposed algorithm.

Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work. We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost.

The UAV deployment as well as power allocation is critical in emergency rescue with the practical diverse re-quirements of data applications. In this paper, a UAV 3-D deployment scheme driven by multi-level quality of service (QoS) is first presented. Specifically, to harvest more performance gain, a fuzzy clustering-based initialization method is proposed, which can achieve more reliable accuracy of clustering compared with traditional clustering methods. Further, considering more comprehensive effect in terms of particle diversity and iteration, a novel inertia weight update method is developed to accelerate convergence. Simulation results demonstrate that our proposed scheme outperforms other schemes.

The rise of Artificial Intelligence of Things (AIoT) makes everything connected smartly, which can enrich industrial productivity. With the help of learning-based wireless communications, terminals in AIoT can communicate with each other and collaborate intelligently. However, one of the critical issues faced in AIoT deployment is the lack of available large datasets for the training of artificial intelligence algorithms in the industrial environment. In this paper, we propose a channel data augmentation algorithm for the dataset limitation cases in intelligent industrial wireless communication systems using generative adversarial networks (GAN). Specifically, we first show the performance of deep learning-based channel state information (CSI) feedback algorithm trained with different size of datasets. Then we develop a GAN for the channel data augmentation to enhance the performance of CSI feedback algorithm. Finally, we apply the enhanced approach to an insufficient dataset for the performance evaluation. Experimental results show that our method can achieve at most 3dB performance improvement than other traditional data augmentation approaches in increasing the accuracy of CSI feedback algorithm when the size of dataset is limited to 10000.

Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.

Modern vehicles are equipped with wireless communication technologies, allowing them to communicate with each other and forming large self-organized ad hoc networks (or vehicular ad hoc networks (VANETs)). VANETs, while promising new approaches for improving road safety, require privacy of vehicles (or drivers) to be protected from a variety of threats. Although pseudonym schemes have provided a promising solution at the upper layers, privacy attacks could still be carried out from medium access control (MAC) layer. In this paper, we first introduce a new MAC layer context linking attack, which could link the old and new pseudonyms of a vehicle by analyzing its transmission characteristics. To deal with the attack, we propose a time division multiple access based MAC-layer-Attack-Resistant Pseudonym (MARP) scheme. Unlike traditional approaches that design the MAC protocols and pseudonym schemes separately, MARP allows vehicles to change their transmission slots and pseudonyms collaboratively. Thus, the unlinkability is guaranteed. Taking the pseudonym age, anonymity set size and time-to-confusion as the location privacy metrics, we derive an analytical model to quantify location privacy achieved in MARP. The analytical model is general to be applied for other pseudonym schemes. Extensive simulation results have validated the analytical model, showed that MARP can resist the MAC context linking attack and guarantee location privacy and efficient transmission for vehicles in VANETs.

To meet the booming demand for higher data rates, Small Base Stations (SBSs) are deployed densely and irregularly, leading to the rapid increases in energy consumption. Meanwhile, the evolution trend of future mobile network is extending to three dimensional (3D) space. Therefore, the energy efficiency for future 3D ultra dense mobile networks is of high significance. In this paper, we focus on analyzing the energy efficiency with sleeping strategy of 3D dense mobile network. Firstly, we derive the expressions of the coverage probability and energy efficiency by using the tool of 3D stochastic geometry, where the locations of SBSs and users are modeled as two independent 3D spatial Poisson Point Processes. In addition, the expression of energy efficiency for BS activation probability is derived to observe explicitly the impact of sleep mode on the energy efficiency. Finally, simulation results confirm the accuracy of our analysis.