The transmission of semantic information related to users’ interests can significantly reduce the network transmission burden. However, if a base station transmits semantic information to a group of users simultaneously, partially similar interests among them inevitably produce repetitive transmission and higher energy consumption. Additionally, the worst channel condition among users impedes enhancements to network capacity. To achieve green communication and enhance network capacity, this paper exploits shared and individualized channels of cooperative rate splitting multiple access (RSMA) for semantic information assignment. Meanwhile, the appropriate relay is selected for network capacity enhancement based on semantic information assignment, energy consumption, and channel status. Then, semantic information assignment, relay selection, time resource allocation, and rate splitting are jointly optimized to minimize energy consumption. The formulated problem is an intractable mixed-integer non-linear programming problem, which can be resolved with the Dinkelbach method and successive convex approximation technique. A customized algorithm, namely SRTR, is proposed for green cooperative RSMA networks. Simulation results reveal that the SRTR algorithm can increase network capacity and flexibly allocate semantic data, resulting in a 21% reduction in energy consumption compared with traditional cooperative RSMA.

This paper investigates an energy-constrained, re-configurable intelligent surface (RIS)-assisted framework that integrates sensing and covert backscatter communication through a stagewise feedback mechanism in the presence of masquerading eavesdroppers located in blind zones. The system operates in two stages on a unified hardware platform. In the sensing stage, the base station cooperates with a terminal-side semi-passive RIS to synthesize directional beampatterns and establish virtual line-of-sight paths for improved angular observability. In the communication stage, the RIS switches to low-power backscatter to deliver data while steering nulls toward suspicious directions inferred from sensing. A stagewise feedback mechanism links the two stages: a KL divergence-based covert requirement is translated into a directional leakage-power budget and then mapped to a Cramér-Rao-style bound sensing-accuracy target for the next round. The coupled waveform designs are formulated for both stages, and an alternating optimization framework with majorization-minimization and convex relaxations (SOCP/SDP subproblems under unit-modulus RIS constraints) is developed. Simulation results show monotonic outer-loop improvement of the legitimate link SNR with the worst-case leakage consistently below the covert cap; inner loops typically converge within a few steps. Flat-top mainlobe shaping further reduces the sensing bound and enhances robustness. Compared with a no-feedback variant, the proposed scheme achieves higher rates under stringent covert requirements, while an optional safety gate that pauses transmission in near-collinear cases preserves feasibility. These results demonstrate that sensing-driven feedback enables energy-efficient covert communication with improved resolution and link quality.

The rise of the low-altitude economy (LAE) is transforming urban development, with multiple unmanned aerial vehicle (multi-UAV) networks serving as a pivotal enabler through their flexibility and cost efficiency, yet the increasing UAV density exacerbates spectrum scarcity, co-channel interference, and energy consumption. In the context of the emerging LAE, we propose a multi-UAV network framework that comprehensively accounts for co-channel interference among users served by the same UAV and across different UAVs. We design a user dissatis-faction metric as the absolute difference between the threshold rate and the achievable rate, which serves as an indicator of the quality of service (QoS) level. Since UAV positioning, channel allocation, and power control are all critical factors affecting communication performance, we formulate a joint optimization problem aimed at minimizing the weighted sum of aggregate user dissatisfaction and the total UAV transmit power. To address this problem, we propose a hybrid genetic algorithm (GA)-sand cat (HGSC) algorithm, in which an improved GA is utilized to optimize channel allocation, and subsequently, an improved sand cat swarm optimization (SCSO) algorithm performs joint optimization of UAV deployment and power control. Simulation results demonstrate that the proposed algorithm achieves supe-rior performance compared with benchmark methods.

Collaborative perception in Internet of Things (IoT) networks exchanges perceptual information to close blind regions caused by limited fields of view and occlusions. Transmitting raw or high-resolution streams is often impractical under bandwidth, latency, and energy constraints. We present a task-oriented semantic communication framework that integrates cooperative perception with semantic mode adaptation. It (i) assigns helpers to a receiver's blind cells; (ii) on each helper-receiver link, selects a semantic mode (image- to- image or image- to- text) and a symbol budget from offline similarity-SINR profiles to meet fidelity and deadline targets; and (iii) performs interference-aware FDMA and power allocation under per-node budgets. We define a value-weighted QoS that combines coverage value, semantic fidelity, and delay, and propose SCRA, a four-stage solver that decomposes the mixed-integer nonconvex problem while preserving feasibility. To date, no end-to-end integration of semantic communication with cooperative perception has been demonstrated. Simulations show consistent gains over tra-ditional communication and fixed-mode semantic baselines in network sum-QoS and value-weighted blind-area coverage across bandwidth regimes, with improved robustness under stronger interference.

Mobile Edge Computing (MEC)-enabled Low Earth Orbit (LEO) satellite networks represent a promising architecture for delivering low-latency computation offloading services, but their performance is hindered by the unbalanced distribution of ground compute requirements. Although previous studies have explored addressing this challenge through computational resource scheduling or inter-satellite offloading, they have largely overlooked the flexible beam coverage capabilities provided by LEO satellites. Accordingly, this paper presents an integrated beam coverage control and task offloading algorithm that dynamically adjusts satellite beam centers and radii in response to variations in ground compute requirements while jointly determining offloading decisions. By developing a detailed system model, the problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem aimed at optimizing average task latency and task success rate. The MINLP problem is subsequently transformed into a Markov decision process (MDP). To solve the MDP, this paper proposes the Advantage Actor-Critic-based Integrated Beam and Offloading Control (AIBOC) algorithm, which employs a DeepSets-based encoder to handle variable numbers of UE feature inputs. Simulation results validate that the proposed method significantly outperforms baseline methods in reducing task latency and enhancing success rates.

Task-oriented semantic communication effectively facilitates the completion of specific tasks by conveying users' interests. Nevertheless, when a base station conveys task-oriented semantic information to multiple users, these users exhibit both shared and distinct interests, which leads to increased redundancy and communication costs. To reduce transmission redundancy and cost, in this paper, a flexible and efficient multicast and unicast transmission scheme based on task-oriented semantic information is investigated for users with overlapping interests. Specifically, a joint energy-delay cost function is adopted to measure the communication cost caused by transmission redundancy, which facilitates a trade-off between energy consumption and transmission delay. Then, to minimize the network cost, this paper leverages the inherent benefits of common and private streams in rate splitting multiple access (RSMA) and design a semantic information allocation strategy. The RSMA common stream is further divided into the super-common and user-common streams. A semantic information allocation mechanism is established according to the common and private streams to flexibly allocate multiple semantic information. Then, semantic information selection on super-common stream, the proportional strategy of user-common stream, and rate splitting are jointly designed to minimize the network cost. The problem is a mixed-integer problem. Block coordinate descent and successive convex approximation technique are adopted to address it. An algorithm, namely SURA, is proposed for semantic information allocation in RSMA networks. Simulation results demonstrate the proposed SURA algorithm can adaptively allocate semantic information and reduce the network cost.

Reconfigurable intelligence surface (RIS) enabled integrated sensing and communication (ISAC) is vulnerable to different hostile attacks, especially the eavesdropping and detection attacks. Unlike the prior studies that tackle these attacks separately, this article investigates the use of an active simultaneously transmitting and reflecting RIS (STAR-RIS) to establish a unified security paradigm for an ISAC network against simultaneous eavesdropping and detection attacks. Thereinto, with the aid of active STAR-RIS, a multiantenna base station (BS) concurrently senses a point-like target and communicates with multiple secrecy and covert users (SUs and CUs) in the full-space. Particularly, target acts as an eavesdropper for wiretapping the transmitted data from BS to SUs, while Willie acts as an extra warden for detecting the existence of the wireless transmission from BS to CUs. To address these attacks, we first explore a joint physical layer secrecy and covert communication strategy. Concretely, BS continuously transmits the secrecy signals to SUs in all time slots, and opportunistically propagates the covert signals to CUs in the selected ones. On this basis, BS exploits the differences of fading channels to limit the amount of SUs’ information wiretapped by target, and uses the Gaussian signaling as an uncertainty to hide CUs’ communication behaviors from Willie. Obeying this, we utilize the Pinsker’s inequality and the large system analytical method to obtain lower bounds on the detection error probability (DEP) of Willie. Furthermore, to effectively balance radar sensing and secure communication, the transmit beamforming and receive filter at BS, and the transmission&reflection beamforming at active STAR-RIS are jointly designed to maximize the average sum of the minimum secrecy and covert rates, while meeting given constraints on the sensing signal-to-interference-plus-noise ratio at BS, the tolerable DEP against Willie, and the transmit power at both BS and active STAR-RIS. To solve this highly nonconvex problem, we develop an efficient overall iterative algorithm by invoking the successive convex approximation, semi-definite relaxation, and sequential rank-one constraint relaxation methods. Finally, numerical results verify the superiority of active STAR-RIS in alleviating the multiplicative fading, and manifest that driven by the proposed algorithm, the studied network greatly outperforms baseline schemes in defeating hybrid attacks.

Federated learning (FL) is recognized as a pivotal paradigm for 6G, offering decentralized model training without compromising data privacy. Recent works have proposed deploying FL in cell-free massive MIMO (CF-mMIMO) networks for reliable model transmission between FL clients and the server. Nevertheless, the problem of simultaneous access point (AP) clustering (i.e., dynamically forming AP groups to facilitate client-server communication) and transmit power allocation has not been thoroughly investigated. Furthermore, most existing solutions do not simultaneously consider the fast decision-making requirements brought by user mobility and the scalability of solutions in large-scale networks. To address this gap, we propose DACPA, a multi-agent deep reinforcement learning (DRL)-based scheme that accounts for client mobility (walking speed) and heterogeneous computing capabilities. DACPA strategically assigns each client a customized AP cluster and corresponding transmit power configuration, thereby optimizing model update latency. Extensive simulation results demonstrate the superior performance of DACPA in terms of convergence stability, spectral efficiency, global model update latency, and average energy consumption.

Integrated sensing and communication (ISAC) is susceptible to eavesdropping because of wireless signals’ broadcast nature, and thus the enhancement of its security becomes of critical importance. In this paper, we investigate the use of active reconfigurable intelligence surface (RIS) to realize covert communication in a millimeter-wave ISAC network with finite blocklength, where a base station (called Alice) performs the single-target sensing, and meanwhile transmits confidential messages to a communication user (called Bob) under the surveillance of target. Especially, owing to its abilities in modifying radio propagation and resisting multiplicative fading, active RIS can not only boost the desired target return, but also concentrate/suppress the covert signal at Bob/target to prevent detection. We first derive the minimum detection error probability (DEP) of target in a closed-form via the large system analytical method. To further enhance Bob’s covert performance, we then propose a hybrid beamforming design in which the transmit beamforming at Alice and the reflection beamforming at active RIS are jointly optimized to maximize the covert rate, while meeting given constraints on the minimum DEP against target and the sensing signal-to-interference-plus-noise ratio at Alice. To solve this problem, an effective overall iterative algorithm is developed by leveraging the alternating optimization and successive convex approximation methods. Finally, numerical results confirm the covertness superiority of the studied network, driven by the proposed algorithm, over baseline schemes.

