Fluid antenna (FA) is an emerging technology in recent years to switch physical locations of antennas in a predetermined small space. This paper proposes a reconfigurable intelligent surfaces (RIS)-cooperative framework with FA-assisted non-orthogonal multiple access (NOMA) system, namely FA-RIS-NOMA. Targeting multi-interference scenarios in urban environments, the considered system incorporates a base station (BS) equipped with a conventional antenna and users each equipped with an FA. Additionally, the RIS is utilized to forward signals between BS and users blocked by obstacles. The non-line-of-sight multipath fading characteristics of the RIS-assisted link are modeled as a Nakagami-mchannel. To overcome the multiuser interference and improve the system performance, we develop the NOMA technique for resource allocation. Meanwhile, to reduce the signal processing complexity at the receiver, a group optimization greedy detection method is proposed. To evaluate the system’s reliability, the outage probability for each user is analyzed by using the copula function. This method enables the derivation of the cumulative distribution and probability density functions for the equivalent user-side channel, from which closed-form outage probability expressions are obtained. Numerical results demonstrate that the proposed framework achieves signal-to-noise ratio gains of approximately 9 dB and 8 dB over fixed-antenna and relay-assisted systems, respectively. Furthermore, the proposed detection method reduces computational complexity by over 75% with less than 0.5 dB performance degradation.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

In this paper, a novel active fully-connected reconfigurable intelligent surface assisted space shift keying and code index modulation (AFRIS-SCIM) scheme is proposed. On one hand, by introducing joint space-code index modulation while maintaining low power consumption and complexity, the proposed scheme achieves higher data rates compared to existing one-dimensional index modulation. On the other hand, the proposed active fully-connected architecture achieves a desirable trade-off between reliability and power consumption compared to conventional passive RIS and active RIS architectures. Additionally, to reduce detection complexity at the receiver, a low-complexity detection algorithm is proposed and the upper bound for the bit error rate (BER) of the system is derived. Mathematical models characterizing the system complexity and power consumption are also established to analyze the overall performance. Both theoretical analyses and simulation results demonstrate that the AFRIS-SCIM scheme outperforms existing RIS-IM schemes as well as multidimensional index modulation systems in terms of BER performance.

Data-driven approaches have been widely used in the field of traction system and equipment fault diagnosis. However, limited training samples can cause data-driven models to face the dilemma of overfitting. In order to supplement sufficient training data in small-sample case, this paper proposes a data generative model based on the Multivariate Normal (MVN) distribution and Mahalanobis Distance (MD). The basic hypothesis of the method is that the diagnostic feature vectors representing the same fault state are subject to an identical MVN distribution. Afterward, its probability density function is unbiasedly estimated by the sample mean vector and sample covariance matrix, and then used to generate samples. During generation, the noise contained in the generated data is limited by the relationship between MD and Chi-square distribution. Finally, the generated samples are combined with original training samples to constitute a mix dataset to train data-driven fault diagnosis models. Taking the fault diagnosis of single-pole traction circuit breaker as an example, this paper illustrates the fault diagnosis framework with the proposed generative model and verifies its effectiveness. The results show that the generated samples cover the range of original samples well, thus increasing the prediction accuracy of the classifiers. Furthermore, three compared generative models are constructed. By comparison to these complicated models, the proposed method has better generation effect, although it limits the generative model capacity.

Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.

The boom in Internet of Things has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination and aroused widespread attention from both academia and industry. In this paper, we develop a theoretical framework to evaluate the statistics of AoI, including its average and violation probability, in wireless networks under different types of sources and updating patterns. The analyses account for the randomness that arises from both the spatial deployment and temporal queueing dynamics, and its accuracy is verified through simulations. Based on the analytical results, we design a locally adaptive power control policy to optimize the sum of average AoI of all nodes, which allows each node to assign transmit power according to its local observation. The proposed scheme has low implementation complexity. Numerical results show that the proposed power control policy can significantly improve information freshness. The scheme is well adapted to variants of network environment and heterogeneous source-destination distance. Further, we evaluate the effect of the retransmission mechanism and updating patterns on the AoI performance.

We consider a homogeneous Poisson bipolar network in which the bipoles represent source-destination pairs. The source nodes need to update their destinations about the new status perpetually, and the communications are taken place over a shared spectrum. The common goal of the source nodes is to minimize the network-wide age of information (AoI). We develop a policy by which every source node can adapt its frequency of generating status updates in a local and decentralized manner. At the same time, the network average AoI is minimized by reducing interference amongst transmitters located in geographical proximity. Following this policy, we also derive mathematical expressions to characterize the distribution of the optimal updating rate at each source node, the network average AoI, and the AoI violation probability, i.e., the probability that the AoI of a typical source node exceeds an age threshold. The analytical results are combined with discrete event simulations to provide a detailed evaluation of the performance of the proposed scheme. Particularly, it is shown that our policy is able to adaptively adjust the updating rate of each source node according to the variant of the network topology. In this manner, it is instrumental in decreasing both the network average AoI and AoI violation probability. Additionally, the scheme can maintain the AoI at a low level even when the network grows in size.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

The boom in Internet of Things (IoT) has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination node and aroused widespread attention from both academia and industry. In this paper, we develop a locally adaptive power control policy for wireless ad hoc networks, which adjusts each node’s transmit power according to its local observation so as to optimize the sum of average AoI of all destination nodes. The proposed scheme has a low implementation complexity. Numerical results show that the proposed scheme is well adapted to variants of network environment and can significantly improve information freshness.

With the deluge of wireless data, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial small base stations (SBSs) to relieve the load of ground macro base stations by establishing wireless backhaul connections with them and providing high-quality service to users. Thanks to the emergence of integrated access and backhaul (IAB), the access and backhaul communication links can work on the same millimeter wave (mmWave) band with huge available bandwidth. This paper studies the quality-of-service (QoS) performance of heterogeneous traffic under equal partition and average load partition spectrum allocation strategies for mmWave UAV-assisted IAB cellular networks. Specifically, we develop a theoretical framework to analyze the mean packet throughput (MPT) of users based on stochastic geometry and queueing theory. Simulation results demonstrate that the deployment of UAVs can promote MPT performance compared to ground SBSs and appropriate UAV height, UAV density, and spectrum allocation play significant roles in improving QoS performance of heterogeneous traffic in the network.

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.