This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency parallel training, it may converge to less accurate model. In contrast, SL achieves higher accuracy through sequential training but suffers from increased delay. To leverage the advantages of both, hybrid split and federated learning (HSFL) allows some devices to operate in FL mode and others in SL mode. This paper aims to accelerate HSFL by addressing three key questions: 1) How does learning mode selection affect overall learning performance? 2) How does it interact with batch size? 3) How can these hyperparameters be jointly optimized alongside communication and computational resources to reduce overall learning delay? We first analyze convergence, revealing the interplay between learning mode and batch size. Next, we formulate a delay minimization problem and propose a two-stage solution: a block coordinate descent method for a relaxed problem to obtain a locally optimal solution, followed by a rounding algorithm to recover integer batch sizes with near-optimal performance. Experimental results demonstrate that our approach significantly accelerates convergence to the target accuracy compared to existing methods.

Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterogeneity, limit its practicality in mobile edge computing. Existing compression methods like sparsification and pruning reduce per-round costs but may increase training rounds and thus the total training cost, especially under heterogeneous environments. We propose a lightweight personalized FL framework built on parameter decoupling, which separates the model into shared and private subspaces, enabling us to uniquely apply gradient sparsification to the shared component and model pruning to the private one. This structural separation confines communication compression to global knowledge exchange and computation reduction to local personalization, protecting personalization quality while adapting to heterogeneous client resources. We theoretically analyze convergence under the combined effects of sparsification and pruning, revealing a sparsity-pruning trade-off that links to the iteration complexity. Guided by this analysis, we formulate a joint optimization that selects per-client sparsity and pruning rates and wireless bandwidth to reduce end-to-end training time. Simulation results demonstrate faster convergence and substantial reductions in overall communication and computation costs with negligible accuracy loss, validating the benefits of coordinated and resource-aware personalization in resource-constrained heterogeneous environments.

As an important issue in the sixth generation communication technologies, the space-air-ground integrated network (SAG IN), mainly composed of satellites, unmanned aerial vehicles (UAVs), and ground stations, can provide global information services. However, it is challenging to provide robust services due to the dynamic characteristics of UAV s and satellites, as well as the resource incompatibility among different nodes. By introducing the network function virtualization technique to SAGIN, tasks can be converted into service function chains (SFCs) composed of multiple virtual network functions in series, and the resource allocation of SAGIN is deemed as the SFC deployment and scheduling. However, the node failure or link disconnections may occur in SAG IN, resulting in failures of SFC implementation. Hence, how to guarantee the robust service recovery of SFCs is challenging. In this paper, we propose the SFC deployment and recovery model to cope with the resource failure. The problem is formulated to minimize the total time consumption to complete the SFC deployment and recovery. Since the problem is an integer linear programming and intractable to solve, we propose an algorithm based on two-sided matching game to implement robust recovery of affected SFCs. Finally, simulation results verify the effectiveness and advantages of the proposed algorithm over other benchmark algorithms.

Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV). Each smart vehicle acts as a mobile client, contributing to the process without uploading local data. This method leverages nonindependent and identically distributed (non-IID) training data from different vehicles, influenced by various driving patterns and environmental conditions, which can significantly impact model convergence and accuracy. Although client selection can be a feasible solution for non-IID issues, it faces challenges related to selection metrics. Traditional metrics evaluate client data quality independently per round and require client selection after all clients complete local training, leading to resource wastage from unused training results. In the IoV context, where vehicles have limited connectivity and computational resources, information asymmetry in client selection risks clients submitting false information, potentially making the selection ineffective. To tackle these challenges, we propose a novel long-term client-selection federated learning based on truthful auction (LCSFLA). This scheme maximizes social welfare with consideration of long-term data quality using a new assessment mechanism and energy costs, and the advised auction mechanism with a deposit requirement incentivizes client participation and ensures information truthfulness. We theoretically prove the incentive compatibility and individual rationality of the advised incentive mechanism. Experimental results on various datasets,including those from IoV scenarios, demonstrate its effectiveness in mitigating performance degradation caused by non-IID data.

In Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users’ tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques.

