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The mobile traffic explosion has caused spectrum shortage and polarization of data usage among users, which will eventually decrease user welfare (UW) in mobile communication services. Governments around the world are planning to make more spectrum available for mobile broadband use, and the key policy issue is to find an efficient spectrum allocation method that will improve UW. In this paper, we propose a data subsidy scheme where the regulator offers a spectrum price discount to mobile network operators (MNOs) in return for imposing the responsibility of providing a predefined data amount to users free of charge. To analyze the subsidy effect, we adopt the two-stage approach of Cournot and Bertrand competition and find a Nash equilibrium (NE) of the competition. An interesting observation is that the increase in UW does not involve MNO profit (MP) loss and that the increasing amount is higher than the regulator's expenses for implementing the data subsidy scheme. Most of the paper concerns the duopoly competition, which is finally extended to the general case. [ABSTRACT FROM PUBLISHER]

One of the goals of this paper is to contribute to finding distributed power control strategies, which exploit efficiently the information available about the global channel state; it may be local or noisy. A suited way of measuring the global efficiency of a distributed power control scheme is to use the long-term utility region. First, we provide the utility region characterization for general utility functions when the channel state obeys an independent block fading law and the observation structure is memoryless. Second, the corresponding theorem is exploited to construct an iterative algorithm, which provides one-shot power control strategies. The performance of the proposed algorithm is assessed for energy-efficient and spectrally efficient communications and shown to perform much better than state-of-the-art techniques, with the additional advantage of being applicable even in the presence of arbitrary observation structures such as those corresponding to noisy channel gain estimates. [ABSTRACT FROM AUTHOR]

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. [ABSTRACT FROM AUTHOR]

Battery energy is always limited in most wireless networked nodes, but wireless power transfer has the potential to address the problem for good. In this paper, we study the problem of collaborative charging decisions in Wireless Rechargeable Sensor Networks (WRSNs). In these networks, multiple Wireless Charging Vehicles (WCVs) need to decide which of the requesting sensors to charge and in what order. Our approach is to convert the decision makers, i.e., WCVs, into game players for higher profit. With the use of Nash Equilibrium, our newly designed scheme, named Game Theoretical Collaborative Charging Scheduling (GTCCS), enables WCVs to make optimal charging decisions. Besides, the result of the game is Pareto-optimal by carefully designing the rules of this game. We introduce the game theory model in this paper and explain how it is played by WCVs, with the goal of increasing overall energy usage efficiency. Extensive simulations and lab/field experiments show that GTCCS is able to achieve a much higher energy usage efficiency and lower the number of dead nodes. The dynamic characteristics of the GTCCS scheme also allow us to introduce the two unique features to further improve system performance, i.e., dynamic warning threshold for WCVs and sacrifice-charge. [ABSTRACT FROM AUTHOR]

In this paper, the problem of decision making for autonomous vehicles changing lanes is addressed by formulating multiple games in normal form for pairs of agents. This formulation generates the optimal action for the Ego vehicle at a given state and does not consider global optimality for all agents. The payoff matrices of the games are designed based on a user-defined set of rules. The constant parameters of these payoffs are then adjusted using neural learning to generate optimal behavior among the vehicles. An algorithm integrating deep reinforcement learning and game theory, regarded as Nash Q-learning, is included in the decision-making scheme. The applicability of the proposed method in a lane-changing scenario is tested via simulation. [ABSTRACT FROM AUTHOR]

This paper investigates a heterogeneous cellular network (HCN) topology, where multiple sub-6 GHz bands are available for access and device-to-device (D2D) links with the full-duplex (FD) mode, and an integrated millimeter-wave (mm-wave) band is added to the D2D links’ sub-channel resource options. D2D links share spectrum resources of cellular users or dedicated mm-wave sub-channel. Densely deployed small cells scenario provides an ideal platform to implement mm-wave, FD and D2D communications. With large bandwidths available, mm-wave has the potential to meet the expected extreme data rate demands. Leveraging highly directional beamforming, severe transmission loss and the blockage phenomenon of mm-wave can be alleviated. Theoretically, FD communications can double the spectral efficiency due to bi-directional communications with the same spectral and temporal resources. However, such benefit comes at the cost of residual self-interference (RSI). Multi-user interference (MUI) and RSI among network uplink, downlink and D2D links are the main challenges. In the paper, we formulate an optimization problem for FD access and D2D links sub-channel allocation underlaying HCNs combining sub-6 GHz and mm-wave bands to make the system transmission rate maximized. Then, a scheme based on the coalition formation game is proposed to deal with this challenging NP-complete optimization. Finally, performance evaluation investigates the suitable conditions for FD and HD operations. Under different network parameter settings, we quantify associated performance of proposed scheme. [ABSTRACT FROM AUTHOR]

