Accurate cellular traffic prediction is crucial for the rational allocation of network resources. Existing methods generally overlook the accurate extraction and full utilization of features across different periods of cellular traffic. Thus, we propose a Relative Long Short-Term Adaptive Spatial-Temporal Targeted Extraction (RLSASTTE) network, which fully extracts long-term trends and short-term dynamics, and then maximizes their utilization. First, to extract long-term spatial-temporal features, we exploit the strength of the Transformer in capturing long-range temporal dependencies, address its limitations in spatial relationship modeling by redesigning a spatial-temporal attention mechanism, and further adopt a pre-decomposition strategy to emphasize seasonal component mining. Second, to capture short-term spatial-temporal features, we define a multi-dilation convolution to explore short-term dependencies and design a novel local strong correlation dynamic attention mechanism to investigate local spatial influences, which also eliminates the limitation of fixed neighboring nodes. Finally, we innovatively employ an adaptive dual gating mechanism to efficiently integrate diverse features. We conducted experiments on three real-world datasets. Compared to the state-of-the-art methods, RLSASTTE achieves reductions of at least 3.1% and 3.7% in mean absolute error and root mean square error, respectively. In addition, we explore auxiliary training based on data augmentation, achieving additional performance improvements of 2.0% to 4.5%.

Rigid gang scheduling, which enables multiple threads of real-time tasks to execute concurrently on a fixed number of different processors, has recently gained attention. Compared to preemptive rigid gang scheduling, non-preemptive rigid gang (NPRG) scheduling improves predictability by requiring fewer context switches. However, NPRG scheduling is vulnerable to 2D-blocking, where lower-priority tasks can block a higher-priority task multiple times, leading to severe priority inversion at runtime and pessimism in schedulability analysis. The root cause is the work-conserving execution behavior, in which tasks are immediately executed whenever the required processors are idle, regardless of priority and the potential blocking of subsequent tasks. This paper focuses on the global fixed-priority NPRG scheduling and introduces the NPRG-SS scheduler. NPRG-SS leverages a selective stalling (SS) mechanism that selectively stalls lower-priority jobs whenever their execution could delay the start time of a higher-priority job in the ready queue. This non-work-conserving approach inherently mitigates multiple blocking. Additionally, we present the first schedulability analysis for NPRG scheduling with SS and propose a novel heuristic priority assignment technique, Iterative Priority Refinement (IPR). Experimental results show that NPRG-SS with IPR effectively mitigates priority inversion, accepts up to 42% more task sets than the baselines at runtime, while our proposed schedulability test accepts up to 80% more task sets than the baselines in worst-case scenarios.

Satellite Edge Computing (SEC) augments the computational capability of Low Earth Orbit (LEO) satellite networks to support latency-sensitive and computation-intensive services. However, limited onboard resources, dynamic network topology, and the lack of trust among heterogeneous nodes hinder efficient task offloading and collaborative processing. To address these challenges, we propose a blockchain-enabled SEC framework that integrates task offloading, resource allocation, and an incentive mechanism via smart contracts, ensuring trusted and autonomous cooperation. We further develop a distributed optimization algorithm based on Successive Convex Approximation and Distributed Alternating Direction Method of Multipliers (SCA-DADMM), enabling decentralized decision-making with only neighbor-level communication. Simulation results show that the proposed approach achieves up to 32.96% higher system revenue and 15.98% lower task latency compared to baseline methods under varying bandwidth and computing resource conditions, demonstrating its potential to enhance both efficiency and trust in resource-constrained satellite edge environments.

In the routing process of low earth orbit satellite networks, frequent topology changes and complex space environment cause routing interrupted and sudden link failures. Traditional terrestrial routing not only fails to manage this problem but also leads to random network congestion. To address these issues and to achieve high adaptability and stability routing strategy, this paper proposes an intelligent distributed routing algorithm based on multi-agent deep reinforcement learning (MADRL) with Lyapunov optimization. Firstly, we build Lyapunov-based network optimization model and analyze the model stability. Then, we combine the Lyapunov optimization with the MADRL framework to stabilize the network. Each satellite agent selects next hop node according to the queue backlogs and the distance between the next hop node and the destination. Evaluation results show that our proposed Lyapunov optimization aided DRL (LOA-DRL) algorithm has better performance in terms of delivery ratio, average delivery time, throughput and average queue backlogs.