Low Earth Orbit (LEO) satellites equipped with mobile edge computing (MEC) servers can break through the coverage limitation of ground base stations and provide ubiquitous services to users, which is an indispensable technique in future networks. However, the satellite’s energy fluctuates with its movement relative to the sun, restricting its battery load capacity, which further hinders it from supplying continuous services to users. In this paper, to maximize the sum of all users’ quality of experience ($\mathrm{Q o E}$) while meeting satellite energy constraints, we propose a dynamic task scheduling and resource allocation scheme, enabled by the cooperation between satellites and the cloud center. As this issue is a long-term decision and optimization problem, we model it as a Markov decision process (MDP) and exploit the improved Proximal Policy Optimization(PPO) algorithm to address it. Simulation results verify the effectiveness of the algorithm, which can significantly improve users’ QoE compared with other baseline algorithms.

This work investigates the secure resource collaboration among selfish edge servers for multi-access edge computing (MEC)-enabled VR systems in a dynamic scenario. Due to the time-varying and stochastic nature of VR user requests, the edge servers usually have significant differences in workload. To this end, we first propose a type judgment method to perceive their service capability and divide them into two types, i.e., the requesting node (RN) with a poor service capability and the cooperative node (CN) with a powerful service capability. To promote collaboration among self-interest nodes, we then model the competitive interactions among RNs and CNs as a multi-leader and multi-follower Stackelberg game. For the RN (as the leader), we design a novel pricing strategy based on deep reinforcement learning (DRL) to motivate CNs to provide resource assistance. Meanwhile, an optimal selling strategy for the CN (as the follower) is presented to maximize its payoffs from the network. To overcome the security problem during the resource collaboration, we finally introduce the blockchain as a secure and trusted platform for resource publishing and trading, where an efficient consensus mechanism called Proof-of-Trust (PoT) is developed to improve the performance of blockchain. The simulation results show that the proposed approach achieves superior performance.

With the rapid growth in demand for Internet services, multi-beam low earth orbit(LEO) satellite networks have received increasing attention in recent years. However, due to the fast movement of LEO satellites, users need to frequently handover between beams. Such handovers can significantly degrade network performance and increase the handover failure rate. In light of these challenges, this paper proposes a user grouping-based beam handover scheme that aims to reduce handover frequency and improve satellite handover decision efficiency. Specifically, we divide users with similar handover moments and similar geographic into groups, and then design separate strategies according to different handover types to achieve load-balancing of the network. For frequent intra-satellite beam handover, because a single beam can only provide a short service time, we design a single-objective function based on the weighted entropy method for to decide handover beam. For the more complex inter-satellite beam handover that requires balanced channel capacity, a Markov decision process(MDP) is further introduced to characterize the state of user groups, and an improved deep reinforcement learning(DRL) algorithm that reduces the dimensionality of the state space is developed to decide handover satellite and handover beam. The simulation results indicate that the proposed scheme can effectively improve the handover success rate and optimize the network performance, especially when the number of users increases.

This paper presents a computing power scheduling framework based on reinforcement learning (CSFRL) for custom fine-grained resource scheduling on Kubernetes. With the rise of edge computing networks, efficiently adapting computing resources is essential to support various services. While Kubernetes is widely used for container orchestration, few studies have implemented fine-grained resource scheduling using AI algorithms. CSFRL enables the scheduling algorithm to be trained according to user requirements and capable of handling complex scheduling environments, leading to more effective computing resource scheduling on Kubernetes. By conducting a detailed analysis of microservices that require different computing power and using the sorting-based PPO algorithm, CSFRL achieves efficient scheduling of computing power. Experimental results show that CSFRL outperforms the default scheduler on Kubernetes and achieves the expected scheduling results.

As global communication demands continue to rapidly expand, traditional terrestrial networks are facing significant challenges in terms of coverage, capacity, and reliability. To overcome these limitations, large-scale low-earth orbit (LEO) satellite networks have emerged as a promising solution, offering ubiquitous and seamless connectivity worldwide. However, this solution brings its own set of challenges, including limited satellite resources, diverse quality of service (QoS) requirements for random service function chain (SFC) requests, the complexity of managing large-scale networks, and the unpredictability of network changes. To tackle these challenges, this paper presents a novel adaptive online SFC deployment algorithm based on deep reinforcement learning. The proposed algorithm effectively handles real-time network changes and diverse service requirements while minimizing resource usage and enhancing QoS, leveraging the sharing of virtual network functions (VNFs) among multiple SFCs on satellite nodes and effectively balancing computing occupancy across satellites. To reduce complexity, we employ subnet segmentation to diminish the dimensionality of the state space. Simulation results validate the effectiveness of the proposed algorithm in significantly reducing resource occupancy and end-to-end delay, even in scenarios involving a large number of requests.

Digital twins for wide-areas (e.g., smart cities) would fulfill the 6G expectation of merging the physical and digital worlds, abbreviated as "DT-WA". It would incorporate an artificial intelligent (AI) model to simulate and predict the physical world, which needs a constant parameter updating process to keep its fidelity. However, the updating process can consume significant energy, where little work exits. This paper proposes an energy-efficient edge cooperation and data collection scheme. The AI model is partitioned into a large amount of sub-models onto different edge servers (ESs) co-located with access points to simulate every part of the wide-area, which are distributed updated using locally-collected data. To reduce system energy, ESs can choose to become either updating helpers or recipients of their neighboring ESs, based on their available sensors and basic updating convergences. Helpers share their updated parameters with their neighboring recipients to reduce the latter workload. To minimize system energy, the paper further proposes a distributed algorithm to adaptively optimize ESs cooperative identities, data collections and heterogenous resource allocations in the dynamic environment. It incorporates several constraint-release methods and a large-scale multi-agent deep reinforcement learning algorithm. Simulation results show that the proposed scheme can reduce the updating energy in DT-WA compared with baselines.

With the development of future smart city, the structure of smart grid is undergoing fundamental changes, where the acquiring and scheduling of local energy become more flexible due to the massive and various access to renewable energy, such as solar energy from residential users. It is urgent to find a measure for efficient local energy management with the influx of a large amount of renewable energy. This paper proposes a peer-to-peer electricity scheduling algorithm with adaptive grouping based on the trading contribution evaluation to maximize local energy consumption. By modeling the electricity demand and the supply of prosumers, the electricity scheduling process is completed, which follows the principle of intra-group priority trading and inter-group auxiliary trading. In the adaptive grouping progress, for highlighting the influence of user engagement on the transaction factor of each user, a trading contribution evaluation is creatively defined to reflect the trading inspiring process. In addition, considering the incomplete sharing of information among prosumers in the electricity trading process, Bayesian game is adopted for the electricity pricing strategy to obtain the optimal balance between the economic benefits and energy transformation under linear strategic equilibrium maximizing the utility of both parties of electricity scheduling. Simulation results show that the proposed algorithm can effectively improve the system prosumer benefit and the local consumption rate of renewable energy.

Reconfigurable intelligence surface (RIS)-enabled integrated sensing and communication (ISAC) is vulnerable to eavesdropping, and thus its secure transmission is a crucial research field. In this paper, we integrate a promising simultaneous transmitting and reflecting RIS (STAR-RIS) into an ISAC network to improve the physical layer security, where with the presence of an eavesdropper, a base station (BS) sends the multicast communication signal combined with the dedicated sensing signal to perform both multi-user communication and single-target detection. Thereinto, the STAR-RIS can reconfigure signal propagation to ensure the sensing and communication quality while preventing the eavesdropping, and also elevate the flexibility of network deployments relying on its full-space coverage. Our goal is to maximize the secrecy rate of the whole network by jointly optimizing the transmit beamforming at the BS and the transmission&reflection beamforming at the STAR-RIS, while satisfying the sensing signal-to-noise ratio (SNR) requirement. To settle the non-convex problem, an overall iterative algorithm is developed by invoking the successive convex approximation and sequential rank-one constraint relaxation methods. Simulations verify the efficiency of the proposed algorithm, and reveal that i) the proposed STAR-RIS-enabled ISAC scheme significantly outperforms other benchmark schemes; ii) the sensing signal can enhance the secrecy rate at the high sensing SNR regions, whereas at the lower sensing SNR regions, it has no extra performance gain and can thus be removed to simplify algorithm design.

With the sixth generation (6G) proposal, collaboration at the edge of the Internet of Things (IoT) has been widely studied to coordinate limited edge resources. Kubernetes has emerged as a promising solution for flexible and efficient resource scheduling. However, the default scheduler of Kubernetes only allocates pods separately according to the resource utilization condition of the cluster, which ignores the effect of the correlation between micro-services on latency. Under this circumstance, we propose a micro-service deployment strategy based on edgeedge collaboration, which takes the correlation between micro-services into account and models it as Service Function Chain (SFC), aiming to reduce the delay and balance the utilization rate in the edge cluster. Furthermore, we propose a model-free Distributed Deep Reinforcement Learning Deployment (DDRLD) algorithm to solve the multi-objective optimization problem. The master node trains the Q network and updates the parameters to the other nodes in the cluster, where each node can determine the deploying decision separately. Simulation results show that the proposed scheduling strategy can reduce user delay while ensuring the balance of the utilization rate.

Green unmanned aerial relaying (UAR) systems, featuring solar-powered unmanned aerial vehicles (UAVs) to provide agile and flexible communication services, have recently gained significant attention for their wide applications. Nonetheless, due to the essential dynamics of wireless channel conditions and solar energy supply, maintaining system stability is a critical concern in practical green UAR systems, as fluctuation in these two factors can significantly affect communication performance. To address this issue, this article proposes a scheme design for stable communications in a UAR system with dynamic solar energy supply and wireless channel conditions. Specifically, we first formulate an optimization problem, called OFL, to control the power allocation and flow data rate assignment to minimize long-term time-averaged energy consumption while ensuring the demanded data rate and system stability. Considering that solving OFL needs complete prior information about the solar power supply and wireless channel conditions, which is hardly acquired, we then explore the Lyapunov theory to transform OFL as an online optimization problem, called ONL. To find the solution to ONL, we subsequently design a distributed algorithm that enables each UAV to make its own decision locally, thus, reducing the computational overhead of each UAV. Particularly, we prove that stability can be guaranteed by employing the designed algorithm. Extensive simulations are conducted to show the performance achieved by the proposed scheme.