Dynamic spectrum leasing (DSL) is a promising method to improve the spectrum efficiency. A lot of work has been done about spectrum sharing issues with static models. In this paper, we want to study the behavior of the primary users (PUs) under uncertainty. For this objective, we propose a model with time-varying parameters. The model captures both technical and economic features. We study the long-term behavior of the PUs in the model with stochastic Cournot game theory and variational inequality theory. We show that the PUs can maximize their long-term utilities with a static supply of spectrum. In addition, we also propose two algorithms based on stochastic approximation theory. The convergence and performance of the algorithms are demonstrated theoretically and numerically. The proposed model and analytical framework can be used to study and predict the behaviors of PUs given a DSL system and provide design insights for the DSL system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a green spectrum and infrastructure leasing framework for sharing unused TV spectrum, in which the ownership of the network infrastructure and spectrum are decoupled, and each can be leased as a service on demand. An incumbent TV spectrum owner (TSO) can lease a share of the infrastructure from a network infrastructure owner (NIO) with a pay-per-use model, to provide new services to its end users. On the other hand, a TSO can rent a portion of its unused spectrum to the NIO for revenue such that the NIO can obtain access to the spectrum to serve its customers. This proposed framework achieves green communications through both resource sharing and energy saving. Our paper aims to analyze the interplay between TSO and NIO, and create a win–win situation to provide sufficient incentives for them to cooperate on this green communications. A multistage Stackelberg game is formulated, where TSO and NIO sequentially determine the total bandwidth supply for sharing, the infrastructure leasing price, the amount of infrastructure to lease, the spectrum leasing price, and the amount of spectrum to lease to maximize their utilities. The best strategies that will be taken by the TSO and NIO under various deployment scenarios are analyzed. Evaluation results show that the proposed green spectrum and infrastructure leasing framework is a promising scheme under which both TSO and NIO can benefit, and their utilities can be maximized in terms of user data throughput and revenue/payment, which provides sufficient incentives for them to cooperate. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the PHY-layer authentication that exploits radio channel information (such as received signal strength indicators) to detect spoofing attacks in wireless networks. The interactions between a legitimate receiver and spoofers are formulated as a zero-sum authentication game. The receiver chooses the test threshold in the hypothesis test to maximize its utility based on the Bayesian risk in the spoofing detection, whereas the spoofers determine their attack frequencies to minimize the utility of the receiver. The Nash equilibrium of the static authentication game is derived, and its uniqueness is discussed. We also investigate a repeated PHY-layer authentication game for a dynamic radio environment. As it is challenging for the radio nodes to obtain the exact channel parameters in advance, we propose spoofing detection schemes based on Q-learning and Dyna-Q, which achieve the optimal test threshold in the spoofing detection via reinforcement learning. We implement the PHY-layer spoofing detectors over universal software radio peripherals and evaluate their performance via experiments in indoor environments. Both simulation and experimental results have validated the efficiency of the proposed strategies. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