Federated learning (FL) and split learning (SL) are two prominent distributed learning modes. FL allows for parallel training but demands significant computational resources on devices to train deep neural network models. Conversely, SL reduces the computational burden on devices and can enhance learning performance, though it often leads to longer training time due to its sequential nature. In this paper, we introduce a novel distributed learning framework, hybrid split and federated learning (HSFL), which combines the advantages of both FL and SL over wireless networks. To achieve a lower training loss within a shorter latency, we start with the convergence analysis of HSFL, followed by a joint optimization problem of the learning mode selection, model splitting, and bandwidth allocation. To solve the problem, we propose a two-stage algorithm. First, we find the optimal bandwidth allocation and model splitting with a fixed learning mode. Then, we select the optimal learning mode based on the above optimal values. Experimental results validate the superior learning efficacy of our proposed algorithm.

Along with the advancement of WiFi technologies, commodity devices of WiFi 6E have now prevailed. In this paper, we conduct a wireless sensing task that utilizes WiFi 6E devices to monitor the respiration pattern of a human being. We discover new features, as well as their causes, in the channel state information (CSI) measured from WiFi 6E devices, which significantly differ from those in legacy IEEE 802.11n/ac/ax devices. As a result, directly applying conventional sensing techniques based on the CSI hardly yields satisfactory performance. In response, we propose a new method that effectively rectifies the measurement defects and recovers the targeted signal. We carry out extensive experiments, the results of which show that our approach substantially outperforms state-of-the-art benchmarks in sensing accuracy, verifying the effectiveness of our scheme.

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully “sliced”, consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

Federated learning (FL) is a privacy-preserving distributed learning framework where model training is performed locally on distributed devices. Unlike traditional FL, which assumes a fixed local dataset, this paper focuses on the more realistic scenario of FL with streaming data. In Streaming Federated Learning (SFL), new data continuously arrives over time, and due to the limited local storage capacity on devices, some data is inevitably discarded. The discarded data may be forgotten by the model, leading to a decline in model accuracy. To this end, we introduce Federated Data Slimming (FedDS), a data selection scheme designed to determine which data should be stored locally. Particularly, FedDS considers both gradient norms and directions when making data selections. We evaluate the performance of FedDS against several previously proposed schemes using various datasets. Our experimental results demonstrate that FedDS surpasses all baseline schemes, achieving the fastest convergence rate and the highest test accuracy.

The Industrial Internet of Things (IIoT) is emerging as a promising technology that can accelerate the application of industrial intelligence to smart factories. Because of the sensitive nature of user data, federated learning (FL) which performs distributed machine learning while preserving data privacy, is leveraged to meet the accuracy and privacy requirements of IIoT end devices/clients. However, the unreliable communications in IIoT may result in possible single-point failures in the typical single-server FL framework, thereby negatively affecting the training efficiency. In this paper, we study on the client scheduling problem in a multi-server FL framework for the communication reliability and training efficiency improvement. Specifically, we focus on a semi-decentralized FL (SD-FL) framework, where edge servers and clients collaborate to train a shared global model through unreliable intra-cluster model aggregation and inter-cluster model consensus because of the model transmission error in client-server and server-server communication. Then, a client-server association optimization problem is formulated, with the objective of minimizing the global training loss. Resorting to the convergence analysis of SD-FL, the original problem is simplified and transformed into an integer nonlinear programming problem to guide us to design a high-efficiency client scheduling scheme. Finally, experimental results show that the proposed scheme significantly outperforms the baselines in terms of the test accuracy and training loss.

On-demand and resource reservation pricing models, widely used in cloud computing, are currently used in Multi-Access Edge Computing (MEC). Nevertheless the edge's resources are distributed and each server has lower capacity. If too much resources were reserved in advance, on-demand users may not get their jobs served on time, jeopardizing MEC's latency benefits. Concurrently, reservation plan users may possess un-used quota. Therefore, we propose a sharing platform where reservation plan users can re-sell unused resource quota to on-demand users. To investigate the mobile network operator's (MNO‘s) incentive of allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game and characterize the optimal strategies of buyers and re-sellers. We show that users’ actions give rise to 4 different outcomes at equilibrium, dependent on the prices and supply levels of the sharing and on-demand pools. Based on the 4 possible outcomes, we characterise the MNO's optimal prices for on-demand users. Numerical results show that having both pools gives the MNO an optimal revenue when the on-demand pool's supply is low, and unexpectedly, when the MNO's commission is low. We develop an interactive prototype, and show that users’ decision distributions in studies on our prototype are similar to that of our decision model.