Edge computing ensures close processing in proximity to mobile devices (MD) via task offloading, resulting in a timely manner to support diversified services with low latency tolerance. Nevertheless, for the proliferation of services, the massive connection between MDs and edge servers (ES) results in huge energy consumption, leading to sustainability issues for greenhouse gas emissions. Besides, quality levels within the same service (e.g., accuracy of object detection, and clarity of video) become subdivided, requiring tighter service environment constraint of ESs for such subdivided quality levels. In this paper, we investigate efficiency-oriented task offloading for services with subdivided quality levels within quality level constraint of ESs. Specifically, we first devise a unified quality-aware service model to abstract out service structure, i.e., the quality-relation, number, and dependency of tasks within it. Then, the offloading algorithm is proposed based on an optimized version of non-dominated sorting genetic algorithm-II (NSGA-II), in which due to the heterogeneity of services, we also embed a scheduling algorithm for services in advance to improve tasks parallelism for concurrent execution and NSGA-II adaptation. Additionally, compared with other algorithms, simulation results demonstrate that our proposed algorithm not only significantly improves the convergence, but also optimizes its energy and latency efficiencies.

Unmanned Aerial wireless Communication Systems (UASs), featuring the low cost and flexible deployment of unmanned aerial vehicles (UAVs), have attracted intensive attention recently to provide wireless communication services in some specific scenarios, e.g., disaster areas and temporary hotspots. Nonetheless, due to UAVs’ limited on-board energy storage, the provisioning of wireless communications can deplete their carried energy, consequently landing on the ground. To mitigate this issue, harvesting solar energy to power UAVs is a promising alternative solution. However, the essential dynamics of solar energy can seriously affect communication performance in UASs. In this paper, we explore dynamic solar energy to supply a UAS with an aerial base station (BS) and aim to minimize the long-term time-averaged energy consumption of the UAS. Particularly, we formulate a Long-term time-averaged Energy Consumption minimization problem (LEC) by jointly taking into account transmission power and data rate. Considering that LEC is time-coupling nonlinear programming (NLP), we reformulate a relaxed online optimization problem, called STP (single-time slot problem), by employing Lyapunov optimization theory. Then, we develop a joint power and rate control algorithm to solve STP. Particularly, we theoretically show that the proposed algorithm can achieve (D/V + C)-approximation and guarantee stability. Extensive simulation results have shown the performance gain, in terms of stability and throughput, achieved by the proposed algorithm.

The low earth orbit (LEO) satellite deployed with the multi-access edge computing (MEC) server is a prospective approach to providing intelligent service in the future intelligent network. However, since the relative location of the LEO satellite to the sun changes in real-time, the available satellite energy endures periodic fluctuation, which limits its capability of providing service. In this paper, we propose the joint task offloading and resource allocation strategy within the satellite cooperative offloading structure to optimize satellite energy consumption. The cooperative structure utilizes the stable intra-orbit inter-satellite links for inter-satellite cooperation and the satellite-terrestrial links for cloud-edge cooperation. Considering the fluctuation of energy harvested by satellite, the energy used for computing is limited to ensure sufficient energy for the satellite to fly out of the shaded area. Then we formulate the task offloading and resource allocation as a mixed-integer nonlinear programming problem with the target of minimizing the satellite energy consumption for computing while satisfying the delay requirement of the task. The problem is solved by the improved non-dominated sorting genetic algorithm II, where the constraint comparator is proposed to make feasible solutions satisfy trade-off constraints. The simulation results show that the proposed algorithm effectively reduces the energy consumption on satellites compared with random offloading algorithm and poll offloading algorithm.

Applying unmanned aerial vehicles (UAVs) to assist Internet of Things (IoT) networks has recently attracted wide attention, thanks to their fast and flexible deployment. Despite the advantages, one key challenge is how to evaluate the service performance and thus provide service-demand-based coverage and form a robust UAV network with limited resources. In this paper, we consider a multi-UAV assisted IoT network with devices of varied service demands. A comprehensive quality of X (QoX) model is proposed to evaluate the service performance. Then, to serve the devices as per their QoX levels, we propose a hierarchical networking scheme. Specifically, based on the device hierarchical architecture built on the QoX order, UAVs are deployed layer by layer sequentially. Meanwhile, bandwidth is reserved for lower-layer devices to ensure their access. Separately in each layer of this hierarchy scheme, the optimization problem is formulated as minimizing the number of UAVs through joint optimization of UAV location, device association, and bandwidth allocation while forming a robust UAV network. To solve this problem, we first apply a problem transformation for variable decoupling and then propose an improved particle swarm optimization (PSO) algorithm. Simulation results show that the proposed scheme reduces the number of UAVs while satisfying the diverse service demands evaluated by our QoX model.

Ambient backscatter communication (ABC) has become an innovative technique for energy-efficient and short-range communication. In this article, we propose a hybrid simultaneously wireless information and power transfer (SWIPT)-assisted relay transmission scheme to extend the communication range of the ABC mode. That is, SWIPT with time-switching (TS) and power-splitting (PS) approaches is adopted into the relay for harvesting energy and processing information. Moreover, after harvesting energy, all the relays simultaneously transmit data according to power-domain nonorthogonal multiple access (NOMA) technology. Therefore, in the ABC-enabled hybrid SWIPT relay networks with power-domain NOMA, there is a problem of how to maximize the network throughput by allocating available power and time resources. We first formulate an optimization problem to achieve the maximum network throughput by finding the optimal PS ratio of relays as well as the optimal time allocation among users and relays. In order to reduce the computational complexity of the problem, we decompose it into two subproblems: 1) PS ratio optimization and 2) time allocation optimization. For PS ratio optimization, we derive the closed form of the optimal PS ratio for relays by the Lagrange dual theory and subgradient method. For time allocation optimization, the Lagrange dual theory and subgradient method are also adopted to obtain the closed form of the optimal time allocation among users and relays. Then, we propose a joint optimization of the PS ratio and time allocation-based iterative algorithm to solve the problem. Finally, simulation results show that our proposed scheme has an obvious throughput superiority over other two benchmark schemes.

The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.

As a key technology of the sixth generation (6G), cloud-edge collaboration has attracted attention in the industrial Internet of Things (IIoT). However, the delay-sensitive and resource-intensive intelligent services in IIoT not only require a large number of computing resources to reduce the delay cost and energy consumption of devices but also require fast and accurate intelligent decisions to avoid service congestion. In this paper, we design an offloading scheme based on cloud-edge collaboration and edge collaboration, including four computing modes, which jointly consider the delay and energy optimization of devices. We propose a parallel deep learning-driven cooperative offloading (PDCO) algorithm, which weighs the real-time and accuracy of offloading scheme. To deal with the difficulty of obtaining labels, a low-complexity hybrid label processing method is designed to reduce the cost of labeling data, and then multiple parallel deep neural networks (DNNs) are trained to generate the best offloading decision timely. Simulation results show that the proposed algorithm can generate offloading decisions with more than 90% accuracy in 0.1s while considering green scheduling.

The upcoming 6G era involves an increasing level of data processing and capacity, where mobile edge computing (MEC) system is deployed in large scale to support more emerging applications. For the purpose of realizing full potential of MEC, it is necessary to allocate computing and caching resources in an intelligent way. The network need to perform decisions adaptively and organize collaboration to further improve the resource utilization, leading to a higher demand for system reliability. Introducing blockchain to the edge network is an effective way to perform resource allocation effectively under the premise of ensuring trustworthiness. To ensure trustworthiness, a credibility evaluation mechanism based on Dempster-Shafer theory is designed in the overlaid blockchain network. Since it is challenging to optimize delay and credibility comprehensively in this dynamic system, we propose a deep reinforcement learning (DRL)-based approach to make offloading decision, and carry out service caching according to both credibility and offloading decisions of multiple users. Finally, the effectiveness of the proposed offloading and caching policies are demonstrated via simulation results.

Cognitive radio (CR) as a key technology of solving the problem of low spectrum utilization has attracted wide attention in recent years. However, due to the open nature of the radio, the communication links can be eavesdropped by illegal user, resulting to severe security threat. Unmanned aerial vehicle (UAV) equipped with signal sensing and data transmission module, can access to the unoccupied channel to improve network security performance by transmitting artificial noise (AN) in CR networks. In this paper, we propose a resource allocation scheme for UAV-assisted overlay CR network. Based on the result of spectrum sensing, the UAV decides to play the role of jammer or secondary transmitter. The power splitting ratio for transmitting secondary signal and AN is introduced to allocate the UAV's transmission power. Particularly, we jointly optimize the spectrum sensing time, the power splitting ratio and the hovering position of the UAV to maximize the total secrecy rate of primary and secondary users. The optimization problem is highly intractable, and we adopt an adaptive inertia coefficient particle swarm optimization (A-PSO) algorithm to solve this problem. Simulation results show that the proposed scheme can significantly improve the total secrecy rate in CR network.

Unmanned aerial vehicle (UAV)-assisted cellular communication is regarded as a promising solution for the data traffic offloading of cell-edge users, who often have throughput bottlenecks. As an air base station (ABS), UAV has high mobility, flexible deployment, high probability of line-of-sight (LoS) link, etc. In this paper, we propose a cooperation schema between multiple ground base stations (GBSs) and UAV to optimize the coverage of GBSs and UAV. Firstly, we jointly optimize the coverage division, the spectrum allocation, and UAV's trajectory to maximize the minimum throughput of cell-edge users. Secondly, we propose an iterative algorithm to solve the non-convex problem and obtain a suboptimal solution. Finally, in the simulation, the other two strategies are compared with the algorithm proposed in this paper to prove its effectiveness.

With the increasing requirements of uplink (UL) data transmission from emerging applications such as virtual reality, ultra dense network (UDN) has been proposed as an effective solution to support high data rate and massive connections in hot spot areas. With short distance between access points (APs) and user terminals, multiple APs may cooperate as a group to provide the access service for a user. Due to the high energy consumption for accessing to multiple APs, energy harvesting (EH) technology is introduced into UDN. We propose a novel APs grouping and resource allocation scheme to maximize the throughput via jointly optimizing energy and time allocation of downlink (DL) and UL in EH-based UDN. In order to ensure that the user's energy harvested from APs is sufficient to transmit data, we present a probability function with the energy relationship of DL and UL to jointly optimize the EH range and the time allocation. Moreover, the APs are divided into groups based on the EH range obtained by the function. Particle swarm optimization (PSO) algorithm is adopted to derive the optimal solution of the formulated power allocation problem for the selected APs. Simulation results show that the proposed scheme has better performance compared with existing APs grouping schemes.

