With the rapid advancement of artificial intelligence (AI), deep learning (DL) has been extensively applied in channel modeling to capture complex nonlinear relationships and uncover underlying signal propagation mechanisms. However, current DL-based channel modeling methods typically rely on extensive training data, which limits their performance and adaptability in dynamic and changing environments. To address this imperative, this paper proposes a DL-enhanced channel parameter prediction model based on adaptive meta mask region-based convolutional neural networks (AMM-RCNN). First, an optimized Mask RCNN network is designed to extract the key environmental information from satellite images. The extracted scatterer feature maps and propagation statistics are used as the multimodal input of network, so as to capture the correlation between propagation environment and channel characteristics. Second, the meta-learning algorithm is applied to improve the prediction accuracy of the network with sparse training samples. The method utilizes measurement data from multiple routes as meta-learning tasks, thereby enhancing the generalization ability of the model in new scenarios. Finally, the proposed model is trained and validated with using real-world channel measurement data collected from a university campus. Simulation results demonstrate that the proposed model can accurately predict channel parameters across diverse routes in campus scenarios.

Millisecond-precision measurement of smart grid presents elevated demands for 6G sensing and communication capabilities. How to ensure timely, reliable, and green delivery of critical information remains a core challenge. In this paper, we address this issue by studying electric semantic short packet communication from a green integrated sensing and communication (ISAC) perspective. First, a novel information timeliness metric named peak age of incorrect semantics (PAoIS) is developed. It describes the entire lifetime of sensing, compression, encoding, transmission, and decoding. Then, a collaborative problem is formulated to jointly minimize PAoIS and ISAC energy consumption by optimizing sensing frequency and semantic compression ratio. An electric multimodal driven green collaborative optimization algorithm is proposed. It enables dynamical adjustment of sampling ratio of electric multimodal experience samples, enhancing optimization performance under sparse modes. Simulation results verify the effectiveness of the proposed algorithm.

As the proportion of renewable energy in the distribution grid continues to rise, the timely transmission of critical state information becomes essential to ensure the balance of energy flow. Existing metrics for information timeliness based on peak age of information (PAoI) and its variants fall short in fully characterizing the intricate influence of information flow on the dynamics of energy distribution. In this paper, a new information timeliness metric named energy dispatch cost-aware PAoI (EPAoI) is introduced from the perspective of integrating information and energy flows. We propose an information-energy flow integrated wireless resource scheduling algorithm based on fuzzy learning to minimize EPAoI. It exceptionally improves learning accuracy by exploiting key features of dual flows to guide resource scheduling optimization. Simulation results validate superior performances of the proposed algorithm in reducing EPAoI and energy dispatch cost.

The rapid advancement of 6G, cloud-fog computing, and internet of things (IoT) has revolutionized the control paradigm of smart grid. With the closed coupling between communication and power domains, control performance heavily relies on timely and secure sensing, transmission, and computing of grid state information. Conventional approaches which treat the four sectors as separate subsystems suffer from slow convergence and even cascading control oscillations. In this paper, we address the key research problem of sensing-transmission-computing-control integrated optimization to minimize the overall voltage deviation. A timeliness-driven integrated optimization algorithm is proposed, where proactive optimization of communication resource adaptation and power-domain control decisions is conducted based on the evolution of information timeliness loss in sensing, transmission, and computing, as well as its impact on control accuracy. Particularly, a self-penalty based cost function is developed to quantify the mismatch between communication-domain resource allocation and voltage control deviation. Moreover, a novel timeliness indicator, named age of trustworthy information (AoTI), is introduced to capture timeliness-trustworthiness performance loss on proportional-integral (PI) consensus control stability margin. Consensus weights are optimized based on AoTI to further enhance convergence speed and improve control accuracy. Simulation results demonstrate that the proposed algorithm significantly improves power-domain control stability, validating the efficiency of AoTI as a critical indicator for control information importance.

The application of multispectral image based partial discharge detection offers a dependable solution for high-voltage substations. Captured visible light and ultraviolet (UV) images are denoised, transmitted and fused to enhance detection performance. However, existing approaches separately design the sensing-layer image denoising, communication-layer image transmission, and computing-layer image fusion, and the lack of unified cooperation hinders the overall performance. To address this issue, it is crucial to integrate sensing, communication, and computing to improve detection accuracy and timeliness. In this paper, we formulate a timeliness and accuracy joint guarantee problem, which aims to minimize the weighted sum of peak age of information (AoI), false-positive detection ratio, and false-negative detection ratio by jointly optimizing sensing-layer filtering window size, communication-layer time division ratio, and computing layer wavelet decomposition level. We propose a multispectral integrated sensing, communication, and computing algorithm based on AoI and false-negative aware multi-experience replay cooperative learning to solve the problem. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of peak AoI, false-positive detection ratio, false-negative detection ratio, and convergence speed.

The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.

In this article, we address the key problem of sensing and communication integrated resource allocation for 6G-empowered distribution grid hierarchical coordinated control. First, we construct a novel information timeliness metric for electric semantic communication, namely, Peak Age of Semantics (PAoS), which covers the entire lifecycle of information sensing, semantic compression, semantic transmission, and semantic decoding. Second, we propose a sensing and semantic communication integrated resource allocation algorithm based on Top-$\text {N}^{2}$and hybrid knowledge–statistic-driven fuzzy reinforcement learning. A deep fuzzy neural network is utilized to build a knowledge model between the grid operating state and decision making. The knowledge is embedded into statistic-driven model of reinforcement learning to enhance accuracy of upper confidence bound (UCB) utility evaluation. Finally, simulations based on realistic application scenarios indicate that compared with two comparison algorithms, the proposed algorithm reduces average PAoS by 4.72% and 9.49%, and the maximum PAoS by 5.76% and 13.57%. Additionally, its end-to-end delay trend and semantic packet decoding success rate align more closely with semantic importance.

Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.

A 5G integrated smart distribution power grid brings a new paradigm shift to realize base station (BS) operation cost reduction, efficient renewable energy utilization, and stable energy supply. However, energy scheduling still faces some major challenges, such as coupling between energy sharing and energy trading, dimensionality curse, and intertwinement of social network attributes and BS load. To tackle these challenges, we propose a social-aware learning-based online energy scheduling (SNES) algorithm, which minimizes BS operation cost minimization under the constraints of energy supply stability. SNES leverages a deep neural network (DNN) to learn the action-state value of energy scheduling and intelligently adjusts purchased, sold, and shared energy based on only casual information. Moreover, SNES achieves social awareness by approximating the nonlinear interconnection between energy scheduling and quality of service (QoS) requirements of social network services. Simulation results verify the superior performance of SNES compared with state-of-the-art energy scheduling algorithms.

The concurrent availability of power-line communication, cellular and other wireless technologies, and the rapid development of concepts like multi-mode power internet of things (PIoT) and digital twin (DT) have made intelligent energy control a reality. However, information timeliness guarantee which determines DT consistency and energy control degradation is still an unsolved issue, as recent studies mainly focus on time-averaged guarantee and ignore the occurrence of extreme events. In this paper, we propose a novel metric named ultra-low age of information (ULAoI), which imposes more stringent constraints on extreme event occurrence probability and high-order statistics characteristics of excess AoI value. Moreover, we minimize energy control performance degradation by optimizing communication control, which acts as the bridge connecting information collection and utilization. A joint device scheduling and multi-mode channel allocation algorithm based on ULAoI-prioritized learning is proposed to achieve communication and control integration. Simulation results verify that the proposed algorithm can significantly reduce energy control performance degradation and achieve ULAoI guarantee.