Priority and mutual interaction among vehicles are crucial for the efficiency and safety control of unsignalized intersections. However, most of the approaches for intersection cooperation are based on first-come-first-served (FCFS) rules, neglecting the influence caused by different priorities. Focusing on the priority and mutual interaction between connected and automated vehicles (CAVs), this paper proposes a game-based hierarchical control strategy to improve the traveling efficiency at unsignalized intersections. The three-layered hierarchical strategy includes the priority negotiation layer (PNL), strategy bargaining layer (SBL) and strategy optimization layer (SOL), respectively. In the PNL, a game-theoretic strategy is designed for priority negotiation, in which the most efficient crossing order is sought by the Pareto-optimal set. In the SBL, with the assigned negotiated priority, a bargaining game is designed for further interaction between CAVs. In the SOL, model predictive control is applied for strategy optimization considering the comfort of drivers and executability of vehicles. Two cases of cooperative intersection, in which fixed priority with comparison of the Nash game method and greedy method, and negotiated priority with comparison of fixed priority are carried out in a hardware-in-the-loop (HiL) test. The results demonstrate that the traffic efficiency is improved with safety guaranteed by the proposed hierarchical strategy. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a context-aware group buying mechanism to reduce users’ data cost based on the content similarity. Each user's cost is formulated as the combination of the content-aware data cost and location-aware sharing cost. Data cost is the payoff of the spectrum owner's channel to download files and sharing cost is the energy and time cost in transmitting files among the coalition. Compared with downloading data alone, users would like to form different groups and download the traffic data first and then share data among the group to achieve a lower cost. The cost reducing problem through group buying mechanism is modeled as a coalition formation game (CFG). Besides the traditional Pareto order, a coalition order maximizing the coalition's benefit and a selfish order maximizing users’ benefit are proposed. The CFGs with the two proposed orders are proved to be potential games, respectively, and the existence of the stable coalition partitions are also guaranteed by Nash equilibria. A cooperative exchange mechanism is designed, where users can make decisions cooperatively to achieve better performance. Simulation results show that the context awareness group buying reduces the cost and improves the benefit significantly compared with the situation without context awareness. The proposed orders both have better performance than the Pareto order. [ABSTRACT FROM AUTHOR]

This paper investigates the joint subcarrier and power allocation problem for the downlink of a multi-carrier non-orthogonal multiple access (MC-NOMA) system. A novel three-step resource allocation framework is designed to deal with the sum rate maximization problem. In Step 1, we relax the problem by assuming that each of the users can use all the subcarriers simultaneously. With this assumption, we prove the convexity of the resultant power control problem and solve it via convex programming tools to get a power vector for each user. In Step 2, we allocate the subcarriers to users by a heuristic greedy manner with the obtained power vectors in Step 1. In Step 3, the proposed power control schemes used in Step 1 are applied once more to further improve the system performance with the obtained subcarrier assignment of Step 2. To solve the maximization problem with fixed subcarrier assignments in both Step 1 and Step 3, a centralized power allocation method based on projected gradient descent algorithm and two distributed power control strategies based, respectively, on pseudo-gradient algorithm and iterative waterfilling algorithm are investigated. Numerical results show that our proposed three-step resource allocation algorithm could achieve comparable sum rate performance to the existing near-optimal solution with much lower computational complexity and outperforms power controlled OMA scheme. Besides, a tradeoff between user fairness and sum rate performance can be achieved via applying different user power constraint strategies in the proposed algorithm. [ABSTRACT FROM AUTHOR]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), energy-efficient resource allocation and intercell-interference management are important issues. In this paper, we address these issues using a two-level dynamic scheme. First, we assign the MUs with the optimum number of subchannels that achieves an operator's required balance between macro users' satisfaction and maximization of network efficiency. Then, the remaining subchannels are left to be shared by a number of small cells. In the latter step, a transmit power adaptation method using a noncooperative game-theoretic approach is developed to reduce cochannel interference in the whole network. The problem is formulated by allowing multiple neighboring small cells to share each subchannel (i.e., universal frequency reuse in the small cell level). We fully characterize the pricing factor in the penalty part of the utility function. The existence and uniqueness of the Nash equilibrium (NE) are analyzed and proved. Then, a distributed iterative algorithm based on the fixed-point theorem is proposed to attain the equilibrium of the game. Simulation results are presented to show the effectiveness of the proposed scheme in different network topologies. [ABSTRACT FROM AUTHOR]