With the rapid development of the Internet of Things(IoT), the amount of data on the IoT is overgrowing, and how to improve the performance of the network is crucial. In this article, we propose the common cold start problem of IoT data. Based on this, we design a novel hybrid caching strategy. We divide the cache space into two parts for proactive caching and reactive caching. Proactive caching uses deep learning methods to predict data popularity, while reactive caching uses an improved LRU-K algorithm. The combination of the two can not only ensure performance but also solve the problem of data cold start. Through the cellular IoT structure, we implement a cooperative caching strategy in small base stations and macro base stations, increasing the cache hit rate and reducing user access delay. We conducted extensive simulations to prove that our proposed solution can solve the cold start problem well and is better than existing solutions.

Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as augmented/virtual reality online games, autonomous driving, and smart everything, which are massive in number, data intensive, computation intensive, and delay sensitive. Due to the mismatch between the fifth generation (5G) and the requirements of such massive IoT-enabled applications, there is a need for technological advancements and evolutions for wireless communications and networking toward the sixth-generation (6G) networks. 6G is expected to deliver extended 5G capabilities at a very high level, such as Tbps data rate, sub-ms latency, cm-level localization, and so on, which will play a significant role in supporting massive IoT devices to operate seamlessly with highly diverse service requirements. Motivated by the aforementioned facts, in this article, we present a comprehensive survey on 6G-enabled massive IoT. First, we present the drivers and requirements by summarizing the emerging IoT-enabled applications and the corresponding requirements, along with the limitations of 5G. Second, visions of 6G are provided in terms of core technical requirements, use cases, and trends. Third, a new network architecture provided by 6G to enable massive IoT is introduced, i.e., space-air-ground-underwater/sea networks enhanced by edge computing. Fourth, some breakthrough technologies, such as machine learning and blockchain, in 6G are introduced, where the motivations, applications, and open issues of these technologies for massive IoT are summarized. Finally, a use case of fully autonomous driving is presented to show 6G supports massive IoT.

Blockchain, an emerging decentralized but trusted system, has been applied in many applications, such as the Internet of Things (IoT), supply chains, and smart grid. However, due to the large amount of computing and storage resources blockchain typically demands, its wide deployment is faced with the sustainability issue. To resolve this issue, a viable solution is to empower the IoT system with fog computing that can offload the computation-demanding tasks. Due to varieties of mining tasks and heterogeneous resource capabilities at fog nodes (FNs), it is not an easy task to schedule mining tasks and manages resource allocation among FNs of conflicting interests and independent IoT devices in a distributed manner. In this article, under the framework of matching theory, we design a distributed matching mechanism to maximize the social welfare of resource-restricted FNs while guaranteeing various mining requirements of FNs. Besides, we also provide formal proof regarding the convergence and computational complexity of a distributed matching algorithm (DMA). Finally, we verify that DMA not only improves the social welfare of FNs but also reduces the mining latency compared with the existing algorithms through extensive simulations.

Mobile edge computing (MEC) is a promising technology that allows users to enjoy efficient computing power at the edge of the network to meet the requirements of computation intensive applications. This paper studies the energy consumption problem of multiple users and single relay cooperation in MEC system. Combining energy harvesting (EH) and full-duplex (FD) technology, an energy-efficient offloading strategy based on relay cooperation is proposed. Specifically, we consider a multiple users MEC system and use the non-orthogonal multiple access (NOMA) technology to simultaneously offload multiple tasks. Meanwhile, considering the long distance transmission, we choose a user node as a relay and adopt FD technology to reduce delay. By optimizing the computational offload strategy and improving the collaboration among users, the total energy consumption of the node can be effectively decreased. In addition, the relay node can use radio frequency EH technology to obtain energy to actively complete the computation and communication work, thereby making full use of the available resources. The simulation results show that compared with the traditional partial offloading scheme, the proposed scheme significantly reduces the total energy consumption of the system.

An unmanned aerial system (UAS), consisting of unmanned aerial vehicles (UAVs) with wireless transceivers, has been considered as an indispensable complement to conventional terrestrial communication infrastructure that cannot fully meet mobile users' demand on ubiquitous connectivity, particularly in some practical areas, such as complex terrains, disaster areas, and temporary traffic hotspots. Thanks to UAVs' high maneuverability and flexible deployment, a UAS can be deployed in these areas to offer ubiquitous connectivity in a timely and cost-effective way. However, due to the limited onboard energy storage capacity, UAVs have to frequently land for energy replenishment, which inevitably affects the provisioning of wireless connectivity services. To prolong UAVs' hovering time, adopting solar energy to power them is an alternative way. In this paper, we explore the joint control of routing, data rate, and transmission power to minimize the energy consumption rate of relaying data in solar-powered UASs. The energy consumption rate minimization problem is formulated as a nonlinear programming (NLP), which is generally NP-hard. To efficiently solve the formulated NLP, we develop an ∊-bounded approximation algorithm that employs a piece-wise linear approximation approach to approximate its nonlinear term and thus reformulate the original problem as classic linear programming (LP). Particularly, we find a theoretical performance gap that is bounded by the approximation error ∊. We conduct extensive simulations to show significant performance improvement.

Internet of Things (IoT) devices are usually limited by battery capacity. Thus, Saving energy consumption of IoT devices is meaningful. By caching IoT data at intermediate nodes to avoid frequent activation of IoT devices to reduce energy consumption. We propose a novel collaborative caching in cellular based IoT network considering the data lifetime to ensure the effectiveness of the cache. The relationship between data lifetime and user access rate determines whether data is cached. With the help of a joint optimization scheme, we unite the problems of capacity allocation and content placement in a hierarchical cache-enabled architecture. We define the formulation of the joint problem to maximize the reduction in energy consumption. Two heuristic algorithms proposed in this paper to reduce the complexity of the problem. Extensive simulations have been performed to prove that our proposed scheme can dynamically adapt to changes in the request rate and outperform existing schemes in terms of energy savings.

Unmanned aerial vehicle(UAV)-aided mobile edge computing network can help resource-constrained users to complete time-sensitive tasks. In recent years, UAV has attracted extensive attention due to their flexibility and low cost. However, UAV's computing power is limited, and how to provide reliable low-delay services for edge users is one of the critical issues. To solve this question, we design a scheme in which UAV and ground base stations cooperate to serve users. When the user offloads the task to the UAV, the UAV can relay some tasks to the ground base station through millimeter-wave to obtain lower calculation delay. We jointly optimize user offload strategy, relay ratio, UAV trajectory, and bit allocation to minimize computational delay for all users. We propose an improved alternating optimization algorithm, which transforms the non-convex problem into three subproblems and obtains the optimal solution through multiple iterations. Simulation results show that the proposed scheme can effectively enhance the computing power of the system and reduce the user's delay significantly.

The past few years have witnessed the explosive growth of mobile user equipment (UEs) and the popularity of computation-intensive applications, leading to a mobile edge computing paradigm for Ultra-dense wireless networks (UDNs). Since numerous UEs need to offload a large amount of computation tasks to edge servers/UEs, it is very challenging to coordinate computation offloading among UEs and edge servers in UDNs. To address this issue, we propose a decentralized computation offloading coordination platform that is based on the blockchain. Specifically, we first establish a blockchain platform for announcing computation offloading requests and coordinate computation offloading among UEs and edge servers. Then, we develop a modified GS-based user matching algorithm to find the matching relationship between offloading requester's computation tasks and the edge server/UEs. In particular, user matching is based on task execution time and energy consumption. We conduct simulations and provide extensive simulation results to show the significant performance improvement achieved by the proposed offloading scheme.

Mobile Edge Computing (MEC) is regarded as a promising technology that migrates cloud computing platforms with computing and storage capabilities to the edge of the wireless access network, enabling rich applications and services in close proximity to the mobile users (MUs). Previous works on computation offloading only focus on limited resources, interference etc. and leave service caching for MUs with heterogeneous computation task and opportunistic networks out of consideration. In this paper, we perform a novel research on the computation offloading strategy taking account into service caching and Device-to-Device (D2D) communication, and introducing opportunistic networks in multi-access networks simultaneously. First, we denote the computation offloading model and formulate the offloading decision as a sequential game problem. In order to solve this problem, we then design a suboptimal algorithm based on game theory. Finally, the performance of the proposed algorithm is verified by comparing with other algorithms. Simulation results corroborate that the proposed computation offloading strategy can not only decrease the overall computation overhead efficiently, but also achieve a Nash equilibrium.

As the explosive growth of smart devices and new applications, traffic volume has been growing exponentially. Thus, mobile edge computing (MEC) technology regarded a promising technology is proposed to enhance the computation capabilities of the networks. Considering available MEC research mostly focused on single-tier base station scenario and single offloading link between mobile devices and MEC servers connected to the macro base station, causing high transmission delay and link congestion problem. A new two-tier small cell architecture equipped with MEC servers is proposed and an efficient task scheduling is designed. Specially, we first formulate the task scheduling problem as a nonlinear mixed integer program problem which is NP-hard. Then we transform this optimization problem into two subproblems, i.e., a linear constrained quadratic programming problem and a convex optimization problem. Finally, Compared our proposed scheme with other existing schemes, the proposed scheme could not only receive 30% energy saving, but also apply to large-scale smart devices.

In wireless networks, network load can be highly unbalanced due to the mobility of user equipments (UEs). Unmanned Aerial Vehicles (UAVs) supported base station with the advantage of flexible deployment, ubiquitous wireless coverage and high speed data rate, is a promising approach to handle with the foregoing problem. However, how to achieve cost-effective UAV deployment in an autonomous and dynamic manner is a significant challenge. Facing this problem, we propose a novel UAV base station intelligent deployment scheme based on machine learning and evaluate its performance on a realworld dataset. First, we conduct data preprocessing to process, clean, and transform raw data into formatted data. Missing values are filled by Conditional Mean Imputation (CMI) method and outliers are corrected by pauta criterion. Then, we use hybrid approach which contains ARIMA model and XGBoost model. Linear predictions are carried out by ARIMA model and later nonlinear model XGBoost are applied on residue of ARIMA. Resultant prediction is obtained by adding linear and nonlinear prediction, hybrid model is estimated by Root Mean Square Error (RMSE) and R2 score. Finally, according to predicted results, UAV base stations can be deployed to cater for dynamically changing demands in the hotspot areas and achieve cost-effective deployment. Simulation results show that the propose scheme is superior to other benchmark schemes in load balancing.