The space-air–ground integrated network (SAGIN) integrates satellites, unmanned aerial vehicles (UAVs), and terrestrial remote clouds to provide seamless network access and high-volume computing services for remote Internet of Things (IoT) devices, thus alleviating geographic and resource constraints. Existing methods typically focus on the network dynamics while overlooking the comprehensive consideration of device dynamics, namely, the time-varying task performance weights, task sizes, and task processing demands. Moreover, the centralized learning-based offloading schemes often lead to substantial signaling overhead. To bridge these gaps, this article proposes a distributed dynamic task offloading mechanism with game-theoretic multiagent stochastic learning (MASL). Technically, a stochastic game is formulated with each device as a player minimizing its weighted sum cost of latency and energy. We prove the existence of Nash equilibrium (NE) for our proposed game and propose a multiagent entropy-enhanced stochastic learning (MESL) algorithm in a fully distributed manner with no information exchange among IoT devices. By introducing the entropy of decision probability for each device, MESL increases decision dimensions, accelerates convergence, and facilitates optimal strategy achievement. Experimental results show that the MESL algorithm significantly reduces the overall cost and greatly enhances the convergence speed in dynamic SAGIN environments compared to existing algorithms.

With the development of Industry 4.0, mobile agents are deployed to coordinate with multi-loop control systems to perform manufacturing tasks, leading to heterogeneous Internet of Things (IoT). Due to shadow fading induced by the movement of mobile agents, the wireless channel closing the control loops is inherently unreliable, which can compromise the control system performance. This paper addresses the co-design problem of transmission scheduling and agents’ movement to ensure both control performance and energy efficiency. A generalized cyber-physical-agent framework is proposed to capture the coupling between IoT systems and a mobile agent through a state-dependent fading channel. Moreover, the movement of the mobile agent among areas exhibiting different levels of shadow effects is modeled by Markov decision process. To address such heterogeneous dynamics, the co-design problem is formulated into the optimization of a logic-based stochastic switched system by utilizing the semi-tensor product technique. Based on state mergence andH-representation methods, a tractable and effective algorithm is then proposed to design co-design policies, minimizing the average joint cost of communication and control. A parallel scheme is further developed to alleviate the computation burden of the algorithm. Theoretical guarantees are provided to ensure control system performance. Finally, simulation results are given to demonstrate effectiveness of the proposed method.

Mobile Edge Cache allows CDN edge nodes to be deployed closer to users, reducing content transmission latency in Vehicular Networks (VANETs). However, how to effectively utilize the limited storage space of cache nodes is the main issue in current research. To address this problem, we propose an incentive-driven hierarchical collaborative caching algorithm based on D3QN combined with user preferences (PC-ID3QN). Firstly, we constructed a UAV-assisted vehicular content-centric network framework, designing different collaborative caching strategy for various layers based on user preferences. Secondly, we proposed a hierarchical incentive mechanism based on social priority to motivate users to participate in collaborative sharing, thereby enhancing the utilization of system caching resources. Next, we modeled the cache placement problem as a system utility maximization problem and proved its NP-hardness. Then, by adjusting the constraint conditions, we conducted theoretical analysis and transform it into a linear programming(LP) problem to obtain an offline theoretical solution. This solution was validated against the simulation optimal solution obtained using the proposed PC-ID3QN algorithm, demonstrating the effectiveness of the algorithm. Finally, we validate the proposed caching strategy using the MovieLens dataset and conduct extensive experiments to verify the applicability and superiority of our solution in improving cache utility. Compared with DDQN, Dueling DQN and DQN, our proposed ID3QN algorithm reduces the request delay by 1.03%,1.73% and 2.21%, and reduces the energy cost by 38.8%, 19.6% and 17.2%, respectively.

To fulfill the requirements of human lunar exploration programs and establish bases on the moon in the distant future, lunar sensors (LSs) will inevitably produce a significant amount of data. It is necessary to construct the Earth-Moon metaverse in order to obtain and utilize lunar information more effectively. Due to the long communication distance between Earth and Moon as well as the lack of communication resources, a hybrid Earth-Moon metaverse network with channel model and transmission model is designed. To ensure the efficiency and stability of transmission in the network, LSs transmit lunar data to Earth clients (ECs) through the active and passive intelligent reflecting surface (IRS) deployed at relay satellites. Then, we propose a Stackelberg game model to describe the adversarial relationship between the satellites, LSs and ECs, and optimal strategies are obtained by solving the Nash equilibrium to maximize their utility. Simulations demonstrate that the network can effectively shorten the transmission delay and improve the utility of ECs and satellites.