Federated learning (FL)-assisted model training plays an important role in distributed energy scheduling of smart park. However, the time synchronization error between edge and end sides and the adversarial routing competition cause poor accuracy and high delay of model training. In this paper, we address this challenge and propose a time synchronization-aware edge-end collaborative deep Q network-based routing management algorithm named TSA-RM. TSA-RM minimizes the weighted sum of model training loss function and delay via routing optimization. TSA-RM achieves time synchronization awareness and avoids adversarial competition by incorporating time synchronization related information in state space construction and relay selection related information in penalty function design. Simulation results verify the superior performance of TSA-RM in terms of global loss function, model training delay, and time synchronization error compared with two state-of-the-art algorithms.

Device scheduling plays a key role in federated learning model training for energy management in low-carbon smart park. It is intuitive to achieve high-accuracy and low-latency model training by scheduling devices with smaller local training loss function and better channel condition. However, the adverse impact of model poisoning attack on model training performance and device scheduling adjustment cannot be neglected. The error model parameters uploaded by malicious attackers-controlled devices significantly reduce model training accuracy and convergence speed. To address this challenge, we propose an Endogenous Security-Aware Deep Q Network (ESA-DQN) based device scheduling algorithm. ESA-DQN integrates model poisoning attach detection with DQN networks to actively adjust device scheduling in accordance with estimated attack probability, thereby achieving endogenous security awareness. Numerical results show that ESA-DQN has excellent performances in terms of model training accuracy and delay.

Abstract Precise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M 2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M 2 and the lossless switching method in time synchronization delay and error.

Abstract Carbon footprint monitoring provides significant basis for computing facilities to improve the computing efficiency and reduce the energy cost, which can enable low/zero carbon computing and facilitate the construction of sustainable cites. Massive sensing devices access to 5G base station to transmit collected carbon emission data, which requires intelligent access management. Fast uplink grant possesses the advantages of reducing signalling overhead and access conflicts while facing the problems of incomplete information and difficulty in guaranteeing priority constraint. This paper examines, the maximum access queuing delay minimization problem is formulated under the long‐term service priority constraint and the short‐term access management constraint. First, Lyapunov optimization is leveraged to decouple the long‐term service priority constraint and short‐term access management optimization, and decompose the long‐term stochastic optimization problem into a series of short‐term deterministic problems. Then, a priority‐aware deep Q‐network (DQN)‐based fast uplink grant access management (PDAC) algorithm is proposed to achieve intelligent access management with differentiated service priority requirements. PDAC utilizes DQN to handle non‐convex high‐dimensional optimization problem with service priority constraint to achieve intelligent access management and priority awareness. Simulation results demonstrate that PDAC outperforms the existing algorithms in access queuing delay, buffer queue backlog, and priority deficit fluctuation.

Age of information (AoI) is an important metric of information timeliness, which determines digital twin (DT) consistency and energy management precision. However, AoI guarantee in the time-averaged sense is unreliable to avoid the occurrence of extreme event. In this paper, we propose a novel information timeliness metric named ultra-low AoI (ULAoI). Compared with AoI, ULAoI further considers the occurrence of extreme event and higher-order statistical characteristics of excess AoI value. Multi-dimensional resources of power internet of things (PIoT) are jointly allocated to achieve ULAoI guarantee from the perspective of sensing-communication-control integration. ULAoI-DT-Prioritized deep Q network (DQN) is proposed to achieve coordinated resource allocation by approximating unobservable information with the assistance of ULAoI-DT, and preventing DQN training from using samples with large AoI based on ULAoI-induced priority. Simulation results demonstrate the superior performance of the proposed algorithm in global loss function, ULAoI guarantee, and energy management optimality.

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named$\text{C}^{3}$-FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that$\text{C}^{3}$-FLOW performs superior in loss function, communication efficiency, and carbon emission reduction.

Space-assisted vehicular networks (SAVN) provide seamless coverage and on-demand data processing services for user vehicles (UVs). However, ultra-reliable and low-latency communication (URLLC) demands imposed by emerging vehicular applications are hard to be satisfied in SAVN by existing computation offloading techniques. Traditional deep reinforcement learning algorithms are unsuitable for highly dynamic SAVN due to the underutilization of environment observations. An AsynchronouS federaTed deep Q-learning (DQN)-basEd and URLLC-aware cOmputatIon offloaDing algorithm (ASTEROID) is presented in this paper to achieve throughput maximization considering the long-term URLLC constraints. Specifically, we first establish an extreme value theory-based URLLC constraint model. Second, the task offloading and computation resource allocation are decomposed by employing Lyapunov optimization. Finally, an asynchronous federated DQN-based (AF-DQN) algorithm is presented to address the UV-side task offloading problem. The server-side computation resource allocation is settled by an queue backlog-aware algorithm. Simulation results verify that ASTEROID achieves superior throughput and URLLC performances.

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

Multi-mode power internet of things (PIoT) provides spatio-temporal coverage for low-carbon operation in smart park through combining various communication media. Heterogeneous resources are dynamically and intelligently managed to improve resource utilization and achieve anti-eavesdropping. However, resource management in multi-mode power IoT confronts challenges such as the mutual contradiction in joint communication and security quality of service (QoS) guarantee and the inadaptability to low-carbon services. In this paper, we propose an Adaptive learNing-based secure and enerGy-awarE resource management aLgorithm (ANGEL) to optimize multi-mode channel selection and power splitting for artificial noise (AN)-based anti-eavesdropping. Based on deep actor-critic (DAC) and “win or learn fast (WoLF)” mechanism, ANGEL can realize multi-attribute QoS guarantee, adaptive resource management, and security enhancement. Simulation results demonstrate its superior performance in energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.

Digital twin (DT) provides a real-time digital representation of electric device state for energy dispatching and control (EDC) model training in power system. However, the large age of information (AoI) deteriorates the consistency of DT and the precision of EDC model. In this paper, we investigate the global loss function minimization problem underthe long-term AoI constraint through coordinated resource management. The optimization problem is decoupled based on telescoping sum and Lyapunov optimization, and solved by the proposed AoI-aware DT-assisted intelligent resource management algorithm named AoI-DT. AoI-DT achievesa balanced tradeoff between AoI guarantee and EDC model precision through device scheduling and channel allocation. Simulation results verify the superior performance of AoI-DT in terms of global loss function and AoI compared withtwo state-of-the-art algorithms.

