Unmanned aerial vehicles (UAVs) play a critical role in enhancing communication and computation capabilities within the Internet of Vehicles (IoV). This paper explores a dual-mode energy harvesting-based aerial computing framework, where a UAV integrated with an edge server supports vehicular task processing. The UAV leverages wireless power transfer (WPT) to harvest energy from the base station (BS) while employing simultaneous wireless information and power transfer (SWIPT) techniques to concurrently collect data and energy from vehicles, ensuring sufficient power supply for communication, aerial computing, and flight operations. To improve resource utilization efficiency, the framework adopts non-orthogonal multiple access (NOMA) technology, enabling multiple vehicles to share the same resource block. Addressing the prolonged task offloading latency challenge in NOMA-based IoV systems, we propose a deep reinforcement learning (DRL) approach named DRL-RPSO (DRL-based Resource management, Power splitting, and Speed Optimization), which utilizes the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the superiority of the proposed method in achieving higher energy efficiency and broader service coverage compared to conventional benchmarks.

In emergency scenarios, unmanned aerial vehicles (UAVs) show significant potential as aerial base stations (BSs) to establish reliable communication links and provide localization services through integrated air-ground collaboration. This paper proposes a novel energy-efficient collaborative framework based on the solo-UAV-rescuer cooperative (SURC) paradigm, which synergistically enhances both communication capacity and localization accuracy. From a system optimization perspective, we formulate an optimization problem using a normalized combination of three critical metrics: achievable data rate, localization accuracy, and energy consumption. Specifically, to maximize the system’s utility, we design a signal perception-based localization method that incorporates angle-of-arrival (AOA) localization information for guidance, and develop a beamforming scheme to facilitate high data rate communication. Building on these methods, we propose a deep reinforcement learning (DRL)-based synergistic communication and localization reinforcement (SYNCORE) approach that dynamically optimizes three key operational parameters: UAV trajectory planning, flight time, and transmission power control, achieving reliable services with energy-efficient operation. Based on the simulation results, we validate that the proposed scheme enhances communication and localization performance, while also improving energy efficiency, surpassing the baseline schemes.

Due to the high level of flexibility, Unmanned Aerial Vehicle (UAV) can be deployed as aerial base station to provide communication coverage enhancement, especially when ground service facilities are lacking or communication is unreliable. UAV-assisted content caching, which can help reduce repeated transmissions on backhaul links, has emerged as a promising solution for edge caching networks. Federated learning enables distributed training, which makes it protect data privacy and reduce communication overhead in large-scale UAV networks. Therefore, in this paper, we study a federated learning-based aerial content caching framework in High Altitude Platform (HAP)-assisted multi-UAV networks. Specifically, we propose a cache replacement and trajectory planning method based on Deep Reinforcement Learning (DRL) with the goal of maximizing the network fair throughput. Simulation results show that the proposed method outperforms the benchmarks in terms of the fair throughput and cache hit rate.

Integrating wireless energy transfer (WET) and data gathering based on the mobile platforms, such as the unmanned aerial vehicle (UAV) has been recognized as a promising technique to prolong the battery lifetime of resource-constrained wireless sensors in the Internet of Things era. However, it is challenging to jointly schedule dynamic renewable energy sources and communications resources to coordinate heterogeneous performance requirements in rechargeable wireless sensor networks (RWSNs). Hence, this article researches rechargeable adaptive energy-efficient dual-mode data gathering (AED2G) using renewable energy sources. First, considering the limited endurance of UAV and the uncertainty of renewable energy harvesting, a life-expectancy-balance-based AED2G strategy is proposed for optimizing the communication energy efficiency of the fixed data gathering (FDG) and mobile data gathering (MDG). Then, considering WET and MDG, the synergy payoff function of rechargeable MDG (RMDG) is designed, and the corresponding synergy payoff maximization problem is established. The problem is nonconvex due to the coupling of MDG and WET, so it is decomposed into two layers to be quickly solved by the designed hierarchical decomposition framework. The simulation results prove that our algorithm can efficiently use renewable energy sources, whether in FDG or RMDG mode, thereby improving the sustainability of RWSN.

