In mobile edge computing (MEC), the computation offloading of massive users could cause the task uploading congestion to deteriorate the users’ offloading delay. The non-orthogonal multiple access (NOMA) enabled MEC is envisioned to address this issue by allowing multiple users to simultaneously upload their tasks on one subchannel. However, the differentiated uploading delay of users may make task uploading completion inconsistent with NOMA decoding order, which complicates the co-channel interference and restricts NOMA to reducing the uploading delay. In this paper, we characterize the interaction between the differentiated uploading delay and co-channel interference for a pair of NOMA users. Furthermore, we propose a computation offloading scheme to reduce the users’ average offloading delay by jointly optimizing offloading decision and resource allocation. Specifically, the proposed scheme first obtains the optimal power allocation based on the characterized interaction and the closed-form solution of computation resource allocation by convex programming. Then, the NOMA user pairing and offloading decision are iteratively determined by semidefinite relaxation and convex-concave procedure. Simulation results show that the proposed scheme effectively mitigates co-channel interference under differentiated uploading delay of users and outperforms in reducing the users’ average offloading delay and increasing the number of users to offload tasks.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this paper, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother-ship assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the mother-ship location, channel allocation and transmit power are jointly optimized to minimize the mean square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this twopart survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and nonterrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II.

This two-part survey provides a comprehensive review of graph optimization and learning for resource management in wireless networks. In Part I, we introduced the fundamentals of graph optimization and provided a recent literature review of graph optimization for resource management in various wireless communication scenarios. In this part, we first present an overview of graph learning and introduce several modern graph neural network models. Then, a state-of-the-art literature review of graph learning for different resource management issues in wireless networks is provided, which covers power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. Furthermore, we discuss current technical challenges and future research directions of graph optimization and learning for resource management in future wireless networks.

Edge sensing can achieve high-performance state estimation in industrial IoT systems by supporting task offloading and data processing at powerful edge estimators. Accurate edge sensing depends on low offloading delay. However, it is challenging to decrease offloading delay due to the harsh industrial environment and limited communication-and-computation resources. In this article, a closed-form expressing of estimation error with respect to offloading delay is derived to indicate that adjusting offload delay on demand is necessary for estimation error reduction. Then, we propose an adaptive edge sensing scheme, aiming to minimize estimation error by jointly optimizing task offloading and sensor scheduling. The required optimization is formulated as a mixed-integer nonlinear programming problem and solved by the designed decomposition and approximation methods. Specifically, the maximum matching is used for sensor scheduling to assign the optimal edge estimator for each sensor. The task offloading algorithm is designed based on the inner approximation method to reduce the offloading delay. Finally, simulation results demonstrate that the proposed scheme has superiorities in reducing estimation error compared with centralized sensing and distributed sensing schemes. Moreover, we find an interesting result that estimation error is delay sensitive when the offloading delay is large.

This paper studies the integration of data collection and offloading for maritime Internet of Things (IoT) systems with multiple unmanned aerial vehicles (UAVs). In the considered multi-UAV maritime IoT system, the UAVs act as the aerial base stations to complete the missions of data collection from buoys and data offloading to the offshore base station (OBS). In this case, the UAVs need to adaptively select the mission mode between data collection and data offloading according to the network resources and mission requirements. In this paper, we aimed to minimize the completion time of data collection and offloading missions for all UAVs by jointly optimizing the UAV trajectories, mission mode selection, transmit power of buoys, and association relationships between the UAVs and buoy/OBS. In order to solve the mixed-integer non-convex minimization problem, we first designed a multi-agent deep reinforcement learning algorithm based on a hybrid discrete and continuous action space to preliminarily obtain the UAV trajectories, mission mode selection, and the transmit power of buoys. Then, we propose an algorithm based on the stable marriage problem to determine the buoy–UAV and UAV–OBS association relationships. Finally, the simulation results show that the proposed algorithms can effectively shorten the total mission completion time of data collection and offloading for the multi-UAV-assisted maritime IoT system.