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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]

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]

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]

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]

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]

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]

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]

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]

Trajectory Following Control (TFC) and lateral stability control are always significant for Autonomous Ground Vehicles (AGVs). However, sometimes it is quite a challenge to achieve excellent tracking performance with less stability loss. In this paper, a novel two-agent non-cooperative game framework is built to explore the coordination approach of the integrated TFC and Direct Yaw Control (DYC) issue. Then the game theory-based control strategies are proposed and two information patterns are investigated. In the open-loop case, the Nash equilibrium of each control cycle is determined by solving discrete-time Riccati equations. When it comes to the closed-loop pattern, the game complexity increases because each player can revise his own game strategy according to the other players’ strategy evolution, and thus the dynamic programming method based on the Bellman's principle is applied to derive the control strategies. Besides, a Linear Parameter-Varying (LPV) method is adopted to facilitate the process of discretizing the state-space model in every control cycle. Finally, simulation tests based on Carsim-Simulink joint platform and experimental tests are carried out to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mechanism – DEFT– to facilitate mobile Internet of Vehicles (IoV) user entities (UEs) with unit tasks to cooperate among one another and offload their tasks to nearby fog nodes (FNs)/roadside units (RSUs) in a decentralized and fair manner. While existing literature depends on offloading schemes based on centralized systems, DEFT provides near real-time computation offloading in a fog-enabled IoV using a two-level game-theoretic approach. Further, this approach allows the UEs to make their own decisions in a dynamic environment. In the first level, FNs play an Oligopoly game for determining the price of their services based on their computation capability and then broadcast the price to the UEs. Further, based on the price transmitted by the FNs, UEs play a non-cooperative Stackelberg game to map the offloading among the user and fog layer. We also ensure that no UE consumes all of the resources in an FN. Additionally, we prove our objective function's convexity and show that the proposed game always attains the Nash Equilibrium. Through extensive real-world emulations, we observe that DEFT minimizes the latency and power consumption, and improves throughput compared to the existing schemes. In the presence of 40, 80, and 120 UEs, the overhead for computation in the fog layer is minimized by $70-75$ % compared to the local computation overhead. [ABSTRACT FROM AUTHOR]

Cooperative localization has emerged as an appealing technology since it can improve the localization performance without any infrastructure change compared with non-cooperative localization. However, some well-localized agents may not be willing to sacrifice additional power to improve the others’ localization accuracy. This paper proposes an incentive mechanism from an economic perspective for cooperative localization, whereby a pricing scheme is designed to lead each agent to the optimal state. A game-theoretic algorithm is proposed where each player (agent) can obtain the optimal budget strategy to minimize its individual utility. To make profits in the game, the relationship between the agent's network condition and its budget strategy is derived. Furthermore, a fairness-aware price allocation rule (PAR) is developed to distribute the budget among the reference agents proportional to each node's contribution. Analytical and numerical results show that agents with better network conditions are more likely to join the cooperation under the proposed incentive mechanism, leading to an improved localization performance. [ABSTRACT FROM AUTHOR]

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]

In a typical traffic scenario, autonomous vehicles are required to share the road with other road participants, e.g., human driven vehicles, pedestrians, etc. To successfully navigate the traffic, an adaptive robust level-k framework can be used by the autonomous agents to categorize the agents based on their depth of strategic thought and act accordingly. However, mismatch between the vehicle dynamics and its predictions, and improper classification of the agents can lead to undesirable behavior or collision. Robust approaches can handle the uncertainties, however, might result in a conservative behavior of the autonomous vehicle. This paper proposes an adaptive robust decision making strategy for autonomous vehicles to handle model mismatches in the prediction model while utilizing the confidence of the driver behavior to obtain less conservative actions. The effectiveness of the proposed approach is validated for a lane change maneuver in a highway driving scenario. [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]

In this paper, we focus on the problem of planning heterogeneous cellular networks powered by renewable energy sources, which are a good alternative for dealing with the growth of mobile data traffic while minimizing energy consumption. The paper proposes a method based on a coalitional game in which small cells are players, with the Shapley value determining the contribution of each potential small cell to the global performance of the system. The characteristic function of the game is based on three key performance indicators: percentage of users served, reduction of grid consumption, and average transmission rate. The success of the coalitional game-based planning process is considered from an energy efficiency perspective, which means a reduction of on-grid consumption and an improvement of network performance according to renewable energy availability. The efficiency of the coalitional design is compared with other heuristic and optimal alternatives in two simulation examples. Our results show that the proposed coalitional planning scheme produces an efficient use of energy compared to other strategies. [ABSTRACT FROM AUTHOR]