Pricing has been widely used to solve network optimization problems. In this paper, we study the case where the network service provider (NSP) has incomplete information about the users, such as the specific user type. Formulating the pricing of the monopolistic NSP and the flow control of the users as a two-level hierarchical game, we first obtain closed-form equilibrium solutions that jointly maximize the benefits of the monopolistic NSP and the users with heterogeneous quality-of-service (QoS) requirements. Then, based on the closed-form solutions, we consider a more general and practical multi-NSP and multiuser model. We further propose a novel one-sided job-hopping matching game to obtain stable NSP-user matching that can optimize network performance in a multi-NSP and multiuser scenario. Simulations demonstrate the effectiveness of the proposed game-theoretic approaches in both the monopolistic and multi-NSP cases. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of channel selection for interference mitigation in opportunistic spectrum access networks using a stochastic game-theoretic approach. The studied network is distributed and dynamic, where each user only has its individual information, and no information exchange is available among users. Moreover, each user is considered to be dynamically active due to its specific data service requirement. Specifically, a user randomly becomes active and then competes for the wireless channel to transmit for a random duration. To capture such dynamic interactions among users, a dynamic interference graph is defined, and based on this, the interference mitigation problem is formulated as a graphical stochastic game. It is proved to be an exact potential game, in which the existence of the Nash equilibrium (NE) is guaranteed. Then, the performance bounds of the NE are theoretically analyzed. Furthermore, we design a fully distributed and online algorithm based on stochastic learning for the interference-mitigation channel selection, which is proved to converge to the NE of the formulated game. Finally, we conduct simulations to validate the effectiveness of the proposed algorithm for interference mitigation and throughput improvement in the distributed and dynamic environment. [ABSTRACT FROM PUBLISHER]

As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network can significantly improve the system capacity and networks coverage. However, it is still a challenge to reduce interference and improve energy efficiency (EE) of UAVs. In this paper, we investigate a downlink power control problem to maximize the EE in an ultra-dense UAV network. Firstly, the power control problem is formulated as a discrete mean field game (MFG) to imitate the interactions among a large number of UAVs, and then the MFG framework is transformed into a Markov decision process (MDP) to obtain the equilibrium solution of the MFG due to the dense deployment of UAVs. Specifically, a deep reinforcement learning-based MFG (DRL-MFG) algorithm is proposed to suppress the interference and maximize the EE by using deep neural networks (DNN) to explore the optimal power strategy for UAVs. The numerical results show that the UAVs can effectively interact with the environment to obtain the optimal power control strategy. Compared with the benchmarks algorithms, the DRL-MFG algorithm converges faster to the solution of MFG and improves the EE of UAVs. Moreover, the impact of the transmit power on EE under the different heights of the UAVs is also analyzed. [ABSTRACT FROM AUTHOR]

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes. [ABSTRACT FROM AUTHOR]

This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constraints. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern. [ABSTRACT FROM AUTHOR]

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing. [ABSTRACT FROM AUTHOR]

An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium, we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages. [ABSTRACT FROM AUTHOR]

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multi-agent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system. [ABSTRACT FROM AUTHOR]

In wireless communication networks, traffics of base stations (BS) can be effectively offloaded by sharing the cached content between mobile devices through wireless device-to-device (D2D) communication. To encourage the content caching of D2D users, a blockchain incentive scheme is proposed; the game theory is introduced to optimize the benefits for both the edge computing server (ECS) and D2D users in the blockchain network. In Stackelberg game, D2D users act as followers. After the successful implementation of the cache strategy, D2D users are rewarded by the system, which is a new block mining process. In this process, the required computing power is provided by the ECS. Therefore, D2D cache users need to pay the cost to purchase computing power from the ECS. As the game leader, the ECS prices the computing resources, and D2D users adjust their caching strategies based on the price. In this paper, we propose four algorithms based on different pricing methods and different cache reward methods. Simulations are performed to compare the cache quality and cache content dispersion of the four algorithms. The results show that the uniform pricing scheme with a linear reward relationship is more suitable for the scenarios that require higher cache quality, and the discriminatory pricing scheme with a nonlinear reward relationship is more suitable for the scenarios that require more evenly cache content distribution. [ABSTRACT FROM AUTHOR]