Ultra Dense Network (UDN) and Mobile Edge Computing (MEC) are two key technologies for the next generation network. Small cells are deployed densely in UDN. MEC brings computation resource and storage resource to users as close as possible. In this paper, we consider a multi-users scenario in UDNs with MEC. Each user offload its task to MEC servers in order to meet the task's delay constraint. A set of small cells are clustered together to provide computing power for a given user. We formulate a overall delay minimization problem by jointly consider small cells clustering, transmission power allocation and computation task allocation in UDN with MEC. Due to the formulated problem is a mixed integer non-linear programming (MINLP)problem which is NP-hard, We decompose the problem and solve it in two steps. We firstly propose a low-complexity heuristic algorithm for small cells clustering. Secondly, for a given set of clusters, we derive resource allocation algorithm based on genetic algorithm that minimize the delay of the task of a user in a cluster and in turn minimize the overall delay of all users. Simulation results demonstrate that our proposed scheme can significantly improve the performance.

Mobile edge computing (MEC) is an emerging technology to support mobile user equipments (UEs) to implement computing-sensitive applications by offloading the computation tasks to the MEC server. However, due to the random movement of the UEs, the offloading procedure and the return of computation results may be interrupted when the UEs move out of the original MEC server's wireless communication coverage. In this paper, we make use of mobile network entities and powerful mobile terminals to form mobile MEC servers, which could provide computing offloading service for surrounding mobile UEs. The mobile MEC server is powered by battery with limited energy. In order to prolong its life time, we introduce the energy harvesting (EH) and dynamic voltage scaling (DVS) techniques. Then based on the requirements of UEs, we discuss the problem of how to jointly optimize the computation speed of mobile MEC server and corresponding power allocation. It is modeled as a nonconvex problem, aiming to minimize the end-to-end computation offloading latency under energy restriction. Then we adopt variable substitution method to transform it into a convex problem, and further propose a latency-optimal computation offloading algorithm to obtain optimal solution. Simulation results show that our proposed algorithm may significantly reduce the energy consumption and the latency.

By bringing computing capabilities to the network edge, mobile edge computing (MEC) has emerged as a promising technique to enable low-latency video streaming services. However, due to the rapid growth of the number of devices and the heterogeneous formats of the video streams, the traditionally centralized content delivery schemes are insufficient to provide secure, adaptive video services with low complexity. To achieve a decentralized content market among untruthful parties (e.g., users and operators), in this paper, we propose an effective video transcoding and delivery approach based on MEC and blockchain. In the proposed approach, we envision a set of blockchain-based smart contracts to build an autonomous content delivery market, where all the participants are financially enforced by smart contract terms. Then, users, small base stations (SBSs), and content provider (CP) are able to autonomously adjust their strategies according to the content market statistics. Moreover, we formulate the optimization problem, including resource allocation, determining content price and quality levels of contents, as a three-stage Stackelberg game. We analyze the subgame equilibrium for each stage and the interplays of the three-stage game. Lastly, an iterative algorithm is proposed to obtain the solution. Simulation results are presented to show the effectiveness of the proposed approach.

Driven by the large-scale video traffic, mobile edge computing (MEC) has emerged as a promising technique that extends cloud-computing capabilities to the proximate small base stations (SBSs) in wireless networks, especially in ultra-dense networks (UDNs). With MEC, video transcoding, which processes the adaptive bitrates of a video and provides the adaptive video streaming to users, can significantly release the backhaul burden of networks. However, video transcoding is a time-consuming task, and how to guarantee quality-of- service (QoS) for large video data with MEC is still challenging. To address this issue, in this paper, we propose a joint SBSs selection, tasks scheduling, and resource allocation approach for achieving a delay- optimal transcoding under the constraints of network cost. Specifically, to reduce the delay, a set of SBSs are formed into a Virtual SBSs Group (VSG) to perform the video transcoding and delivering in parallel for a given user. Then, the joint tasks scheduling and feasible resource allocation are performed to minimizing total delay while maintaining a low network cost. The optimization problem is formulated as a mixed integer non- convex programming problem and a three-stage search solution is proposed to solve it. Simulation results show that our proposed approach can significantly improve the transcoding performance while satisfying the resource consumption constraint.

Ultra-dense network (UDN), as well as nonorthogonal multiple access (NOMA), has been emerging as promising techniques to meet the growing demand of data traffic in next-generation wireless networks. However, due to the spectrum sharing among SBSs and users, interference management (IM) is becoming a more important issue in NOMA-based UDN. Moreover, the massive small base stations (SBSs) with various types and overlapped coverage require more intelligent and efficient mechanisms for the IM problem. Thus, in this paper, to reduce interference and improve operation efficiency, we propose a self-optimizing resource allocation (SORA) scheme for IM with joint consideration of the dynamic interference conditions and fierce resource competition among SBSs. Concretely, each SBS constructs the interfering SBSs group adaptively to represent the potential interference from other SBSs. Then, to reduce interference and meet users' requirements, each SBS performs the resource allocation including sub-band and power allocation independently. Moreover, we formulate the problem as a non-cooperation satisfaction game, where a satisfaction function is established for evaluating each SBS's utility. When every SBS's utility is above a preset threshold, the game is considered to reach the satisfaction equilibrium. A distributed algorithm is designed to enable each SBS to learn the satisfaction equilibrium and allocate the resource autonomously. Simulation results show the effectiveness of the proposed scheme compared with the traditional schemes.

The smallcell network (SCN) is restricted by capacity of backhaul, which forces the smallcells (SCs) to prefer acquiring demanded contents from the neighbor SCs rather than data centers far away. In this paper, we investigate the content update problem aiming to maximize the local hit rates in a cache-enabled SCN where a macro BSs (MBS) is overlaid with a tier of SCs with caches. By employing mankind mobility model (MMM) and hidden Markov model (HMM), we describe the movement-in-area nature of human mathematically. Moreover, we derive the transition probability between each movement result and further predict the content demands on SCs. Based on the prediction, we transform the maximum sum hit rate content update problem as a kind of modified multi-commodity max flow problem (MC-MFP). Inspired by the previous works, we design an algorithm to solve the MC-MFP and then propose a Prediction-Based Content Update Scheme (PB-CUS). Simulation and numerical results show the efficiency of PB-CUS.

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.

Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

With the rapid development of small cell networks, resource allocation problem has attracted great attention. Some works have been studied by introducing other advanced technologies, such as full duplex (FD) and massive multiple-input and multiple-out (MIMO). However, with dense deployment of small cell base stations (SBSs), caching is regarded as a promising technology. In this paper, we propose a novel resource allocation scheme in the cache-enabled small cell networks with massive MIMO and FD where SBSs could act as FD relays between other SBSs and users. Furthermore, we formulate a joint spectrum and power allocation problem. Due to complex interference and the non-convexity of the optimization problem, we tackle this problem in three steps with low computation complexity. First, for a given spectrum allocation, we transfer the original problem as a difference of convex program (DCP) and solve the problem by the successive convex approximation (SCA). Then, spectrum allocation is optimized. Thirdly, an iteration algorithm is developed to jointly optimize spectrum and power allocation. Finally, the convergence and effectiveness of the proposed scheme are validated by extensive simulations.

Caching content at the edge of the network is a promising way to improve content delivery efficiency. In most existed research, content caching strategies are typically designed to maximize local hit rates, improve energy efficiency or reduce network cost. However, this metric cannot guarantee the utility of content providers (CPs). To encounter with this challenge, we construct a hierarchical content distribution problem, within this which, two layers are included called caching selection and content delivery, respectively. The former layer intends to find content caching places, i.e. service providers(SPs) for contents while guaranteeing CP's utility, and the latter layer utilizes multi-seller multi-buyer multi- content trading auction (MMMTA) to characterize the competition between the SPs and users. To solve the proposed problem, a hierarchical content distribution iteration (HCDI) method is designed. Various simulation results show the property of the proposed scheme.

At the edge of the network with cache servers, researchers usually focused on how to cache the contents or how to deliver cached contents from edge network to user equipment (UE). In this paper, we study the content delivery scheme in ultra-dense network (UDN) scenario. Since small cells (SCs) are densely and largely deployed in UDNs, it is hard to operate the SCs with a central unit. Thus, our delivery scheme should be implemented in a distributed way. We firstly utilize auction to model the content delivery problem, where UE intend to acquire interested contents from SCs. Then, the content delivery relationship is constructed by the content trading process. Then, an auction based truthful and distributed content delivery (ATDCD) algorithm is designed. Within ATDCD, a distributed auctioneer protocol is introduced to effectively prevent SCs falling in to collusion and guarantee the c-resiliency for sellers. Extensive simulation results are done to show that ATDCD can achieve truthfulness as well as high performance.

The rapidly growing mobile traffic calls for higher quality of service (QoS) and lower on-grid energy cost. Integrating energy harvesting (EH) to small cell network (SCN) with caching has been regarded as a promising solution. However, considering the limited resource of the small cell base stations (SBSs), how to cache contents and serve the mobile users (MUs) is a crucial problem. In this paper, we design a green content caching mechanism in the SCN, where the number of MUs' content requests handled by the SBSs is maximized. First, the content caching problem is formulated, which is NP-hard. To decrease the complexity of the problem, we divide it into two subproblems: MU-BS association and content placement. When the number of SBSs is large, the two subproblems can't be efficiently solved by conventional centralized approaches. Thus we adopt the exact potential game (EPG) to model the subproblems. Finally, we propose a two-dimensional iteration algorithm (TDIA) to solve the proposed problem. The simulation results show that the proposed algorithm can achieve significant performance.

Forecasting the users movement and behavior is extremely valuable for communication networks to support the explosive mobile data in the outdoor crowded area, in the respects such as network deployment, resource allocation and mobility management. Due to the large users number and complex individual behavior, it is difficult to accurately predict the user's movement. In this paper, we take an academic campus as a study example and propose a novel mobility prediction scheme based on data mining algorithm. First, we divide the whole area into several prediction areas based on the number of mobile users, and divide the prediction time into several periods according to the scenario feature. Then, we classify the trajectories of the mobile users into groups based on Fuzzy C-means (FCM) clustering, and discover the frequent mobility patterns in each prediction area at different periods using sequence pattern mining. Finally, we determine the group for the new user and find the most matched mobility pattern to predict its future location. Simulation results show that the proposed scheme achieves a better performance compared with exiting schemes in terms of the handoff numbers and dwell time.

