With the number of users growing rapidly, the federated learning (FL) algorithm gradually moves towards a multi-layer framework to ensure learning performance. In this article, considering the non-independent and identically distributed scenario, a new hierarchical federated meta-learning (HFML) framework is studied. The Hessian-free Model-Agnostic Meta-Learning is introduced into our model to personalize the local models of edge users (EUs), which is more computationally efficient than the traditional meta-learning. To alleviate the learning performance reduction due to the scarce available bandwidth resources, a multilayer perceptron model prediction scheme based on the attention mechanism is deployed at the side of edge nodes (ENs). To achieve the tradeoff between learning time and model accuracy, the semi-synchronous cloud aggregation mechanism based on the learning states and parameter freshness is proposed. The convergence analysis of the proposed HFML algorithm is also provided to prove that the upper bound of the loss decay exists. To solve the complex nonconvex optimization problem whose target is to maximize the learning efficiency of HFML, considering device selection and communication resource allocation, a decentralized algorithm based on Jacobi-Proximal ADMM (JP-ADMM) is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed method. Particularly, compared with the traditional hierarchical federated learning algorithm, the proposed HFML achieves better learning performance while reducing the latency.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been recently proposed as a promising modulation scheme for next-generation communication systems with high mobility. Despite its benefits, ODDM modulation and other DD domain modulation schemes face the challenge of excessive equalization complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM transmitter, to make the DD domain single-tap equalizer feasible, thereby reducing the equalization complexity. In our design, we first pre-cancel the inter-symbol-interference (ISI) using the linear time-varying (LTV) channel information. Second, different from classical THP designs, we introduce a modified modulo operation with an adaptive modulus, by which the joint DD domain data multiplexing and time-domain ISI pre-cancellation can be realized without excessively increasing the bit errors. We then analytically study the losses encountered in this design, namely the power loss, the modulo noise loss, and the modulo signal loss. Based on this analysis, BER lower bounds of the ODDM system with time domain THP are derived when 4-QAM or 16-QAM modulations are adopted for symbol mapping in the DD domain. Finally, through numerical results, we validate our analysis and then demonstrate that the ODDM system with time domain THP is a promising solution to realize better BER performance over LTV channels compared to orthogonal frequency division multiplexing systems with single-tap equalizer and ODDM systems with maximum ratio combining.

Based on emerging artificial intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge-terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

Supporting massive machine type communications (mMTC) has been one of the key concerns in the next generation wireless network, where a large number of devices access the network. The cell-free (CF) massive MIMO is introduced as a promising enabling technology for mMTC. In this paper, we focus on the device activity detection (AD) for grant-free random access in CF massive MIMO systems. First, the CF massive random access signal model for mMTC scenarios is given, and we formulate the devices AD as a maximum likelihood estimation (MLE) problem by leveraging the block diagonal property of the covariance matrix of the received signals. Next, we propose an improved device AD performance algorithm, which consists of a serial processing algorithm and a parallel processing algorithm. The parallel processing algorithm can further reduce the computational overhead. In addition, through the asymptotic analysis of the MLE via its associated Fisher information matrix and solving a quadratic programming problem, we derive a theoretical prediction bound of devices AD performance, which can numerically predict theoretical results of devices AD with the coordinate descent algorithm. Finally, simulation results show that the proposed AD scheme can achieve a better performance-complexity trade-off than existing schemes, and the theory prediction bound can match well with the simulation of devices AD as the antenna number increases.

In this article, an efficient wireless federated learning (FL) framework based on blockchain (BC) assistance is studied. Many existing frameworks adopt lots of third-party servers as consensus nodes, which is vulnerable to collusion attacks. In our framework, the blockchain-assisted FL (BFL) model selects edge users as blockchain nodes without any third-party intervention. Besides, the convergence analysis of this FL algorithm considering transmission outages is provided to prove the effects of wireless factors on FL. To solve the low efficiency of the BC based on the conventional linear chain structure, the Directed Acyclic Graph (DAG) blockchain is introduced into our work. Moreover, since the design and optimization of FL and BC in most existing works are done separately, this may result in sub-optimal performance. To achieve an excellent trade-off between FL efficiency and BC performance, a joint optimization problem regarding DAG-BFL is formulated. The optimization objective is to maximize the FL performance and DAG-BC throughput. To solve the complex nonconvex optimization problem, considering the resource-constrained BFL system, the joint communication and computing resource allocation as well as block designing schemes are proposed, which are based on the twin-loop penalty dual decomposition (PDD) method and the successive block minimization technique (BSUM). Extensive simulations are performed to demonstrate the effectiveness of the proposed method. Particularly, compared with the traditional alternative optimization, the proposed PDD-based algorithm achieves better performance. © 1967-2012 IEEE.