Wireless power transfer (WPT) based on mobile platforms, such as unmanned aerial vehicle (UAV), has been recognized as a promised technique to prolong battery lifetime of resource-constrained wireless sensors in the Internet of Things (IoT) era. However, it is challenging to collaborate multiple mobile wireless chargers (MWCs) for coordinating heterogeneous energy requirements of massive rechargeable wireless sensors, where the efficient optimization of quantitative collaboration utility among MWCs is difficult. Hence, this article investigates the adaptive payoff balance among MWCs for rechargeable wireless sensor networks (RWSNs). First, the collaborative wireless powered system based on charging sectors control among MWCs is proposed. Then, the charging payoff function and the corresponding optimization problem for maximizing the minimum payoff are designed, and it is decomposed into two layers by the hierarchical decompose method to be solved quickly. In the bottom layer, the payoff of each MWC with the given charging sector is maximized by the convex optimization theory. According to the payoff feedback of the bottom layer, intelligent charging sectors allocation is realized by the deep reinforcement learning. The simulation results show that our algorithm can ensure efficient energy allocation of any single MWC, and the overall utility of collaborative wireless powered system on this basis can be optimized by rationally allocating charging sectors, which significantly improves the sustainability of RWSN.

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent’s parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95$\%$over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78$\%$higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced.

As more and more applications in smart transportation emerge, the timely and efficient collection of data in the Internet of Vehicles (IoV) has become an important issue. Dy-namically changing traffic streams make it difficult for roadside units (RSUs) to collect vehicle data. Unmanned aerial vehicles (UAVs) with high mobility can be easily deployed anywhere to compensate for the limited communication coverage of ground-based infrastructure. Based on the above considerations, we propose an air-ground cooperation framework for vehicular data collection in real-time traffic scenarios. Specifically, we maximize the data transmission success rate (DTSR) within a given time duration by simultaneously optimizing the UAV's flight trajectory, access decision, and resource allocation. This problem is non-convex and time-continuous, and cannot be solved by conventional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) approach based on the twin delayed deep deterministic policy gradient (TD3) algorithm. A real road-based traffic scenario is constructed by Simulation of Urban Mobility (SUMO) in our work. Simulation results show that the proposed method outperforms the benchmarks in terms of DTSR.

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The hybrid-powered networks composed of wireless-powered communication (WPC) terminal and solar-powered UAV can solve the energy supply problem of the IoT networks, and the nonorthogonal multiple access (NOMA) technique can solve the massive access problem of IoT terminals. Exploiting these benefits, we investigate joint UAV 3-D trajectory design and time allocation for aerial data collection in hybrid-powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, Quality of Service (QoS) requirements, and flight conditions. The problem is nonconvex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called fair communication is accomplished by trajectory design and time allocation (FC-TDTA), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that our proposed algorithm performs better than benchmarks in fair throughput maximization. The proposed FC-TDTA algorithm can make the UAV: 1) fly in appropriate direction and speed, so that the UAV can arrive at the charging station before the energy runs out and 2) conduct WPC energy transmission and data collection to achieve fair communication.

The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studies on the MVP problems tend to set evading vehicles to move randomly or in a fixed prescribed route. The opponent modeling method has proven considerable promise in tackling the non-stationary caused by the adversary agent. However, most of them focus on two-player competitive games and easy scenarios without the interference of environments. This paper considers a Team-to-Team Multi-vehicle Pursuit (T2TMVP) problem in the complicated urban traffic scene where the evading vehicles adopt the pre-trained dynamic strategies to execute decisions intelligently. To solve this problem, we propose an opponent-aware reinforcement learning via maximizing mutual information indicator (OARLM2I2) method to improve pursuit efficiency in the complicated environment. First, a sequential encoding-based opponents joint strategy modeling (SEOJSM) mechanism is proposed to generate evading vehicles' joint strategy model, which assists the multi-agent decision-making process based on deep Q-network (DQN). Then, we design a mutual information-united loss, simultaneously considering the reward fed back from the environment and the effectiveness of opponents' joint strategy model, to update pursuing vehicles' decision-making process. Extensive experiments based on SUMO demonstrate our method outperforms other baselines by 21.48% on average in reducing pursuit time. The code is available at https://github.com/ANT-ITS/OARLM2I2.

The multi-vehicle pursuit (MVP), as a problem abstracted from various real-world scenarios, is becoming a hot research topic in the Intelligent Transportation System (ITS). The combination of Artificial Intelligence (AI) and connected vehicles has greatly promoted the research development of MVP. However, existing works on MVP pay little attention to the importance of information exchange and cooperation among pursuing vehicles under the complex urban traffic environment. This paper proposed a graded-Q reinforcement learning with information-enhanced state encoder (GQRL-IESE) framework to address this hierarchical collaborative multi-vehicle pursuit (HCMVP) problem. In the GQRL-IESE, a cooperative graded Q scheme is proposed to facilitate the decision-making of pursuing vehicles to improve pursuing efficiency. Each pursuing vehicle further uses a deep Q network (DQN) to make decisions based on its encoded state. A coordinated Q optimizing network adjusts the individual decisions based on the current environment traffic information to obtain the global optimal action set. In addition, an information-enhanced state encoder is designed to extract critical information from multiple perspectives and uses the attention mechanism to assist each pursuing vehicle in effectively determining the target. Extensive experimental results based on SUMO indicate that the total timestep of the proposed GQRL-IESE is less than other methods on average by 47.64%, which demonstrates the excellent pursuing efficiency of the GQRL-IESE. Codes are outsourced in https://github.com/ANT-ITS/GQRL-IESE.

