Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge intelligence. Despite its promise, FL faces critical challenges in Internet of Things (IoT) networks, particularly the combined impact of data heterogeneity and communication delays. This paper examines the theoretical and empirical impact of these factors in Asynchronous Federated Learning (AFL). Initially, we categorize existing memoryless asynchronous strategies as Asynchronous Updates with Delayed Gradients (AUDG). Our theoretical analysis of AUDG reveals the coupling effect of delays exacerbate the adverse impact of data heterogeneity, causing the global model to drift towards frequently active clients. To address this, we propose a gradient reusing mechanism, termed Pseudo-Synchronous Updates by Reusing Delayed Gradients (PSURDG). By leveraging storage to reuse historical gradients, PSURDG effectively decouples the correlation between delay and data heterogeneity. Crucially, we conducted a comprehensive convergence analysis covering both convex and non-convex settings, confirming the algorithm’s effectiveness in diverse optimization landscapes. Finally, both schemes are validated through rigorous analysis and extensive simulations on multiple datasets. The results demonstrate a clear trade-off that AUDG remains efficient under low data heterogeneity, while PSURDG improves convergence in high-heterogeneity scenarios with moderate delays, thereby providing a theoretical guideline for aggregation strategy selection in different edge intelligence scenarios.

Unmanned aerial vehicle (UAV) swarms are extensively used in emergency communications, area monitoring, and disaster relief. Their operations are coordinated by control centers, making them well-suited for federated learning (FL) frameworks. However, current UAV FL methods ignore the rich information contained in UAV images and the potential of using a single dataset to accomplish multiple tasks. For instance, in disaster relief scenarios, images acquired by UAVs can support tasks like crowd detection, road passability analysis, and disaster impact assessment. These tasks exhibit time-varying demands and may have potential correlations. To meet these requirements, this paper introduces two core mechanisms: a dynamic task attention mechanism to evaluate task importance for efficient resource allocation, and a task affinity (TA) metric to capture inter-task correlations for knowledge sharing. Building on these innovations, we propose FedDya, a novel UAV swarm-based one-dataset multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks leveraging a single dataset. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply the block coordinate descent method for optimization. For the outer problem, a novel two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption violation and multi-task performance, characterized by anO( √V, 1/V) relationship. Extensive simulation results further validate the effectiveness of the proposed scheme.

The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief. Due to their limited battery capacity and processing power, multiple UAVs are often required for complex tasks. In such cases, a control center is crucial for coordinating their activities, which fits well with the federated learning (FL) framework. However, conventional FL approaches often focus on a single task, ignoring the potential of training multiple related tasks simultaneously. In this paper, we propose a UAV-assisted multi-task federated learning scheme, in which data collected by multiple UAVs can be used to train multiple related tasks concurrently. The scheme facilitates the training process by sharing feature extractors across related tasks and introduces a task attention mechanism to balance task performance and encourage knowledge sharing. To provide an analytical description of training performance, the convergence analysis of the proposed scheme is performed. Additionally, the optimal bandwidth allocation for UAVs under limited bandwidth conditions is derived to minimize communication time. Meanwhile, a UAV-EV association strategy based on coalition formation game is proposed. Simulation results validate the effectiveness of the proposed scheme in enhancing multi-task performance and training speed.

Long-range depth is crucial for roadside perception, which helps vehicles detect potential threats earlier, respond promptly, and avoid collisions. However, a notable challenge with existing roadside perception methods is their difficulty in accurately perceiving objects at long-range depth. To this end, we propose a Priori Geometric-Guided long-range Depth estimation framework, named PGDepth. First, inspired by the human ability to perceive depth by referencing objects, we utilize priori Geometric as reference information for road objects, assigning each pixel a predefined depth range. Second, a coarse-to-fine approach is introduced to continuously refine the accuracy of depth distribution for pixels. Furthermore, we propose a new loss function to effectively supervise the depth distributions of road objects. Extensive experimental results on the DAIR dataset demonstrate that the proposed method surpasses previous state-of-the-art competitors.