In this paper, we propose a game theoretic framework for secure physical layer authentication (PLA) by applying the spatial correlations of received signal strength (RSS) in the air-to-ground channels between an unmanned aerial vehicle (UAV), a legitimate transmitter, and a spoofer. Based on our hypothesis model, we first derive the false alarm rate and miss detection rate for the UAV receiver. We then formulate a zero-sum PLA game where the UAV receiver selects its RSS detection threshold while the spoofer selects its attack probability to maximize their respective utilities. We derive the Nash equilibrium (NE) and its existence condition for the proposed PLA game. Monte Carlo simulation results accurately verify our analytical expressions for the false alarm rate and miss detection rate. Numerical results highlight the complex interplay between the detection threshold, spoofing attack probability and UAV altitude in the derived NE. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) are likely to provide ubiquitous access to mobile broadband services as they involve various small cells, each targeting a different environment in the network. In this paper, we are interested in improving the system performance at the cell edge. Hence, we consider a HetNet composed of an eNodeB (eNB) coexisting with different relay nodes (RNs) situated at the cell border. We propose a cooperative physical resource blocks (PRBs) sharing scheme in a dual-hop LTE-Advanced in which every node can transmit on some of the PRBs assigned to a coexisting node without causing it harmful interference. To do so, we use a powerful mathematical tool, i.e., game theory. First, we consider two nodes in the macrocell and define a new repeated game in which a selfish node is encouraged to cooperate under the threat of punishment. Then, we enlarge the number of RNs in the network and find the optimal pairs of nodes that will cooperate, due to a new coalition formation game. Simulation results show that the proposed cooperative PRB sharing scheme significantly improves the utilization of the spectrum and the achieved total throughput in the system. We also demonstrate that both games rapidly attain Pareto efficiency. [ABSTRACT FROM PUBLISHER]

Security is a critical element to the existing Internet of Things (IoT) deployment, where any user may actively or passively attack the content sharing of others reusing the same channel. As most smart devices are carried by human, we may leverage their owners’ social trust to avoid being intercepted by untrusted users, which conforms to the Social Internet of Things (SIoT) paradigm. In this paper, we propose a secure content sharing (SCS) scheme to strike the trade-off between security and quality of experience (QoE) by exploring the social trust. Firstly, to dynamically extract the social trust, the random walk strategy is employed for prediction based on the proposed “User-Content-Social Group” graph which models users’ preference over time. Given the social trust value, we propose a hierarchical game model to decouple the optimization problem into two sub-problems: user pairing and channel selection. More specifically, the user pairing sub-problem is formulated as a matching sub-game with peer effect, and the embedded rotation-swap matching algorithm can accommodate the dynamics caused by mutual interference. The second sub-problem can be formulated as a secure channel selection sub-game with the directed hypergraph being game space, which is proved to be an exact potential game. Then, we design an uncoupled-user concurrent learning algorithm (UUCL) to search for the optimal pure Nash equilibrium, and thereby the global optimum of this sub-game is achieved. Finally, simulation results generated on realistic social dataset verify that our proposed scheme can notably enhance the security without sacrificing users’ QoE. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented Gale-Shapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations. [ABSTRACT FROM AUTHOR]

The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by first, cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by second, D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: first, cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; second, Lyapunov optimization based dynamic channel and UAVs’ activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog. [ABSTRACT FROM AUTHOR]

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices’ priority. This work applies game theory to control devices’ strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices’ strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature. [ABSTRACT FROM AUTHOR]

With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency. [ABSTRACT FROM AUTHOR]

The cognitive femtocell network (CFN) integrated with cognitive-radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environments for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum rate under constraints of intratier and intertier interference while maintaining the average delay requirement for cognitive femtocell users. Specifically, the aggregated interference from cognitive femtocell users to the macrocell base station (MBS) is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs). Then, instead of maximizing the sum rate in all cognitive femtocells, we only maximize the sum rate of each DJG. After that, we formulate the optimization problem as a coalitional game in partition form, which obtains suboptimal solutions. Moreover, distributed algorithms are also proposed for allocating resources to the CFN. Finally, the proposed framework is tested based on the simulation results and shown to perform efficient resource allocation. [ABSTRACT FROM PUBLISHER]