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.

Mobile edge computing (MEC) has been regarded as a promising technology to liberate the resource-limited users from computation-intensive and latency-sensitive tasks by computation offloading. Furthermore, implementing non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energy consumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the strong coupling between offloading decision and resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can efficiently lower energy consumption of users compared to other benchmark schemes with an acceptable complexity.

Mobile edge computing (MEC) is regarded as a key technology to reduce the network pressure from the computing-intensive and latency-sensitive applications in future wireless networks. Non-orthogonal multiple access (NOMA) can achieve high spectral efficiency by allowing multiple users to reuse the same resources. In this paper, we consider a novel NOMA-based MEC system to improve the energy efficiency during task offloading process. With multiple access points (APs) being deployed, the optimization problem is joint user association and resource allocation while the objective is to minimize the total energy consumption of all users subject to the task execution deadline. We formulate the problem as a many-to-one matching game with externality due to the co-channel interference among users, and then, propose a matching-coalition approach coupled with computing resource allocation and power control. Simulation results show that the proposed approach can efficiently reduce the total energy consumption in comparison to other simplified approaches.

Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (ADMM) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate the superior performance of the proposed algorithm.

With the ubiquity of wireless network and the intelligentization of machines, Internet of Things (IoT) has come to people's horizon. Device-to-device (D2D), as one advanced technique to achieve the vision of IoT, supports a high speed peer-to-peer transmission without fixed infrastructure forwarding which can enable fast content distribution in local area. In this paper, we address the content distribution problem by multihop D2D communication with decentralized content providers locating in the networks. We consider a cross-layer multidimension optimization involving frequency, space, and time, to minimize the network average delay. Considering the multicast feature, we first formulate the problem as a coalitional game based on the payoffs of content requesters, and then, propose a time-varying coalition formation-based algorithm to spread the popular content within the shortest possible time. Simulation results show that the proposed approach can achieve a fast content distribution across the whole area, and the performance on network average delay is much better than other heuristic approaches.

In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and longlasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash- stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.

Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D-V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D-V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.

Industrial Internet of unmanned aerial vehicles (IIoUAVs) that enable autonomous inspection and measurement of anything anytime anywhere have become an essential component of the future industrial Internet of things (IIoT) ecosystem. In this paper, we investigate how to apply IIoUAVs for power line inspection in smart grid from an energy-efficiency perspective. First, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization, such as trajectory scheduling, velocity control, and frequency regulation, and the small-timescale optimization, such as relay selection and power allocation. Then, the original NP-hard problem is transformed into a two-stage suboptimal problem by exploring the timescale difference and the energy magnitude difference between the large-timescale and the small-timescale optimizations, and is solved by combining dynamic programming (DP), auction theory, and matching theory. Finally, the proposed algorithm is verified based on real-world map and realistic power grid topology.

Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.

Device-to-device (D2D) cooperative relay can assist users with inferior channel conditions to implement multi-hop transmissions, improving network coverage and throughput. However, energy efficiency is an important issue to be optimized because of the limited battery capacity of handheld equipments. Considering a two-hop D2D relay communication scenario, this paper proposes a resource management approach that jointly optimizes relay selection, spectrum allocation, and power control, so that the total energy efficiency of D2D links is maximized while guaranteeing the quality of service (QoS) requirements of D2D and cellular links at the same time. Since the formulated joint optimization problem involves a four-dimensional matching that is NP-hard, we propose a pricing-based two-stage matching algorithm to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three- dimensional matching is conducted to match users, relays, and the spectrum resources reused by transmitter-to-relay links. The optimal transmit power is solved during the preference establishment process in the second stage. As shown in simulation results, the proposed algorithm not only performs good on energy efficiency, but also enhances the average number of served users in comparison to the case without any relay.