In wireless networks, retransmissions are used by senders to recover lost packets. The conventional retransmission schemes usually retransmit only one lost packet each time, resulting in a low retransmission efficiency. However, using broadcasting nature of a wireless channel with network coding (NC), we can recover more than one lost packet per transmission. Different from single-sender multiple-receiver wireless networks as considered in the existing works, this paper explores the ways to use NC for retransmissions in multiple-sender multiple-receiver scenarios. In addition, this paper also aims to investigate the advantages and shortcomings of using NC in retransmissions. In order to maximize the gain and minimize the impact in NC retransmissions, a probabilistic network coding retransmission (PNCR) scheme is proposed, where a buyer/seller game-theoretic approach is adopted to optimize the performance. The experimental results confirm the effectiveness of the PNCR scheme. [ABSTRACT FROM PUBLISHER]

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]

Network slicing provides great opportunities for network service providers (SPs) to scale up and achieve higher revenue by enabling fast and efficient creation of new services and customizable networks for enterprises and user equipment (UEs). This can only be achieved at the expense of more complicated radio resource management and service/trading interactions among different involved stakeholders. In this paper, we study the resource allocation and pricing problem for network slicing that captures interactions among access/backhaul service providers and their UEs by using the multi-leader multi-follower (MLMF) Stackelberg game approach. Toward this end, we show how to formulate such a Stackelberg game and prove the existence of a unique game equilibrium. Then, we develop a distributed algorithm based on updating underlying best-response functions, which is proved to converge to the game equilibrium. Numerical results are presented to provide important insights into the interactions among the involved stakeholders and demonstrate the economical efficacy of the proposed design with respect to existing benchmarks. [ABSTRACT FROM AUTHOR]

We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens. We investigate two problems; P1) network power minimization for the first scenario and P2) social cost minimization for the second scenario, defined as the total cost paid by the nodes of the network for receiving data. In this paper, to address P1 and P2, we propose centralized and decentralized approaches that determine which of the nodes of the network should act as transmitting nodes, find their transmit powers and their corresponding receiving nodes. For the sake of energy efficiency, in our model, we employ maximal-ratio combining (MRC) at the receivers so that a receiver can be served by multiple transmitters. The proposed decentralized approach is based on a non-cooperative cost-sharing game (CSG). In our proposed game, every receiving node chooses its respective transmitting nodes and consequently, a cost is assigned to it according to the power imposed on its chosen transmitting nodes. We discuss how the network is formed in a decentralized way, find the action of the nodes in the game and show that, despite being decentralized, the proposed game converges to a stable solution. To find the centralized global optimum, which is a benchmark to our decentralized approach, we use a mixed-integer-liner-program (MILP). Simulation results show that our proposed decentralized approach outperforms the conventional algorithms in terms of energy efficiency and social cost while it can address the need for an incentive for collaboration. [ABSTRACT FROM AUTHOR]

With limited wireless resources but increasing demands for wireless applications, non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless networks. Nevertheless, it remains challenging on motivating the mobile users (MU) to join NOMA networks, scheduling MUs on the same sub-bands, reducing interference among the MUs and setting optimal transmit power in the NOMA networks. In this paper, first we consider the quantization error due to the limitation of receivers’ equipments and the decoding error during the successive interference cancellation (SIC) procedure. Second, we model the resource allocation problems as the four-level equilibrium problems with equilibrium constraints (EPEC), where the wireless operator, on behalf of each MU, first predicts the utilities and assign the MU as a NOMA user (NMU) or a traditional MU (TMU). For the NMUs, the interference penalty pricing strategy of other NMUs are proposed. Based on the announced prices of all the NMUs, the sub-channel allocation is managed in either distributed or collaborative ways. According to the announced interference prices of other NMUs and the coalition results, the optimal transmit power to gain the maximum utility is considered. Through extensive simulation analysis, the superior performance obtained by using the proposed scheme is demonstrated. [ABSTRACT FROM AUTHOR]

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]

Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The Blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for microtransactions or frequent data transfer among the nodes in the vehicular network. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) and nonorthogonal multiple access (NOMA) are two enabling technologies in the 5 G network and beyond. MEC admits user equipments (UEs) running many more compute-intensive applications by providing computing capabilities at the network edge and within radio access networks, while NOMA enables multiple UEs to share the same resource block, thus leveraging considerable advantages such as greater spectral efficiency and a larger number of supported UEs. The state-of-the-art showed that the combination of NOMA and MEC can lower the energy consumption and/or overall latency; however, they mostly focused on single-carrier NOMA. In this paper, we investigate the computation offloading problem in multi-carrier NOMA enabled MEC systems and solve it from the cooperative game theory viewpoint using coalition formation game. Particularly, UEs are considered as game players and subcarriers are regarded as coalitions that can be used for computation offloading of multiple UEs. Based on the introduced coalition formation game, we develop a low-complexity algorithm with convergence guarantee to achieve the Nash-stable solution. Numerical results are provided to validate the effectiveness of the proposed coalition game based algorithm as well as its comparison with three baseline schemes. [ABSTRACT FROM AUTHOR]

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time. [ABSTRACT FROM AUTHOR]

Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for the power grid, stimulates new ideas in current smart grid research. Since EVs are selfish individuals owned by different parties, how to motivate them to provide ancillary services becomes an issue. In this paper, game theoretic approaches using non-cooperative and cooperative game are proposed to motivate EVs to provide frequency regulation services for the power grid. In a non-cooperative V2G system, the interaction between the EV aggregator and EVs is formulated as a non-cooperative Stackelberg game. The EV aggregator as the leader decides the electricity trading price, and EVs as the followers determine their charging/discharging strategies. In a cooperative V2G system, a potential game is formulated to achieve the optimal social welfare of the V2G system. The existence and uniqueness of the Nash equilibrium of these two games are validated. Our simulation results show that the proposed game theoretic approaches can motivate EVs to smooth out the power fluctuations from the grid while EVs schedule their charging/discharging activities to maximize their utilities. This demonstrates the effectiveness of the use of the V2G game in providing regulation services to the grid. Through cooperation and extra information exchange, the social welfare of EVs and the EV aggregator can be improved to the global optimum and the V2G regulation services can also achieve near-optimal performance. [ABSTRACT FROM AUTHOR]

Instead of delivered directly by the base station (BS), in a device-to-device multicast (D2MD) system, the multimedia content is broadcasted to the request nodes (RNs) by the head node (HN) that has cached the content, and thus the system's performances can be greatly improved. However, the content delivery consumes energy. If the cost cannot get compensated from the cooperation, the HN may refuse to serve the RNs due to the selfish and rational nature. In such a case, how to stimulate the HN to cooperate is an essential issue. In this paper, we propose an indirect reciprocity game framework to investigate the D2MD stimulation problem, where the service level of the content and the location distribution of the RNs are taken into account. In the game, the HN will be assigned a reputation value after its delivery (i.e., action), based on which the HN can get some rewards in the future. Due to the channel fading gain, the HN's action and reputation value will be distorted. Taking such a distortion into consideration, we propose a reputation updating policy to update the HN's reputation, where the deviation of the HN's reputation is captured by the concept of reputation distributions. We prove theoretically that even under the influence of channel fading, there exists a stationary reputation distribution of the whole system. To find the optimal action rule of the HN in the game, we propose a modified value iteration algorithm. We prove that within an appropriate cost-to-gain ratio, the optimal action for the HN is to serve all the RNs. Such an optimal strategy is also proved to be an evolutionarily stable strategy (ESS) under the perturbation effect of the HN's action selection. The simulation results show that the proposed scheme is superior to the existing schemes in terms of portion of the served RNs, system throughput and energy efficiency. [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]

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]

In a smart community (SC) with renewable energy sources (RES), flexible charging service can be provisioned to electric vehicles (EVs), where EVs can choose clean energy, traditional energy, or the mixture of them on demand in the vehicular networks. Considering the existence of various entities in the SC and the limited generation capacity of RES, it becomes of significance yet very challenging to optimally schedule the charging service for EVs with different consumption preferences. In this paper, we propose a charging scheme for EVs in a SC integrated with RES using a game theoretical approach. First, a three-party energy network is proposed to model the interactions among the main grid, EVs, and aggregators in the smart grid. Second, the trust model is presented to improve the safety of energy trading by evaluating the reliability of aggregators based on direct trust and indirect trust. Third, based on the four-stage Stackelberg game, the optimal strategies of three energy entities are analyzed. The Stackelberg equilibrium can be obtained by the proposed accelerated gradient descent based iteration algorithm. Furthermore, a weighted max–min fairness based energy allocation algorithm is proposed to allocate the limited renewable energy for EVs in a fair and efficient manner. Finally, extensive simulations are carried out to evaluate and demonstrate the effectiveness of the proposed scheme through comparison with conventional schemes. [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]

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]

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]

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]

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]