The integration of Mobile Edge Computing(MEC) with the Radio Access Network(RAN) has arisen as a futuristic technology. It offers the cloud-computing capabilities and high-speed wireless transmission to close to User Equipment(UEs) by deploying MEC servers on the Base Stations(BSs) in Ultra Dense Network(UDN). Mostly current research mainly focus on that the MEC server is a coadjutant to offload the computational tasks and reduce the energy consumption of UEs. However, the heterogeneity of servers and the agglomeration effect of users bring new challenge for the fair resource sharing and load balancing among MEC-BSs. In this paper, to efficiently relieve the unfairness of MEC-BS servers and utilize the whole computing resources, we envision a MEC collaborative architecture to achieve the resource sharing among MEC-BSs in UDN. Our design aims at reducing the time consumption of all tasks, where multiple weight and number of tasks can be delivered to different servers at random. We consider the time delay, resource consumption and the state of wireless channel to establish the system model and design an optimal model based on the transfer time and computation time consumption. Finally, we conduct the simulations of optimal task collaboration mechanism to evaluate the performance. The simulations result demonstrate a better performance improvement of the proposed strategy over the simply approaches in terms of average time consumption of per task and rate of tasks successfully completed.

With the explosive increase of wireless data traffic, caching is regarded as a promising technology to combine with heterogeneous cellular networks (HCNs), which can offload cellular traffic and improve the system performance effectively. In this paper, we investigate the communication scenario about cache-enabled HCNs with device-to- device (D2D) communications. Both small cell base stations (SBSs) and D2D user equipments (DUEs) have the caching hardware that can store popular contents to serve users locally. With the caching technology introduced, heavy traffic load in the HCNs could be relieved and the request latency could be decreased, which results in better user experience. Meanwhile, due to the constraints of the limited resource, access selection of users and resource allocation are two significant problems that need to be carefully studied. Thus, we propose a novel scheme to study the access selection and resource allocation jointly. First, we formulate the access selection, spectrum allocation as a joint optimization problem to maximize the system capacity, where bandwidth resource is allocated flexibly and the quality of service (QoS) of users is satisfied. Since the original problem is a mixed combinatorial problem, which is a non-convex optimization, an efficient solution is proposed to transfer the original problem to a convex problem so as to reduce computational complexity. In the simulation, we compare our scheme with other three schemes. Simulation results are presented to validate the effectiveness of our proposed scheme.

A user-centric ultra-dense network (UUDN) is proposed as one of the promising solutions to provide very high area throughput density and flexible access service for users in the fifth-generation systems. On the one hand, network densification provides opportunities to cooperate among a large number of access points (APs) for serving a given user. On the other hand, the limited radio resources cause the serious competition among numerous APs and may degrade the network performance. Therefore, to support large number of connections and break through the restriction of limited frequency resource, non-orthogonal multiple access (NOMA), which supports multiple signals to transmit on the same frequency resource, is introduced into the UUDN. However, NOMA with network densification arises a series of challenges. And the method to group APs efficiently on the same frequency to support for a given user is a critical problem. Thus, in this paper, we propose a user-centric access framework for providing efficient access service and the flexible resource management in NOMA-based UUDN. Under the proposed framework, we then investigate the access scheme that organizes multiple APs into respective AP group (APG) cooperatively to provide access service for each user, aiming at maximizing the system energy efficiency. First, considering the users' requirement and network environment, a grouping evaluation model is set up to organize APG efficiently. Then, we formulate the resource allocation problem of APG as a mix-integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem and propose low-complexity algorithms based on matching and differ of convex programming theories to obtain a feasible solution. Extensive simulation results are presented to demonstrate the significant performance improvement compared with the existing schemes.

With the explosive growth of smart devices and mobile data traffic, limited fronthaul capacity has become a notable bottleneck of green communication access networks, such as cloud radio access networks(C-RANs). In this paper, we proposed a joint user association and downlink beamforming scheme for green C-RANs to minimize the network power consumption with the limited fronthaul links. We first formulate the design problem as a mixed integer nonlinear programming (MINLP), and then transformed the MINLP problem into a mixed integer second-order cone programming (MI-SOCP) which is a convex programming when the integer variables are fixed. By relaxing the integer variables to continuous ones, an inflation algorithm, which can be finished within polynomial time, was proposed to solve the problem. The simulation results are presented to validate the effectiveness of our proposed algorithm compared with the the scheme adopted by LTE-A.

With the deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most existing works do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. By jointly considering them, we propose a novel self-backhaul scheme for small cell networks in this paper, where the macro base station is equipped with massive MIMO antennas, and the SBSs have the FD communication capability. Then, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency and then spectrum efficiency is improved. Furthermore, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transfer the original problem into a difference of convex program by using successive convex approximation method and variable substitution, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted to verify the effectiveness of the proposed scheme.

In this paper, we aim at green small cell networks by jointly achieving self-backhaul and energy harvesting. We study full duplex (FD) technology and massive multiple-input and multiple-output (MIMO) technology to enhance the performance. In order to improve the energy efficiency (EE) further, we design a precoding scheme to eliminate the inter-tier interference. Moreover, we formulate the cell association and power allocation problem as an optimization problem to optimize the EE of the considered network with considering the energy arrival rate and remainder battery energy in SBSs. The formulated optimization problem implies a sleep mechanism to control the on/off of SBSs, which will reduce the energy consumption of small cell networks. Considering the high computation complexity for solving the non-convex optimization problem, we transform the original problem into a difference of convex program, which can be efficiently solved by using a constrained concave convex procedure based algorithm. Extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.

Nowadays, in Massive MIMO Relay Networks (MM-RNs), delay jitter decrease the video quality of users when they enjoy the Instant Video Communication (IVC) service. Cache is a promising technology to deal with the problem. In this paper, we study a high video quality guarantee problem. To mitigate the jitter, caching is applied to adjust the rate of transmitting side to adapt the rate of receiving side. The objective of this problem is to guarantee high video quality for IVC users. We derive a novel expressions for video quality with fairness in consideration. We also deduce the expression of delay and obtain a limit for the video frame queuing in cache. Since the problem is convex, we solve it by employing the convex optimization methods. Based on bisection searching, a Video Quality Guarantee (VQG) algorithm is designed to achieve the methods. Various simulations are shown to demonstrate the effectiveness of our proposed algorithm.

Ultra-dense network (UDN) has been considered as one of the advanced technologies in 5G. With the increasing requirements on quality of service (QoS), large numbers of connections and very high area throughput density, various access points (APs) are deployed densely to provide desired services to user terminals. Then how to choose the optimal AP among neighbor candidate APs for accessing to the network is an interesting problem. Meanwhile, some research work has pointed out that the backhaul capability may become the bottleneck for UDN. Therefore, the backhaul links should be considered when making the accessing decision. However, there is few well recognized solution so far. In this paper, we investigate the energy-efficient access scheme in UDN, with joint consideration on backhauling capability. On the basis of the proposed UDN architecture, an access evaluation model is set up with flexible weight adjustment on delay and power consumption of the candidate AP's backhaul links. Then, the resource allocation problem is formulated with joint scheduling on time slot, subchannel and power to optimize system energy efficiency. Simulation results have proven that compared with traditional access algorithm, our proposed scheme has obvious improvement in the performance.

With the dense deployment of small cell networks, the powering and backhaul problem of small cell base stations (SBSs) has attracted great attention, and energy harvesting technology and self-backhaul technology have been proposed as promising solutions. Although some excellent works have been done on energy harvesting and self-backhaul in small cell networks, most existing works do not consider them jointly. In this paper, we aim at green small cell networks by jointly achieving self-backhaul and energy harvesting. In addition, full-duplex and massive multiple-input and multiple-output technologies are also exploited to enhance the system performance. In order to improve the energy efficiency (EE) further, a novel precoding scheme is designed to eliminate both the inter-tier and multi-user interference. Based on the proposed precoding scheme, we formulate the cell association and power allocation problem as an optimization problem to optimize the system EE performance, with the energy arrival rate and remaining battery energy in SBSs involved. The formulated optimization problem implies a sleep mechanism to control the ON/OFF of SBSs, which will further reduce the energy consumption of small cell networks. In addition, to reduce the computation complexity to solve this non-convex problem, we propose to transform the original problem into a difference of convex program, which can be efficiently solved via a constrained concave convex procedure-based algorithm. Extensive simulation results are presented to justify the effectiveness of the proposed scheme with different system configurations.

To enjoy rich Internet services, contemporary mobile devices generate heavy loads of computation tasks, which cannot be suffered locally due to the limited computing and energy capacities of each device. Cloud radio access network (Cloud RNA) based heterogeneous structure is widely accepted as a promising way to make up devices' shortage and computation need. To determine whether the computation task should be migrated from the device to the Cloud RAN, traditional work usually aims to improve resource utilization efficiency and constrain the latency of computation task. However, less concerned on the energy efficiency problem for the device. Through maintaining higher energy efficiency, device can utilize the limited energy in a reasonable way. In this paper, we formulate an energy efficient resource allocation problem for the computation tasks in Cloud-RAN based heterogeneous Network (Cloud-RAN HetNet). Within this problem, two subproblems are proposed. The first is determining whether computation task on the device should be offloaded to the the Cloud RAN. The second is energy efficiency based resource allocation while considering latency limitation for the computation tasks. To solve the proposed problem, a computation location selection and resource allocation (CCSRA) method is put forward. Finally, we utilize Various simulation results show the property of the strategy.

Recently, induced by incorporating the ubiquitous data gathering capability of wireless sensor networks (WSNs) as well as the powerful data storage and data processing abilities of cloud computing (CC), WSN-CC integration is attracting growing interest from both academia and industry. However, WSN-CC integration is still in its infancy and a lot of research efforts are expected to emerge in this area. Towards WSN-CC integration, this paper first presents four ignored research issues about WSN-CC integration. Further, our accomplished work and ongoing work regarding solving the identified research issues are briefly described. The analytical and experimental results conducted in our work show that the approaches proposed can effectively relieve the corresponding research problem. We hope our work can attract more researches into WSN-CC integration to make it develop faster and better.

Wireless Sensor Networks (WSNs) have been widely deployed in monitoring and surveillance areas. With the improvement of sensor nodes' processing capability, multi-media transmission in WSNs has become a new trend. Since multi-media has strict Quality of Service (QoS) requirements, which calls for efficient scheduling algorithm that could guarantee the end-to-end delay and reduce the energy consumption. Therefore, in this paper we propose a utility-based scheduling algorithm that could enhance the energy efficiency and meet the QoS requirements of multimedia. Our proposed scheduling algorithm includes two steps: firstly, a semi-Markov model is deployed to predict the arrival rate of multi-media; secondly, a utility function is designed which based on the consideration of energy consumption and the QoS constraints. Simulations show that, compared to conventional algorithms, our proposed scheduling algorithm performs better in terms of packet loss rate due to buffer overflow and end-to-end delay.