To solve the sudden inter-satellite link failures of low earth orbit satellite networks (LEO-SNs), a robust routing algorithm against link failures is proposed in this paper. Firstly, we introduce a 2-D Markov model for LEO-SNs to study the problem about how to minimize the probability of encountering link failures in minimum-hop path set. Theoretical results indicate that forwarding in the more-hop direction has a lower probability to encounter link failures. Based on the results, we propose a More-Hop Direction Priority routing algorithm with routing Recovery strategy by Extending Path Area (MHDPREPA). The algorithm consists of three components which are routing preparation, routing calculation and routing recovery. In the routing process, each node aims to avoid encountering link failures when selecting the next hop node. If the node encounters link failures, a routing recovery strategy is adopted to bypass the failed links. Simulation results show that our proposed algorithm can effectively improve delivery ratio and average throughput compared with baseline algorithms.

With the rapid development of applications with different use cases and service demands for edge network, network slicing is an emerging solution for satisfying service-oriented requirements, while the low earth orbit (LEO) satellite caching-assisted communication has been considered as one of the key elements for effective services. With limited resources at the edge of the radio access network (RAN), it is challenging to take advantage of the LEO content cache to joint allocation of communication, computing and caching space (3C) resources. To this end, we investigate the problem of resource slicing and scheduling of joint 3C resources in RAN edge scenario assisted by LEO content caching. A hierarchical resource slicing framework is proposed for dynamic allocation of multidimensional resources. The optimization variables are relaxed and the constraints are adjusted. The sequential quadratic programming (SQP) iteration algorithm is proposed as theoretical offline baseline. Due to its complex solving process and limited real-time performance, we incorporate Long Short-Term Memory (LSTM) into the Soft Actor-Critic (SAC) algorithm to aware extract the distribution characteristics of historical information and propose the deep reinforcement learning algorithm of LSTM-SAC. Meanwhile, the proportional priority based scheduling algorithm is employed in the intra-slice. Compared to SAC, TD3 and DDPG algorithms, the proposed algorithm is the closest to the theoretical value, improves the objective function by 6.95%, 9.52% and 11.52% respectively, which can significantly improve the system rate while satisfying the service level agreements.

Video surveillance in intelligent transportation systems is advancing rapidly, with video analytics technology being used to enhance the security of the Internet of Vehicles (IoV) system. However, the sheer volume of video data from cameras and the computational intensity of video analysis pose significant challenges to the IoV network. To address this, mobile edge computing (MEC) has been introduced to offload video tasks from cameras to mobile edge servers/groups formed by vehicles. However, the resource-constrained nature of edge servers and vehicle groups necessitates the design of effective offloading strategies. Additionally, ensuring the security of user data during transmission and computation is a pressing issue. Moreover, the heterogeneous devices in the IoV system may be reluctant to participate in the collaborative processing of video tasks due to mistrust and lack of incentives. To tackle these challenges, we propose a cooperative computing offloading and resource allocation framework that integrates blockchain and MEC to provide secure and low-latency computing offloading services for the IoV system. We also design an efficient incentive mechanism to promote the collaborative processing of video tasks. Our framework formulates computing offloading and resource allocation as a joint optimization problem to maximize the system revenue, and we propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the distributed optimization problem with fast convergence and low complexity. Simulation results demonstrate that compared to the typical baselines, our scheme can achieve the maximum system revenue and effectively reduce the system delay.

With the widespread adoption of resource-intensive mobile applications, mobile edge computing (MEC) has emerged as a solution to enhance the computational power of mobile user equipments (UEs) and minimize their computational delay by offloading tasks to edge servers (ESs). This paper delves into the computing offloading challenge for multiple UEs in dynamic Internet of Things (IoT) networks with partial information-sharing. In such settings, the transmission bandwidth for each UE varies over time, and they can only access the historical data of their peers. Since UEs are self-interested in offloading computational tasks to ESs that possess limited computational resources, we model the UEs’ offloading decision-making in this dynamic, privacy-bound scenario as a game. Subsequently, this game is further formulated as a multi-agent Partially Observable Markov Decision Process (POMDP). To address the POMDP and attain a near-optimal Nash equilibrium (NE) of the structured game, we introduce an algorithm grounded in multi-agent reinforcement learning, integrating Differentiable Neural Computer and Advantage Actor-Critic framework (abbreviated as DNA). Through this method, each UE autonomously decides the optimal computing offloading strategy based on its game history, without obtaining the detailed offloading policies of other UEs. Experimental outcomes reveal that DNA surpasses the state-of-the-art benchmark methods by at least 8.3% in computing offloading utilities and 3.98% in convergence rate, highlighting its effectiveness in a dynamic IoT environment with partial information-sharing between UEs.

