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We consider a rate-splitting multiple access (RSMA)-based communication and radar coexistence (CRC) system. The proposed system allows an RSMA-based communication system to share spectrum with multiple radars. Furthermore, RSMA enables flexible and powerful interference management by splitting messages into common parts and private parts to partially decode interference and partially treat interference as noise. The RSMA-based CRC system thus significantly improves spectral efficiency, energy efficiency and quality of service (QoS) of communication users (CUs). However, the RSMA-based CRC system raises new challenges. Due to the spectrum sharing, the communication network and the radars cause interference to each other, which reduces the signal-to-interference-plus-noise ratio (SINR) of the radars as well as the data rate of the CUs in the communication network. Therefore, a major problem is to maximize the sum rate of the CUs while guaranteeing their QoS requirements of data transmissions and the SINR requirements of multiple radars. To achieve these objectives, we formulate a problem that optimizes i) the common rate allocation to the CUs, transmit power of common messages and transmit power of private messages of the CUs, and ii) transmit power of the radars. The problem is non-convex with multiple decision parameters, which is challenging to be solved. We propose two algorithms. The first sequential quadratic programming (SQP) can quickly return a local optimal solution, and has been known to be the state-of-the-art in nonlinear programming methods. The second is an additive approximation scheme (AAS) which solves the problem globally in a reasonable amount of time, based on the technique of applying exhaustive enumeration to a modified instance. The simulation results show the improvement of the AAS compared with the SQP in terms of sum rate.

In this paper, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To reduce the probability of paying claim, the CSSV has an incentive to invest in improving its cloud security service. To meet these requirements, we propose a two-stage Stackelberg game. In the first stage, the CSSVs jointly set the price charging the users and determine the security investment to improve their offered cloud security service. In the second stage, the users decide on the amount of the cloud security plan to purchase based on the prices of the cloud security plan and the perceived cyber breach probability of the cloud security service. The existence and uniqueness of the equilibrium of the Stackelberg game are proven analytically. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game.

Federated Learning (FL) as a promising technique is able to address the privacy issues in machine learning. However, due to the broadcast nature of wireless channel, one of the key challenges of FL is its vulnerability to wireless security threats. Thus, in this paper, we consider the model update security in FL. In particular, we propose to adopt a covert communication technique with which a friendly jammer transmits jamming signals to prevent a warden from detecting local model update transmissions of mobile devices in FL. The use of jamming signals reduces the transmission rate of the devices. Thus, we formulate an optimization problem that jointly determines the jamming power, local model transmission power, and local training accuracy to minimize the FL latency, given a security performance requirement. The problem is non-convex, and we propose an alternating descent algorithm to solve it. Extensive simulations are conducted and the results demonstrate the effectiveness and network performance improvement of the proposed algorithm

Recently, due to the staggering growth of wireless data traffic, heterogeneous networks have drawn tremendous attention due to the capabilities of enhancing the capacity/coverage and to save energy consumption for the next generation wireless networks. In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Namely, the users select the network based on not only their instantaneous achievable service experience but also their past service experience within their memory. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We analytically prove that the equilibrium of the fractional evolutionary game exists, is unique and uniformly stable. We also numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive numerical results have been conducted to evaluate the performance of the fractional evolutionary game. The numerical results have revealed some insightful findings. For example, the user in the fractional evolutionary game with positive memory effect can achieve a higher cumulative utility compared with the user in the fractional evolutionary game with negative memory effect. Moreover, the fractional evolutionary game with positive memory effect can reduce the loss in the user's cumulative utility caused by the small-scale fading.

A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.

By enabling revenue sharing between the network operators and the sponsors, the sponsored data has been proven to be a promising solution and is becoming a ubiquitous trend in the fifth generation (5G) networks for improving data connectivity for the users, increasing mobile engagement for the sponsors, and ensuring revenue for the network operators. In this paper, we investigate the data sponsored 5G system on a long-run basis. Compared with the conventional dynamic, i.e., long-run, model, the users in the system are memory-affecting, i.e., the users’ decision-making is affected by their past service experience. In the system under our consideration, the users decide on the communication service access by jointly taking into account their instantaneous achievable utility and the history of their service experience, e.g., the past improved utility corresponding to the data sponsorship. The 5G system works as the utility provider for managing the communication service. Specifically, by using the concept of the power-law fading memory and the classical evolutionary game theory, we formulate a population game to model and study the dynamic behaviors of the players in the data sponsored 5G system. In the game, the interaction among the memory-affecting rational users is formulated as a fractional evolutionary game, and the communication service management of the 5G system is formulated as a classical evolutionary game. We analytically prove the existence and uniqueness of the solution to the population game. We both analytically and numerically verify the stability of the solution. The performance evaluation shows some insightful results. For example, the data sponsorship can significantly increase the data consumption for the users when they are heavily memory-affecting. Following this, we study a data sponsorship pricing problem with the objective to maximize the data consumption at the expense of the minimal data sponsorship.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

