Federated learning (FL) over wireless networks has become a key enabler for privacy-preserving distributed artificial intelligence (AI). However, high learning latency remains a critical bottleneck due to the presence of stragglers, limited wireless resources, and frequent model uploads. While model quantization can mitigate this issue by reducing communication overhead, its effectiveness is sensitive to device heterogeneity and time-varying channel conditions. To address this issue, we proposeFedDamQu, a communication-efficient FL framework with doubly-adaptive model quantization, which dynamically adjusts quantization bit-widths across devices and communication rounds to balance latency and accuracy. Our objective is to maximize the model performance under learning latency constraints. The main contributions are summarized as follows. 1) Convergence Analysis under Unreliable Channels: We derive a novel convergence error upper bound forFedDamQu, which explicitly quantifies the impact of device selection, unreliable transmission, and quantization error on the global model performance, under both fixed and dynamic quantization gain settings. 2) Joint Optimization Framework: Based on the knowledge from the proposed theoretical bound, we formulate a joint mixed integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Efficient Solution Design: The MINLP problem is decomposed into three subproblems, where closed-form solutions for quantization bit-width configuration and bandwidth allocation subproblems are derived, and a lightweight yet effective iterative algorithm is developed to obtain a suboptimal solution for the device selection subproblem. Extensive experiment results validate the theoretical analysis and demonstrate thatFedDamQuconsistently outperforms existing methods in terms of convergence rate and model accuracy, while significantly reducing the overall learning latency.

Wireless federated learning (FL) is an emerging artificial intelligence (AI) technique capable of leveraging the data and computing capacity of networked wireless devices while ensuring their individual data privacy and security. However, in geographical areas with poor wireless signal coverage, implementing FL is challenging. Additionally, intensive computation and communication put significant strain on resource-limited wireless devices. To address these issues, firstly, we propose a user equipment (UE)-to-network relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes device scheduling, network topology construction, and multi-type resource allocation to achieve low latency and low energy consumption. The second contribution is threefold. 1) For solving the device scheduling problem, we propose a voting-based strategy to identify the most suitable wireless UEs as relays. 2) Regarding the network topology optimization problem, we derive the optimal solutions under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For solving the multi-type resource allocation problem, we analyze its mathematical structure and propose an iterative algorithm that has significantly lower computational complexity than the traditional method. This algorithm is capable of jointly optimizing the usage of transmission time resource, computing capacity, and transmit power. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

To address the latency bottleneck in wireless federated learning (FL) systems, we propose FedDamQu, a communication-efficient framework that adaptively adjusts model quantization bit-width across both devices and communication rounds to cope with device heterogeneity and dynamic wireless conditions. Our objective is to maximize global model performance under strict latency constraints. The key contributions are threefold. First, we derive a novel convergence error upper bound that explicitly characterizes the effects of device selection, unreliable transmission, and quantization error. Second, we formulate a joint mixed-integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Third, we decompose the MINLP into three tractable subproblems, obtain closed-form solutions for quantization bit-width configuration and bandwidth allocation, and develop a lightweight iterative algorithm for device selection. Extensive experiments demonstrate that FedDamQu consistently outperforms existing methods in terms of convergence speed and model accuracy, while significantly reducing the overall learning latency.

The integrated sensing and communication (ISAC) technology has emerged as an enabler that promises to transform the traditional mobile communication networks into the multifunctional perceptive mobile networks (PMNs), where precise positioning and motion state estimation of network nodes can be achieved relying on wireless communications within the network itself. However, in a practical PMN, multiple types of individually estimated parameters corresponding to multiple sensing targets are not naturally associated with each specific target, which may cause severe obstacles to subsequent signal processing tasks, such as positioning and motion state estimation. To address this challenge, a high-performance low-complexity sensing parameter association algorithm is proposed in this paper. Different from previous works, we first develop a novel spatial filter by exploiting the convolutional beamspace based beamformer to separate paths with different directions of arrival (DOA), and then leverage a low-complexity correlation-based algorithm to associate the DOA estimates with the corresponding paired range-velocity estimates. Extensive simulation results are provided to validate the superior performance of the proposed parameter association algorithm over state-of-the-art schemes.

This paper addresses the difficulty of implementing federated learning (FL) in geographical areas with poor wireless signal coverage, and alleviates the high burden imposed by intensive computation and communication on resource-limited wireless devices. Firstly, we propose a user equipment (UE)-tonetwork relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes resource allocation, network topology construction, and device scheduling, to achieve low latency and low energy consumption. The second contribution is threefold. 1) For allocating resources, we propose a linear-complexity algorithm which is capable of jointly optimizing the transmission time resource and the computing power. 2) For constructing network topology, we derive the optimal closed-form solution under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For scheduling devices, we propose a voting-based device scheduling algorithm that is near-optimal. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical “window function” by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.

Asynchronous radio transceivers often lead to significant range and velocity ambiguity, posing challenges for precise positioning and velocity estimation in passive-sensing perceptive mobile networks (PMNs). To address this issue, carrier frequency offset (CFO) and time offset (TO) synchronization algorithms have been studied in the literature. However, their performance can be significantly affected by the specific choice of the utilized window functions. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We first derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then develop a practical window selection criterion and test a special window generated by the super-resolution algorithm. Numerical simulation has verified our analysis.