This paper considers a downlink cell-free network under limited fronthaul capacity, where the fronthaul constraint is modeled as rate-distortion theory. Our objective is to maximize the spectral efficiency (SE) by jointly optimizing user association (UA) and power control (PC). To this end, we first represent the cell-free network as a heterogeneous undirected bipartite graph and then develop a message passing-enhanced heterogeneous Graphormer network (MP-HGraphormer) algorithm. In this algorithm, three graph structural encoding mechanisms are introduced to effectively capture both global and local graph structural information. These mechanisms are specifically designed to exploit the constructed graph, and consequently the computational complexity of these mechanisms is reduced. Furthermore, UA and PC are optimized by applying a threshold-based decision rule according to the joint node and edge outputs of MP-HGraphormer. Simulation results show that the proposed algorithm outperforms traditional Transformer and graph neural network models in improving the SE while achieving better generalization across different numbers of user equipments.

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates.

This paper investigates the edge caching in maritime wireless networks to cope with traffic surge issue in offshore region. A buoy equipped with wireless cache servers is introduced to provide wireless communication services for ships and users to relieve traffic load of onshore base station(OBS). Due to limited cache capacity and transmit power on the buoy, the transmission delay for ships and users can be increased. To tackle this problem, a joint cache placement and power allocation algorithm is proposed based on the Dinkelbach method and successive convex approximation. Simulation results show that the proposed algorithm can improve the cache hit rate with reducing the system cost.

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.

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 article, 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 (MS)-assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the MS 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.

In cache-enabled wireless networks (CWN), the unbalanced traffic distribution due to the node's sociality may lead to local congestion, which significantly degrades system throughput. Especially, nodes prefer to share content with those that have social relationships with them, which may result in heavy traffic load in the nodes with great social relationships. Therefore, it is crucial to capture the interplay among sociality, content caching and traffic distribution. In this paper, we design a caching strategy through jointly considering sociality and load balance to maximize the throughput capacity. To this end, efficient betweenness (EB) is adopted to quantify the traffic distribution, where EB is the number of content delivery paths through a node. Aided by EB, the impacts of key system parameters including sociality and caching strategy on throughput capacity are elaborated. According to the critical condition of the steady state in CWN, we formulate an optimization problem aiming to maximize throughput capacity. Due to the non-convexity of the initial problem, we propose an effective heuristic algorithm to solve it, which can balance traffic load according to the node's sociality and transmission capacity. Simulation results show that the proposed algorithm can increase the throughput capacity by 35.7% against benchmark approaches.

In smart ocean, multiple unmanned surface vehicles (USVs) are deployed, which generally perform multiple monitoring missions with different requirements of transmission performance. For the monitoring mission, the transmission latency is quite important for marine IoT systems to achieve the ubiquitous situation awareness. However, it is quite challenging due to the location-depended path loss and battery-powered sensors. To address this issue, this paper adopts the Age of Information (AoI) to mathematically express the impact of transmission delay on state estimation, and proposes a mothership assisted cooperative transmission scheme to enhance the estimation performance with limited energy. Moreover, the locations of mother-ships is optimized to minimize the mean squared 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 could achieve smaller the estimation error.

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.

Non-orthogonal multiple access mobile edge computing (NOMA-MEC) is proposed to enhance the connectivity between the edge node and users for low- latency computation offloading. However, it is asynchronous for users to complete the task uploading, which complicates the co-channel interference between NOMA users to affect overall offloading delay. In this paper, we first characterize the impact of this asynchronism in task uploading on interference management in NOMA enabled computation offloading. The optimal power allocation is proposed to coordinate the co-channel interference between both NOMA users. Then, we propose a multi- user offloading scheme to jointly optimize offloading decision and NOMA user pairing, aiming to minimize the users' average delay on executing their tasks. The proposed offloading scheme is designed by formulating a binary nonlinear problem, which is solved by the proposed relaxation method and heuristic algorithm. Simulation results demonstrate that compared with other NOMA based schemes, our proposed scheme can effectively reduce the average delay of users and increase the number of users to perform computation offloading.

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness.

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

In this paper, we investigate the resource allocation for mobile edge computation offloading in non-orthogonal multiple access (NOMA) cellular networks. Leveraging NOMA, the massive connectivity can be supported to enable multiple cellular users to simultaneously upload their computation-intensive tasks on the same orthogonal resources, which improves spectral efficiency and reduces transmission delay. However, the co-channel interference in non- orthogonal spectrum sharing may potentially degrade the achievable rate of offloading computation tasks. Moreover, the overall delay of all cellular users in finishing computation offloading will increase if the computation resources at the edge server are not properly allocated. To minimize the maximum overall delay of all users, we formulate an optimization problem that jointly allocates communication resources and computation resources. Due to the non-convexity of the primal problem, we divide it into three subproblems. By exploiting their specific structures, an efficient algorithm is designed to obtain the suboptimal solution with low computational complexity. Simulation results are presented to demonstrate that our proposed algorithm can effectively reduce the overall delay of cellular users and fully exploit the benefit of NOMA on spectral efficiency, especially when the number of users is large.

Device-to-Device (D2D) communication underlaid cellular networks has been regarded as a technology with great promise to provide higher transmission rate, lower latency and better energy efficiency in services between user terminals in the future fifth generation (5G) wireless network. In this paper, we consider the resource allocation problem to enhance the system performance. Specifically, we use hypergraph to characterize the interference among cellular uplinks and D2D links when sparse code multiple access (SCMA) is applied as the multiple access strategy. Targeting at maximizing system sum rate, we propose an Interference-Aware Hypergraph based Codebook Allocation (IAHCA) algorithm. Using IAHCA, each orthogonal SCMA resource, i.e., SCMA codebook, is allowed to be shared by one cellular uplink and more than one D2D links. As a consequence, available SCMA resources can be fully exploited, thereby effectively achieving higher system throughput and activating more D2D links. Simulation results confirm that IAHCA outperforms conventional graph based algorithm and other hypergraph based algorithms.