Lithium-ion batteries, which are vital for powering mobile devices, experience performance degradation over time due to capacity fading and other aging phenomena, thereby presenting safety risks. This work introduces the DeTransformer-Physics model, a predictive model for battery capacity. This model synergistically integrates physical knowledge and a de-noising autoencoder to enhance predictive accuracy. Firstly, it employs a denoising autoencoder as a preprocessing step to reconstruct denoised input data, thereby facilitating the extraction of more effective input data. Subsequently, a model describing the procedure for lithium-ion battery capacity degradation is derived and incorporated into a neural network, with the model training constrained by physical principles. Finally, using the lithium-ion battery degradation dataset from NASA, we demonstrate that incorporating the denoising autoencoder and embedding physical knowledge substantially improves the predictive accuracy of the model.

The continuous increase in carbon dioxide (CO2) concentrations has become a primary driver of global warming. Tracking CO2levels in high-emitting enterprises is crucial for achieving carbon neutrality. Although high-precision ground-based sensors provide real-time monitoring, their widespread deployment is limited by cost, leading to data gaps in certain areas. To tackle this issue, we design an aerial-ground cooperative sensing system that enables three-dimensional CO2observation of high-emitting enterprises. The system comprises a long-term monitoring module employing ground-based sensors and a periodic monitoring module using Unmanned Aerial Vehicles (UAVs) with portable sensors. Based on the collected data, we develop a Kriging model to estimate the CO2concentrations at unobserved locations and construct a fine-grained three-dimensional CO2distribution map. The experimental results demonstrate that our proposed scheme can achieve large-scale CO2assessment using limited sensing resources, thereby serving as a valuable reference for effectively regulating corporate CO2emissions. Our implementation has been deployed around a high-emitting enterprise in Nanjing since February 2024.

Federated learning has provided a new approach to coordinating a group of clients to train a machine learning model collaboratively, which can be easily embedded into Unmanned Aerial Vehicle (UAV) swarms. Compared with the terrestrial wireless networks, the UAV swarm faces more precarious communication conditions, rendering synchronous aggregation no longer tenable. Additionally, the data collected from UAVs tend to be heterogeneous due to different deployment regions or requirements. To overcome these restrictions, this paper has proposed a novel two-stage Asynchronous Federated Learning scheme for the UAV swarm. Initially, the convergence property of both convex and non-convex models trained by the proposed scheme is analyzed. In the pre-training stage, we modeled the learning process as a cooperative game with demonstrated monotonicity and submodularity. Furthermore, the Shapley Value is imported to quantify data values of UAVs, and the upper bound of its estimation error rate is derived. In the training stage, a new concept named Network Age of Updates (AoU) is proposed to address the fairness issue, quantifying the model’s generalization capability with data value consideration, and a sequential UAV selection scheduling is performed through the AoU minimization by Whittle Index method. Finally, the system performance is validated through both theoretical analysis and simulations.

Nowadays, the Unmanned Aerial Vehicle (UAV) has emerged as a powerful platform for diversified application development. In traffic areas specifically for the much-anticipated Intelligent Transportation System (ITS), UAVs can coordinate with the ground internet of things infrastructure, such as the Road Side Unit (RSU), to perform 3D data collection and processing, improving traffic safety and transportation efficiency. In ITS, decision-making is crucial and relies heavily on proper and timely processing and transmission of the massive amounts of data generated by ubiquitous sensors, especially in emergency disposal scenarios. In this paper, the latency incurred from RSU-aided data offloading and UAV-aided data relay plays a decisive role, characterized by both the average and risk performance metric, Conditional Value at Risk (CVaR), for quantifying the risk incurred from the latency violation over a certain threshold in probability. Specifically, the peak Age of Information (AoI) with its distribution is adopted to underlying the CVaR analysis in relay latency. Besides, the matching algorithm to coordinate the mutual transmission between UAVs and RSUs is proposed, achieving low computation complexity and minimum risk for both sides. The system performance is validated through analysis, simulation, and field experiments.

Wireless communication system incorporating unmanned aerial vehicles (UAVs) has gained much popularity recently, especially in video transmission application. This paper investigates the base station (BS) selection scheme for cellular-connected UAVs that possess the function of video collection and streaming to BS for online decision in remote processing center. We aim to minimize the expected access latency while the throughput requirement is satisfied. To this end, a sequential BS selection scheme is proposed by designing an optimal transmission rate threshold for each candidate BS. Due to the mission-driven nature, the access rate varies as the UAV moves, so an effective average transmission rate rather than instantaneous transmission rate is considered. A recursive algorithm is proposed to obtain the rate thresholds which can be used to guide whether UAV should stop or continue measuring the links of the remaining candidate BSs. The proof of the optimality of this algorithm is given. Simulation results validate the effectiveness of the proposed scheme on access latency performance compared with conventional throughput-oriented scheme.