With the rapid development of wireless communications, integrated sensing and communication (ISAC) has attracted considerable attentions, which enables both data transmission and target detection simultaneously by spectrum sharing. The adaptive Orthogonal Frequency Division Multiplexing (OFDM) shared waveform design can dynamically adjust power allocation based on the preferences of the radar or communication system, which achieves optimal ISAC performance with given static channel conditions. For the perfect channel state information (CSI) is hard to obtain due to the feedback errors, we then propose a robust OFDM shared waveform design, which achieves better performance under the worst-case channel states. The Karush-Kuhn-Tucker (KKT) conditions are formulated and an improved greedy algorithm is introduced to adjust the bit and power allocation on each subcarrier adaptively. Theoretical analysis and simulation results verify the effectiveness of the proposed algorithm for the joint optimization of both radar and communication systems.

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.

As a supplement of cloud computing, fog computing has attracted wide attention due to its lower latency in data offloading. At present, most researches can only offload data through fixed nodes or can not effectively reduce the offloading delay of different types of data. And it is still a great challenge to develop an effective mechanism for offloading fog nodes selection. Due to the consumption of bandwidth, storage capacity, power and other resources in the process of data transmission, we must formulate a pricing strategy to ensure the revenue of fog nodes. Game theory is a widely adopted method to analyze the pricing strategy between clients and fog nodes. Therefore, this paper uses Stackelberg game to model the interaction between clients and fog nodes. And put forward the best strategy of clients and fog nodes by seeking their Nash equilibrium. Finally, the simulation results show that this mechanism can effectively reduce the offloading delay of clients and improve the revenue of fog nodes.

Mobile edge computing (MEC) enables terminals to migrate their tasks to edge servers instead of the central cloud for efficient execution. However, most researches on task offloading are limited to binary offloading for atomic tasks with a single edge server, while in practice, serial tasks of computation-intensive applications are more important. Therefore, we jointly study the task offloading and resource allocation for serial tasks in the multi-terminal multi-server scenario. A serial task is divided into multiple sub-tasks that are executed sequentially, leading to a better utilization of fragmented resources. The offloading mechanism of inter-coupled terminals is formulated as a noncooperative stochastic game, with evaluation indexes defined by the joint task priority, average task delay and energy consumption. Aiming at minimizing the long-term cost of the whole system, we adopt a multi-agent reinforcement learning (MARL) algorithm with dynamically adjusted offloading strategies, subchannels, transmit power, and allocated resources with only the partial state information. Simulation results demonstrate the feasibility of the proposed algorithm to solve the formulated problem in a distributed way. Compared with the other five benchmark algorithms, it has better system cost performance, and can schedule delay-sensitive tasks with higher priority earlier on the basis of a lower task failure rate.

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

In order to minimize the total energy consumption of a cloud-fog network, one of the most essential challenges is the assignment of subtasks from the task node (TN) to suitable fog nodes (FNs). In this paper, we apply a many-to-one matching to deal with this problem. Specifically, we first introduce two concepts, Service Efficiency (SE) and Energy Efficiency (EE), as the indexes of the preference list (PL) of TNs and FNs, respectively. Then a stable matching algorithm for energy-minimized task offloading (SMETO) is proposed, which is comprised of two key components: (i) Deferred Acceptance Algorithm Based On Energy Efficiency (EEDA) and (ii) Energy-Minimized Task Allocation (EMTA). Algorithm (i) is an iterative procedure that matches TNs and helpers based on PLs. Algorithm (ii) minimizes the energy consumption in the network by allocating the subtasks to helpers according to the result of matching. Finally, numerical simulations indicate the stability and energy-minimization of our proposed SMETO.

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.

