We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario. Most existing MP-MAB methods are impractical for real-world OSA systems as they assume many ideal conditions, incur a heavy computational cost, and most importantly, ignore the impact of channel noise which is directly related to the quality of service. In this study, we embody this impact by modeling channel noise as a perturbation of the arm’s reward function in MP-MAB. As there is an implicit correlation between channel state information and channel noise, we take the former as a context for MP-MAB to present the perturbation caused by the latter. We investigate two types of correlation between the context and the perturbation—linear and nonlinear, and derive two index policies, respectively. These policies learn the correlations through a linear model and a neural network, and use estimated noise value to adjust the upper confidence bound. Numerical experiments demonstrate that the proposed policies can achieve lower regret and select sub-optimal arms in a more reasonable way.

We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

We aim to develop a system-level security solution for a large-scale device-to-device (D2D) network against adversaries based on covert communication. The D2D network underlays a downlink cellular network to reuse the cellular spectrum and is enabled for simultaneous wireless information and power transfer (SWIPT). In the D2D network, the D2D transmitters communicate with the D2D receivers, and the D2D receivers extract information and energy from their received radio-frequency (RF) signals. In the meantime, the adversaries aim to detect the D2D transmission. The D2D network applies power control and leverages the cellular signal to achieve covert communication (i.e., hide the presence of transmissions) so as to defend against the adversaries. We model the interaction between the D2D network and adversaries by using a two-stage Stackelberg game. Therein, the adversaries are the followers minimizing their detection errors at the lower stage and the D2D network is the leader maximizing its network utility constrained by the communication covertness and power outage at the upper stage. Both power splitting (PS)-based and time switch (TS)-based SWIPT schemes are explored. We characterize the spatial configuration of the large-scale D2D network, adversaries, and cellular network by stochastic geometry. We analyze the adversary’s detection error minimization problem and adopt the Rosenbrock method to solve it, where the obtained solution is the best response from the lower stage. Taking into account the best response from the lower stage, we develop a bi-level algorithm to solve the D2D network’s constrained network utility maximization problem and obtain the Stackelberg equilibrium. We present numerical results to reveal interesting insights. For example, the PS-based SWIPT scheme outperforms the TS-based SWIPT scheme in terms of both network performance (e.g., link reliability and power outage probability) and resistance to the adversary, i.e., steady network utility against increasing aggressiveness of the adversary.

To alleviate the spectrum scarcity problem, exploiting the vast available spectrum provided by the Millimeter-Wave (mmWave) frequency band and underlaying cellular network by Device-to-Device (D2D) communication are two promising solutions. In this paper, we focus on D2D-underlaid cellular network, where the D2D communication is performed on a hybrid manner (i.e., operating over either mmWave or microwave frequency band). To secure the hybrid D2D communication against vigilant adversary, we apply covert communication to hide its presence. In particular, the D2D transmitters perform power control and communication mode switch as well as leveraging the cellular signal to avoid the transmission detection by the adversaries. We model the conflict between the D2D transmitters and adversaries in the framework of a two-stage Stackelberg game. The D2D transmitters are the leaders to maximize their utility subject to the constraints on communication covertness at the upper stage. The adversaries are the followers to minimize their detection errors at the lower stage. We apply stochastic geometry to mathematically characterize the network spatial configuration and consider a large-scale D2D-underlaid network, enabling the study from system-level perspective. We analyze the game equilibrium and obtain it by adopting a bi-level algorithm. Numerical results are provided and insightful conclusions are drawn. Compared with the conventional D2D communication, hybrid D2D communication shows a significant advantage regarding throughput under the same security requirement while weak resistance to the more stringent security requirement.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