We consider a device-to-device (D2D) underlaid cellular network, where each cellular channel can be shared by several D2D pairs and only one channel can be allocated to each D2D pair. We try to maximize the sum rate of D2D pairs while limiting the interference to cellular links. Due to the lack of global information in large scale networks, resource allocation is hard to be implemented in a centralized way. Therefore, we design a novel distributed resource allocation scheme which is based on local information and requires little coordination and communication between D2D pairs. Specifically, we decompose the original problem into two cascaded subproblems, namely channel allocation and power control. The cascaded structure of our scheme enables us to cope with them respectively. Then a two-stage algorithm is proposed. In the first stage, we model the channel allocation problem as a many-to-one matching with externalities and try to find a strongly swap-stable matching. In the second stage, we adopt a pricing mechanism and develop an iterative two-step algorithm to solve the power control problem.

In device-to-device (D2D) underlaid cellular networks, several D2D pairs can share one channel to improve the system throughput. Most existing works allow D2D pairs to reuse all the channels, which may incur complicated interference management. Therefore, each D2D pair is limited to reuse at most one channel to reduce overhead. We aim to maximize the throughput of D2D pairs while suppressing the interference to cellular links. However, the optimization problem is an intractable mixed integer non-linear programming (MINLP) problem. Meanwhile, as network size increases, acquiring the global channel state information (CSI) is expensive even impossible in practice. Therefore, we propose a novel local CSI-based distributed channel-power allocation scheme. The base station (BS) broadcasts a control signal to indicate its received interference from D2D pairs. Upon this signal, each D2D pair executes channel selection and power update independently and iteratively. Specifically, the channel allocation problem is formulated as a many-to-one matching game with externalities. The power control problem is modeled as a Stackelberg game. We prove the existence of two-sided swap-stable matching and show that the outcomes of the Stackelberg game are locally optimal under some mild conditions. Simulation results show that our scheme is efficient with low overhead.

Caching at the small base station (SBS) is a promising architecture to alleviate the highly-loaded wireless video networks. SBS can cache popular video files, thus serves mobile users without going through backhaul connection to the core network and provides content- level offloading. This paper proposes a novel method for the content caching problem that optimizes cache performance. Our proposed algorithm learns the content popularity profile by predicting the probability of files to be requested, and then refreshes the cache based on the learned content popularity. Popularity learning method runs in an online fashion and has no assumption of the file requests, thus it can be used for predicting either fixed or time-varying popularity. Our simulation results show that our proposed algorithm has similar performance compared to the traditional algorithms when the content popularity profile is fixed, and performs better than other algorithms when the content popularity profile is time-varying, which is more realistic.

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the “least” information from content providers.

The explosion of mobile video traffic imposes tremendous challenges on present cellular networks. To alleviate the pressure on backhaul links and to enhance the quality of experience (QoE) of video streaming service, small cell base stations (SBS) with caching ability are introduced to assist the content delivery. In this paper, we present the first study on the optimal caching strategy of scalable video coding (SVC) streaming in small cell networks with the consideration of channel diversity and video scalability. We formulate an integer programming problem to maximize the average subjective quality of SVC streaming under the constraint of cache size at each SBS. By establishing connections between subjective quality and caching state of each video, we simplify the proactive caching of SVC as a multiple-choice knapsack problem (MCKP), and propose a low-complexity algorithm using dynamic programming. Our proactive caching strategy reveals the structural properties of cache allocation to each video based on their popularity profiles. Simulation results manifest that the SBSs with caching ability can greatly improve the average quality of SVC streaming, and that our proposed caching strategy acquires significant performance gain compared with other conventional caching policies.

The device-to-device (D2D) communication theoretically provides both the cellular traffic offloading and convenient content delivery directly among proximity users. However, in practice, no matter in underlay or overlay mode, the employment of D2D may impair the performance of the cellular links. Therefore, it is important to design a spectrum sharing scheme, under which the performance of both links can be improved simultaneously. In this paper, we consider the cell-edge user (CEU) scenario, where both sides have the demand to improve the quality of experience or service. Therefore, CEUs and D2D users both have intentions to form pairs, namely, CEU-D2D pairs, to cooperate mutually. Different from the conventional equilibrium point evaluation, the stable matching between D2D users and CEUs are formulated under matching theory framework instead. For each CEU-D2D pair, a two-stage pricing-based Stackelberg game is modeled to describe the willingness to cooperate, where the win-win goal is reached finally.

