Device-enhanced mobile edge computing (MEC) is an emerging technology designed to handle intensive and delay-sensitive tasks through device-to-device (D2D) communication. In this paper, we present a terminal-edge collaborative computing network to investigate device-enhanced distributed tasks scheduling (DDTS) with specific application deployment. Our optimization focuses on offloading choices, bandwidths, and computing frequencies, aiming to minimize execution costs, including processing delay and energy consumption. We decouple the joint multiple goals optimization problem into several sub-problems which are solved by math optimization methods except the NP-hard offloading choices sub-problem. This NP-hard problem is modeled as a multitask scheduling game (MTSG), which we demonstrate to be a potential game with at least one Nash equilibrium solution. However, considering further the dynamic nature of real-world application deployment and the complexity of large-scale games, the problem evolves into a stochastic game with a Markov policy (SGMP). Thus, we propose a multi-agent DDTS algorithm based on a dueling double deep Q-network (D3QN) to approximate an optimal solution. Extensive experiments confirm the feasibility and efficiency of our approach.

Benefiting from wide coverage and low expense, satellite constellations show potential in numerous applications. As satellite constellations expand with more diverse and smarter payloads, traditional networking methods become increasingly rigid. This rigidity hinders the flexible and efficient use of on-board resources in mega-constellations, and limits the ability of satellite networks to provide customized services. Hence, a taskoriented clustering (TOC) method based on Markov process is proposed for multi-layer mega-constellations, enabling flexible networking of satellites with different payloads according to task requirements. Finally, numerical simulation results demonstrate that the TOC method outperforms existing works.

Edge computing (EC), positioned near end devices, holds significant potential for delivering low-latency, energy-efficient, and secure services. This makes it a crucial component of the Internet of Things (IoT). However, the increasing number of IoT devices and emerging services place tremendous pressure on edge servers (ESs). To better handle dynamically arriving heterogeneous tasks, ESs and IoT devices with idle resources can collaborate in processing tasks. Considering the selfishness and heterogeneity of IoT devices and ESs, we propose an incentive-driven multilevel task allocation framework. Specifically, we categorize IoT devices into task IoT devices (TDs), which generate tasks, and auxiliary IoT devices (ADs), which have idle resources. We use a bargaining game to determine the initial offloading decision and the payment fee for each TD, as well as a double auction to incentivize ADs to participate in task processing. Additionally, we develop a priority-based intercell task scheduling algorithm to address the uneven distribution of user tasks across different cells. Finally, we theoretically analyze the performance of the proposed framework. Simulation results demonstrate that our proposed framework outperforms benchmark methods.

As one of the core directions in 6G technology evolution, computing and network convergence enables coordinated orchestration and unified management of multi-dimensional resources such as computing and communication, thereby providing users with highly reliable and low-latency computing services. However, with the increasing complexity of intelligent applications, modern applications gradually exhibit structural characteristics of the coexistence of parallel tasks and multi-level dependent tasks. The intricate interdependencies among these tasks present significant challenges for the optimal allocation of computing and network resources. Therefore, this paper investigates task offloading and resource allocation for applications with dependent tasks. The objective is to minimize the system’s energy consumption while meeting the application completion delay requirement. Considering the coupling between discrete and continuous variables in this NP-hard problem, we propose a Joint Offloading and Resource Allocation Hybrid Proximal Policy Optimization (JOR-HPPO) algorithm to handle the hybrid discrete-continuous action space comprising offloading decisions and resource allocation. Furthermore, we employ the Beta distribution to model the resource action space and design an action masking mechanism to optimize the resource allocation strategy in the algorithm. Simulation results demonstrate that the proposed JOR-HPPO algorithm significantly outperforms the baseline methods. Specifically, compared with the single action space optimization method, JOR-HPPO reduces the system energy consumption by 26.01 % while improving the application request success rate to 30.72%

With the rapid growth of global traffic and the construction of large-scale low-orbit satellites, the hybrid network of multitier LEO satellite has become the future trend, which also brings challenges to satellite routing. In this article, we propose a flexible satellite cross-tier routing method under the control of gateway, considering on-board processing ability and satellite congestion to realize low lantency routing. First, we model the multitier satellite constellations as marked Poission point process (MPPP), in which each tier follows Poission point process (PPP) distribution with marked different satellite queueing length. Then, the analytical expressions of routing outage probability, end-to-end delay are derived, considering search radius, packet arrival rate, routing distance and intertier distance. Finally, the search range expansion approach is proposed to solve the problem of traffic congestion, and the tradeoff between routing success probability and search range deflection angle is obtained. Numerical results show that the cross-tier routing mechanism can significantly reduce the routing outage probability under high load and reduce the end-to-end delay under medium load, respectively. In addition, the search range expansion approach can significantly reduce the routing outage probability without influencing the sum delay significantly. This work is expected to be instructive to the future sixth generation (6G) network.