To meet the surging wireless traffic demand, underlaying cellular networks with device-to-device (D2D) communication to reuse the cellular spectrum has been envisioned as a promising solution. In this paper, we aim to secure the D2D communication of the D2D-underlaid cellular network by leveraging covert communication to hide its presence from the vigilant adversary. In particular, there are adversaries aiming to detect D2D communications according to their received signal powers. To avoid being detected, the legitimate entity, i.e., D2D-underlaid cellular network, performs power control aiming to hide the D2D communication. We model the conflict between the adversaries and the legitimate entity as a two-stage Stackelberg game. Therein, the adversaries are the followers intending to detect D2D communication at the lower stage while the legitimate entity is the leader and aims to maximize its utility constrained by the D2D communication covertness and the cellular quality of service (QoS) at the upper stage. Different from the conventional works, the study of the combat is conducted from the system-level perspective, where the scenario that a large-scale D2D-underlaid cellular network threatened by massive spatially distributed adversaries is considered and modeled by stochastic geometry. We obtain the adversary’s optimal strategy as the best response from the lower stage and also both analytically and numerically verify its optimality. Taking into consideration the best response from the lower stage and based on the successive convex approximation (SCA) method, we devise a bi-level algorithm to find the optimal strategy of the legitimate entity, which together with the best response from the lower stage constitute the Stackelberg equilibrium. Numerical results are presented to evaluate the network performance and reveal practical insights that instead of improving the legitimate utility by strengthening the D2D link reliability, increasing D2D transmission power will degrade it due to the security concern.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

Unmanned aerial vehicles (UAVs) are an emerging technology that can be effectively utilized to perform data collection tasks in the Internet of Things (IoT) networks. However, both the UAV and the sensors in these networks are energy-limited devices, necessitating an energy-efficient data collection procedure to ensure network lifetime. In this paper, we consider a UAV-assisted network, where a UAV flies to the ground sensors according to a predetermined schedule and controls the sensor’s transmit power when hovering above the sensor. Our goal is to minimize the total energy consumption of the UAV and the sensors, which is needed to accomplish the data collection mission. We formulate this problem into two sub-problems of UAV navigation and sensor power control and model each part as a finite-horizon Markov Decision Process (MDP). We deploy the deep deterministic policy gradient (DDPG) method to generate the best trajectory for the UAV in an obstacle-constrained environment and to control the sensor’s transmit power during data collection. Our simulations show that the UAV can find a safe and energy-efficient path for each trip. In addition, continuous sensor power control achieves better performance against the fixed-power and fixed-rate approaches in terms of the total energy consumption during data collection.

Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.

In this paper, we study a covert Internet of Things (IoT) system. Compared with conventional IoT systems that apply cryptography and information-theoretic secrecy approaches to secure the transmission, our considered IoT system adopts the covertness technique and intends to hide the legitimate transmission from the observant adversaries. In the IoT system, the IoT devices randomly transmit the collected data to their associated IoT gateways (GWs). In the meantime, the adversaries attempt to detect the existence of legitimate transmission based on their received signal power and launch hostile attacks accordingly. To avoid being detected by the adversaries, the IoT system applies uplink power control to achieve covert legitimate transmission. Moreover, to distort the observation of the adversaries so as to mislead their decisions, we propose an artificial noise (AN)-assisted covert communication design, where the AN is transmitted by in-band full-duplex (IBFD) IoT GWs as a jamming operation. We formulate a Stackelberg game to study the interaction between the adversaries and the legitimate entities including the IoT GWs and IoT devices, where the legitimate entities, as the leaders, decide on the powers of legitimate and AN transmissions at the upper level and the adversaries, as the followers, aim to minimize their detection errors at the lower level. Thereafter, considering the large scale of IoT system, we further cast the Stackelberg game into a mean-field Stackelberg game and incorporate the stochastic geometry and statistical channel model to capture the location heterogeneity and channel dynamics among and of the system entities, respectively. In the performance evaluation, we verify the practicability of the mean-field Stackelberg game. Moreover, we demonstrate the effectiveness of AN in improving the transmission covertness.

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 reducing 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 simulations 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.

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.

Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved.

With the increasing use of smart objects, such as wearable health gadgets, household automation devices, and personal electronics, there is a growing demand for a globally interconnected information network, known as the Internet of Things (IoT). IoT is featured with low-power communications among a massive number of ubiquitously-deployed and energy-constrained electronics, like sensors and actuators. In this context, wireless-powered cooperative relaying emerges as a promising solution to extend coverage and solve energy scarcity problems for IoT devices. In this paper, we propose a novel hybrid relay by combining wireless-powered communications and ambient backscattering functions for improved applicability and performance. To well adapt the hybrid relay to the network environments, we design a mode selection protocol to coordinate between the two functions. Moreover, we analyze the successful transmission probability of a dual-hop relaying system with the hybrid relay. Through numerical results, we demonstrate the performance gain of the hybrid relay and the impact of the system parameters.