In this paper, we consider the scenario of the mobile network operator (MNO) incentivizing femtocell access points (FAPs) to form a shared network. We propose an incentive mechanism under which the licensed femtocell user (FU) of each FAP can use a portion of another FAP's spectrum resources when moving into that FAP's coverage. The FAPs are rewarded based on both the amount of provided resources and the quality of service (QoS). We formulate the problem as a Stackelberg game. The MNO acts as the leader to decide incentive price. When observing the price, each FAP decides the amount of provided resources according to its type information (resource constraint, QoS and especially its licensed FU's mobility pattern). The best response functions of FAPs are first obtained and the existence of the Nash Equilibrium (NE) is investigated. And then we investigate the optimal strategy of the MNO given the FAPs' strategies. Simulation results show that the proposed mechanism can effectively motivate FAPs to share their resources with each other. We will also mainly analyze the influence of FUs' mobility patterns on their adopted strategies.

In this paper, a low-complexity sparse channel estimation scheme is proposed for massive three dimensional multi-input multi-output (3D-MIMO) systems. In outdoor propagation environment, 3D-MIMO channels exhibit joint sparseness in both temporal and angular domains, sharing the common support in delay domain. By taking prior knowledge of the structured sparseness, the proposed heuristic channel estimation method can greatly reduce the complexity of channel estimation, and achieve a near optimal performance. Simulation results verify the effectiveness of the proposed algorithm.

This paper investigates simultaneous wireless information and power transfer (SWIPT) in K-user multiple-input multiple-output (MIMO) interference channels. In particular, the power splitting (PS) technique is leveraged at each receiver to divide the received signal into two flows, for information decoding (ID) and energy harvesting (EH), respectively. As a whole system, our objective is to minimize the total transmit power of all transmitters by jointly designing transmit beamformers, power splitters, and receive filters, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for ID and the harvested power constraint for EH at each receiver. Due to the coupling nature of all variables, the formulated joint transceiver design problem is nonconvex, and has not yet been well addressed in the literature. In this paper, we first propose a semidefinite relaxation-based alternating optimization (SDRAO) solution to approach the optimal solution of the problem. Then, we semidecouple the joint optimization by the derived diversity interference alignment (DIA) technique, and obtain a solution of lower complexity. Finally, a closed-form solution is further developed relying on the transmitter-side zero-forcing (TZF), which can be implemented in a distributed manner, with the lowest computational complexity and CSI exchanging overhead.

This paper investigates the beamforming for three- dimensional multiple input multiple output (3D-MIMO) systems with inaccurate channel state information (CSI). From the view of angle-domain, the 3D-MIMO channel is sparse on the high 3D resolution provided by planar antenna array with large number of antenna elements at the base station (BS) in 3D-MIMO systems. Prior knowledge of sparsity is not only beneficial to channel estimation in literature, but also implies more efficient beamforming with inaccurate CSI at transmitters as discussed in this paper. We prove that the optimal beamforming vector is correspondingly sparse in angle-domain with a sparse channel. Therefore, we add the ℓ1-norm penalty to the beamforming vector in optimization design in angle-domain, which can fight against the perturbation due to inaccurate CSI. Technically, the problem is reformulated as a second order cone program (SOCP) form that can be solved efficiently. Simulation results demonstrate that the proposed beamforming method can achieve considerable system sum-rate improvement with high CSI error.

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as buffering time and starvation behavior interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on realistic streaming traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the videos can be categorized into two classes, short and long viewing time with separated time scales. We then map the traffic pattern of large-scale measurement to bandwidth sharing cellular networks. We propose two models to compute the close-form starvation probability and mean sojourn time on the basis of ordinary differential equations (ODEs). Extensive trace-driven simulations validate their accuracy. The proposed models precisely capture how the QoE metrics of video streaming in each class are influenced by the scheduling algorithms at a base station.