The low-carbon operation of smart park requires to deploy massive internet of things (IoT) devices to provide real-time monitoring and control services. Digital twin (DT) provides accurate guidance for communication network resource management in low-carbon smart park by establishing a digital representation of physical entities. Facing the strict requirements of DT on delay and accuracy, as well as the constraints of access priority and energy consumption, we propose a federated learning-based DT framework and a Latency-awarE diGital twIn assisted resOurce maNagement algorithm (LEGION). LEGION can achieve a well tradeoff between delay and accuracy performances under the long-term constraints of access priority and energy consumption. Compared with existing algorithms, LEGION has superior performance in average iteration delay, DT loss function, energy consumption, and access priority deficit.

Collaborative vehicular networks (CVNs) provide on-demand data processing via computation offloading empowered by edge and fog computing. However, intelligent computation offloading in CVNs still faces several challenges such as long-term quality of service (QoS) guarantee, inefficient utilization of environmental information, and asynchronous information exchange. In this paper, we aim at maximizing the throughput under long-term QoS constraints such as queuing delay. The problem of server-side computation resource allocation and user vehicle (UV)-side task offloading is decoupled by Lyapunov optimization. Firstly, we propose an asynchronous federated deep Q-learning network based task offloading (AF-DQN) algorithm to solve the task offloading subproblem by exploring the semi-distributed learning framework. Secondly, we develop a heuristic queue backlog-aware algorithm to solve the computation resource allocation subproblem. Simulation results demonstrate that the proposed algorithm effectively reduces end-to-end queuing delay.

Abstract Multi‐mode green internet of things (IoT) network that integrates multiple communication media can well meet data transmission and processing demands of low‐carbon smart building. However, network resource management optimisation including joint optimisation of gateway and channel selection still faces technical challenges such as differentiated quality of service (QoS) demand guarantee, coupling between optimisation problems with different timescales, and adversary caused by multi‐device competition. To address these challenges, an adversarial learning‐based multi‐timescale network resource management algorithm for multi‐mode green IoT is proposed. Specifically, the minimisation problem of weighted difference between energy consumption and throughput under the long‐term queuing delay constraints is formulated to achieve differentiated QoS guarantee. Large‐timescale gateway selection is decoupled from small‐timescale channel selection by establishing matching preferences based on empirical performance, and optimised by using bilateral matching with quota. Finally, an exponential‐weight algorithm for exploration and exploitation (EXP3)‐based small‐timescale channel selection algorithm is proposed to achieve adversary awareness. Simulation results demonstrate that compared with asynchronous greedy matching algorithm and auction‐based many‐to‐many matching algorithm, the proposed algorithm performs superior in terms of energy consumption and throughput.

Abstract The green internet of things with heterogeneous communication technologies can provide data transmission and computing services for low‐carbon operation of smart buildings. However, latency‐sensitive task offloading in smart buildings for multi‐mode green internet of things still faces several challenges such as coupling between multi‐mode channel and multiple gateway selection, diversified quality of service requirement guarantee, and contradiction of long‐term performance guarantee and short‐term optimisation objectives. To address these challenges, a three‐dimensional quota matching‐based latency‐sensitive task offloading algorithm is proposed to minimise the weighted difference between energy consumption and throughput under the long‐term queuing delay constraints. Specifically, the minimisation problem is decoupled by Lyapunov optimisation. The three‐dimensional quota matching among devices, gateways, and channels is employed to solve the conflicts between gateway selection and channel selection. Finally, the three‐dimensional quota matching is converted to a two‐side quota matching to further reduce complexity and solved iteratively. Numerical results demonstrate that compared with H3CG and MMCS, the proposed algorithm improves the weighted difference between energy consumption and throughput by 21.85% and 27.91%, respectively, and reduces the sensor‐side average queuing delay by 30.82% and 16.83%, and gateway‐side average queuing delay by 16.57% and 26.71%, respectively.

As the fast development of vehicular network, the layout of the roadside unit (RSU) is indispensable. Due to the shackles of factors, such as coverage and cost, there is an urgent need for effective solution to solve the contradiction that RSU cannot be deployed on large scale. Parked cars provide a feasible solution for replacing RSUs and effectively reducing the arrangement of edge nodes. Inspired by this, parked cars as RSUs (P-RSUs) are leveraged to support cities’ vehicular network in this article. We first construct the P-RSU-based cellular-V2V heterogeneous networks (C-V2V HetNets) system model, and then formulate an optimization problem to maximize the energy efficiency (EE) of C-V2V HetNets with parked cars. Since the proposed issue is an NP-hard mixed-integer nonlinear programming (MINLP) problem coupled with P-RSU incentive, we reformulate it into two subproblems, which are the P-RSU recruitment and the joint resource allocation. For the first subproblem, an effective reverse auction-based mechanism is given to encourage parked cars participate and become P-RSUs. For the second subproblem, nonlinear fractional programming is used to optimize transmission power, and many-to-one matching is utilized to effectively obtain channel reusing scheme constrained by QoS. Moreover, a multihop-based transmission strategy is given to further expand vehicular network coverage. Algorithms are evaluated based on real-world scenarios using SUMO. Numerical results demonstrate that the proposed approach can both effectively recruit P-RSUs with low cost and achieve excellent system performance in terms of EE, spectrum efficiency, and network coverage compared to other benchmark algorithms.

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.

The combination of air–ground integrated power Internet of Things (AGI-PIoT) and cloud-edge-end collaboration enables flexible coverage and real-time data processing. However, how to achieve intelligent cloud-edge-end collaboration in AGI-PIoT faces several challenges such as dynamics of aerial networks, coupling of resource allocation in multiple layers, timescales, and dimensions, incomplete information, and dimensionality curse. In this article, we propose a FEderated Deep rEinforcement leaRning-based multi-lAyer multi-Timescale multi-dImensional resOurce allocatioN algorithm (FEDERATION). The multilayer multitimescale multidimensional resource allocation problem is decomposed into three subproblems based on Lyapunov optimization. For the subproblem of joint task offloading and power control, a federated deep actor-critic-based semidistributed algorithm is developed. The subproblem of admission control is solved by quadratic programming. The third subproblem is addressed through smooth approximation and Lagrange dual decomposition. Simulation results indicate that FEDERATION outperforms existing algorithms in queuing delay, energy consumption, and convergence.

The heat–electricity integrated energy system (HE-IES) with energy hubs (EHs) and prosumers is the typical form of future energy systems. An EH can efficiently model the integration of different energy carriers and coordinate an integrated energy sharing system of multiple prosumers in HE-IES. In this article, a closed-loop framework is designed to integrate the contract theory and consensus algorithms for a bilevel heat–electricity energy sharing system. First, the energy sharing scheme between prosumers and an EH in the lower level energy system is formulated. Contract theory is utilized to maximize the profits of an EH under the information asymmetry scenario, and different contract items are designed for the electric and thermal networks. In particular, a set of new bidirectional contract items is proposed in the electrical network. Furthermore, a consensus-based energy sharing framework among various EHs and utility grid in the upper level system is developed and network constraints are considered. Two types of Lagrange multipliers are defined to decouple the electric and thermal network, and an interactive mechanism is designed to solve the problem. Finally, the simulation results verify the convergence of the bilevel closed-loop framework and the feasibility and performance of the scheme.

Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

The integration of air-ground electricity internet of things (AGE-IoT) and machine learning, enables flexible network coverage and intelligent task offloading. However, dynamics of AGE-IoT networks, incomplete information, and resource allocation coupling are still major challenges in achieving intelligent AGE-IoT. In this paper, we investigate a joint multi-timescale task offloading and power control optimization problem to minimize the queuing delay of all the EIoT devices under the long-term constraint of energy consumption. We firstly decompose the joint optimization problem and transform it to large-timescale task offloading optimization and small-timescale power control optimization. Then, we propose a fed-erated deep actor-critic-based task offloading algorithm (FDAC) with two actor-critic networks for multi-timescale optimization. Numerical results show that FDAC has excellent performances in queuing delay and energy consumption compared with existing algorithms.

Collaborative vehicular network is a key enabler to meet the stringent communication and computing requirements of user vehicles (UVs). A UV dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as queuing delay guaranteeing, incomplete information, and dimensionality curse. In this paper, a Deep Reinforcement lEarning-based queue-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the long-term queuing delay constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, convergence, and queuing delay.

Air-Ground Integrated Power Internet of Things (AGI-PIoT) is a key enabler to meet the stringent communication and computing requirements of PIoT devices. In AGI-PIoT, the computation-intensive and delay-sensitive tasks can be either offloaded to edge servers through unmanned aerial vehicles (UAVs) or offloaded to cloud servers through ground base stations (GBSs), while the computational resources of edge servers and cloud servers should be jointly allocated. However, the joint optimization of task offloading and resource allocation faces several challenges such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this paper, we propose a learning-based QUeue-AwaRe Task offloading and rEsouRce allocation algorithm (QUARTER). Specifically, by exploiting Lyapunov optimization, the joint optimization problem is decomposed into task offloading and server-side resource allocation. For the first subproblem, we propose a Queue-aware Actor-Critic-based task offloading algorithm named QAC to cope with dimensionality curse. A low-complexity heuristic algorithm is developed to solve the second subproblem. Compared with existing task offloading and resource allocation algorithms, simulation results demonstrate that QUARTER has superior performances in throughput, queuing delay, and convergence.

Abstract In this work, an artificial neural network (ANN) based time‐varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN‐based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high‐voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

Space-air-ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas. In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites. However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER). Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation. The first subproblem is solved by the Lagrange dual decomposition. For the second subproblem, we propose a queue-aware actor-critic-based task offloading algorithm to cope with dimensionality curse. A greedy-based low-complexity algorithm is developed to solve the third subproblem. Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence.

In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency communication (URLLC) constraints have imposed new challenges for task offloading optimization. In this article, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability, occurrence probability of extreme events, and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose a URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. URLLC awareness is achieved by dynamically balancing the URLLC constraint deficits and energy consumption through online learning. We provide a rigorous theoretical analysis to show that guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. Finally, the effectiveness and reliability of UTO-EXP3 are validated through simulation results.

The papers in this special section focus on the topic of green industrial Internet of Things (IIoT). These papers aim to consolidate the current state of the art in terms of fundamental research ideas and network engineering, geared toward exploiting greenness of IIoT. The IIoT is a new ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine–human collaboration to improve productivity, efficiency, and reliability. IIoT connects billions of mobile digital devices, manufacturing machines, industrial equipment, etc., and generates an unprecedented volume of industrial data. The gap between the rapidly growing demands of data rate and existing bandwidth-limited network infrastructures has become ever prominent. Moreover, the interaction and connection of things in IIoT will consume substantial energy in contrast with limited energy storage of the things. Therefore, the greenness of IIoT is crucial for the success of IIoT. In particular, with the prevalence of mobile devices, electronic devices, cameras, social networks, social media, etc., ourworld is generating big data and multimedia big data, which further aggregate the energy demand in terms of the data transmission and processing of IIoT.

The exponential growth of data generated from increasing smart meters and smart appliances brings about huge potentials for more efficient energy production, pricing, and personalized energy services in smart grids. However, it also causes severe concerns due to improper use of individuals' private data, as well as the lack of transparency and auditability for data usage. To bridge this gap, in this article, we propose a secure and auditable private data sharing (SPDS) scheme under data processing-as-a-service mode in smart grid. Specifically, we first present a novel blockchain-based framework for trust-free private data computation and data usage tracking, where smart contracts are employed to specify fine-grained data usage policies (i.e., who can access what kinds of data, for what purposes, at what price) while the distributed ledgers keep an immutable and transparent record of data usage. A trusted execution environment based off-chain smart contract execution mechanism is exploited as well to process confidential user datasets and relieve the computation overhead in blockchain systems. A two-phase atomic delivery protocol is designed to ensure the atomicity of data transactions in computing result release and payment. Furthermore, based on contract theory, the optimal contracts are designed under information asymmetry to stimulate user's participation and high-quality data sharing while optimizing the payoff of the energy service provider. Extensive simulation results demonstrate that the proposed SPDS can effectively improve the payoffs of participants, compared with conventional schemes.

Vehicular fog computing has emerged as a complementary framework for edge computing by leveraging the under-utilized computational resources of vehicles. However, how to reduce task offloading delay, queuing delay, and handover cost with incomplete information while simultaneously ensuring privacy, fairness, and security remains an open issue. In this paper, we develop a secure and intelligent task offloading framework to address these challenges. We exploit blockchain and smart contract to facilitate fair task offloading and mitigate various security attacks. Then, we design a subjective logic-based trustfulness metric to quantify the possibility of task offloading success, and develop a trustfulness assessment mechanism. An online learning-based intelligent task offloading algorithm named QUeuing-delay aware, handOver-cost aware, and Trustfulness Aware Upper Confidence Bound (QUOTA-UCB) is proposed, which can learn the long-term optimal strategy and achieve a well-balanced tradeoff among task offloading delay, queuing delay, and handover cost. Finally, extensive theoretical analysis and simulations are carried out to demonstrate the reliability, feasibility, and efficiency of the proposed secure and intelligent task offloading scheme.

Existing task offloading mechanisms are developed on some single and rigid quality of service (QoS) performance metrics, which is widely apart from satisfying the true intent of a user vehicle (UV), thereby resulting in low quality of experience (QoE), large queuing latency, and poor reliability. There is an unprecedented demand for an intent-aware task offloading strategy that provides improved QoE and guarantees reliability. In this paper, we develop a novel task offloading framework for air-ground integrated vehicular edge computing (AGI-VEC), which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. IUCB can achieve three-dimension intent awareness including QoE awareness, URLLC awareness, and trajectory similarity awareness. Simulation results demonstrate that IUCB significantly outperforms existing EMM, sleeping-UCB, and UCB mechanisms in terms of QoE, end-to-end delay, queuing delay, throughput, and times of task offloading failure.

The Cognitive Internet of Vehicles (CIoV) is an intelligent network that embeds the cognitive mechanism in the Internet of Vehicles (IoV) to sense the environment and observe the network states to learn the optimal policies adaptively. However, one of the key challenges in CIoV systems is to design a smart agent that can smartly schedule the packet transmission for ultra-reliable low latency communication (URLLC) under extreme random and noisy network conditions. We propose a software defined network (SDN) based scheduling algorithm that leverages generative adversarial network (GAN) based deep distributional Q-network (GAN-DDQN) for learning the action-value distribution for intelligent transmission scheduling. A reward-clipping technique is proposed for stabilizing the training of GAN-DDQN against the effect of broadly spanning utility values. The extensive simulation results verify that GAN-Scheduling achieves higher spectral efficiency (SE), service level agreement (SLA), system throughput, transmission packet rate with lower transmission delay, and power consumption compared to the existing reinforcement learning algorithms.