With the development of satellite communication technology, satellite-terrestrial integrated networks (STINs), which integrate satellite networks and ground networks, can realize global seamless coverage of communication services. Confronting the intricacies of network dynamics, the resource heterogeneity, and the unpredictability of user mobility, dynamic resource allocation within networks faces formidable challenges. Digital twin (DT), as a new technique, can reflect a physical network to a virtual network to monitor, analyze, and optimize the physical networks. Nevertheless, in the process of constructing a DT model, the deployment location and resource allocation of DTs may adversely affect its performance. Therefore, we propose a STIN model, which alleviates the problem of insufficient single-layer deployment flexibility of the traditional edge network by deploying DTs in multi-layer nodes in a STIN. To address the challenge of deploying DTs in the network, we propose a multi-layer DT deployment problem in the STIN to reduce system delay. Then we adopt a multi-agent reinforcement learning (MARL) scheme to explore the optimal strategy of the DT multi-layer deployment problem. The implemented scheme demonstrates a notable reduction in system delay, as evidenced by simulation outcomes.

Mobile edge computing mitigates the shortcomings of cloud computing caused by unpredictable wide-area network latency and serves as a critical enabling technology for the Industrial Internet of Things (IIoT). Unlike cloud computing, mobile edge networks offer limited and distributed computing resources. As a result, collaborative edge computing emerges as a promising technology that enhances edge networks' service capabilities by integrating computational resources across edge nodes. This paper investigates the task scheduling problem in collaborative edge computing for IIoT, aiming to optimize task processing performance under long-term cost constraints. We propose an online task scheduling algorithm to cope with the spatiotemporal non-uniformity of user request distribution in distributed edge networks. For the spatial non-uniformity of user requests across different factories, we introduce a graph model to guide optimal task scheduling decisions. For the time-varying nature of user request distribution and long-term cost constraints, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time subproblems that do not require prior knowledge of future system states. Given the NP-hard nature of the subproblems, we design a heuristic-based hierarchical optimization approach incorporating enhanced discrete particle swarm and harmonic search algorithms. Finally, an imitation learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Comprehensive theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed schemes.

Mobile edge computing (MEC) is a promising computing paradigm that offers users proximity and instant computing services for various applications, and it has become an essential component of the Internet of Things (IoT). However, as compute-intensive services continue to emerge and the number of IoT devices explodes, MEC servers are confronted with resource limitations. In this work, we investigate a task-offloading framework for device-assisted edge computing, which allows MEC servers to assign certain tasks to auxiliary IoT devices (ADs) for processing. To facilitate efficient collaboration among task IoT devices (TDs), the MEC server, and ADs, we propose an incentive-driven pricing and task allocation scheme. Initially, the MEC server employs the Vickrey auction mechanism to recruit ADs. Subsequently, based on the Stackelberg game, we analyze the interactions between TDs and the MEC server. Finally, we establish the optimal service pricing and task allocation strategy, guided by the Stackelberg model and priority settings. Simulation results show that the proposed scheme dramatically improves the utility of the MEC server while safeguarding the interests of TDs and ADs, achieving a triple-win scenario.

The potential new application scenarios in the era of 6G will generate a large number of mobile computing tasks. In order to meet the greater resource demands, 6G base stations will not only have traditional communication capabilities but also possess information perception and intelligent computing capabilities, realizing ubiquitous sensing and computing integration. This paper will first introduce the concept and architecture of the 6G computing power dedicated network, which can release the surplus computing power of base stations during idle times in mobile communication services, providing ubiquitous computing services for mobile users near the base station side. The 6G computing power dedicated network can not only reduce the end-to-end latency of services but also effectively improve the utilization rate of edge computing power, avoiding task overload in cloud resource pools.