In this study, a novel energy‐efficient resource allocation (RA) scheme is proposed for device‐to‐device communication underlaying cellular networks from an end‐to‐end energy‐efficient perspective. The time slot, sub‐channel (frequency) and power resources are allocated together to optimise the energy‐efficiency (EE) performance. Furthermore, to match the practical communication situations and achieve the best EE performance, the time–frequency resource units (RUs) are used in a complete‐shared pattern. Then, the multiuser interference is very severe and complex. With all these considerations, the energy‐efficient RA problem is formulated as a mixed integer and non‐convex optimisation problem, which is an non‐deterministic polynominal (NP)‐hard problem and extremely difficult to solve. To obtain a desirable solution with a reasonable computation cost, the authors tackle this problem with two steps. Step 1, the RU allocation policy is obtained via a greedy search method, and the original optimisation problem is reduced to a non‐convex fractional programming problem. Step 2, exploiting the properties of fractional programming and after some manipulations, they transform the reduced problem to a concave optimisation problem, and obtain the sub‐optimal power allocation strategy through the Lagrange dual approach. Finally, simulation results are presented to validate the effectiveness of the proposed RA scheme.

This paper discusses the problem of minimizing the energy used to transmit packets over a memoryless channel, with a constraint on the delay suffered by packets and a constraint on peak transmitter power. The problem is studied within discontinuous reception (DRX) supported system. Specifically, we seek a DRX-compatible transmission policy that can solve the problem without the knowledge of fading distribution and packet arrival process. To achieve this goal, we formulate the problem as a variation of finite horizon classical secretary problem (CSP) with arbitrary monotonic utility. We present key structure property of optimal solution, and utilize it to obtain the optimal policy which can be described as a threshold rule. Moreover, we also propose an off-line calculation method to obtain the thresholds with low computing complexity. With the help of the thresholds, the optimal policy can make transmission decision only based on the relative rank of channel state over present time slot and the inter-packet deadlines. The performance of the optimal transmission policy is studied via simulation. The results show that the optimal transmission policy can work efficiently in DRX-supported system with low packet drop rate and high energy efficiency.

Providing reliable and quality of service (QoS) guarantee service is basic requirement for communication system. However, the intrinsic features of high-speed railway communication (HSRC), such as serious channel distortion, lack of perfect channel state information (CSI), severe Doppler effect, and sensitive to algorithm computation complexity, make this requirement hard to be met. In this paper, considering the intrinsic features of HSRC, a novel link adaptation scheme is proposed to improve HSRC systems goodput while guaranteeing prescribed error rate target. We formulate the link adaptation as a partially observable Markov decision process (POMDP). With this stochastic optimization formulation, the proposed link adaptation scheme can tackle the imperfect CSI and has very low computation complexity. In addition, cross-layer design methodology is exploited, where diversity/multiplexing gain selection in physical layer and frame size in link layer are considered together to improve system goodput. Simulation results demonstrate the effectiveness of the proposed link adaptation scheme.

For energy-efficient (EE) resource allocation, only limited work has considered end-to-end energy consumption. In this paper, a novel EE resource allocation algorithm is proposed for orthogonal frequency division multiple access (OFDMA) downlink networks, where both transmitter energy consumption (base station (BS) transmission and BS circuit energy consumption) and receiver energy consumption (user equipment (UE) circuit energy consumption) are taken into account. The time slot, subchannel (frequency) and power allocation policies are joint considered to optimize system energy efficiency. In addition, different quality of service (QoS) requirements including minimum-rate guarantee service and best effort service are supported in our considering system. With all these considerations, the EE resource allocation problem is formulated as a mixed combinatorial and non-convex optimization problem, which is extremely difficult to solve. To obtain a desirable solution with a reasonable computation cost, an algorithm based on quantum-behaved particle swarm optimization (QPSO) is proposed. Finally, extensive simulation results are presented to validate the effectiveness of the proposed algorithm.

With the dense deployment of femtocell, interference control is becoming a major challenge for resource management in femtocell networks. Most of the existing works focus on improving throughput or reducing the total power consumption respectively, neglecting the energy consumption. In this paper, we investigate energy efficiency aspect in a femtocell network and propose an interference-mitigation channel allocation algorithm aiming to maximize the energy efficiency with QoS constraints. The algorithm also mitigates the co-tiered interference and the cross-tiered interference. Moreover, we formulate the energy efficiency of the femtocell networks as an optimization problem. An iteration algorithm based on Chemical-reaction is proposed to solve the energy efficiency maximum problem. Simulation results show that the proposed algorithm can improve energy efficiency by about 17.6% and throughput by about 30% compared with the existing algorithm.

Orthogonal frequency division multiplexing (OFDM) brings severe co-channel interference for cell-edge users (CEUs). To deal with this problem, traffic-pairing scheme based on particle swarm optimization (TP-PSO) is proposed as an efficient scheme in downlink Coordinated Multi-Point multi-user multi-input multi-output (CoMP-MU-MIMO) transmission/reception. This paper jointly considers CEUs' signal to interference plus noise ratio (SINR) and exponential/proportional fairness (EXP/PF) to maximize the total channel capacity in traffic-pairing process by formulating a penalty function. To simplify the penalty function's computation, particle swarm optimization (PSO) algorithm is introduced. Simulation results demonstrate that the TP-PSO scheme can not only obtain the optimal total channel capacity while guarantee each user's SINR and traffics QoS, but also largely reduce computational complexity. As a result, the poor communication quality of CEUs can be enhanced.

This paper concentrates on the problem of how to schedule a great quantity of heterogeneous traffic for one mobile node in LTE. To solve such problem in High Speed Rail (HSR), we present a decoupled time/frequency domain packet scheduler based on the analysis of HSR MIMO channel properties. In time domain, a novel single-user adaptive EXP/PF algorithm is adopted. In frequency domain, a fast exhaustive search-based frequency resource allocation algorithm (FRAA) named SU-OP is proposed, which can utilize the scarce frequency resources with constraint of LTE practical limits. Furthermore, we also introduce an alternative FRAA named SU-Greedy which has lower complexity and could obtain similar performance while the SNR is not high. By our original calculation rule the complexity of both algorithms is reduced further and thus more practical. Based on the system simulation, we find that the proposed scheduler has better delay and throughput performance than the well-known joint time domain and frequency domain proportional fair (TD-FD-PF) scheduler under HSR scenario.

In high-speed railway communication system, due to its intrinsic features, on-going services are more prone to drop than in common communication systems. Therefore, existing admission control (AC) schemes designed for common communication systems cannot be directly used, otherwise the service dropping probability (SDP) requirement cannot be guaranteed. In this paper, a cross-layer AC scheme is designed to improve system SDP performance in high-speed railway communication system. Firstly, we clearly classify the main potential origins of service dropping into two types: modulation and coding scheme (MCS) changed service dropping and handoff service dropping. And then corresponding adaptive resource reservation algorithms are proposed to improve SDP performance, where the influence of MCS change and the unique characteristics of high-speed railway communication are taken into account. Simulation results show that compared with traditional cutoff AC scheme, the proposed AC scheme can effectively improve SDP performance while maintaining fairly desirable performance of service blocking probability and system bandwidth utilization.

Resource allocation problem in high-speed railway wireless communication networks is one of the key issues to improve the efficiency of resource utilization. However, traditional resource allocation methods cannot be directly applied to this special communication system. In this paper, we propose a resource allocation approach for high-speed railway downlink orthogonal frequency-division multiplexing (OFDM) system with multiple-input multiple-output (MIMO) antennas. Sub-carriers, antennas, time slots, and power are jointly considered, which is formulated as a mixed-integer nonlinear programming problem. The effect of the moving speed on Doppler shift is analyzed to calculate the inter-carrier interference power. The objective is to maximize the throughput under the constraint of total transmission power. In order to reduce computational complexity, suboptimal solution to the optimization problem is obtained by quantum-behaved particle swarm optimization. Simulation results show that the proposed resource allocation strategy has a better performance compared with an existing one.

Considering the rampant growth of data traffic and exponential increasing energy consumption in recent years, energy efficiency is becoming more and more crucial in cooperative communication systems where the wireless devices are battery operated. In this paper, we focus on the energy efficient transmission problem in a one source relay-based cooperative network, aims to find the most energy efficient relay node for the source node when it broadcasts a cooperation-request. We model the relationship of the source node and the relay nodes as a first price auction game, when the source node needs cooperation, the relay nodes compete for it and the one who sends a bid which can minimize the cost of the source node for cooperating is selected. If the relay node wins the game, it can gain reward by means of a product of the power it spends to help the source node and the price it charges for the power. Moreover, by reinforcement learning (RL), the relay node's reward can be maximized gradually in an iterative way. Simulation results are presented to show that the proposed scheme can improve energy efficiency significantly compared to a centralized one which can maximize the source node's transmission rate by allocating optimal power to the relay nodes.

In this study, the authors consider the application of a system architecture called cognitive radio (CR) with fibre‐connected distributed antennas in IEEE 802.22 wireless regional area networks (WRANs) as it could bring the benefits of much shorter wireless transmission distances, lower transmission power and the possibility of utilising multi‐antenna transmission techniques. In this architecture, the authors study the secondary user (SU) access problem in uplink, where the SU to primary user (PU) link estimation is subject to random errors because PU could not assist link estimation of SU. This SU access problem is divided into two parts: antenna selection and access control. Thus, first antenna selection problem is modelled as a restless bandit problem, which is solved by the primal‐dual index heuristic algorithm based on first order relaxation. In addition, the access control problem is modelled as a stochastic knapsack (SASK) problem with random weight, and then relaxed to be a deterministic second order cone programming problem. With the deduced upper bound, the access control problem is solved by the branch and bound algorithm, which yields the SU access based on SASK scheme. Simulation results illustrate the significant performance improvement of SASK scheme, compared with existing SU access methods.