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, and effective solution design. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friendly expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.

Driven by the widespread applications of Space-Air-Ground Integrated Networks (SAGINs) in a number of practical fields, the volume of space data grows rapidly. However, the large volume of space data in SAGINs is typically intractable to be offloaded from space to the ground under the high dynamic network topology and the stochastic data arrivals. Furthermore, most nodes in SAGINs are battery-powered and energy-constrained, thereby implying that energy consumption becomes one major bottleneck for data offloading. Towards this end, this paper studies online joint data offloading and power control in SAGINs to maximize long-term time-averaged data offloaded amount under the constraints of average energy consumption. First, we propose a novelty Two-timescale Time-Expanded Graph (TTEG) to characterize the rapid change of the network topology in large-timescale slots and capture the stochastic data arrivals in small-timescale slots. Based the TTEG model, we formulate a stochastic optimization problem and transform it into a series of per-time-slot subproblems to obtain an efficient online solution. Through theoretical analyses, we show that the performance gap with optimal solution is bounded. Finally, extensive simulations demonstrate that the maximum performance gap of our proposed online solution to the optimal solution is less than 2% in a low computation cost.

Federated learning (FL) can be used in mobile-edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a model-agnostic meta-learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous data sets. However, existing research simply combines MAML and FL without explicitly addressing how much benefit MAML brings to FL and how to maximize such benefit over mobile-edge networks. In this article, we quantify the benefit from two aspects: 1) optimizing FL hyperparameters (i.e., sampled data size and the number of communication rounds) and 2) resource allocation (i.e., transmit power) in mobile-edge networks. Specifically, we formulate the MAML-based FL design as an overall learning time minimization problem, under the constraints of model accuracy and energy consumption. Facilitated by the convergence analysis of MAML-based FL, we decompose the formulated problem and then solve it using analytical solutions and the coordinate descent method. With the obtained FL hyperparameters and resource allocation, we design an MAML-based FL algorithm, called automated FL (AutoFL), that is able to conduct fast adaptation and convergence. Extensive experimental results verify that AutoFL outperforms other benchmark algorithms regarding the learning time and convergence performance.

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among multiple graphs. However, in practice, a more intricate topological pattern, comprising simultaneously ofhomogeneousandheterogeneouscomponents, would exhibit in multiple graphs. In this paper, we propose a general graph estimator based on a novel structural fusion regularization that enables us to jointly learn multiple graphs with such complex topological patterns, and enjoys rigorous theoretical guarantees. Specifically, in the proposed regularization term, the structural similarity among graphs is characterized by a Gram matrix, which enables us to flexibly model different types of network structural similarities through different Gram matrix choices. Algorithmically, the regularization term, coupling the parameters together, makes the formulated optimization problem intractable, and thus, we develop an implementable algorithm based on the alternating direction method of multipliers (ADMM) to solve it. Theoretically, non-asymptotic statistical analysis is provided, which precisely characterizes the minimum sample size required for the consistency of the graph estimator. This analysis also provides high-probability bounds on the estimation error as a function of graph structural similarities and other key problem parameters. Finally, the superior performance of the proposed method is demonstrated through simulated and real data examples.

Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). However, most existing PFL implementations rely on synchronous training to ensure good convergence performances, which may lead to a serious straggler problem, where the training time is heavily prolonged by the slowest UE. To address this issue, we propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS2), over mobile edge networks. By jointly optimizing the wireless bandwidth allocation and UE scheduling policy, it not only mitigates the straggler problem but also provides convergent training loss guarantees. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. On this basis, the bandwidth allocation problem can be solved using analytical solutions and the UE scheduling policy can be obtained by a greedy algorithm. Experimental results verify the effectiveness of PerFedS2 in saving the training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.