In the Internet of Health Things based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the ultra-reliable and low-latency communication (URLLC) constraints, and the adversarial competition among IoHT devices have imposed new challenges for task offloading optimization. In this paper, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability of queuing delays and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose an energy-aware and URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. Guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. The effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Reliable and flexible emergency communication is a crucial challenge for search and rescue in the circumstance of disasters, specifically for the situation when base stations (BS) are no longer functioning. Unmanned aerial vehicle (UAV)aided networking is becoming a prominent solution to establish emergency networks with the underlay device-to-device (D2D), which also should be energy-efficient. In this article, we study energy-efficiency (EE) maximization for interference-aware underlay D2D-enabled UAV-aided 5G systems. All the interference scenarios are taken into account while modeling the system architecture. Afterward, we formulate an objective function to optimize EE maximization, which shows the characteristic of an NP-hard nonconvex research problem. Therefore, we transform the nonconvex problem into a convex one by reformulating the constraint functions with the cubic inequality method. Several criteria are developed to satisfy the non-negativity of the reformulating constraint. This leads the problem to be solved as a convex optimization method and results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrangian dual decomposition with a projected gradient method. In the end, we analyze the convergence behavior of the studied algorithm and also compared it with another existing algorithm through numerical simulations.

In this paper, we study the channel selection problem in edge computing-empowered cognitive machine-to-machine (CM2M) communications, where a massive number of machine type devices (MTDs) offload their computational tasks to a nearby edge server by opportunistically using the spectra that are temporarily unoccupied by primary users (PUs). We formulate the channel selection problem as an adversarial multi-armed bandit (MAB) problem, and combine the exponential-weight algorithm for exploration and exploitation (EXP3) and Lyapunov optimization to develop a learning-based energy-efficient solution named SEB-EXP3. It can find the long-term optimal channel selection strategy with guaranteed performance based on local information, while simultaneously achieving service reliability awareness, energy awareness, and data backlog awareness. Four heuristic algorithms are compared with SEB-EXP3 to demonstrate its effectiveness and reliability under various simulation settings.

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

In this article, a simultaneous wireless information and power transfer (SWIPT) system with full-duplex (FD) Internet of Things (IoT) nodes is considered and investigated. We induce the adaptive interference decoding (AID) strategy as well as the switch and inverter structure with antenna selection (SIAS)-based hybrid precoding scheme to the SWIPT system. A joint optimization of hybrid precoder, decoding rule, and power splitting (PS) ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it is a nonconvex problem, we propose a suboptimal solution with three stages. In the first stage, a search algorithm which can reduce the complexity of exhaustive search is proposed to decide the sending mode of each node. In the second stage, we utilize the semidefinite relaxation (SDR) approach to find the optimal digital precoder and PS ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise (IAN) systems and the digital precoding schemes. Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system. Moreover, the self-interference causes little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by the antenna separation technique.

In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).

Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.

Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand-supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex-concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader-follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis.

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations.

In this paper, the 5G wireless communication system with hybrid energy supply is considered, where the energy arrival variations and the channel fading are of different time-scales. In such a system, there exists multi-dimension randomness caused by the variations of energy harvesting, channel fading and electricity price. Faced with this challenge, we address the two time-scale cross-layer resource allocation problem to maximize the user experience while minimizing the energy cost from a long-term perspective. The formulated problem can be decoupled into three subproblems based on Lyapunov optimization, including the energy management subproblem over the large time-scale, and the rate control subproblem as well as the joint channel and power allocation subproblem over the small time- scale. Next, these subproblems are solved by combining linear programming, convex optimization, and matching theory, without the requirement of noncausal information. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance while guarantee reliable data transmission and efficient energy utilization.

With the wide development of smart devices, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands in Internet of things (IoT). However, there are two major obstacles hindering the wide deployment of IoT fog computing, i.e., how to realize server recruitment under information asymmetry and reliable task assignment under information uncertainty. In this article, we develop a robust two-stage task offloading algorithm by integrating contract theory with computational intelligence. In the first stage, we propose a contract based server recruitment scheme to motivate servers to share residual computational resources. In the second stage, by leveraging multi-armed bandit (MAB), we develop a reliable volatile upper confidence bound (RV-UCB) algorithm to minimize the long-term delay of task assignment, which takes into account task awareness, occurrence awareness and location awareness. Finally, a series of stimulation results are carried out to validate the performance of the proposed algorithm.

In this paper, we emphasize on energy-efficient resource allocation for the energy harvesting based cognitive machine-to-machine (EH-CM2M) communication. We consider how to maximize the energy efficiency of M2M transmitters (M2M-TXs) via the joint optimization of channel selection, peer discovery, power control, and time allocation. We propose a two-stage three-dimensional matching algorithm. In the first stage, M2M-TXs, M2M receivers (M2M-RXs) and resource blocks (RBs) are temporally matched together, and then the joint power control and time allocation problem is solved by combining alternating optimization (AO), nonlinear fractional programming, and linear programming to construct the preference lists. In the second stage, the joint channel selection and peer discovery problem is solved by the proposed pricing-based matching algorithm based on the established preference lists. Simulation results confirm that the proposed algorithm can approach the optimal performance with a low complexity.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.

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.

In this study, the performances of commonly used precoding schemes are evaluated based on channel measurements carried out at 28 GHz in a railway station for a massive antenna system configuration. And a hybrid precoding algorithm for a fifth generation (5G) multi‐user antenna system is proposed and its performance is evaluated based on real measured channels. Specifically, An upper bound for achievable sum‐rate of phased zero forcing (PZF) algorithm is derived and a precoding technique by reduction of phase shifters (PSs) named reduced‐PZF (RPZF) is proposed, which can obviously reduce power consumption in hybrid precoding systems. In addition, the authors give the closed‐form expression for achievable sum‐rate when RPZF is used. There are no requirements in the authors’ proposed scheme with complicated matrix decomposition and optimisation techniques. The simulation results show that it is possible to reduce 30 and 50% of the PSs by using the proposed hybrid precoder with better system performance in the line‐of‐sight (LoS) and non‐LoS scenarios, respectively. In addition, the digital PSs with 3 bits low resolution can achieve the similar performance when using analogue ones.

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.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

Multi-Access Edge Computing (MEC) is an emerging technology that leverages computing, storage and network resources deployed at the proximity of users to offload terminal from computational- and delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since offloading tasks incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in MEC deployments are: (i) offload the workload to the edge or compute it in terminals? (ii) which edge, among the available ones, should the task be offloaded to? Hence, we propose a matching theory based task assignment mechanism which takes into account the devices' and MEC servers' computation capabilities, wireless channel conditions, and delay constraints. The main goal of our task assignment mechanism is to reduce overall energy consumption, while satisfying task owners' heterogeneous delay requirements and supporting good scalability. Simulations are conducted to evaluate the efficiency of our proposed mechanism.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). However, the on- board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we investigate the joint optimization of route planning and task assignment for UAV-aided MCS from an energy efficiency perspective. The formulated NP-hard problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on dynamic programming (DP), and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley (GS) algorithm. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

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.