The rapid expansion of computing needs from emerging applications pushes a large amount of deployment of computing infrastructures and corresponding energy cost and greenhouse gas emissions of computing generate great concern. In this paper, we study how to maximize the platform profit by optimizing task scheduling in the Space-Air-Ground integrated Computing Power Network supplied by green energy while considering both the user requirements and dynamics of green energy. First, we formalize the problem as a binary integer linear programming problem that is NP-hard. The problem is then further modeled as a Markov decision process. Considering the dual dynamics of user requests and the generation of green energy, we propose a task scheduling strategy based on deep reinforcement learning, which can predict power generation based on the current operating status of each hydroelectric power station and also provide a scheduling strategy. Extensive experiments demonstrate that the proposed algorithm performs better than the baseline algorithms.

The introduction of edge computing provides a broad application scenario for the Internet of Vehicles. Service programs that were not able to be handled timely by On-Board Unit (OBU) can now be placed on Road Side Unit (RSU) to meet users' requirements of End-to-End (E2E) latency and reliability. However, based on the microservice architecture, services are decomposed of multiple microservices, and the complex dependencies between microservices pose new challenges to their placement. To tackle this problem, we first model the dependencies as a directed acyclic graph (DAG), and the long-term interference-aware placement model is then established to depict the load balance between RSUs and network. After that, we formulate it as an integer linear programming (ILP) problem with the aim to achieve a tradeoff between node load cost and transmission cost while reducing the E2E latencies. Considering the local features of DAG topology, an iterative two-phase heuristic microservice placement algorithm is then proposed. Finally, a simulation environment based on real-world electric taxis trajectory data is constructed, and intensive experiments with several baseline algorithms are conducted to verify the superiority of our proposed algorithm.

With the development of the Internet of Things (IoT), certain IoT devices have the capability to not only accomplish their own tasks but also simultaneously assist other resource-constrained devices. Therefore, this article considers a device-assisted mobile edge computing system that leverages auxiliary IoT devices to alleviate the computational burden on the edge computing server and enhance the overall system performance. In this study, computationally intensive tasks are decomposed into multiple partitions, and each task partition can be processed in parallel on an IoT device or the edge server. The objective of this research is to develop an efficient online algorithm that addresses the joint optimization of task partitioning (TP) and parallel scheduling (PS) under time-varying system states, posing challenges to conventional numerical optimization methods. To address these challenges, a framework called online task partitioning action and parallel scheduling policy generation (OTPPS) is proposed, which is based on deep reinforcement learning (DRL). Specifically, the framework leverages a deep neural network (DNN) to learn the optimal partitioning action for each task by mapping input states. Furthermore, it is demonstrated that the remaining PS problem exhibits NP-hard complexity when considering a specific TP action. To address this subproblem, a fair and delay-minimized task scheduling (FDMTS) algorithm is designed. Extensive evaluation results demonstrate that OTPPS achieves near-optimal average delay performance and consistently high-fairness levels in various environmental states compared to other baseline schemes.

Beam hopping (BH)-enhanced satellite-enabled Internet of Things (S-IoT) is a significant complement to terrestrial Internet of Things (IoT), and is also a key component of the nonterrestrial network (NTN)-enabled IoT. For BH low-Earth orbit (LEO) satellite IoT, efficient resource management is crucial for improving system performance. The joint allocation of multidimensional resources, such as time, frequency, and power, needs to be investigated urgently, with multiple purposes of maximizing the long-term throughput, minimizing the average delay of real time (RT) services and assuring the fairness. Involving both discrete and continuous variables, the multidimensional resources allocation problem is formulated as a multiobjective mixed integer programming problem. To address this problem, we transform it into two subproblems. First, the power optimization (PO) subproblem is approximated as a convex optimization problem and further solved. Subsequently, the beam scheduling subproblem is modeled as a Markov decision process. Furthermore, an action masking multiobjective double deep Q network (AMM-DDQN) algorithm is proposed based on Chebyshev scaling and action masking strategy. The simulation results demonstrate the convergence of the proposed AMM-DDQN algorithm, which outperforms the baseline methods in terms of multiple performances. Specifically, compared with the greedy with distance limit strategy, TopKDQN without PO method, TopKDQN method, genetic algorithm, and random method, the average delay of RT services of the proposed algorithm is reduced by 22.51%, 10.82%, 4.42%, 34.41%, and 52.13%, respectively, achieving QoS guarantees in BH LEO S-IoT.

