In IoT-based water quality monitoring, traditional three-dimensional (3D) fluorescence spectroscopy is difficult to deploy directly on terminal sensors due to its large size, high cost, and complex structure, despite its rich information being highly advantageous for water pollution detection. In this work, a novel approach is proposed through designing a compact sensor integrated with a super-resolution reconstruction algorithm to meet the requirement of entire 3D fluorescence spectra in cloud deployment of monitoring network at minimal cost. In the design of the terminal sensor, multiple LEDs with different wavelengths serve as excitation light source and multiple filters are used to switch emission wavelengths, enabling the acquisition of a sparse 3D fluorescence spectrum. In the reconstruction algorithm, a point spread function (PSF) is employed as a constraint module to simulate the degradation process of spectra from high to low resolution by modeling the spectral characteristics of the optical components responsible for degradation, including LEDs and optical filters, which can enhance the authenticity of the reconstructed spectra. Furthermore, a joint training strategy is introduced for better robustness by jointly optimizing the parameters of PSF and deep learning network. Experiment results demonstrate that the proposed method achieves the highest reconstruction quality (PSNR: 47.57 dB, SSIM: 0.9903) and pollutant identification accuracy (98.1%) compared with seven other reconstruction methods. The proposed approach can obtain high-resolution and entire 3D fluorescence spectra for water pollution detection with low manufacturing cost and minimal data, thereby meeting the practical application requirements in the field of IoT-based water quality monitoring.

Satellite-enabled multi-access edge computing (MEC) networks have emerged as a promising solution for low-latency data processing in areas lacking infrastructure. However, these satellite MEC networks face significant security vulnerabilities and high communication latency due to the open-air interface and large-scale data transmission. To address these challenges, we propose a secrecy-aware adaptive federated learning (AFL) approach for a satellite MEC network. In this network, terrestrial devices perform local model training using their own data and periodically transmit updated model parameters to a satellite server in the presence of an eavesdropper. To secure both model uploading and downloading, idle devices act as friendly jammers, transmitting jamming signals to disrupt eavesdropping attempts. Our goal is to minimize the overall federated learning latency by jointly optimizing the number of quantization bits, the transmit power of MEC devices, the satellite’s transmit power, and the jammer selection strategy. To solve this problem, we first propose an AFL framework that minimizes the model uploading size while ensuring the required model accuracy. Building on this, the problem is divided into two subproblems of model uploading and model downloading, which are solved using a successive convex approximation (SCA)-based algorithm. Additionally, to improve secrecy performance, we introduce a low-complexity jammer selection strategy that significantly enhances the secrecy rate for both model uploading and downloading. Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods in terms of AFL convergence, secrecy performance, and overall latency.

Non-orthogonal multiple access (NOMA) enables multiple terminal devices to simultaneously share wireless resources, providing efficient computing offloading services for wireless devices in networks that integrate unmanned aerial vehicles (UAVs) with mobile edge computing (MEC). However, the broadcast characteristics of UAV line-of-sight (LoS) communication introduce serious security issues for NOMA-based UAV-MEC systems, especially when facing an aerial warden. To address this issue, we propose a covert communication scheme for NOMA-based UAV-MEC systems against an aerial warden, where the aerial warden monitors the task offloading behavior of terminal devices. In the proposed scheme, the average computing capacity is maximized by jointly optimizing the UAV trajectory and system resources while ensuring the covert performance requirements. Firstly, considering the terminal devices have a fixed number of computing tasks, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the non-convex original problem into several subproblems and solves them iteratively. Secondly, considering the case of dynamic tasks arrival at terminal devices, we propose a double-deep Q-learning (DDQN)-based algorithm, where the optimal strategy for trajectory planning and resource allocation is obtained. Simulation results demonstrate that the proposed scheme using two algorithms outperform their respective baselines.

The rapid advancement of AI-generated content (AIGC) has enhanced the Artificial Intelligence of Things (AIoT) by offering a novel approach to content generation and creation. However, the heterogeneity of AIGC services and the large scale of AIGC models present significant challenges for providing these services. In this paper, we propose an edge-cloud collaborative framework to facilitate the provisioning of heterogeneous AIGC services. In this framework, we focus on three kinds of representative AIGC services, including lightweight AIGC services, computation-intensive AIGC services, and preprocessing-based AIGC services. We jointly optimize resource allocation and AIGC model deployment at an edge server to minimize the service delay for AIoT devices. The delay minimization problem involves mixed-integer nonlinear programming, which is inherently complex. To address this issue, we propose a dual-layer optimization algorithm that decouples the problem into an inner-layer resource allocation subproblem and an outer-layer model deployment subproblem. These subproblems are then addressed using the Karush-Kuhn-Tucker conditions and a cross-entropy-based technique. Finally, simulation results demonstrate the effectiveness of our proposed joint optimization scheme, which achieves an average performance improvement of approximately 23.2%.