In this paper, we propose a time-dependent pricing (TDP) scheme for bandwidth consumption scheduling of multimedia streaming applications. By decoupling the network control functions from data delivery, software defined network (SDN) enables multimedia streaming users to negotiate their QoS parameters in a on-demand basis. Our key idea is to employ TDP as an incentive mechanism in SDN to motivate users to shift their delay-tolerant traffic demand, and free up resources for delay-sensitive applications in peak-time. Then, a Stackelberg game is formulated to analyze the interactions between the ISP and users. Next, we proposed a greedy algorithm to obtain the optimal congestion price and bandwidth slicing in each time slot based on real-time traffic load as well as users' preferences for delay. Finally, simulation results confirm that the proposed method can significantly flatten out traffic fluctuation.

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.

The advancement in vehicles has enabled crowdsensing in vehicular fog computing (VFC), where vehicles are recruited to be assigned different subtasks and participate sensing activities that may disclose their sensitive information. To stimulate more participants, VFC systems should be able to provide reliable and privacy-preserving data transmission and processing mechanisms for the sensing report. To ensure the report process, we present a reliable and privacy- preserving task recomposition (REPTAR) for multiple subtasks sensing in VFC. Modified homomorphic Paillier encryption and superincreasing sequence are employed for aggregating hybrid subtasks into one ciphertext. Reliability is verified by means and variances of each aggregated subtasks from different vehicular fog nodes. Detailed security analysis and performance evaluation are provided to demonstrate the security, privacy-enhancement, efficiency and low complexity of the proposed REPTAR.

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.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

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.

Multiuser multiple input multiple output (MU‐MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam‐vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal‐to‐leakage plus noise ratio (SLNR), we analyze the ergodic sum‐rate capacity using statistical Eigen‐mode (SE) and zero‐forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum‐rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum‐rate capacity.

In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution delay constraints. In order to address the formulated problem, an alternating direction method of multipliers (ADMM)-based distributed algorithm is proposed. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over the other existed schemes can be observed.

In this paper, we present an Internet of Things (IoT)-based health care system implementation scheme using Hidden Markov Model (HMM) chain and ElectroCardioGram (ECG) sensors within the context of e-Health. The scheme aims to facilitate improved monitoring and timely intervention for Cardio Vascular Diseases (CVD) patients thereby enhancing medical services for such patients. As real-time monitoring of patients from different locations remains a critical challenge for IoT-based health care systems, this implementation employs patient path estimator, patient table and alert management schemes within the hospital to facilitate the localisation and timely intervention for the treatment of CVD patients.

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.

In wireless communication systems powered by harvested energy, besides the channel fading, there is another dimension of dynamics induced by energy arrival variations, which makes the design of wireless transmission policies nontrivial. In this paper, we propose a framework for analyzing the energy harvesting powered wireless transmissions where the channel fading and the energy arrival variations are of different timescales. We define the duration between two consecutive changes of energy arrival rate as an energy harvesting frame, which consists of N channel fading slots. The power allocation problem can be formulated as an Markov decision process (MDP), and can be decoupled into two sub-problems. The inner problem deals with the power allocation in channel fading timescale in every N slots where the energy arrival rate keeps constant, and the outer problem deals with the energy management in energy harvesting timescale among frames. The two sub-problems can be solved by finite horizon dynamic programming (DP) and infinite horizon DP, respectively. Numerical simulations show that the average rate decreases slightly as N increases, and the rate under i.i.d. channel is higher than that under Markov channel.

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

With the support of cloud computing, large quantities of data collected from various WSN applications can be managed efficiently. However, maintaining data security and efficiency of data processing in cloud- WSN (C-WSN) are important and challenging issues. In this paper, we present an efficient data outsourcing scheme based on CP-ABE, which can not only guarantee secure data access, but also reduce overall data processing time. In our proposed scheme, a large file is divided into several data blocks by data owner (DO) firstly. Then, the data blocks are encrypted and transferred to the cloud server in parallel. For data receiver (DR), data decryption and data transmission is also processed in parallel. In addition, data integrity can be checked by DR without any master key components. The security analysis shows that the proposed scheme can meet the security requirement of C-WSN. By performance evaluation, it shows that our scheme can dramatically improve data processing efficiency compared to the traditional CP-ABE method.

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.

The non-regenerative massive multi-input-multi-output (MIMO) non-orthogonal multiple access (NOMA) relay systems are introduced in this paper. The NOMA is invoked with a superposition coding technique at the transmitter and successive interference cancellation (SIC) technique at the receiver. In addition, a maximum mean square error-SIC receiver design is adopted. With the aid of deterministic equivalent and matrix analysis tools, a closed-form expression of the signal to interference plus noise ratio (SINR) is derived. To characterize the performance of the considered systems, closed-form expressions of the capacity and sum rate are further obtained based on the derived SINR expression. Insights from the derived analytical results demonstrate that the ratio between the transmitter antenna number and the relay number is a dominate factor of the system performance. Afterward, the correctness of the derived expressions are verified by the Monte Carlo simulations with numerical results. Simulation results also illustrate that: 1) the transmitter antenna, averaged power value, and user number display the positive correlations on the capacity and sum rate performances, whereas the relay number displays a negative correlation on the performance and 2) the combined massive-MIMO-NOMA scheme is capable of achieving higher capacity performance compared with the conventional MIMO-NOMA, relay-assisted NOMA, and massive-MIMO orthogonal multiple access (OMA) scheme.

Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS to total number of user requests. With finite battery capacity, Markov decision process-based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based policy and the energy-efficient threshold-based policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high.

Other than separately investing the energy efficiency (EE) merits of information-centric networking’s (ICN’s) caching and sharing (CS) mechanism in wireless communications, here we comprehensively compare the EE performances of ICN’s CS mechanism in different scenarios. A modified system model is first proposed while introducing the CS mechanism into the in-network router, base station (BS), and neighboring user sides. Afterwards, the system achievable sum rate as well as the power consumptions in wireless and wired sections is investigated. The EE performances of different scenarios are finally obtained by dividing the achievable sum rate by the consumed power. While comparing the three scenarios, numerical results demonstrate that the optimal place to cache the content is mainly determined by the distance and hub number of the core routers that passed.

In this paper, we explore the content information to design the joint caching and push mechanism in the small-cell base stations (SBSs) powered by renewable energy. The problem is formulated as a Markov decision process by exploring the features of content popularity and renewal and by taking into consideration the energy consumption for both content fetch from core network and push to the users. The objective is to minimize the number of requests which cannot be met by the SBSs. We adopt the policy iteration algorithm to obtain the optimal caching and push policy. According to the numerical results, the performance gain with large SBS cache size is marginal due to the limited energy. We also find that the optimal policy reveals noticeable performance gain compared with the greedy fetch policy and the non-push policy. In addition, simulations shows the tradeoff between the number of cached contents in the SBS and the available energy for content push.

