In the realm of the Internet of Vehicles (IoV), the concept of grouping autonomous vehicles into platoons stands out as a promising driving scenario. A platoon comprises interconnected vehicles, with the foremost vehicle designated as the Platoon Leader (PL), while each of those trailing behind is a Platoon Member (PM). In such contexts, information freshness quantified using the Age of Information (AoI) critically ensures road traffic safety. This paper explores the joint packet transmission scheduling and power allocation problem with the objective of minimizing AoI in multi-platoon vehicular networks; these latter exhibiting high dynamics incurring notable uncertainty and complexity. To alleviate this optimization problem’s complexity a decentralized partially observable Markov Decision Process (Dec-POMDP) formulation is adopted. Then, an AoI-aware joint scheduling and power control scheme based on Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm is proposed. In addition, in order to improve the efficiency of the MATD3’s learning phase, the algorithm has been augmented with Priority Experience Replay (PER). Simulation results show that this approach outperforms the baseline MATD3 method by 17.3% in terms of the achieved mean AoI.

Virtual reality (VR) demands extensive computation while imposing strict requirements for ultra-low latency, placing a significant burden on wireless communication systems. In recent years, there has been a growing interest in leveraging unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) as a promising technology to provide flexible computing resources at the edge of wireless networks. To meet the computational demands of VR, we propose a collaborative three-layer edge computing framework assisted by multiple UAVs. This framework enables VR rendering tasks to be executed locally on user devices or offloaded to UAVs and base station (BS) for execution. By jointly optimizing the flight trajectories of UAVs and the rendering modes of users, we aim to maximize the average rendering completion rate, defined as the ratio of successfully completed VR rendering tasks within the specified delay constraints, while minimizing the average energy consumption of UAVs. To enhance adaptability, we adopt a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach that provides an efficient strategy for multi-UAV-assisted VR rendering, even in partially observable scenarios. Simulation results validate our proposed approach and demonstrate that the MATD3 algorithm surpasses the classical multi-agent deep deterministic policy gradient (MADDPG) algorithm in terms of convergence speed and the average rendering completion rate.

The emergence of intelligent mobile technologies and the widespread adoption of 5G wireless networks have made Federated Learning (FL) a promising method for protecting privacy during distributed model training. However, traditional FL frameworks rely on static aggregators such as base stations, encountering obstacles such as increased energy demands, frequent disconnections, and poor model performance. To address these issues, this paper investigates an innovative aUtonomous Aerial Vehicle (UAV)-assisted FL framework, aiming to utilize UAVs as mobile model aggregators to collaborate with devices in training models, while minimizing the total energy consumption of devices and ensuring that FL can achieve the target model accuracy. By adopting the Distributed Approximate NEwton (DANE) method for local optimization, we analyze the convergence of FL and derive device scheduling constraints that aid in convergence. Accordingly, we formulate a problem of minimizing the total energy consumption of devices, integrating a constraint on global model accuracy, and jointly optimizing the UAV trajectory, device scheduling, bandwidth allocation, time slot lengths, as well as the uplink transmission power, CPU frequency, and local convergence accuracy. Then, we decompose this non-convex optimization problem into three subproblems and propose an iterative algorithm based on Block Coordinate Descent (BCD) with convergence guarantee. Simulation results indicate that, compared with various benchmark methods, our proposed UAV-assisted FL framework significantly reduces the total energy consumption of devices and achieves an improved trade-off between energy and convergence accuracy.