Recently, a new architecture regarding a crowd of mobile devices as mobile cloud has been proposed, where the neighboring mobile devices are pooled together for resource sharing. Most existing works focus on mobile cloud framework design and assume that the nearby users are willing to share their resources. Since each user is rational and self-interested, a proper market mechanism is needed to incentivize users to participate and allocate resources among multiple users. In this paper, we consider a broker-based mobile cloud, where multiple supplying users have idle resources to share, and multiple demanding users suffer from resource limitations. We propose and analyze a double-sided bidding mechanism where each demanding user submits a bid to choose a demand resource-price function, and each supplying user submits a bid to choose a supply resource-price function. We consider the cases with price-taking users and price-anticipating users who would anticipate the influence of their own bids on the price, respectively. For the case with price-taking users, we first show that the proposed mechanism admits a unique competitive equilibrium that maximizes the social welfare of the mobile cloud and then develop an optimal distributed algorithm to achieve the desired equilibrium point. For the case with price-anticipating users, we first formulate the interaction among multiple users as a strategic game and prove the existence and uniqueness of the Nash equilibrium and then develop a distributed algorithm to compute the Nash equilibrium. Numerical results validate the efficacy of our proposed algorithms and illustrate that the social welfare achieved at the Nash equilibrium is very close to the optimal social welfare. [ABSTRACT FROM PUBLISHER]

This paper studies the resource-allocation problem for a heterogeneous network (HetNet) in which the spectrum owned by a macrocell operator (MCO) can be shared by both unlicensed users (UUs) and licensed users (LUs). We formulate a novel hierarchical game theoretic framework to jointly optimize the transmit powers and subband allocations of the UUs and the pricing strategies of the MCO. In our framework, an overlapping coalition formation (OCF) game has been introduced to model the cooperative behaviors of the UUs. We then integrate this OCF game into a Stackelberg game-based hierarchical framework. We prove that the core of our proposed OCF game is nonempty and introduce an optimal subband-allocation scheme for UUs. A simple distributed algorithm is proposed for UUs to autonomously form an optimal coalition formation (CF) structure. The Stackelberg equilibrium (SE) of the proposed hierarchical game is derived, and its uniqueness and optimality are proved. A distributed joint optimization algorithm is also proposed to approach the SE of the game with limited information exchanges between the MCO and the UUs. [ABSTRACT FROM PUBLISHER]

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit–receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R \le \text{2} at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use R=\text{2}$ as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput. [ABSTRACT FROM PUBLISHER]

The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction. [ABSTRACT FROM PUBLISHER]

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBSs), namely, small-cell caching, is an efficient technology for mitigating redundant data transmissions over backhaul channels in heterogeneous networks (HetNets). In this paper, we consider a commercialized small-cell caching system consisting of a video retailer (VR), multiple network service providers (NSPs), and mobile users (MUs). The VR leases its popular videos to the NSPs to make profits, and the NSPs, after placing these videos to their SBSs, can efficiently reduce the repetitive video transmissions over their backhaul channels. We study such a system within the framework of the Stackelberg game. We first model the MUs and SBSs as two independent Poisson point processes (PPPs) and develop the probability of the event that an MU can obtain the demanded video directly from the memory of an SBS. Then, based on the derived probability, we formulate a Stackelberg game to maximize jointly the average profit of the VR and the NSPs. Moreover, we investigate the Stackelberg equilibrium (SE) via solving an optimization problem. Numerical results are provided for verifying the proposed framework by showing its effectiveness on pricing and resource allocation. [ABSTRACT FROM PUBLISHER]