In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might be excess demand at some locations and underutilized resources at other locations. To address such load-unbalanced problem in MEC systems, in this paper we propose virtual machines (VMs) sharing across base stations. Specifically, we consider the joint VM placement and pricing problem across base stations to match demand and supply and maximize revenue at the network level. To make this problem tractable, we decompose it into master and slave problems. For the placement master problem, we propose a Markov approximation algorithm MAP on the design of a continuous time Markov chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing auction, where all users are truthful. Furthermore, given users' potential untruthful behaviors, we propose an incentive compatible auction iCAT along with a partitioning mechanism PUFF, for which we prove incentive compatibility and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the original problem, and analyze the optimality gap. Simulation results show that collaborative base stations increases revenue by up to 50%.

Higher communication rates are required given that cloud radio access network (C-RAN) becomes a significant component of 5G wireless communication, yet the problem of using sparse beamforming to maximize the achievable sum rate in the long term subject to transmit power constraints still remains open in C-RAN. Inspired by the success of Deep Reinforcement Learning (DRL) in solving dynamic programming problems, we propose a DRL-based framework for online sparse beamforming in C-RAN. Particularly, the DRL agent is in charge of remote radio head (RRH) activation based on the defined state space, action space, and reward function, and meanwhile makes a decision on transmit beamforming at active RRHs in each decision period. Through simulations, we evaluate the performance of the proposed framework by comparing it with traditional ways and show that it can achieve higher sum rate in time-varying network environment.

The unmanned aerial vehicle (UAV) network that is convinced as a significant component of 5G and emerging 6G wireless networks is desired to accommodate multiple types of service requirements simultaneously. However, how to converge different types of services onto a common UAV network without deploying an individual network solution for each type of service is challenging. We tackle this challenge in this paper through slicing the UAV network, i.e., creating logical UAV networks customized for specific requirements. To this end, we formulate the UAV network slicing problem as a sequential decision problem to provide mobile broadband (MBB) services for ground mobile users while satisfying ultra-reliable and low-latency requirements of UAV control and non-payload signal delivery. This problem, however, is difficult to be directly solved mainly due to the sequence-dependent characteristic and the lack of accurate location information of mobile users and accurate and tractable channel gain models in practice. To overcome these difficulties, we propose a novel solution approach based on learning and optimization methods. Particularly, we develop a distributed learning method to predict mobile users’ locations, where partial user location information stored on each UAV is utilized to train user location prediction networks. To achieve accurate channel gain models, we design deep neural networks (DNNs) that are trained by signal measurements at each UAV. To cope with the challenging sequence-dependent characteristic of the problem, we develop a Lyapunov-based optimization framework with provable performance guarantees to decompose the original problem into a sequence of separate optimization subproblems based on the learned results. Finally, an iterative optimization scheme joint with a successive convex approximation technique is exploited to solve these subproblems. Simulation results demonstrate the accuracy of the learning methods as well as the effectiveness of the Lyapunov-based optimization framework.

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD's task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD's transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption.

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users' satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Deployed with computation resources, fog radio access network (F-RAN) provides a promising solution for computation offloading. To take full advantage of two-tier computing in the F-RAN, on one hand, it is inevitable to design, between edge and cloud, an efficient and flexible fronthaul transmission strategy, and fronthaul resource allocation should be jointly optimized with allocation of tasks and other resources. On the other hand, a robust computation provisioning strategy that can avoid failures caused by estimation errors of available computation resources is necessary. In this work, considering the fronthaul compression and the uncertain computation capacity, we design an energy-efficient computation offloading mechanism in the F-RAN. The formulated problem is challenging to solve due to coupled communication and computation resource constraints and binary variables for task placement. We show that the problem can be recast as a convex problem if binary variables are relaxed. On top of this result, we propose an efficient algorithm to find a stationary solution. Through simulation, we demonstrate that the proposed algorithm outperforms the baseline algorithm significantly and converges to the near-optimal point solution. Besides, we compare the F-RAN with single-tier computing systems and show the excellence of the F-RAN in energy conservation for mobile devices.

As the network becomes dense, multi-server mobile edge computing (MEC) systems with multiple candidate MEC servers, bring new opportunities to enrich user experience through computation offloading. Specifically, MEC server selection, as a new dimension, arises to strike a well balance between energy consumption and task execution delay of mobile device (MD). Since channel conditions between the MD and MEC server (or its connected access point) as well as available computing capability at MEC servers are time-varying, this paper aims to devise dynamic computation offloading mechanism to account for delay-energy tradeoffs in multi-server MEC systems. To this end, we jointly optimize transmit power and MEC server selection for the MD to minimize time average expected task execution delay, under the constraint of average energy consumption. With partial current network status available, we then combine Lyapunov optimization framework and multi-armed bandit framework for an online learning based computation offloading algorithm, whose feasibility and regret bound are given through theoretical analyses. Finally, simulation results are presented to demonstrate its efficiency and superiority.