Fog computing is expected to excavate and make full use of the inherent idle communication, cache, computation, and control resources of massive devices, and to relieve the pressure of cloud computing on link congestion, delay, and energy consumption. However, how to accurately sense the resources of all fog nodes (FNs) in real time is vital to efficient resource scheduling in the fog computing networks. Frequent sensing will result in both high sensing accuracy at the fog controller (FC) and cost at the FNs. To this end, we propose a novel incentive framework to motivate the FNs to feed back their resource sensing data frequently to the FC based on Stackelberg game. The FC plays as the leader with the sensing reward prices as its strategy, and the FNs play as the followers with the sensing frequency as their strategies. The utility functions of the FC and the FNs are proposed, considering the payment for resource sensing, the accuracy of sensing and the cost of sensing. The existences of the global optimum of both utilities for the FC and the FNs are proved. Closed-form solutions for the optimal sensing frequencies of the FNs are derived. Numerous simulations are done verifying our theoretical analyses and indicating the importance of our proposed incentive framework for resource sensing in the fog computing network.

In this paper, we propose an energy-efficient power control algorithm (EPCA) using a game theory approach, which can be exploited for non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets). Firstly, we formulate the energy efficiency (EE) maximization problems for users in the macrocell and femtocell, respectively. Then, to reduce information exchanged between the base stations and users, we present a centralized implementation of the EPCA based on a noncooperative game. By studying the properties of the derived game, we develop convex optimization problems to deduce the unique Nash equilibrium (NE). Simulation results show that the proposed EPCA could converge to the equilibrium with higher system-level EE and spectrum efficiency.

Fog computing is expected to cope with the long latency and heavy link burden existing in cloud- based networks. Computing tasks of the terminal node can be offloaded to nearby fog nodes thus achieving much lower processing delay than that of cloud-based networks. Existing researches for energy consumption in fog computing networks mainly focus on the total energy consumed by processing a task. However, fair offloading among multiple fog nodes while maintaining a low task delay is of great significance especially for the battery-powered fog nodes. This paper proposes an analytical framework of the fair task offloading for fog computing networks. Task delay and the corresponding energy consumption are formulated. Then, a fairness scheduling metric is constructed for each fog node. A two-step Fair Task Offloading (FTO) scheme is proposed finally, which selects offloading fog nodes according to the fairness metric and then offloads tasks to the selected nodes based on a rule that minimizes the task delay. Numerical simulations and comparisons indicate the satisfactory performance of the proposed task offloading scheme for maintaining a relatively high fairness index for energy consumption and low task delay in the fog computing networks.

In this paper, an incentive proactive cache mechanism in cache-enabled small cell networks (SCNs) is proposed, in order to motivate the content providers (CPs) to participate in the caching procedure. A network composed of a single mobile network operator (MNO) and multiple CPs is considered. The MNO aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the MNO's small base stations (SBSs) to improve the quality of service (QoS) of their users. This problem is formulated as a Stackelberg game where a single MNO is considered as the leader and the multiple CPs willing to cache files are the followers. The followers game is modeled as a non-cooperative game and both the existence and uniqueness of a Nash equilibrium (NE) are proved. The closed-form expression of the NE which corresponds to the amount of storage each CP requests from the MNO is derived. An optimization problem is formulated at the MNO side to determine the optimal price that the MNO should charge the CPs. Simulation results show that at the equilibrium, the MNO and CPs can all achieve a utility that is up to 50% higher than the cases in which the prices and storage quantities are requested arbitrarily.

We consider a heterogeneous network (HetNet) in which multiple small cell base stations (SBSs) aim to offload a quantity of macro cell user equipments (MUEs) to reduce the energy consumption of the network while guaranteeing the QoS requirements of all UEs. We design an ascending-bid auction mechanism to achieve this goal. Unique and closed form solutions for the demand and supply quantities of offloading MUEs are derived. When the MBS has knowledge about the utilities and strategies of the SBSs, the proposed auction can be formulated as a Stackelberg game where the clinching bid price is obtained in closed form. Numerical results verify the theoretical analysis for different scenarios and show that the proposed auction clinches fast at the unique clinching price, thereby resulting in a win-win solution that improves the energy consumption of the HetNet.

When there are strategic and malicious users in a wireless network, the resource allocation is complicated due to the information limitation about the nature of users and network parameters. Bayesian games are appropriate tools to analyze the network resource allocation with heterogeneous users. We consider a scenario with arbitrary number of malicious users in the network, in which individual users gather probabilistic information about the density of malicious users. Users and the base station observe the network over a long time period and modify their actions accordingly. The power allocation in wireless networks which we consider in this paper, is subject to Quality of Service (QoS) requirements. We consider Bayesian pricing mechanisms where the prices are modified using the Bayesian information about types of the users to satisfy the QoS requirements. We also give detection methods based on regression learning algorithms which are used for forming the probability of a user being malicious. The utilities of the users are formed by observing the power strategies of the users and the anomalies are detected. We obtain numerically, the Bayesian Nash Equilibrium (BNE) points of the Bayesian games. We also evaluate the effect of incomplete information on the satisfaction of the QoS requirements of the users in the mechanisms. These mechanisms are with prices which were originally developed for networks with complete information.