Due to the fading and broadcast nature of wireless medium, it is challenging to provide full wireless coverage and secure the transmitted signals from unintended users in cellular networks. As a result, cyber risks, such as service outage and secrecy outage, would inevitably occur and cause loss/damage to the users. To transfer the cyber risks and mitigate the impact of loss, cyber insurance appears to be a promising solution for the economics of wireless services. In this paper, we introduce a cyber insurance framework for wireless users to relieve loss from the cyber risks. In this framework, each user pays a premium to an insurer. If the user experiences an outage, he/she will claim the loss, and the insurer will pay the corresponding %claim or indemnity to the user. Under the network model of a large-scale massive multiple-input multiple- output (MIMO) cellular networks and cyber insurance, we first characterize the user performance in terms of both service outage probability and secrecy outage probability using stochastic geometry analysis. Based on these performance results, we quantify the ruin probability of the cyber insurer, which indicates the chance that the insurer does not have enough capital reserve to afford the claims from the outage users. Through numerical evaluation, we show that the ruin probability of the insurer can be efficiently reduced by equipping a larger number of antennas at base stations or increasing network frequency reuse.

The fifth-generation (5G) wireless networks are expected to provision value-added services with ubiquitous coverage, which makes data security unprecedentedly critical. In this context, physical layer security has emerged as a promising solution to safeguard data transmission by exploiting characteristics of the wireless medium. Despite the recent technological advance in physical layer security and wireless transmission, secrecy outages (i.e., data breaches) and service outages (i.e., connection failures) will inevitably happen and incur financial losses. This economical consequence is a fact that is mostly overlooked by the existing literature. To provide financial protection against secrecy outage and service outage, we introduce a cyber-insurance framework for wireless users in cellular networks, where each user pays a premium to an insurer for a future financial compensation if an outage occurs to him/her. In particular, we derive the network risks of the cellular users in terms of secrecy outage probability and service outage probability as well as the financial risk of the cyber insurer in terms of the ruin probability that indicates the chance that the insurer experiences a deficit in affording the losses of outage users. Through numerical evaluation, we demonstrate the impact of network performance on the financial risk of the insurer. The numerical results also show that the ruin probability of the insurer can be effectively reduced by equipping a larger number of antennas at the base stations or increasing network frequency reuse.

The recent advanced wireless energy harvesting technology has enabled wireless-powered communications to accommodate wireless data services in a self-sustainable manner. However, wireless-powered communications rely on active RF signals to communicate and result in high power consumption. On the other hand, ambient backscatter technology that passively reflects existing RF signal sources in the air to communicate has the potential to facilitate an implementation with ultra-low power consumption. In this paper, we introduce a hybrid device-to-device (D2D) communication paradigm by integrating ambient backscattering with wireless-powered communications. The hybrid D2D communications are self-sustainable, as no dedicated external power supply is required. However, since the radio signals for energy harvesting and for backscattering come from the ambient, the performance of the hybrid D2D communications depends largely on environment factors, e.g., distribution, spatial density, and transmission load of ambient energy sources. Therefore, we design two mode selection protocols for the hybrid D2D transmitter, allowing a more flexible adaptation to the environment. We then introduce analytical models to characterize the impacts of the considered environment factors on the hybrid D2D communication performance. Together with extensive simulations, our analysis shows that the communication performance benefits from larger repulsion, transmission load, and density of ambient energy sources. Furthermore, we investigate how different mode selection mechanisms affect the communication performance.

Self-sustainable communications based on advanced energy harvesting technologies have been under rapid development, which facilitate autonomous operation and energy-efficient transmission. Recently, ambient backscattering that leverages existing RF signal resources in the air has been invented to empower data communication among low-power devices. In this paper, we introduce hybrid device-to-device (D2D) communications by integrating ambient backscattering and wireless-powered communications. The hybrid D2D communications are self-sustainable, as no dedicated external power supply is required. However, since the radio signals for energy harvesting and backscattering come from external RF sources, the performance of the hybrid D2D communications needs to be optimized efficiently. As such, we design two mode selection protocols for the hybrid D2D transmitter, allowing a more flexible adaptation to the environment. We then introduce analytical models to characterize the impacts of the considered environment factors, e.g., distribution, spatial density, and transmission load of the ambient transmitters, on the hybrid D2D communications performance. Extensive simulations show that the repulsion factor among the ambient transmitters has a non-trivial impact on the communication performance. Additionally, we reveal how different mode selection protocols affect the performance metrics.

