With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm’s high energy efficiency and effectiveness.

Due to obstructions such as trees and buildings, single-modal satellite or aerial images are insufficient for continuous high-precision representation of road features. To address this problem, this article proposes a lightweight cross-modal information interaction for road feature extraction (LCIRE-Net) from high-resolution remote sensing images (HRSIs) and GPS trajectory/LiDAR images. We design two parallel encoders for modality feature learning, using pairs of multimodal information as inputs to the encoders. By designing a cross-modal information dynamic interaction (CMIDI) mechanism, thresholds are used to decide whether to supplement redundant information from another modality, solving the issue of ineffective fusion calculations due to minor differences in multimodal feedback. A multimodal feature fusion module (MFFM) is proposed after the encoder output to achieve effective dual-modal fusion while addressing the interference of redundant noise generated during extraction. Subsequently, we present the feature refinement and enhancement module (FREM), which successfully captures edge features of the image using the receptive field of dilated convolution kernels. Additionally, in terms of lightweight design, we employ a novel SOTA method on D-LinkNet by replacing the original residual blocks with an enhanced ghost basic block. Extensive experiments are conducted on the BJRoad, Porto, and TLCGIS datasets, demonstrating that our network, with smaller parameters and FLOPs, outperforms other road-based semantic segmentation methods.

In the 6G era, space–air–ground integrated networks (SAGINs) can provide ubiquitous coverage for Internet of Things (IoT) devices. Multiaccess edge computing (MEC) and blockchain are two enabling technologies, which can further enhance the services capabilities of SAGINs, where MEC demonstrates a notable capability in efficiently minimizing both the task execution delays and system energy consumption, and blockchain can provide trust guarantee for task offloading and wireless data transmission among the entities operated by different operators in SAGIN. In this article, we present an MEC and blockchain enabled SAGIN architecture, which consists of two subsystems. In the MEC subsystem, a satellite and multiple unmanned aerial vehicles (UAVs) act as the edge nodes to provide IoT devices with computing power. Moreover, the satellite serves as the block generator and the client, and the UAVs serve as the consensus nodes of the blockchain subsystem. We intend to minimize the energy consumption within the network, which is achieved through the IoT devices’ task segmentation, the UAVs, and satellite’s bandwidth allocation among their served IoT devices. And moreover, the computing power of UAVs and the satellite also allocated in task processing and blockchain consensus. Considering the high dynamics of the network, it is impossible to obtain real-time and accurate channel information, so we remodel this problem as a Markov decision process, and propose a low-complexity adaptive optimization algorithm based on the deep deterministic policy gradient (DDPG). Our simulation results indicate that the proposed algorithm exhibits commendable performance in minimizing the network energy consumption and DDPG agent’s accumulated reward maximization.

Vehicular mobile edge computing (vMEC) and non-orthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this paper, we propose a novel multi-agent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model’s predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multi-agent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

In scenarios where road obstacles complicate feature extraction, designing a lightweight convolutional neural network (CNN) model with minimal parameters and flops while maintaining competitive segmentation accuracy poses one of the most challenging research tasks in remote sensing imaging. Finding the optimal balance between segmentation performance and computational efficiency is crucial. We introduce a novel method for global road feature extraction by strategically employing the light ghost basic block to develop a tiny-ghost link network (TG-LinkNet). A multiscale feature fusion (MSFF) module, which combines the parallel channel position attention mechanism (PCPAM) to deliver accurate road structure information, further supports the goal. We present a solution to the issue of feature fusion information retrieval-induced excessive redundant noise, which might cause serious interference. Furthermore, to efficiently extract edge features and capture long-distance reliance on global features, we create a global context feature extraction (GCFE) module, ultimately resulting in the lightweight global road feature extraction network (LGRF-Net). To facilitate efficient training, we implement a 1:2 weight design within our deep supervision technique, termed hybrid loss (weighted cross entropy (WCE)-Dice). Extensive experiments were conducted on the DeepGlobe ($1024\times 1024$,$512\times 512$) and SpaceNet road datasets. This demonstrates that our network possesses smaller parameters and flops compared to other road-based semantic segmentation methods.

Conventional channel codes are designed to recover channel errors by adding controlled redundancy to transmit bits; however, the main underlying assumption is that information bits are independent and identically distributed (i.i.d.). Short term and linear temporal correlations are assumed to be exploited by the preceding source encoders. This assumption is flawed in some scenarios since many types of data (e.g, audio samples, video frames, and sensor measurements) exhibit long-term relations and intricate dependencies that are not exploitable by conventional source encoders. Furthermore, sending plain information is still commonplace in wireless networks. Therefore, it is essential to design channel encoders that accommodate these conditions. It is well-known that the underlying hidden patterns can be captured by deep learning methods. This important capability is not yet fully utilized in channel encoder design. This work is a primary step towards developing a predictive channel decoder that learns the intricate dependencies within and between data frames using an embedded learning module at the receiver to enhance the bit decoding performance, especially in high-noise regimes. The learning module is integrated with the belief propagation algorithm over bipartite graphs appropriate for low-density parity-check (LDPC) codes. The proposed method is universal since no specific correlation model is adopted and the learning-based prediction is performed at the bit level. The proposed method is fully implemented at the receiver side, making it compatible with generic LDPC encoders. Our simulations demonstrate the superior performance of the proposed method compared to standard LDPC decoders. For instance, about 1.7 dB gain at the 10-4BER level is achieved when recovering noisy audio files.