Grant-free (GF) random access has emerged as a promising solution for massive machine-type communications (mMTC). However, the non-uniform distribution of user equipment (UE) and real-world limitations on base station (BS) placement lead to random access imbalance among BSs and heavy access collisions in some cells. To this end, an unsupervised learning-based location-aware random access (ULL-RA) scheme is proposed in this paper to select the accessing BSs and channels simultaneously. Specifically, ULL-RA adopts a deep learning model named ULL-RA-Net, comprising parameter-shared feedforward neural network (FFNN) layers that enable each active UE to select a BS and an access channel to maximize the achievable rate. The model is trained in an unsupervised manner to maximize a designed differentiable objective, mapping UE locations to channel access probabilities. Notably, we propose a rate-collision loss tailored to the model architecture, which combines a collision-free channel capacity term and a sparsity-inducing collision penalty term to reduce access collisions and enhance the achievable rate. Experimental results using the Deep-MIMO dataset indicate that ULL-RA outperforms conventional GF access in both the average achievable rate and the access success rate.

A novel near-field wideband integrated sensing and communication framework is proposed to address the prohibitively high pilot overhead challenge in extremely large-scale MIMO systems. Unlike conventional approaches that rely on exhaustive two-dimensional codebook search, a unified architecture leveraging true-time-delay-based rainbow beamforming with controllable distance-dependent beam squint is proposed to extend spatial coverage. Furthermore, the inter-antenna phase ambiguity is harnessed to introduce beam split phenomena, enabling simultaneous multi-angle and multi-distance sensing within a single pilot transmission. Based on this architecture, a two-stage low-complexity sensing protocol is carried out, where distance-ring identification via beam-split-enhanced rainbow beams is performed in the first stage using sub-array structures, followed by angle refinement in the second stage. To mitigate frequency-dependent beamwidth variations, a frequency-compensated joint reconstruction algorithm based on virtual grid mapping and sparse optimization is proposed. Additionally, an echo-aided velocity estimation method exploiting intra-symbol Doppler diversity across subcarriers is developed, eliminating the need for multiple pulse transmissions. Simulation results demonstrate that: 1) complete spatial coverage is achieved with only two OFDM symbols, representing over 98% overhead reduction compared to exhaustive search methods; 2) the proposed scheme achieves superior localization accuracy with root-mean-square errors below 0.001 in normalized angle domain and 0.01 in distance-ring domain at moderate SNR; 3) communication rates are improved by 7% to 15% compared to conventional near-field beam training approaches under identical pilot budgets.

ABSTRACT With the evolution of wireless communication technologies towards the sixth generation (6G) mobile communication system, the space‐air‐ground‐sea integrated network architecture has emerged as a critical development direction for achieving global seamless coverage. Focusing on the unmanned aerial vehicle (UAV)‐to‐ship maritime communication scenario within this network framework, a three‐dimensional (3D) geometry‐based stochastic model is proposed. The model adopts a combined structure of elliptical and cylindrical components to comprehensively characterize multipath propagation mechanisms, including line‐of‐sight, sea surface reflection, as well as single‐bounced and double‐bounced components. By introducing the wave equation of sea surface to establish the 3D motion trajectory model of the ship and integrating it with the 3D rotational motion model of the UAV, the time‐varying propagation distance‐induced channel non‐stationarity is accurately captured. Based on this model, key statistical characteristics such as the space‐time‐frequency correlation function (STF‐CF) and Doppler power spectral density are derived. Furthermore, the impacts of sea surface wind speed, UAV rotation, ship oscillation, and ship size on channel statistical properties and space‐time non‐stationarity are thoroughly analysed. These numerical results provide theoretical foundations for the design and performance optimization of UAV‐assisted communication systems in complex maritime environments.

Ultra dense networks (UDNs) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between the half-duplex (HD) and full-duplex (FD), are expected to support high-density transmissions. However, to centrally handle a large network is costly, while distributed processing may suffer from the severe performance loss due to the complicated intercell interferences in the UDNs. This article aims to balance the system performance and clustering cost of the SD UDNs by dividing all small cells into several clusters. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multiagent MDP to maximize the average reward of all small cells. Next, a multiagent deep reinforcement learning (DRL) is proposed to jointly implement the dynamic clustering for noncenter small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD in UDNs, and the proposed multiagent DRL outperforms other clustering schemes under the considered scenarios.

The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of the era of “Internet of everything” but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT system is characterized by a large amount of data, strict confidentiality, and high-security requirements, and they are usually high-risk information on privacy leakage. The proposed method is conducted to the sentence similarity analysis model based on a convolutional neural network (CNN) in the CEC scene to test the feasibility of the method. Compared with the original CNN, the accuracy of the model using the confrontation training method is improved by 4.8%. At the same time, the security value of our model is 2.1% higher than that of the simple CNN model, and it has the best security performance among the four comparison models. Further experiments have demonstrated that the model performs better in its capacity of resisting disturbance and can effectively help multiple organizations to implement data usage and sentence information on the requirements of user privacy protection, data security, and government regulations.

With the rapid development of wireless communications, the understanding of three-dimensional (3D) propagation channels becomes essential for design and testing of some new wireless technologies, e.g., massive multiple-input multiple-output (MIMO) and full dimensional beamforming. To not only fully exploit the 3D multiplexing but also circumvent the size limitation of base station (BS), antenna elements in massive MIMO are usually arranged both horizontally and vertically. Based on an elaborate channel measurement campaign conducted at 11 GHz in a lobby environment, a 3D extended cluster-based channel model is proposed in this paper for massive multiple-input single-output (MISO) multi-user communications. In the model, the channel characteristics in both azimuth and elevation dimensions, and the visibility regions which are parametrized by observed cluster lengths across the large-scale array in both horizontal and vertical directions, are taken into consideration. Moreover, the spherical wavefront phenomenon observed from the measurements is also incorporated in the model. Model parametrization, implementation, and validation are presented in detail. Validations show that the proposed model can accurately reflect the realistic channel, and the spatial non-stationarity and the spherical wavefront should be carefully considered in the channel models for large-scale array communications.