Cooperative relaying technology among secondary users (SUs) in cognitive radio networks (CRN) is shown to yield a significant performance improvement, such as improving spectrum utilization as well as system fairness in CRN. This paper investigates the problem of the relay selection and resource allocation in a downlink OFDMA cognitive relay network. The objective of this optimization problem is to maximize both system throughput and system fairness, which is achieved through our proposed non-transferable utility coalition graph game algorithm (NTU-CGGA). In this algorithm, the SUs with more available channels can help SUs with less available channels to improve their utility in terms of the throughput and fairness by forming a directed tree graph. The coalition graph is formatted according to spectrum availability and traffic demands of SUs by using merge-split rule. So it can effectively exploit both space and frequency diversity of the system. Simulation results show that, NTU-CGGA significantly improves system throughput while not reducing the fairness level, which has a better performance comparing with other existing algorithms.

With the wide deployment of high-speed railway at more than 300 kilometers per hour, providing various services effectively for users in the train becomes both practical demand and interesting challenge for wireless communication. Resource allocation problem in high-speed railway communication system is one of the key issues to improve the efficiency of resource utilization. In this paper, we propose a multidimensional resource allocation strategy for high-speed railway downlink orthogonal frequency-division multiplexing (OFDM) system with multiple-input multiple-output (MIMO) antennas. Sub-carrier, antenna, time slot, and power are considered jointly, which is modeled as a nonlinear integer programming problem. The effect of the moving speed on inter-carrier interference is analyzed to calculate the transmitted power. The objective is to minimize the total transmitted power while satisfying quality of service requirement of each user. Moreover, optimal and approximate solutions are obtained by linearization and quadratic fitting, respectively. Simulation results have proved that approximate solution has lower total transmitted power than optimal solution while the computation complexity is higher.

Along with the wide deployment of high-speed railway at more than 300 km/h, providing various services effectively for users in the train becomes both practical demand and interesting challenge for wireless communication. Due to the specific environment for high-speed railway, existing radio admission control (RAC) algorithms could not suit the requirements such as frequent handover, quick decision-making duration and fast-changing physical transmission condition. The investigation on novel and proper RAC is of great importance. In this paper, we propose an effective radio admission control scheme based on Rician shading model for high-speed railway communication with MIMO antennas. Layered networks access architecture is designed in which Rician channel is used to analyze physical transmission condition. The effect of bit error rate on data rate at high moving speeds is taken into account for the RAC decision-making to get better quality of service (QoS). Moreover, different priorities for different services are considered to resolve simultaneous multiple users' access requests. Simulation results have proved that the proposed scheme may improve QoS obviously compared to the scheme that does not consider bit error rate and service priority.

Spectrum sensing is an essential functionality of cognitive radio networks. In this paper, a noncooperative game framework is proposed for studying the interactions between multiple secondary strategic users in spectrum sensing. The licensed spectrum of single primary user is divided into K sub-bands, each secondary user operates exclusively in one sub-band. In each time interval, secondary users are optimally selected to perform cooperative sensing. We model this scenario as a noncooperative game and analyze it by exploring the properties of Nash equilibrium point. We further develop a distributed learning algorithm so that the secondary users approach the NE solely based on their own payoff observations. The simulation results show that the proposed scheme can significantly increase the total throughput than having all secondary users sensing in every time slot. Moreover, the average throughput per user in the sensing game is higher than the case where secondary user sense individually without cooperation.

Collaborative spectrum sensing among secondary users (SUs) in cognitive radio (CR) networks is shown to yield a significant performance improvement. However, much previous work in CR networks focus on single channel sensing performed by SU cooperatively while spectrum sensing on multiple channels is largely ignored. In order to overcome the foregoing shortcoming, coalition game theory is introduced. In this paper, we mainly study the throughput maximization problem which considers optimizing the network throughput and channel sensing period synchronously. Moreover, the the missing detection and false alarm probability are also constrained. Then a modified merge and split algorithm (MMSA) is designed. Extensive simulation results illustrate that the proposed MMSA algorithm can significantly improve the overall network throughput and guarantee low missing detection and false alarm probability for channels.

Large-scale multi-AP WLANs with a high density of users and access points (APs) have emerged widely in various hotspots, where intra-cell interference is detrimental to the performance of WLANs. In order to mitigate the interference, a new system called cognitive WLAN over Fiber is presented in recent years. In this paper, based on Hidden Markov Modeling (HMM) detector's spectrum detection for Primary Users (PUs), we model the RAU allocation problem for CWLANoF not only to maximize the overall system capacity, but also to guarantee PUs' transmission. In order to cope with this problem, RAU allocation based on Antenna Allocation for Maximizing System Capacity (RAMSC) algorithm is proposed with low computation complexity, using local search. Finally, extensive simulation results illustrate that the proposed RAMSC algorithm significantly enhances the overall system capacity and guarantees the transmission of PUs in density CWLANoF systems.

In this paper, we propose a distributed joint relay selection and power allocation scheme in wireless multi-hop cooperative networks, taking both instantaneous channel state information (CSI) and relay nodes' residual energy into consideration. Specifically, we formulate the cooperative relaying network as a restless bandit system, which has been successfully applied in stochastic control and operations research problems. The first-order finite-state Markov chain is used to characterize the time-varying channel and residual energy state transitions. With this stochastic optimization formulation, the optimal policy for joint relay selection and power allocation has indexability property that dramatically reduces the computation and implementation complexity. Analytical and simulation results demonstrate that the proposed scheme can efficiently enhance the expected system reward, while guaranteeing a good tradeoff between achievable date rate and average network lifetime.

Most previous work in CR relay networks concentrates on maximizing physical layer QoS (e.g., capacity, spectral efficiency and achievable data rate) as relay selection criteria. However, the energy state of relay nodes is largely ignored, which has significant effects on the network lifetime. This paper proposes a distributed relay selection scheme for cooperative transmissions over underlay CR networks, while considering adaptive modulation and coding (AMC) strategy and energy state of relay nodes. The objective is to increase spectral efficiency as well as prolong the average network lifetime (i.e., achieve a good tradeoff between them). With the stochastic optimization formulation, the optimal relay selection policy has indexability property that dramatically reduces the computation and implementation complexity. Simulation results are presented to show the effectiveness of the proposed scheme.

In this paper, we propose a distributed best-relay node selection scheme to maximize the achievable data rate for cooperative communications over underlay-paradigm based CR networks, and meanwhile guarantee that the primary link is provided with a minimum-rate for a certain percentage of time. Specifically, we formulate the CR relay network as a restless bandit system, where the finite-state Markov channel model is used to characterize the time-varying channel state. With this stochastic optimization formulation, the optimal relay node selection policy is obtained by a primal-dual priority-index heuristic, which can dramatically reduces the computation and implementation complexity. Simulation results are presented to show the effectiveness of the proposed scheme.

This paper considers multi-hop cellular networks in which nodes use accumulation for cooperative broadcasting in order to save total energy with low delay. Our purpose is to build up Green Cellular Networks (GCN) which represent the trend of Next-Generation Networking (NGN) and cater for people's demand for health and energy savings. Therefore, we assume a radiation and harmful region and show that power is in direct proportion to the area of the region so that we can focus on the power property. We also propose Virtual Tree (VT) to determine hop number in order to evaluate delay property of accumulative routing algorithms. Then, we propose a centralized broadcast routing algorithm specific for the asymmetric cellular based on accumulation named Cellular Accumulative Broadcast (CAB) Algorithm, which aims to realize low-delay minimum-energy broadcast. Further, we figure out the accumulative relay region for better analysis of relay cooperation. Simulation results show that our CAB algorithm performs superior to other existing accumulative or non-accumulative broadcast algorithms, which means GCN built up with CAB could be cost-effective with low delay.

Cooperative communication is an emerging and effective technology which can overcome the limitation and improve overall system performance of wireless networks. As important design parameters, relay selection, power and bandwidth allocation have been investigated respectively in previous literature. However, most of the existing schemes mainly consider single-relay selection, which may result in imbalance of resource utilization, and moreover, the "emergence" diversity gain among multiple relays cannot be achieved. In this paper, we propose a novel multi-relay nodes selection strategy, taking both instantaneous channel state information (I-CSI) and remaining energy as weighted metrics. Besides, an optimal power allocation algorithm using convex optimization method is presented. Theoretical analysis and simulation results show that the proposed scheme can significantly improve energy efficiency and channel capacity while maintaining relatively low implementation complexity in wireless cooperative relay networks.

An effective node failure recovery scheme is very important in wireless peer-to-peer (P2P) file sharing networks. Most of the existing failure recovery protocols are based on backup path mechanism, in which a new path will be used if the original one breaks. However, due to the limited resources in wireless environments, the maintenance of backup paths is expensive. Moreover, the time spending on path switch may be unacceptable long. In this paper, we propose a novel peer grouping scheme to select a qualified peer from a group to replace the failed node. The main criteria in deciding the backup node is derived with the help of recent advances in fuzzy cognitive maps (FCM). Several influential factors are considered, including energy, movement, lingering time and security, with further investigation on their respective contributions to node failure risk. We compare the performance of the proposed scheme with that of traditional path redirection and multi-path backup algorithms. The FCM-based peer grouping scheme has significant improvement in failure recovery time and file transfer time.

In order to fully utilize spectrum, auction-based dynamic spectrum access has become a promising approach which allows unlicensed wireless users to lease unused bands from spectrum license holders. Traditionally, the property of spectrum goes to a unique winner with the highest bid after the auction, and the bidders with low bids would be probably not served. This also may result in spectrum resource wasting when the assigned band is larger than the original request. In fact, because the spectrum is divisible goods, it can be shared among a group of users. In this paper, we propose a novel virtual bidder group (VBG) mechanism in spectrum auction which allows multiple winners to obtain the spectrum item simultaneously. Furthermore, a heuristic phase-optimization algorithm is proposed to reduce the crucial exponential computing time issue of band allocation optimization in double-side bandwidth auction scenario with multiple auctioneers. Simulation results prove that the VBG scheme could significantly improve the realized system data rate and the bandwidth utility, which indicates higher revenue for the spectrum license holder; meanwhile, the proposed phase-optimization algorithm exhibits relative low complexity and could provide a near-optimal performance.

Selecting an appropriate peer from the discovered file holders is one of the key steps in wireless peer-to-peer (P2P) file sharing systems. Most of existing peer selection schemes are based on the min-hops selection criterion, which uses the number of hops as the only factor in peer selection. However, due to the distinct characteristics in wireless mobile networks, a number of important factors should be considered in wireless P2P networks, such as time-varying channels, node energy, security and user movement. In this paper, we propose a novel peer selection scheme that simultaneously considers multiple selection criteria in wireless P2P networks. The proposed scheme is based on recent advances in fuzzy cognitive maps theory. The main influential factors and their complex relationships for peer selection in wireless P2P networks are investigated. The candidate peer and corresponding intermediate peers along the path are treated as a team for evaluation. Simulation results show the effectiveness of the proposed scheme.