Mobile edge computing (MEC) can extend the computing capability of mobile users (MUs), by offloading computational tasks from MUs to proximal MEC servers. Specifically, offloaded tasks are firstly transmitted to access points (APs) via wireless channels, and then relayed to MEC servers through backhauls. Due to differentiated wireless channels and input data sizes, offloaded tasks from different MUs arrive at the AP and MEC server in an asynchronous manner. Considering such asynchrony, we design sequential execution model for backhaul transmission and task processing in the MEC server and present its merits in terms of maximum task completion time for different backhaul configurations: 1) With ideal backhaul, the optimal task scheduling order in the MEC server follows the first coming and first scheduling rule; 2) With limited backhaul, an effective two-stage task scheduling order on the backhaul and MEC server is determined by the Johnson's rule. Further, we make suboptimal decisions on wireless resource provisioning and task offloading, with respect to the attained task scheduling order and derived necessary and sufficient conditions under which full local processing and full offloading are optimal. Finally, simulation results demonstrate the superiority of our devised sequential execution model and computation offloading mechanisms.

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.

Content caching is a promising way to overcome backhaul limitations in small cell networks. However, in such type of networks, small base stations (SBSs) are always deployed with limited cache storages. Thus, it is necessary for SBSs to adjust their contents for better caching efficiency, so as to reduce backhaul traffic. In this paper, we study the content replacement problem to minimize the traffic flowing into the costly backhaul links. However, in small cell networks where SBSs make up backhaul mesh networks, the effectiveness of reducing backhaul traffic depends on the hop distance from the content location to the requesting user. On this basis, we formulate a hop minimization problem that is inherently combinatorial. Through log-sum-exp approximation, we can solve the problem and arrive at a close-form solution with guaranteed performance gap to the optimal solution. By exploiting the properties of continuous-time Markov chain (CTMC), the solution can be implemented by designing a CTMC that can instruct the content replacement process. As a consequence, a concise, efficient, and flexible content replacement strategy is proposed. Simulation results verify our analysis and show that our proposed strategy outperforms the conventional strategies.

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.

By integrating software defined network and network function virtualization, software defined satellite networks (SDSNs) can enable flexible virtual network function (VNF) deployment to process and forward end-to-end traffic flows. Since one traffic flow has to go through all its required VNFs, the VNF deployment has a significant impact on traffic routing. In this regard, with time-varying network topology and limited network resources taken into account, we aim to match VNF deployment and routing to fulfill traffic flows' requirements in the SDSN in a cost-effective manner. Specifically, we first evolve the traditional time evolving graph as a software defined time evolving graph (SDTEG) to depict the time-varying network topology and meanwhile, provide a shared platform for elastic network resource provisioning. On this basis, we then formulate a cost minimization problem as a multi-slot integer linear programming problem to make a judicious decision on VNF deployment and routing for each traffic flow. To address this challenging problem effectively, we further propose a heuristic algorithm, referred to as time-slot decoupled algorithm (TDA). Finally, the effectiveness of the TDA as well as the superiorities from the joint optimization of VNF deployment and routing are demonstrated through simulation results.

By allocating antenna time blocks to spacecrafts, data relay satellites are of vital importance for the space network to relay data within their visible intervals (i.e., time windows). Existing works concentrate only on the allocation of transmission resources (i.e., antenna time blocks) in time windows and may result in transmission conflicts hard to efficiently resolve, especially when multiple missions are activated simultaneously. To this end, we propose to further integrate computation with transmission resource allocation, to enable data compression so as to alleviate conflicts. Specifically, aiming to maximize the number of completed missions and minimize data loss, we first formulate the joint transmission and computation resource allocation problem as a mixed integer linear programming (MILP) one. Then, for the complexity reduction, we transform the MILP into an integer linear programming (ILP) one by fixing maximal data compression. Meanwhile, by constructing a conflict graph to characterize resource allocation conflicts, a time window scheduling algorithm is proposed to solve the ILP problem efficiently. Next, we further develop a data compression control algorithm to reduce data loss on the prerequisite of invariant mission number. Finally, simulation results show that the space network can benefit from the combination of transmission and computation resources in terms of both mission number and data loss.