In this work, we investigate the frequency synchronization and system performance in massive multiple-input multiple-output (MIMO) systems with one-bit analog-to digital converters (ADCs). First, we tackle the challenges arising from severe multi-user interference (MUI) and the non-linearity inherent in one-bit ADCs in orthogonal frequency division multiplexing (OFDM) based on Bussgang decomposition and receive beamforming. To assess the accuracy of the CFO estimation, we analyze its theoretical mean square error (MSE) and investigate how quantization noise influences synchronization precision. Additionally, we introduce a multi-user inference (MUI)-plus-noise whitening technique to mitigate the correlation of the equivalent noise. Finally, we derive an approximate expression for the uplink achievable rate using maximal-ratio combining (MRC) detection scheme. Extensive numerical simulations confirm the effectiveness of the proposed approach.

In the Internet of Everything (IoE) era, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) have emerged as crucial enabling technologies to support delay-sensitive and computation-intensive IoE services. Despite the potentials of RISs and MEC, achieving efficient service provisioning in IoE scenarios still faces significant challenges due to interdependencies among different types of resources. To address this issue, we propose a digital twin (DT)-empowered IoE framework that leverages real-time monitoring to virtually replicate network conditions, thereby assisting in decision-making in a physical IoE scenario. Specifically, the IoE scenario comprises a MEC server empowered by prestoring some service programs for task execution and a RIS that assists computation offloading. Taking into account deviations between DT and physical networks, we aim to minimize devices’ total task completion delay by jointly optimizing the service caching at the MEC server, the computation offloading of devices, the computing resource allocation at the MEC server, and the beamforming of the RIS. To handle the problem involving discrete and continuous factors, we develop a hybrid deep reinforcement learning (HDRL) algorithm that integrates the double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) approaches. In our HDRL algorithm, DDQN plays a crucial role in determining discrete variables representing service caching and computation offloading decisions, while DDPG focuses on optimizing resource allocation and RIS beamforming. We conduct simulations to evaluate the performance of the proposed scheme and compare it with several baselines. Simulation results demonstrate the superiority of our scheme in minimizing the task completion delay.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

Unmanned Aerial Vehicles (UAVs) are considered as promising data collection tools because of their maneuverability and line-of-sight conditions, especially for operations at sea. In this paper, we thus deploy a UAV-enabled offshore operating network, in which the UAV acts as an airborne base station and receives data from the sensing devices at sea. Considering the limited spectrum resources, the non-orthogonal multiple access technology is used for data transmission in parallel to improve the spectrum efficiency. In our scheme, we aim to minimize the total system latency by jointly optimizing the trajectory of the UAV and the number of hovering points, under the constraints of the maximum energy threshold of the UAV and the required data size to be collected. Since the proposed problem is non-convex, we use the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection search to obtain the minimum total system latency. Specifically, we first get the optimal UAV trajectory by the DDPG algorithm for a given number of hovering points. Then, the optimal number of hovering points is derived using the bisection search algorithm, based on the requirement of the data amount to be collected. Lastly, the optimal latency is obtained by alternately iterating the DDPG and bisection search algorithms. Through numerical verification, we can effectively minimize the system latency using our proposed algorithm, with a minimum reduction of about 7.4% to a maximum reduction of about 17.7% in comparison with the existing algorithms A2C and DQN.