This paper studies the performance of overlay device-to-device (D2D) communication links via carrier-sense multiple access (CSMA) protocols. We assume that the D2D links have heterogeneous rate requirements and different willingness to pay, and each of them acts nonaltruistically to achieve its target rate while maximizing its own payoff. Spatial reuse is allowed if the links are not interfering with each other. A noncooperative game model is used to address the resource allocation among the D2D links, at the same time leveraging on the ideal CSMA network (ICN) model to address the physical channel access issue. We propose a Stackelberg game in which the base station (BS) in the cellular network acts as a Stackelberg leader to regulate the individual payoff by modifying the unit service price so that the total D2D throughput is maximized. The problem is shown to be quasi-convex and can be solved by a sequence of equivalent convex optimization problems. The pricing strategies are designed so that the network always operates within the feasible throughput region. The results are verified by simulations. [ABSTRACT FROM PUBLISHER]

Cognitive radio networks (CRNs) provide an effective solution to address the increasing demand for spectrum resources. The cooperation among secondary users (SUs) improves the sensing performance and spectrum efficiency. In this paper, we study the traffic-demand-based cooperation strategy of SUs in multichannel CRNs, in which each SU makes its own cooperative sensing decision according to its traffic demand. When an SU has high traffic demand, it can choose to sense multiple channels in the sensing period and obtain more chances to use spectrum resources. If an SU has no data to transmit, it can choose not to perform spectrum sensing. We formulate this problem as a nontransferable utility (NTU) overlapping coalitional game. In this game, each SU implements a cooperation strategy according to its expected payoff, which takes into account the expected throughput and energy efficiency. We propose two different algorithms, namely, an overlapping coalition formation (OCF) algorithm and a sequential coalition formation (SCF) algorithm, to obtain a coalition structure. The OCF algorithm guarantees that the coalition structure is stable, whereas the SCF algorithm has lower computational complexity and less information exchange among SUs. Moreover, when being assigned a certain channel, SU implements an adaptive transmission power control scheme to further improve its energy efficiency. Simulation results show that our proposed algorithms achieve a higher aggregate throughput than the disjoint coalition formation algorithm from the literature, where each SU can join only one coalition. [ABSTRACT FROM PUBLISHER]

Device-to-device (D2D) communication has been recently proposed to reduce cellular traffic by directly connecting the local nodes. In this paper, we consider a cellular-based primary network coexisting with a cognitive D2D pair. The primary network consists of a base station and multiple cellular users, which can sell their spectrum to the D2D user. Additionally, under low-interference conditions, the spectrum can be simultaneously shared between cellular users and D2D-capable users, which is called the nonorthogonal sharing mode. We introduce a double-sided bandwidth-auction game for cognitive D2D communication. In the auction, the D2D transmitter has three types of service providers (SPs) or sharing modes. The SPs operate on different frequency spectra, and a D2D-capable pair intends to share these spectra. This situation is formulated as an auction game, where a D2D transmitter bids a demand price–bandwidth curve, and each SP offers a price–bandwidth supply curve. The Nash equilibrium point of the auction game is analytically obtained. Then, a learning method is proposed for the decision making of the players in an incomplete-information repeated game. Moreover, the convergence of the proposed learning method to the Nash equilibrium point is studied through the converse Lyapunov theorem. Finally, the proposed auction game is extended to the multiple-D2D case, and the optimal bandwidth allocation strategy to maximize the sum rate is derived. [ABSTRACT FROM PUBLISHER]

Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles’ communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, offloading certain computing tasks to the mobile ad hoc cloud (MAHC) could help mobile devices reduce execution time. However, if multiple resource demanders (RDs) offload tasks to the MAHC without a proper scheduling policy, it may cause unbalanced load distribution among resource providers (RPs), which will affect the overall quality of service (QoS). To this end, in this paper, we propose a multi-agent independent learning approach aiming to optimize the QoS of MAHC. Firstly, for the distributed MAHC, we formulate the QoS optimization model as a non-cooperative game, where each RD competes for maximizing its own utility. Secondly, based on the potential game theory, we prove the existence of Nash equilibrium. A multi-agent independent learning algorithm is then proposed to obtain the equilibrium points, and the convergence of this algorithm is analyzed. Simulation results confirm that the proposed approach helps balance the load distribution and enhances the QoS of MAHC. [ABSTRACT FROM AUTHOR]