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.

To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.

In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.

This paper considers two base stations (BSs) powered by renewable energy serving two users cooperatively. With different BS energy arrival rates, a fractional joint transmission (JT) strategy is proposed, which divides each transmission frame into two subframes. In the first subframe, one BS keeps silent to store energy, while the other transmits data, and then, they perform zero-forcing JT (ZF-JT) in the second subframe. We consider the average sum-rate maximization problem by optimizing the energy allocation and the time fraction of ZF-JT separately. First, the sum-rate maximization for given energy budgets in each frame is analyzed. We prove that the optimal transmit power can be derived in closed form, and the optimal time fraction can be found via bi-section search. Second, an approximate dynamic programming algorithm is introduced to determine the energy allocation among frames. We adopt a linear approximation with the features associated with system states and determine the weights of features by simulation. We also operate the approximation several times with random initial policy, named policy exploration, to broaden the policy search range. Numerical results show that the proposed fractional JT greatly improves the performance. In addition, appropriate policy exploration is shown to perform close to the optimal.

Motivated by the recent development of energy harvesting communications, and the trend of multimedia contents caching and push at the access edge and user terminals, this paper considers how to design an effective push mechanism of energy harvesting powered small-cell base stations (SBSs) in heterogeneous networks. The problem is formulated as a Markov decision process by optimizing the push policy based on the battery energy, user request and content popularity state to maximize the service capability of SBSs. We extensively analyze the problem and propose an effective policy iteration algorithm to find the optimal policy. According to the numerical results, we find that the optimal policy reveals a state dependent threshold based structure. Besides, more than 50% performance gain is achieved by the optimal push policy compared with the non-push policy.

Massive Multiple-Input Multiple-Output (Massive MIMO) has been realized as a promising technology element for 5G wireless mobile communications, in which Spectral Efficiency (SE) and Energy Efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy consumption of cellular system comes from the operator, mostly used to feed the base station (BS). Yet previously, the User Equipment(UE) is focused on while studying the EE issue instead of BS. In this case, in this paper, an EE scheme that focuses on the optimization of BS energy consumption is proposed. Apart from the previous studies, which divides the coverage area by circuit section, the coverage area is divided by fan section with the help of Propagation theory for zoom in or zoom out. In the proposal, transmission model and parameters related to EE is deduced first. Afterwards, the Cellular Partition Zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area or zoom out to save the energy. Comprehensive simulation results demonstrate that CPZ presents better EE performance with negligible impact on the transmission rate.

The introduction of micro grids (MGs) into the utility grid poses new challenges in the energy management design due to the intermittent characteristics of renewable energy sources and limited storage capacity. In this paper, we proposed a distributed energy management algorithm by taking into consideration the interactions and interconnections among utility companies, MGs, and customers. We model the energy management problem as a two-stage Stackel berg game, in which utility companies and MGs are game leaders, and customers are game followers. Utility companies and MGs make decisions about what price to offer their electricity to customers. Customers adjust their electricity procurement amounts based on the prices offered by utility companies and MGs. We prove that a Nash equilibrium exists in the proposed two-stage Stackel berg game, and the optimum solutions obtained by the distributed energy management algorithm is exactly the Nash equilibrium. We have analyzed and verified the relationships among utility functions, electricity prices, electricity demands, electricity procurement amounts, and pollutant parameters through computer simulations. We have also compared the performance of the proposed distributed algorithm with the centralized algorithm under different simulation conditions.

In this work, a game theoretical approach for addressing the virtual network service providers (NSPs), small cell provider (SCP) and user interaction in heterogenous small cell networks is presented. In particular, we consider the users can select the services of different NSPs based on their prices. The NSPs have no dedicated hardware and need to rent from the SCP in term of radio resources, e.g., small cell base stations (SBSs) in order to provide satisfied services to the users. Due to the fact that the selfish parties involved aim at maximizing their own profits, a hierarchical dynamic game framework is presented to address interactive decision problem. In the lower-level, a Stackelberg game is formulated to model and analyze the adaptive service selection of non-atomic users. In the upper-level, the NSPs and SCP sequentially determine the leasing and pricing strategies, respectively, by taking into account the service selection in the lower-level game. Performance evaluation shows the effectiveness and advantages of the proposed game theoretic approaches.

Despite the numerous benefits brought by device‐to‐device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design because of the co‐channel interference caused by spectrum reuse and limited battery life of user equipment's (UEs). Most of the previous studies mainly focus on how to maximise the spectral efficiency and ignore the energy consumption of UEs. In this study, the authors study how to maximise each UE's Energy Efficiency (EE) in an interference‐limited environment subject to its specific quality of service and maximum transmission power constraints. The authors model the resource allocation problem as a non‐cooperative game, in which each player is self‐interested and wants to maximise its own EE. A distributed interference‐aware energy‐efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The authors prove that the optimal solution obtained by the proposed algorithm is the Nash equilibrium of the non‐cooperative game. The authors also analyse the tradeoff between EE and SE and derive closed‐form expressions for EE and SE gaps.

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

The combination of energy harvesting and large-scale multiple antenna technologies provides a promising solution for improving the energy efficiency (EE) by exploiting renewable energy sources and reducing the transmission power per user and per antenna. However, the introduction of energy harvesting capabilities into large-scale multiple antenna systems poses many new challenges for energy-efficient system design due to the intermittent characteristics of renewable energy sources and limited battery capacity. Furthermore, the total manufacture cost and the sum power of a large number of radio frequency (RF) chains can not be ignored, and it would be impractical to use all the antennas for transmission. In this paper, we propose an energy-efficient antenna selection and power allocation algorithm to maximize the EE subject to the constraint of user's quality of service (QoS). An iterative offline optimization algorithm is proposed to solve the non-convex EE optimization problem by exploiting the properties of nonlinear fractional programming. The relationships among maximum EE, selected antenna number, battery capacity, and EE-SE tradeoff are analyzed and verified through computer simulations.

In this paper, we consider the application scenario where multiple Base Stations (BSs) cooperate to transmit signals to a mobile terminal in the same frequency and any of the cooperative BSs is allowed to enter into sleeping mode to save energy. The mobile terminal employs the Joint Maximum Likelihood Sequence Estimation (JMLSE) based Joint Signal Detection (JSD) to simultaneously detect multiple co-channel signals and judge whether a cooperative BS is active or not. The detection error probability of JSD is analyzed in this paper. For the case of BS sleeping, the error probability is computed based on a tentative modulation scheme which incorporates the M constellation points of conventional M-QAM and the additional constellation point 0. For the case of BS cooperation, the error probability bounds are derived based on a genie-aided receiver, and a new Tighter Lower Bound (TLB) is derived by replacing the genie with a less generous one. Simulation results have verified that the computed error probability can provide a rapid and accurate estimation of the Symbol Error Rate (SER) performance.