Mobile edge computing (MEC) pushes computing and caching resources close to the network edge, which allows devices to offload computation-intensive tasks to MEC servers. Considering that wireless signals may be easily blocked by obstacles, reconfigurable intelligent surface (RIS) has emerged as a promising technique to improve the efficiency of computation offloading. In this paper, we consider a RIS-assisted MEC network, where a MEC server caches service programs required for task execution and a RIS helps computation offloading in the presence of eavesdropping. Due to the diversity of services and the broadcast nature of wireless channels, it is challenging to achieve efficient and secure computation offloading in this network. Therefore, we first formulate a task completion delay minimization problem by jointly optimizing service caching, computation offloading decisions, RIS passive beamforming, and transmit power subject to the constraints of secure offloading rate and limited storage space. To address the highly non-convex nature of the problem, we then develop a dual-layer optimization algorithm via a vertical decomposition on its layered structure. The outer-layer problem, which deals with service caching and computation offloading decisions, is solved by a cross-entropy-based caching and offloading learning algorithm. For the inner-layer problem that optimizes RIS passive beamforming and transmit power, we utilize a horizontal decomposition by invoking the block coordinate descent method. Finally, simulation results demonstrate that the proposed scheme exhibits performance improvements compared to several baseline schemes.

Mobile Edge Computing (MEC) has envisioned to be a promising technology to provide more efficient services for computation-intensive but delay-sensitive onboard mobile services. In this paper, the Non-Orthogonal Multiple Access (NOMA) technology is applied in a vehicular edge computing network, in which vehicular users (VUs) can offload partial computation tasks to MEC servers over wireless channels for remote execution. In this network, an optimization problem for the long-term energy consumption of the system is presented and aims to minimize it by jointly optimizing the Successive Interference Cancellation (SIC) ordering of NOMA, the VUs’ transmit power for computation offloading, and computation resource allocation of the MEC server. To deal with the intractable long-term optimization problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC ordering sub-problems. For the resource allocation sub-problem, we exploit its convexity through the transformation and reparameterization, and derive the optimal solution in accordance with the Karush-Kuhn-Tucker (KKT) conditions and the gradient descent algorithm. After that, we propose a low-complexity algorithm by leveraging the Tabu search to obtain the sub-optimal SIC ordering. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to Frequency Division Multiple Access (FDMA).

In this paper, the non-orthogonal multiple access (NOMA) technology is applied in a vehicular edge computing network, in which mobile vehicles can offload partial computation tasks to the MEC server for remote execution. In this network, a long-term energy consumption minimization problem is presented by jointly optimizing the successive interference cancellation (SIC) order, transmit power, and computation resource allocation. To deal with the formulated problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC order subproblems. For the resource allocation subproblem, we exploit its convexity through the transformation and reparameterization and then derive the optimal solution by the Karush-Kuhn-Tucker (KKT) conditions. After that, we propose a low-complexity algorithm by leveraging tabu search to obtain the suboptimal SIC order. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to frequency division multiple access (FDMA).

This paper investigates the performance of cooperative device-to-device (C-D2D) communications in a cellular network, where the simultaneous wireless information and power transfer (SWIPT) technology is adopted by D2D transmitters (DTs). In this network, DTs can act as relays that consume a portion of energy harvested by a time switching (TS) strategy to satisfy the quality of service (QoS) requirements of cellular users (CUs) with poor channel conditions, in exchange for spectrum resources of CUs for D2D communications. To achieve the sum-throughput maximization of the network while guaranteeing the QoS requirements of both D2D and cellular links, we formulate a novel optimization problem that jointly determines user pairing between DTs and CUs, time allocation for energy harvesting and information transmission, and power allocation at DTs for relaying information and performing D2D communications. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) problem which is computationally prohibitive. To overcome this issue, a two-step policy-based algorithm is proposed to solve the problem in polynomial time. Simulation results validate the convergence of the proposed algorithm and the effectiveness of the joint user pairing and resource allocation scheme for improving network throughput.

To establish reliable wireless communication systems in underground mines, accurate channel modeling plays an indispensable role in the design phase. In this paper, we propose ray tracing-based channel modeling in room-and-pillar mines. Specifically, the propagation paths of signals are firstly determined in the horizontal plane of a room-and-pillar mine using the ray tracing method. Then, the two-dimensional (2D) propagation paths are converted into their corresponding three-dimensional (3D) counterparts by deriving the heights of intersection points at obstacles and the locations of reflection points on mine ceilings and floors. Since the roughness of mine surfaces has a non-negligible influence on radio propagation properties, it is also taken into account when the electric fields of propagation paths are calculated. To validate the accuracy of the proposed ray tracing-based room-and-pillar channel model, we compare simulation results with experimental measurements conducted in an actual mine in terms of mean excess delay and root mean square (RMS) delay spread. The comparisons show simulation results have a good consistency with measurement data.