A wireless powered communication network is a potential application of wireless energy harvesting to improve convenience and flexibility. However, wireless energy transfer from a wireless energy source has to be scheduled to minimize energy usage while meeting quality of service (QoS) requirements of sensor nodes in the network. In this paper, we consider wireless powered sensor network whose sensor nodes have auxiliary energy sources in addition to dedicated wireless energy transfer. We propose a distributed wireless energy transfer scheduling to achieve the aforementioned objective and meet the requirements. We formulate a constrained stochastic game model to obtain a multi-policy constrained Nash equilibrium of wireless energy transfer request. This equilibrium instructs the sensor node to request for wireless energy transfer based on its local state. The performance evaluation shows that the analytical model is well verified by numerical simulations.

RF-enabled wireless power transfer and energy harvesting has recently emerged as a promising technique to provision perpetual energy replenishment for low-power wireless networks. The network devices are replenished by the RF energy harvested from the transmission of ambient RF transmitters, which offers a practical and promising solution to enable self-sustainable communications. This paper adopts a stochastic geometry framework based on the Ginibre model to analyze the performance of self-sustainable communications over cellular networks with general fading channels. Specifically, we consider the point-to-point downlink transmission between an access point and a battery-free device in the cellular networks, where the ambient RF transmitters are randomly distributed following a repulsive point process, called Ginibre α-determinantal point process (DPP). Two practical RF energy harvesting receiver architectures, namely time-switching and power-splitting, are investigated. We perform an analytical study on the RF-powered device and derive the expectation of the RF energy harvesting rate, the energy outage probability and the transmission outage probability over Nakagami-m fading channels. These are expressed in terms of so-called Fredholm determinants, which we compute efficiently with modern techniques from numerical analysis. Our analytical results are corroborated by the numerical simulations, and the efficiency of our approximations is demonstrated. In practice, the accurate simulation of any of the Fredholm determinant appearing in the manuscript is a matter of seconds. An interesting finding is that a smaller value of α (corresponding to larger repulsion) yields a better transmission outage performance when the density of the ambient RF transmitters is small. However, it yields a lower transmission outage probability when the density of the ambient RF transmitters is large. We also show analytically that the power-splitting architecture outperforms the time-switching architecture in terms of transmission outage performances. Lastly, our analysis provides guidelines for setting the time-switching and power-splitting coefficients at their optimal values.

In wireless powered networks, a user can make a request and use the wireless energy transferred from an energy source for its data transmission. However, due to broadcast nature of wireless energy transfer (e.g., RF energy), a malicious node (i.e., an attacker) can also intercept the energy and use it to perform an attack by jamming the data transmission of the user. We consider such a jamming attack where the user and attacker are aware of each other. We formulate a game theoretic model to analyze the energy request and data transmission policy of the user and the attack policy of the attacker when the user and the attacker both want to maximize their own rewards. We use an iterative algorithm designed based on the best response dynamics to obtain the solution defined in terms of the constrained Nash equilibrium. The numerical results show not only the convergence of the proposed algorithm, but also the optimal reward of the user under different energy cost constraints.

The advance in RF energy transfer and harvesting technique over the past decade has enabled wireless energy replenishment for electronic devices, which is deemed as a promising alternative to address the energy bottleneck of conventional battery-powered devices. In this paper, by using a stochastic geometry approach, we aim to analyze the performance of an RF-powered wireless sensor in a downlink simultaneous wireless information and power transfer (SWIPT) system with ambient RF transmitters. Specifically, we consider the point-to-point downlink SWIPT transmission from an access point to a wireless sensor in a network, where ambient RF transmitters are distributed as a Ginibre α-determinantal point process (DPP), which becomes the Poisson point process when a approaches zero. In the considered network, we focus on analyzing the performance of a sensor equipped with the power-splitting architecture. Under this architecture, we characterize the expected RF energy harvesting rate of the sensor. Moreover, we derive the upper bound of both power and transmission outage probabilities. Numerical results show that our upper bounds are accurate for different value of a.