Supporting cooperative radio among remote radio heads (RRHs) and elastic cloud service in the baseband unit (BBU) pool, cloud radio access network (C-RAN) is perceived as a promising solution for the next mobile network. In C-RAN, cooperative radio can enhance power saving at RRHs, along with impact on computation effort and power saving in the BBU pool. In turn, power saving at RRHs, benefited from cooperative radio is restricted by the constrained computation capacity provisioned by processors in the BBU pool. Besides, limited fronthauls, which support baseband signal transfer between the BBU pool and RRHs, affect the cooperative radio design and power consumption at RRH as well. By jointly optimizing transmit beamforming among RRHs and processor sleeping in the BBU pool, we exploit such interplay between communication and computation for system power minimization in C-RAN with limited fronthaul and computation capacity. Specifically, we formulate this problem as a mixed-integer non-linear programming problem (MINLP), and then leverage the special structure of the MINLP to make a near optimal decision on the set of active processors and transmit beamforming vectors with high efficiency. Finally, extensive numerical results demonstrate that our proposed algorithms can enforce processor sleeping and reduce system power consumption significantly.

Cloud radio access network (C-RAN) is perceived as an energy-efficient solution for the next mobile network. The cloud-based baseband unit (BBU) pool is capable of dynamically provisioning computational resources for mobile users to improve hardware utilization such that unused processors can be switched off for power saving in the BBU pool. Besides, cooperative radio among remote radio heads (RRHs) can optimize transmit beamforming to reduce power consumption at RRHs. Thus, to achieve more judicious system power saving, this is need to consider power consumption in the BBU pool and that at RRHs together. In this paper, we aim to minimize system power consumption by jointly exploiting transmit beamforming and processor sleeping. Specifically, we formulate a mixed integer non-linear system power minimization problem, which is hard to solve. For tractability purpose, we transform this problem to an equivalent clustering problem embedded with a series of transmit beamforming problems and processor sleeping problems. On this basis, we first focus on solving the embedded problems with the given clustering and then propose a low-complexity clustering algorithm to search out the optimal clustering with minimum system power consumption. Finally, simulation results show that our proposed algorithms can save system power significantly.

Cloud radio access network (C-RAN), regarded as a promising green network architecture, facilitates cooperative transmission among remote radio heads (RRHs) while enabling flexible computation provisioning in the virtualized baseband unit pool. By jointly optimizing cooperative transmission, i.e., transmit power allocation with zero-forcing precoding adopted, and computation provisioning, i.e., virtual machine assignment, this paper minimizes the system power consumption comprised of transmit power and processing power in downlink C-RAN. Specifically, subject to per-RRH power constraint (PRPC) and per-MU quality of service constraint, the system power consumption minimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem. To solve the challenging MINLP, we reformulate the MINLP as a minimum weight perfect matching problem to get the initial solution without considering the PRPC. On this basis, a power-aware greedy algorithm is further devised to modify the solution such that the PRPC is satisfied. Finally, extensive simulations show the superiority of the proposed scheme on system power saving and the tradeoff between transmit power and processing power.

By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.

The deployment of smallcell eNodeBs (SeNBs), overlaid on existing macrocell eNodeB (MeNB) is widely accepted as a key solution for improving spectral efficiency (SE). However, both SeNB and MeNB may suffer significant performance degradation due to inter/intra-tier interference. Meanwhile, the energy efficiency (EE) is another promising requirement especially when SeNB/MeNB are densely deployed. In this paper, a utility function with an α-adjustable parameter is proposed to achieve an optimal tradeoff between EE and SE. Then, an energy-aware capacity offload between the MeNB and multiple SeNBs is formulated as a Nash bargaining game, which is significantly simplified during the following analysis. To attain a win-win optimality for both the relatively involved SeNBs and MeNB, an energy-aware trigger of source-MeNB, interference-related right selected target-SeNB, and mutual interference compensation are all provided in this paper, which help to attain more dimensions of diversities and gains. Finally, simulation results show the improved performance of our proposed scheme.