Timely gathering of sensing data is critical in wireless sensor networks (WSNs). However, in delay-sensitive applications, maintaining the freshness of collected data poses a significant challenge. To tackle this issue, an age of information (AoI)-aware data collection method leveraging unmanned aerial vehicle (UAV) and intelligent reflective surface (IRS) is proposed in this work. Particularly, a UAV is employed to traverse over ground sensor nodes (SNs) and reliably collect their sensing data where the received signal strength is enhanced through IRS. The UAV’s flight trajectory and its association with SNs, as well as the IRS phase control strategy are jointly optimized to minimize the weighted sum of the average AoI of the SNs and energy consumption of the UAV. However, this optimization is complicated by potential inaccuracies in IRS channel state estimation. To tackle this challenge, we propose an enhanced deep reinforcement learning (DRL) framework that incorporates a dual-network agent with two nested neural networks (NNs): UAV-NN, which jointly optimizes the UAV trajectory and SN association, and IRS-NN, which dynamically adjusts IRS phase shifts based on sampled channel states, UAV position, and associated SN. By integrating this architecture into proximal policy optimization (PPO) and deep Q-network (DQN), we develop two novel algorithms: PPO-RAC and DQN-RAC, tailored for IRS-assisted UAV data collection. Extensive simulations validate their effectiveness across diverse scenarios, demonstrating significant AoI reduction compared to baseline methods.

Network Function Virtualization (NFV) is considered one of the key technologies of 5G/B5G because of its advantages of flexibility, scalability, and manageability. In NFV networks, the flow of network service needs to go through a certain number of Virtual Network Functions (VNFs) which form Service Function Chain (SFC). Compared to link protection in traditional networks, the backup transmission links for different types of VNFs need to be considered to improve the SFCs’ reliability, since any failure of transmission link may interrupt the network service. Due to the uncertainty of VNF placement and routing, the flexible selection of link backup for each VNF to satisfy the reliability requirement of SFC becomes a remarkably challenging problem. In this paper, a Flexible virtual Link Protection (Fle_LP) mechanism is proposed to calculate backup resources accurately, enhancing the reliability of NFV-enabled network service. We mathematically formulate the problem as a Mixed Integer Nonlinear Program (MINLP). An Extended Least Square (ELS) method is introduced to deal with the nonlinear constraints, which transforms MINLP to Mixed Integer Linear Programming (MILP). Owing to the MILP’s remarkable complexity, a Column Generation-based Link Protection (CG_LP) algorithm is proposed, which generates an acceptable sub-optimal solution. Numerical results show that CG_LP reduces the computing time (8-node network: 92.3 %, 16-node network: 99.6 %) while achieving the same bandwidth consumption as MILP.

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT) networks. Information freshness in data collection can be characterized by the Age of Information (AoI). It is highly challenging to schedule multiple energy-constrained UAVs to improve information freshness especially when the generation instants of sensing samples are unpredictable. To deal with this issue, we leverage state-of-art reinforcement learning (RL) methods to design flight trajectories of UAVs without knowing the sampling mode each sensor node (SN) adopts. Each SN can sample the environment at periodical or random intervals. Multiple energy-constrained UAVs are dispatched to collect update packets from the SNs when flying over them. The UAV trajectory planning problem for AoI minimization is formulated as a Markov decision process (MDP). The objective is to minimize the average AoI of the SNs under the constraints of energy capacity and collision avoidance for the UAVs. Then, we propose two learning algorithms based on the Sarsa and value-decomposition network (VDN), respectively, which allow the UAVs to fulfill data collection tasks requested by the SNs. By learning directly from the environment, the Sarsa-based algorithm can approach the optimal policy asymptotically when certain conditions are satisfied. As one of the most popular multiagent deep RL methods, the VDN-based algorithm enables each UAV to make its own decision independently on its flight and data collection based on the partially observed network information. Simulation results validate the effectiveness of the proposed two learning-based algorithms compared with baseline policies.

This paper studies Physical-layer Network Coding (PNC) in a two-way relay channel (TWRC) operated based on OFDM and QPSK modulation but with the presence of carrier frequency offset (CFO). CFO, induced by node motion and/or oscillator mismatch, causes inter-carrier interference (ICI) that impairs received signals in PNC. Our ultimate goal is to empower the relay in TWRC to decode network-coded information of the end users at a low bit error rate (BER) under CFO, as it is impossible to eliminate the CFO of both end users. For that, we first put forth two signal detection and channel decoding schemes at the relay in PNC. For signal detection, both schemes exploit the signal structure introduced by ICI, but they aim for different output, thus differing in the subsequent channel decoding. We then consider CFO compensation that adjusts the CFO values of the end nodes simultaneously and find that an optimal choice is to yield opposite CFO values in PNC. Particularly, we reveal that pilot insertion could play an important role against the CFO effect, indicating that we may trade more pilots for not just a better channel estimation but also a lower BER at the relay in PNC. With our proposed measures, we conduct simulation using repeat-accumulate (RA) codes and QPSK modulation to show that PNC can achieve a BER at the relay comparable to that of point-to-point transmissions for low to medium CFO levels.