Ultra-Reliable and Low-Latency Communications (URLLC) is essential for sixth generation communications, with Cell-Free massive Multiple-input-Multiple-Output (CF mMIMO) being a promising architecture to support these demands. This paper addresses the challenge of optimizing energy efficiency in CF mMIMO systems for URLLC, focusing on the probabilistic delay bounds and finite blocklength communications. We propose a theoretical framework that considers tail distributions to evaluate extreme reliability and latency requirements, instead of relying on asymptotic analysis. In particular, a closed-form expression for the signal-to-interference-plus-noise ratio (SINR) distribution is derived, accommodating imperfections in channel state information caused by pilot contamination. Then, the paper also presents a comprehensive reliability analysis, incorporating both delay violation probability and average decoding error probability, utilizing stochastic network calculus for accurate statistical modeling. Finally, an innovative power control algorithm is proposed to maximize effective energy efficiency (EEE), the ratio of the effective data rate to total power consumption, while meeting stringent Quality-of-Service (QoS) constraints and power limits. Extensive simulations validate the theoretical framework and the efficacy of the proposed algorithm, demonstrating its ability to enhance EEE in various scenarios and providing insights into the interplay between EEE, delay, and reliability metrics.

Multi-UAV cooperative search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a multilayered aerial computing network (MACN) scenario, which consists of a low-altitude platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a high-altitude platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of search probability map (SPM), and meanwhile maximizing the number of target discovery and coverage rate. The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a multiagent deep reinforcement learning (MADRL) approach based on the parameter sharing and action mask (PSAM), called PSAMMA, where the state-action-reward-state-action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that 1) the proposed PSAMMA algorithm outperforms existing algorithms in the literature, and can increase the average utility by 9.89%-31.15% and 2) we evaluate the search performance by analyzing the average uncertainty, target rate, and coverage rate under different parameter settings.

In this study, we address the issue of secretive coded caching in fog radio access networks (F-RANs). Our approach involves a hierarchical placement scheme during the placement phase, which efficiently leverages the cache resources of both fog access points (F-APs) and users while adhering to secrecy constraints. In the delivery phase, we employ an access points cooperation based delivery scheme that maximizes the cooperative transmission capability among multiple F-APs. By integrating the placement and delivery schemes, we propose an access points cooperation based secretive coded caching scheme that minimize the sum rate. We further derive a closed-form expression for the worst-case sum rate and obtain an information theretic lower bound on the sum rate by using cut-set arguments. Our analysis reveals that the proposed scheme achieves the information theoretic lower bound within a factor that depends on the number of F-APs. Additionally, we analyze the decodability and secrecy aspects of the proposed coded caching scheme. Simulation results demonstrate that when the cache capacity of each F-AP is large, the performance of our proposed scheme approaches the theoretical optimum.

Wireless Fidelity (Wi-Fi) communication technologies hold significant potential for realizing the Industrial Internet of Things (IIoT). In this paper, both Single-Input Single-Output (SISO) and polarized Multiple-Input Multiple-Output (MIMO) channel measurements are conducted in an IIoT scenario at the less congested Wi-Fi band, i.e., 5.5 GHz. The purpose is to investigate wireless characteristics of communications between access points and terminals mounted on automated guided vehicles as well as those surrounding manufacturing areas. For SISO channel measurements, statistical properties including the delay Power Spectral Density (PSD), path loss, shadowing fading, delay spread, excess delay, K-factor, and amplitude distribution of small-scale fading are analyzed and compared with those observed in an office scenario. For MIMO channel measurements, results show that there are multiple Dense Multipath Component (DMC) processes in the delay PSD. An estimation algorithm based on the algorithm for a single DMC process is proposed to effectively process the multi-processes data. Moreover, delay, angular, power, and polarization properties of DMCs are investigated and compared with those of specular multipath components. Furthermore, effects of DMCs on Singular Values (SVs) and channel capacities are explored. Ignoring DMCs can overestimate SVs and underestimate channel capacities.

The space-air-ground integrated network (SAGIN) greatly improves coverage and reliability for millimeter-wave (mmWave) communication in high-speed railway (HSR) scenarios. However, a significant challenge arises in the transmission scheduling due to the rapid changes in channel state, link selection for train mobile relays (MRs), and order of the flow scheduling. To tackle this challenge, we introduce an optimization problem focused on maximizing the weighted sum completed flows that satisfy the quality of service (QoS) requirements for HSR mmWave communication in SAGIN. To facilitate the simultaneous scheduling of flows by base station-MR (BS-MR), satellite-airship-MR, and satellite-MR links, we propose a link selection algorithm, which can help each flow choose a suitable set of links in every frame and determine whether the BS networks need the assistance of the satellite and airship. Furthermore, taking into account the priority and occupied time slots (TSs) resource of different flows, we propose a multi-link weighted flow scheduling (MWFS) algorithm. This algorithm not only prioritizes scheduling high-priority flows but also aims to maximize the weighted sum completed flows for MRs. Our simulation results confirm that the proposed algorithm significantly increases the weighted sum completed flows and the total transmitted bits. Additionally, the proposed algorithm can achieve the optimal flow transmission in different link switching periods and enhance the scheduling of high-priority flows compared to other algorithms.