This paper studies the spectrum sharing framework for motivating the hybrid access in the two-tier macro-femtocell networks. The power allocation of each user equipment (UE) is subject to its quality-of-service (QoS) requirement as a function of the signal-to-interference plus noise ratio (SINR). The macro base station (MBS) offloads the macro UEs (MUEs) to the femto access points (FAPs) in order to improve the energy efficiency of the entire system since certain MUEs are closer to the FAP(s) than to the MBS. When multiple femtocells exist in the network, auction mechanism is appropriate to establish the hybrid access. Each FAP bids for serving extra MUEs while the MBS acts as the auctioneer. In order to reduce the overhead of the information exchange, we assume that the FAPs decide their bids independently of each other by maximizing their own utilities. After receiving the bids, the MBS searches the winner FAP(s) and optimizes the number of offloaded MUEs. The compensation fee based on the bid is paid by the MBS to the winner FAP(s) for serving the additional MUEs. Numerical results show that the lowest bidder wins the auction. The proposed auction for motivating the hybrid access in the two-tier macro-femtocell network results in a win-win solution since both utilities of the MBS and FAPs are maximized.

We develop the noncooperative game with individual pricing for the general multiple access channel (MAC) system without successive interference cancellation (SIC). Each user allocates its own power by optimizing the individual utility function with clever price adaptation. We show that by the proposed prices, the best response (BR) power allocation of each user converges rapidly. The individual prices are proposed such that the Shannon rate-based quality-of-service (QoS) requirement of each user is achieved at the unique Nash equilibrium (NE) point. We analyse different behavior types of the users, especially the malicious behavior and the resulting NE power allocation and achievable rates of all the users with malicious users. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations.

The success of future wireless networks depends on the correct and robust operation with selfish or even malicious nodes. Game theory provides methods to design such wireless systems. In this paper, we study a general multiple access system (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements and pay a corresponding virtual fee to the regulator depending on their transmit power. The regulator ensures the QoS requirements of all users by clever non-linear pricing and prevents cheating. The simple system optimizer solves the system utility maximization problem to allocate the power. The feasible utility region, power allocation, weights, the universal pricing, which is linear in the pricing parameters and logarithmic in power, and the resulting cost terms are derived in closed form. The user misbehavior is analyzed. Finally a repeated game is formulated with the worst case strategy for all the honest users and trigger strategy for the cheater. Analysis and simulation results show that the proposed framework is strategy-proof.

A novel compensation framework to motivate the hybrid access in the uplink transmission of the femtocell network is discussed in this paper, based on the power allocation to each user equipment (UE) in order to achieve the signal-to-interference-plus-noise-ratio (SINR) based quality-of-service (QoS) requirement. The energy efficiency of the whole macro-femtocell network is the utility of the macrocell base station (MBS). Hybrid access, in which the femtocell access points (FAPs) can serve the certain number of nearby macro UEs (MUEs), helps to save the sum power consumption. However, the rate-based utility of the registered femtocell UEs (FUEs) is decreased with the additionally served MUEs. Therefore, the MBS compensates the FAP to motivate the hybrid access. A Stackelberg game is formulated where the MBS serves as a leader and the FAP serves as a follower. The optimal number of accepted MUEs in the hybrid access and the optimal compensation price are derived. Numerous simulations are conducted showing that the proposed compensation framework can lead to a win-win solution.

This paper studies universal cheat-proof pricing by a repeated game for the general multiple access channel (MAC) without successive interference cancelation (SIC). We model the system by three entities: regulator, system optimizer and users. The regulator is designed to ensure the signal-to-interference plus noise ratio (SINR) based quality-of-service (QoS) requirements of all users and prevent cheating. The feasible utility region, power allocation, corresponding weights, the universal pricing which is linear in pricing parameters and logarithmic in power, and the resulting cost terms are provided. The user misbehavior to maximize their own user-utility is analyzed. A repeated game is formulated with worst case strategy for all the honest users and trigger strategy with trigger pricing for the malicious user once cheating is detected. Analysis and simulation results show that it is possible for the regulator to compute a trigger pricing such that misbehavior is prevented in the repeated game.