Device-to-Device (D2D) communications, which allow direct communication among mobile devices, have been proposed as an enabler of local services in 3GPP LTE-Advanced (LTE-A) cellular networks. This work investigates a hierarchical LTE-A network framework consisting of multiple D2D operators at the upper layer and a group of devices at the lower layer. We propose a cooperative model that allows the operators to improve their utility in terms of revenue by sharing their devices, and the devices to improve their payoff in terms of end-to-end throughput by collaboratively performing multi-path routing. To help understanding the interaction among operators and devices, we present a game-theoretic framework to model the cooperation behavior, and further, we propose a layered coalitional game (LCG) to address the decision making problems among them. Specifically, the cooperation of operators is modeled as an overlapping coalition formation game (CFG) in a partition form, in which operators should form a stable coalitional structure. Moreover, the cooperation of devices is modeled as a coalitional graphical game (CGG), in which devices establish links among each other to form a stable network structure for multi-path routing. We adopt the extended recursive core, and Nash network, as the stability concept for the proposed CFG and CGG, respectively. Numerical results demonstrate that the proposed LCG yields notable gains compared to both the non-cooperative case and a LCG variant and achieves good convergence speed.

In wireless energy harvesting communication networks, a user receives wireless energy released by an ambient or dedicated energy source, and uses that energy for delay constrained data transmission. However, such data transmission can be susceptible to a jamming attack from a nearby attacker also harvesting from the wireless energy source. In this paper, we consider such a scenario and present performance analysis. In particular, we develop an analytical model for the network based on a Markov chain to obtain various performance measures for the user including throughput and delay distribution. The performance evaluation shows some interesting results. For example, under the jamming attack, there is a maximum achievable throughput of the user. We also validate the analytical model using simulation.

Publish-subscribe mobile social networks enable content providers to disseminate up-to-date contents to end users with the help of mobile content relays by opportunistic wireless contacts. Since content providers, relays and end users are independent and self-interest entities in the mobile social networks, the content relay has to take a content requesting/transferring action to achieve the lowest cost. In this paper, we propose and solve a Markov decision process (MDP) based scheme for the content relay to optimally take the actions to receive contents from content providers, and to transfer/forward contents to end users. The relay takes an action based on the observed content price, the number of end users of contents, as well as the queue length. The proposed MDP scheme aims to minimize an expected cost of the content relay. The numerical results show that the proposed MDP scheme significantly outperforms baseline schemes.

Ambient radio frequency (RF) energy harvesting technique has recently been proposed as a potential solution for providing proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic geometry approach. Specifically, we consider the point-to-point uplink transmission of a wireless sensor in a stochastic geometry network, where ambient RF sources, such as mobile transmit devices, access points and base stations, are distributed as a Ginibre α-determinantal point process (DPP). The DPP is able to capture repulsion among points, and hence, it is more general than the Poisson point process (PPP). We analyze two common receiver architectures: separated receiver and time-switching architectures. For each architecture, we consider the scenarios with and without co-channel interference for information transmission. We derive the expectation of the RF energy harvesting rate in closed form and also compute its variance. Moreover, we perform a worst-case study which derives the upper bound of both power and transmission outage probabilities. Additionally, we provide guidelines on the setting of optimal time-switching coefficient in the case of the time-switching architecture. Numerical results verify the correctness of the analysis and show various tradeoffs between parameter setting. Lastly, we prove that the RF-powered sensor performs better when the distribution of the ambient sources exhibits stronger repulsion.

Ambient RF (Radio Frequency) energy harvesting techniques have recently been proposed as a potential solution to provide proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic-geometry approach. Specifically, we consider a random network model in which ambient RF sources are distributed as a Ginibre α-determinantal point process which recovers the Poisson point process when α approaches zero. We characterize the expected RF energy harvesting rate. We also perform a worst-case study which derives the upper bounds of both power outage and transmission outage probabilities. Numerical results show that our upper bounds are accurate and that better performance is achieved when the distribution of ambient sources exhibits stronger repulsion.

In wireless mesh network (WMN), multiple service providers (SPs) can cooperate to share resources (e.g., relay nodes and spectrum), to serve their collective subscribed customers for better service. As a reward, SPs are able to achieve more individual benefits, i.e., increased revenue or decreased cost, through efficient utilization of shared network resources. However, this cooperation can be realized only if fair allocation of aggregated payoff, which is the sum of the payoff of all the cooperative SPs, can be achieved. We first formulate such cooperation as a coalitional game with transferable utility, specifically, a linear programming game, in which, each SP should obtain the fair share of the aggregated payoff. Then we study the problem of allocating aggregated payoff which leads to stable service coalition of SPs in WMN based on the concepts of dual payoff and Shapley value.