This paper focuses on the simultaneous wireless information and power transfer (SWIPT) in coordinated multi-cell networks. In particular, the considered system has a location-aware feature, i.e., the conventional coordinated multi-point (CoMP) information transmission is available to the cell-edge users; meanwhile, the emerging wireless power transmission is available to the cell-center users. Subject to the SINR constraints for information decoding at edge CoMP users and the received power constraints for energy storage at center users, our objective is to minimize the total transmit power of all coordinated beamformers. Although the celebrated semi-definite relaxation (SDR) technique can be applied to obtain a centralized optimal solution, we prefer to decompose the original problem and derive a decentralized closed-form solution to decrease the cost of inter- cell cooperation. With this low-complexity solution, some important insights are further presented on the benefit of the location-aware SWIPT setup and the potential user scheduling mechanism. Simulation results validate our derivation and show that the decentralized solution can asymptotically approach the centralized optimal solution.

Many signal processing problems in wireless sensor networks can be solved by graph filtering techniques. Finite impulse response (FIR) graph filters (GFs) have received more attention in the literature because they enable distributed computation by the sensors. However, FIR GFs are limited in their ability to represent the global information of the network. This letter proposes a family of GFs with infinite impulse response (IIR) and provides algorithms for their distributed realization in wireless sensor networks. IIR GFs bring more flexibility to GF designers, as they can be designed and realized even when the graph spectrum is unknown. Numerical results show that IIR GFs are more accurate in approximating ideal GFs and more robust against network variation than FIR GFs.

Channel prediction is an appealing technique to mitigate the performance degradation due to the inevitable feedback delay of the channel state information (CSI) in modern wireless systems. We first propose a general MIMO-OFDM channel prediction framework, which exploits both the spatial and temporal correlations among antennas. Then we derive two predictors which select data for auto-regressive (AR) predictors in different ways based on the proposed framework. The first predictor chooses the data set via minimizing the mean square error (MSE) of prediction model. The second predictor chooses the data in a heuristic way, which aims to reduce the computational complexity. Our algorithms can be applied to improve the precoding performance in multi-user MIMO-OFDM systems. Simulation results show that the proposed methods can overcome the feedback delay effectively, even when the channel changes rapidly.

Identifiability is a fundamental issue in blind signal processing. It is well known that there are two types of inherent ambiguities: the scalar and permutation ambiguities. However, this is obtained under the condition that no a priori information of input signals is assumed. We show that if the information of source constellation is exploited, with a general constellation, there is an inherent discrete phase ambiguity, which is uniformly spaced on the unit circle. We also illustrate our identifiability results to blind channel estimation in orthogonal frequency division multiplexing (OFDM) systems.

Blind channel estimation is a promising technique to reduce the pilot overhead. Unfortunately, most existing algorithms suffer from the scalar ambiguity problem, and hence only achieve semi-blind identification. In this paper, we show that with the information of source constellation, the phase of the ambiguous scalar can be divided into a fractional part and an integer part. Then we propose a multiple-constellation scheme enabling totally blind identification regardless of constellation type for OFDM systems. The necessary and sufficient condition for eliminating the scalar ambiguity is given. An application example shows that our scheme can help other algorithms circumvent the annoying ambiguity.

A total rejection of interferences under any noise power is difficult, if not impossible, by current filtering technology. In this presentation, we will show that this is indeed achievable by a new filtering technique that extracts the part of the statistics from the received signals, which depend only on the interferences, not on the signals and noises. The algorithm is blind to the signal and interference channels; and hence it needs not to be updated when these channels vary with time. It is also important when the channels cannot be or is too costly to be estimated by pilot signals. The computation is based on statistics that is noise-invariant and hence its accuracy is not impacted by the noise power. Simulation confirmed what is proven that interferences are totally rejected over a wide range of noise power.

In this paper, a sampling based channel estimation as well as an iterative particle filter (PF) signal detection scheme for fast flat fading multiple-input multiple-output (MIMO) channels is proposed, which aims at achieving the required bit error rate (BER) performance under the scenario that the accurate channel estimation is difficult to attain. The channel estimation is comprised of two parts: the adaptive iterative update on the channel distribution mean and a regular update on the "adaptability" via pilot. In the detection procedure, the particle filter (PF) is employed to produce the optimal decision given the known received signal and the sequence of the channel samples, where an asymptotic optimal importance density is constructed, and in terms of the asymptotic update order, the parallel importance update (PIU) and the serial importance update (SIU) scheme are implemented respectively.