The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) has sparked considerable interest in academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid network, vehicles and charging stations can, respectively, make better charging and pricing decisions via real-time information sharing. In this paper, we study the charging problem in an intelligent transportation system, which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location, while the objective of a charging station is to maximize its revenue, given the charging strategy of the vehicles. We employ a multi-leader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that the equilibrium of the followers’ subgame played by the vehicles exits, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable using the proposed charging scheme. Finally, the performance of the proposed approach is demonstrated via extensive trace-driven simulations. [ABSTRACT FROM AUTHOR]

Conducting reputation management is very important for Internet of vehicles. However, most of the existing work evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation, which cannot represent the real scenarios. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users’ attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate non-cooperative radio resource management in a vehicle-to-vehicle communication network. The technical challenges lie in high-vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued data packets, aiming to optimize the expected long-term performance. The frequency allocation at the beginning of each slot at the RSU is regulated by a sealed second-price auction. Such interactions among VUE-pairs are modeled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the original game into an equivalent stochastic game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium, which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE solution. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm. [ABSTRACT FROM AUTHOR]

Computation offloading is a promising solution to extend the capacity of robot swarms for computation-intensive applications because it allows robot swarms to benefit from the powerful computing resources of modern data centers. However, the existing computation-offloading approaches still face challenges: 1) multi-hop cooperative computation offloading, 2) joint computation offloading and routing, and 3) task slicing. In this paper, we propose a quality of service (QoS)-aware cooperative computation-offloading scheme for robot swarms using game theory. We analyze the multi-hop cooperative communication model in robot swarms and investigate the computation offloading and routing decision-making problems with the goals of both latency minimization and energy efficiency. We formulate the joint optimization problem as a multi-hop cooperative computation-offloading game and show the existence of a Nash equilibrium (NE) of the game for both unsliceable and sliceable tasks. We further propose a QoS-aware distributed algorithm to attain an NE and provide an upper bound on the price of anarchy in the game. Finally, our simulated results show that our algorithm scales well as the swarm size increases and it has a stable performance gain in various parameter settings. [ABSTRACT FROM AUTHOR]

Modern models for public safety networks (PSN) utilize commercial user equipment (UE) and exclusively rely on cellular architecture. This paper introduces a heterogeneous PSN mechanism where multiple wireless protocols are used to establish resilient communications. Specifically, each UE acts as a learning automaton and using a reinforcement-learning approach selects the most appropriate operation protocol, in terms of environment/topology needs, protocol specifications, and PSN battery-life extension. In addition, we follow a UE coalition formation approach, using a Chinese restaurant process that considers UEs’ communication interest, physical distance, and energy availability. Finally, each UE's optimal transmission power is obtained using the formulation of a utility-based power control problem as a non-cooperative, distributed game among UEs, which converges to a unique Nash equilibrium. The performance of the proposed approach is evaluated using modeling and simulation under several scenarios; also, its superiority is demonstrated. [ABSTRACT FROM AUTHOR]

LTE over unlicensed band (LTE-U) has emerged as an effective technique to overcome the challenge of spectrum scarcity. Using LTE-U along with advanced techniques such as carrier aggregation, one can boost the performance of existing cellular networks. However, if not properly managed, the use of LTE-U can potentially degrade the performance of co-existing Wi-Fi access points, which operate over the unlicensed frequency bands. Moreover, most of the existing works consider single operator in their proposals. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is studied. The goal is to enable the cellular network to use LTE-U with CA to meet the quality-of-service (QoS) needs of its users while protecting Wi-Fi access points (WAPs) for a network with multiple operators. In particular, the problem of LTE-U sum-rate maximization is addressed under user QoS and WAP-LTE-U coexistence constraints. To solve this problem, a cooperative Nash bargaining game (NBG) and a one-sided matching game are proposed. Here, the NBG solves the coexistence issue between LTE-U and Wi-Fi system, while the matching game solves the resources allocation problem in the LTE-U system. These two games repeat until convergence. Simulation results show the quality of the proposed approach over other comparing methods in terms of the per-user achieved rate, percentage of unsatisfied users, and fairness. The result also shows that the proposed approach can better protect the performance of Wi-Fi users, compared to the conventional listen-before-talk scheme. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]