The extensive deployment of femtocells introduces co-channel interference to the macro cell-edge users (MCEUs), which degrades the throughput improvement of the total network. In this paper, we consider the scenario in which the femtocell base station (FBS) allows the hybrid access of MCEUs on the condition that the MCEUs rent the power resource from it. However, the FBS is power limited. If almost all of its power is used to serve the MCEUs and its original femtocell users (FUEs), when newly authoritized FUEs are switched on, there will be little power left to serve these newly authoritized FUEs. In this situation, the FBS can select one MCEU as a relay to coordinate its information transmission to the FUE. To reward the cooperation of the MCEUs, the FBS will agree to support the service of MCEUs without charging. Stackelberg game is used to find the optimal power and price value for this procedure. An MCEU relay selection criterion is proposed for the FBS. The optimal power that the MCEU can obtain from the FBS and the power that the MCEU can obtain from the FBS and the power that the MCEU can share with the FUEs are obtained. The simulation results show that the throughputs of both the FUEs and the MCEUs are improved.

This paper aims at improving the demodulation performances of Four-element Single Sideband signals by proposing a new receiver structure involving an Inter-Signal Interference cancellation filter. The filter makes use of the estimated symbols generated by the receiver to approximate the interference between multiplexed signals inherent to the use of low complexity Hilbert Transform filters at the transmitter. Simulation results show that the new receiver structure performs better than the one formerly proposed in early works on this topic.

This paper proposes a study of performances of the Orthogonal Four Single Sideband elements modulation previously proposed by Ohta [1]. Realistic parameters such as receiver noise and various channel effects are introduced in the derivation of the modulation. Transmission of two QPSK signals multiplexed in one 4-SSB (Single Sideband) signal over OFDM (Orthogonal frequency-division multiplexing) is considered and a demodulation process exploiting a turbo equalizer is presented. Computer simulations are used to verify the performances of the proposed modulation scheme. Results show that 4-SSB QPSK signal over OFDM can achieve performances close to QPSK-OFDM scheme while carrying twice more information, provided perfect channel equalization. However, we also show that in a non-ideal equalization case, performances of 4-SSB QPSK OFDM become highly sensitive to channel effects.

The wireless communication system is evolving towards a ubiquitous wireless network in which multiple-radio devices can roam anytime and anywhere across different access networks. Minimal latency and low packet loss ratio during handover (HO) are the indispensable requirements to reduce service disruption and support real-time services. However, minimizing service disruption is a challenging issue to multiple access networks since HO latency is much higher than that of homogeneous networks. Link layer trigger or layer 2 trigger has been proposed to address this issue. Nevertheless, the precise timing and definitive criteria for initiating L2 trigger which can greatly affect the handover performance is not specified. This paper considers the issue of how to timely initiate handover decision from the link layer by using adaptive filtering algorithm to predict forthcoming received signal. Particularly, we propose Recursive Least Squares (RLS) as the predictive method for layer 2 triggering and compare its performance with various conventional filtering techniques. A simulation in which the proposed predictive algorithm is implemented to a handover scenario from Wi-Fi to WIMAX is also presented. Our numerical analysis and simulation results show that the proposed algorithm can achieve better performance than that of the other algorithms and can be used to enhance the handover performance in heterogeneous wireless networks.

In the previous work, a single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) has been proposed. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In another work, in order to solve this problem, a subcarrier identification and interpolation algorithm was proposed, in which the slowly converging subcarriers are identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. However, this correlation relationship was only found based on simulation results and no clear mathematical proof was given. The performance of the algorithm was only evaluated for the case of single interference. In this paper, the mathematical proof of the correlation relationship between MSE and MSD is given. Furthermore, we generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement over single antenna. The performance of LMS-BJMLSE is also evaluated for the case of dual interference.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) would greatly degrade the bit error rate (BER) performance of cell-border users. In the previous work, a blind single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) has been proposed. The proposed LMS-BJMLSE algorithm is blind with respect to interfering signals and neither the training sequence (TS) nor pilot signal from interferers is needed. However, the conventional LMS-BJMLSE requires a long training sequence (TS) for channel estimation. In this paper, we propose a TS reduction scheme in which the subcarriers are divided into small groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired signal. The identified subcarrier's channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm could reduce the required TS length by 80%.

Mobile Sensor Networks (MSN) are used for network load balancing, prolonging network lifetime, and improving network coverage by monitoring critical areas where manual sensor deployment cannot be performed. Addressing the problem of how to achieve maximum network coverage and network uniformity, after deploying sensors in critical areas randomly, is of significant importance recently. In this paper, we design an energy efficient distributed self-deployment algorithm, which is based on the diffusion of mobile sensors in the Region of Interest (ROI). Mobile sensors are diffused from denser sensors area to lesser or uncovered area in ROI, on the basis of localized information. Our algorithm considers ROI with the absence as well as presence of obstacles. Resemblance in the numerical and simulation analysis confirms the concreteness of our algorithm.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) would greatly degrade the bit error rate (BER) performance of cell-border users. Joint maximum likelihood sequence estimation (JMLSE) based single antenna interference cancellation (SAIC) algorithms have been under intense research. In this paper, both the upper and lower error probability bounds for JMLSE are extended to MQAM-OFDM systems based on a genie-aided receiver. The derived upper and lower bounds are valid for any MQAM and an arbitrary number of interferers. A tighter lower bound is derived by replacing the genie with a less generous one. We prove that this new lower bound is much tighter compared to the conventional lower bound.

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) operating and coexisting with the desired BS in the same frequency channel would greatly degrade the bit error rate (BER) performance of mobile receivers which are near to cell borders. CCI cancellation algorithms by using only one receiving antenna named single antenna interference cancellation (SAIC) have been under intense research. However, most of these algorithms require additional information from interfering signals. Furthermore, the orthogonality of a asynchronous interfering signal is destroyed and therefore, conventional SAIC algorithms cannot be applied. In this paper, we propose a blind SAIC algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) for asynchronous interference cancellation. The proposed LMS-BJMLSE algorithm is blind with respect to interfering signals and neither the training sequence (TS) nor pilot signal from interfering signals is needed. Its complexity is independent of channel length compared to the conventional time domain JMLSE algorithm. Simulation results show that the proposed LMS-BJMLSE algorithm could greatly improve the BER performance in both synchronous and asynchronous interference conditions.

Due to limited frequency resources, the co-channel interference (CCI) from adjacent femto cells operating and coexisting with conventional macro cells in the same frequency channel would greatly degrade the bit error rate (BER) performance of mobile terminals. CCI cancellation algorithms for the 2G GSM and the 3G CDMA systems have been under intense research. However, since orthogonal frequency division multiplexing (OFDM) has been adopted in the next generation mobile communication systems, dealing with CCI is more complex in OFDM than in GSM or CDMA because it requires dynamic power control and frequency allocation with advanced coordination among base stations. In this paper, we propose a single antenna interference cancellation (SAIC) algorithm named power adaptive joint maximum a posterior (PA-JMAP) for OFDM systems. The proposed PA-JMAP algorithm jointly decodes the desired and interfering data by using a joint trellis, which defines all possible combinations of each base station's encoder trellis. Signal to interference ratio (SIR) estimated by a sequentially updated estimator is used to track power changes of the received signal. Simulation results show that the proposed PA-JMAP decoder could greatly improve the BER performance for OFDM systems even under severe CCI conditions when compared to a conventional viterbi decoder (CVD).