By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

Energy-efficiency (EE) is critical for cognitive device-to-device (D2D) communications due to limited battery capacity of user equipments (UEs) and hash quality of service (QoS) requirements. In this paper, we address the EE optimization problem by proposing a game theory and matching based resource allocation algorithm. Noncooperative game is adopted to analyze the interactions among UEs and establish mutual preferences, both of which vary dynamically with channel states and interference levels. We then employs the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs (CUs), which is proved to be stable and weak Pareto optimal. We also extend the algorithm to address scalability issues in large-scale networks by introducing some tie-breaking and preference deletion rules. Simulation results demonstrate that the proposed algorithm achieves significant EE performance and UE satisfaction gains compared to heuristic algorithms.

In this paper, we address the energy-efficient power allocation problem for energy-harvesting device-to- device (EH-D2D) communications, which enable user equipments (UEs) to harvest energy from ambient environments. The challenge is how to optimize energy efficiency (EE) with the intermittent and dynamic characteristics of energy arrivals. We model the offline power allocation problem as a non- cooperative game over a finite horizon. Various practical constraints such as circuit power consumption, energy causality, battery capacity, quality of service (QoS), and maximum transmission power have been taken into consideration. A low- complexity iterative power allocation algorithm is developed by exploiting properties of non-linear fractional programming and Lagrange dual decomposition. Simulation results demonstrate that the proposed algorithm outperforms the power-greedy algorithm by 55% and 84% for D2D and cellular UEs, respectively.

Device-to-Device (D2D) is an emerging technology that is typically employed as an underlay of the uplink (UL) cellular networks. The downlink cellular spectrum, however, is rarely reused by D2D links due to the strong interference from the base station to the D2D receivers. To this end, we propose to enable D2D transmissions in the downlink by multiplexing D2D and cellular users using beamforming techniques. In particular, a cross-layer design approach is adopted to jointly optimize beamforming, spectrum allocation, and power control, so that the total system transmission power is minimized. To deal with the non-convex and combinatorial nature of the problem, we transform the formulation into a convex optimization problem by identical deformation and relaxations, and propose a semidefinite relaxation (SDR) based algorithm to approximate the optimal solution. Moreover, we focus on the feasibility of the convex problem, and propose an improved algorithm which reduces the probability of users out of service. The simulation results show that the performance of our proposed algorithm is close to the optimal solution, and the improved algorithm gives a higher efficiency on user admission control.

Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput, and extending the battery life of user equipment. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered bidders that compete for channel resources and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication and present numerical simulations to verify the proposed algorithm.

An auction-based joint subcarrier and power allocation approach is investigated to improve the performance of device-to-device (D2D) communication underlay cellular networks with uplink (UL) resource sharing. To maximize the system sum rate over the resource reuse of multiple D2D pairs, we introduce a reverse iterative combinatorial auction to formulate the optimization problem. In the auction, cellular channels are viewed as bidders competing to obtain rate increase while packages of D2D pairs and the corresponding transmit power are auctioned as goods in each round. We first give the evaluation of bidders' optimal value for packages, and then explain a descending price auction in detail, also give properties of convergency and low-complexity. The simulation results are finally provided to indicate the efficiency of the proposed auction-based algorithm.

Device-to-device (D2D) communication as an underlay to cellular networks brings significant benefits to users' throughput and battery lifetime. The allocation of power and channel resources to D2D communication needs elaborate coordination, as D2D user equipments (UEs) cause interference to other UEs. In this paper, we propose a novel resource allocation scheme to improve the performance of D2D communication. Battery lifetime is explicitly considered as our optimization goal. We first formulate the allocation problem as a non-cooperative resource allocation game in which D2D UEs are viewed as players competing for channel resources. Then, we add pricing to the game in order to improve the efficacy, and propose an efficient auction algorithm. We also perform simulations to prove efficacy of the proposed algorithm.

Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.

An innovative auction-based allocation scheme is proposed to improve the performance of device-to-device (D2D) communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which compete to obtain business as bidders while packages of D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit for packages of D2D links. And then a detailed non-monotonic descending price auction algorithm is explained. Further, we prove that the proposed scheme is cheat-proof, converges in a finite number of iteration rounds, and has lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.

An innovative resource allocation scheme is proposed to improve the performance of device-to-device (D2D) communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a sequential second price auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which are auctioned off by groups of D2D pairs in sequence. We first formulate the value of each resource unit for each D2D pair, as a basis of the proposed auction. And then a detailed auction algorithm is explained using a N-ary tree. The equilibrium path of a sequential second price auction is obtained in the auction process, and the state value of the leaf node in the end of the path represents the final allocation. The simulation results show that the proposed auction algorithm leads to a good performance on the system sum rate, efficiency and fairness.