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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]

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]

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]

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]

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]

Featuring unprecedentedly high data rates and high connection densities, 3GPP New Radio (NR) vehicle-to-everything (V2X) communications have been regarded as the most promising remedy to support the next generation driving use cases. For this purpose, although dedicated bands on 5.9 GHz spectrum have been allocated for Intelligent Transportation Systems (ITS), the limited bandwidth may not afford the desired service requirements. This engineering concern thus drives the utilization of unlicensed bands on 5 GHz spectrum adjacent to the ITS bands. However, subject to the fairness constraint in sharing unlicensed spectrum, various communication regulations have been imposed, and four different types of multi-band channel access procedures are consequently developed both in 3GPP LTE-A Licensed-Assisted Access (LAA) and NR on unlicensed spectrum (NR-U), to lead to a phenomenon that occupying more unlicensed bands may not suggest a proportional growth in the throughput. To empower high-throughout and high-density V2X communications, in this paper, we consequently propose the optimum channel access strategies for each V2X transmitter to decide the optimum number of unlicensed bands to access. To this end, analytic models for four types of multi-band channel access procedures in 3GPP NR-U are provided, which capture both inter-system and intra-system interference from coexisting WiFi transmitters and V2X transmitters. Subsequently, game-theoretically optimum strategies for each type of multi-band channel access procedures are derived. Performance evaluation results fully demonstrate the optimality of the proposed strategies, to serve the urgent demands in supporting the next generation driving use cases on unlicensed spectrum. [ABSTRACT FROM AUTHOR]

In this paper, the problem of strategic resource management in fog networks is discussed while considering a pay-per-use model, similar to that used in cloud. Fog networks are distributed in nature, because of which resource management in these networks is an NP-hard problem. In the existing literature, the researchers focused on resource management in fog networks, while considering the network delay constraint. However, none of these works considered the effect of pricing policy while deciding on resource allocation. Hence, there is a need for pricing-based resource management in fog networks. In this work, we proposed a dynamic pricing-based resource allocation scheme, named FogPrime, for analyzing the trade-off between the service delay and the associated price. In FogPrime, we use dynamic coalition-formation game to decide the resource allocation strategy locally within a cluster. On the other hand, we use utility game to choose the fog nodes, strategically, while considering the aforementioned trade-off. Through simulation, we observed that FogPrime outperforms the existing schemes in terms of satisfaction of the involved entities – the end-user and the fog nodes. Using FogPrime, the satisfaction of the end-users and the fog nodes increases by 24.49–47.82%, respectively. Additionally, we observe that FogPrime ensures an even distribution of profit among the fog nodes and enables the end-users to pay less at most by 15.88–47.27%. [ABSTRACT FROM AUTHOR]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

A cellular unmanned-aerial-vehicle (UAV)-enabled system is studied in this research, where multiple UAVs work cooperatively to upload the collected sensory data to the base station (BS). The UAVs form a two-tier network with two kinds of communication modes, i.e., UAV-to-Network (U2N) and UAV-to-UAV (UAV) communications. We investigate the communication resource allocation for UAVs to maximize the aggregate energy efficiency of the system. The formulated problem is mixed-integer and NP-hard. To make the formulated problem tractable, an efficient matching-based algorithm is proposed to complete the transmission mode selection and subchannel allocation, respectively. Then the transmission power is optimized based on game theory. The simulation results reveal that the uplink energy efficiency can be improved with the proposed scheme, compared with the maximum power strategy. [ABSTRACT FROM AUTHOR]

We investigate the resource blocks (RBs) allocation problem in the Single-Carrier Frequency Division Multiple Access (SC-FDMA) based heterogeneous uplink networks from a game-theoretical viewpoint. First, a general network model that considers the co-tier, cross-tier, and inter-cell interference caused by the co-channel deployment is presented. Then, we design an effective throughput-optimizing utility function and prove it an exact potential game. The $\gamma$ -logit is a learning algorithm which commonly used to achieve the optimal solution in potential game and is very sensitive to the changes of the network structures, making it hard in practice. To obtain the optimal solution under the different network structures stably, we propose a Self-adaptive logit algorithm, which can achieve the optimal solution automatically and is a variant of the well-established $\gamma$ -logit learning algorithm. Additionally, to speed up the convergence, we propose the corresponding suboptimal algorithm based on the better response principle. Simulation results show that the proposed Self-adaptive logit algorithm is throughput optimal and network structure adaptive, outperforming the existing Binary-logit, Max-logit, and Round Robin algorithm. Moreover, the proposed suboptimal algorithm is near-optimal throughput achieved and converges efficiently. In addition, we also study the impact of the initial point selection on our proposed two alogithms. [ABSTRACT FROM AUTHOR]