We investigate the problem of target tracking using a wireless sensor network with asynchronous sensors. To study the impact of sensor clock imperfection on target tracking in practical situations, we build a testbed and collect data from an outdoor experiment. After analyzing the collected data, we find that the TDOA (time‐difference‐of‐arrival) and FDOA (frequency‐difference‐of‐arrival) measurements have notable bias, which is caused by asynchronous sensors or more precisely by the sensor clock drift. Based on the model of clock drift, the measurement bias and the target position are integrated into a state‐space model. Both can be estimated in the framework of the extend Kalman filter. In some circumstance, the target trajectory is tracked successfully.

This paper considers physical-layer network coding (PNC) in vehicular ad-hoc network (VANET) to solve the problem of short contact time between fast-moving vehicles. PNC enables data exchange between nodes in a relay network within a short airtime, e.g., twice faster than relay networks based on traditional communication, and can be a powerful performance booster in VANET. One of the most important challenges in applying PNC to VANET, however, is the Doppler shift caused by vehicular motions. Doppler shift leads to carrier frequency offset (CFO) that induces inter-carrier interference (ICI) in OFDM systems. The ICI destroys the orthogonality of modulated symbols, causing degradation in PNC signal detection. This paper puts forth a detection method to mitigate the CFO/ICI effect on PNC. The method, referred to as BP-VPNC, makes use of a belief propagation (BP) algorithm to process the outputs of the OFDM correlators. BP extracts useful hidden information embedded in ICI to improve signal detection in VANET PNC. Our study shows that the BER performance of PNC VANET operated with BP-VPNC can be achieved close to that of traditional VANET at various CFO levels. These results suggest that with BP-VPNC, a potential shortcoming of PNC, vulnerability to CFO, can be circumvented, and that PNC can be used to overcome the short vehicular contact time in VANET.

As a challenged network, the delay-tolerant network (DTN) generally has unpredictable delay for the packet delivery due to the insufficiency of the network connectivity. Thus, it is inadequate to support some time-sensitive applications. In this paper we aim to mitigate this unpredictability of the delay in DTN to broaden its applications by proposing a new routing strategy to make use of the node mobility. Our designed solution is called purpose-movement assisted routing (PMAR) aiming for a more realistic mobility model, group mobility, which has been paid attention in many realistic scenarios. Different from the traditional store-carry-forward DTN routing strategy, in which nodes wait for the connectivity passively, PMAR employs nodes to actively create new connection by altering the node movement, and thus the packet could be delivered in an expedited manner. More importantly, the proposed PMAR could be taken as a component to be integrated into different DTN routings for group mobility to make them more aggressive in delivering packets. In this paper, we integrate PMAR into a previously proposed routing, group-epidemic routing (G-ER). PMAR could be triggered whenever some packets need to be delivered to the destinations in a faster way, but without any connection to their destinations. By computer simulation, it is found that PMAR could greatly improve G-ER, especially the packet delay.

In this paper we propose a new routing scheme called group-epidemic routing (G-ER), which is specifically designed for group mobility in mobile ad hoc networks. G-ER is based on one proposed delay tolerant routing protocol called epidemic routing (ER). So, it could tolerate the disconnection characteristic among groups in group mobility. By considering each group as a single node, G-ER has made a great improvement to ER, which is shown in the simulation. In addition, we introduce the buffer sharing mechanism inside each group for G-ER. Also, the simulation has demonstrated the effectiveness of the buffer sharing mechanism, especially in a more disconnected scenario for group mobility.