Decentralized security function deployment (activation) is an important solution to achieve reliable and secure operations of wireless networks. In this paper, we consider the joint problem of security function activation and traffic routing in a wireless multihop network with the aim of minimizing total energy consumption of all nodes in the network while meeting the security requirements of the users. Firstly, we analyze the tradeoff of energy consumption between preventing security damages with protection functions and remedying it with recovery functions. The analysis takes the uncertainty of attacks and network capacity availability into account. Secondly, if the protection function is to be activated, the activation has to be performed along the route of traffic flows, which means that the traffic routing should also be jointly optimized. Therefore, we formulate this problem as a stochastic optimization model. To obtain the optimal solution, we apply a distributed algorithm, i.e., accelerated distributed augmented Lagrangian algorithm which is ensured to converge. Furthermore, we analytically demonstrate that the solution obtained by the accelerated distributed augmented Lagrangian algorithm is optimal to our stochastic optimization model. Based on the setting from real experiments, the performance evaluation reveals that the optimal solution depends largely on the energy budget and energy consumption of the nodes for transferring traffic and running security functions.

The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model., Comment: 16 pages, double-column version, accepted by IEEE Internet of Things Journal

In this paper, we address dynamic network selection problems of mobile users in an intelligent reflecting surface (IRS)-enabled wireless network. In particular, the users dynamically select different service providers (SPs) and network services over time. The network services are composed of adjustable resources of IRS and transmit power. To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve. For this, the replicator dynamics is used to model the service selection adaptation of the users. To allow the users to take their past service experiences into account their decisions, we further adopt an enhanced version of the evolutionary game, namely fractional evolutionary game, to study the SP and network service selection. The fractional evolutionary game incorporates the memory effect that captures the users' memory on their decisions. We theoretically prove that both the game approaches have a unique equilibrium. Finally, we provide numerical results to demonstrate the effectiveness of our proposed game approaches. In particular, we have reveal some important finding, for instance, with the memory effect, the users can achieve the utility higher than that without the memory effect.

In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive numerical results have been conducted to evaluate the performance of the fractional evolutionary game.

With the advancement and emergence of new network services, such as social network, Internet of Things, and crowdsensing, large volume of diverse data is collected, shared, and leveraged to develop analytics services. The data analytics service has become a key commodity that can be traded among various economic entities. In this paper, we address the optimal pricing mechanisms and data management for data analytics services and further discuss the perishable services in the time varying environment. We first propose a data market model and define the data utility based on the impact of data size on the performance of data analytics, e.g., prediction and verification accuracy. For perishable services, we study the perishability of data that affects the service quality and provide a quality decay function. The data analytics services are considered as digital goods and uniquely characterized by “unlimited supply” compared to conventional goods. Therefore, we apply the Bayesian profit maximization mechanism in selling data analytics services, which is truthful, rational, and computationally efficient. The optimal service price, data amount, and service update interval are obtained to maximize the profit under different customer’s valuation distributions. Finally, experimental results on real-world datasets show that our data market model and pricing mechanism effectively solve the profit maximization problem and provide useful strategies for the data analytics service provider.

With the tremendous success of machine learning and increasingly powerful mobile devices, federated learning has gained growing attention from both academia and industry. It capitalizes on vast number of distributed data to support machine learning based applications while maintaining data privacy. In this paper, we consider a federated learning system, in which the mobile devices allocate their data and computation resources among the machine learning applications, i.e., model owners. Specifically, we formulate an evolutionary game mode for the mobile devices with bounded rationality to adapt their training strategies aiming to maximize the device's individual utility. The uniqueness and stability of the equilibrium of the game are analysed theoretically. Besides, Extensive experiments are conducted to determine the functions fitted for the accuracy and energy consumption metrics.

For the sake of protecting data privacy and due to the rapid development of mobile devices, e.g., powerful central processing unit (CPU) and nascent neural processing unit (NPU), collaborative machine learning on mobile devices, e.g., federated learning, has been envisioned as a new AI approach with broad application prospects. However, the learning process of the existing federated learning platforms rely on the direct communication between the model owner, e.g., central cloud or edge server, and the mobile devices for transferring the model update. Such a direct communication may be energy inefficient or even unavailable in mobile environments. In this paper, we consider adopting the relay network to construct a cooperative communication platform for supporting model update transfer and trading. In the system, the mobile devices generate model updates based on their training data. The model updates are then forwarded to the model owner through the cooperative relay network. The model owner enjoys the learning service provided by the mobile devices. In return, the mobile devices charge the model owner certain prices. Due to the coupled interference of wireless transmission among the mobile devices that use the same relay node, the rational mobile devices have to choose their relay nodes as well as deciding on their transmission powers. Thus, we formulate a Stackelberg game model to investigate the interaction among the mobile devices and that between the mobile devices and the model owner. The Stackelberg equilibrium is investigated by capitalizing on the exterior point method. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the Stackelberg game.