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and computing power of devices can affect its training or communication process in complex network environments. Computing and network convergence (CNC) of sixth-generation (6G) networks, a new network architecture and paradigm with computing-measurable, perceptible, distributable, dispatchable, and manageable capabilities, can effectively support federated learning training and improve its communication efficiency. By guiding the participating devices’ training in federated learning based on business requirements, resource load, network conditions, and computing power of devices, CNC can reach this goal. In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating devices. The simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters. The results show that the methods we proposed can cope well with complex network situations, effectively balance the delay distribution of participating devices for local training, improve the communication efficiency during the transfer of model parameters, and improve the resource utilization in the network.

Edge computing servers have been widely deployed in recent years to address the requirements of diverse tasks that are sensitive to delays and computationally intensive. However, due to their independent nature and uneven distribution of service requests, certain clusters may be relatively idle, while others may be overloaded. This situation can result in increased latency for certain tasks, and it prevents the full utilization of resources in the edge clusters. To mitigate this problem, we design and implement a prototype testbed for task offloading, aimed at achieving computing and network convergence. This testbed facilitates collaboration among multiple edge computing clusters. We construct multiple clusters using Intel NUC mini computers and incorporate key enabling technologies into the system. We assess the testbed's performance by employing multiple video processing services that require low latency and high computational capacity. In scenarios with uneven service requests, load balancing can be achieved across the edge computing clusters, resulting in reduced response latency for user tasks.

Low earth orbit (LEO) mega-constellations in future 6G have attracted the attention from both academia and industry. However, due to the high dynamic characteristic of the LEO satellite network topology and the limited on-board resources, existing approaches relying on high on-board processing capabilities and monitoring the global network state, result in intolerable packet loss rate and excessive signalling overhead. In this paper, a routing algorithm based on area segmentation for LEO mega-constellations is proposed according to the topological characteristics of the network. Specifically, we divide the LEO mega-constellations into multiple areas with four quadrant parts. In addition, the transmission cluster is defined consisted of two adjacent parts based on transmission direction. With the relative geographical location and transmission clusters, we joint intra-area and inter-area routing to realize multi-path routing and forwarding by periodically updating the link state, instead of globally calculating. Simulation results demonstrate that the proposed algorithm can achieve higher throughput, decrease the packet loss rate by 22% and reduce the signalling overhead significantly.

With the evolutionary development of computing-intensive and delay-insensitive applications, partial computing offloading in cooperative edge computing networks is considered as a promising technology to reduce tasks execution delay. However, existing researches focus on either splitting one task to several subtasks without exact proportion or splitting each of multiple tasks into hard two parts. In this paper, we consider splitting multiple computing-intensive tasks to several subtasks simultaneously. Accordingly, a joint tasks segmentation and parallel scheduling with cooperative computing problem is formulated to minimize total tasks execution delay. To tackle this intractable mixed integer non-convex problem, firstly, we decouple it into separated multiple tasks segmentation and subtasks parallel scheduling problems. Secondly, the multiple tasks segmentation problem is further decomposed into single task segmentation problem, where the optimal task segmentation ratio function is proposed and proved. Thirdly, the Advantage Actor Critic (A2C) algorithm is applied to choose computation node for subtasks parallelly in an online manner for the time-varying network. Finally, the multiple tasks segmentation scheme is incorporated into A2C algorithm to achieve end-to-end joint optimization of tasks segmentation and parallel scheduling with cooperative computing. Simulation results represent the superiority and effectiveness of the proposed algorithm compared with the benchmarks, such as binary tasks offloading.

Recently deep neural networks (DNNs) are widely used in various fields. These intelligence applications, such as target recognition, are often computation-intensive and latency-sensitive. Since a single UAV's computing resource is limited, it is difficult to complete the DNN inference task independently. Partitioning the deep neural work into numerals subtasks and distributing them to multiple UAVs for collaborative computing seems a better way to finish the task. However, UAV usually works in a harsh environment, such as battlefield, disaster area, etc., and the link interruption or node failure in the inference process caused by uncertain factors may lead to failure of inference task. Hence, the reliability of DNN inference is of high importance. In this paper, we propose a deep Q learning-based DNN partitioning strategy for minimizing the energy consumption of DNN collaborative inference among multi-UAVs within latency and reliability requirements. To validate the effectiveness of the proposed strategy, a series of experiments are conducted on four kinds of typical DNNs (i.e., AlexNet, VGG19, GoogleNet, and ResNet). The simulation results prove the proposed strategy can effectively reduce the DNN inference cost under constraints.

In large-scale Internet of Thing (IoT), ambient backscatter communication has become a new green technology of concern. At present, the research scenario on ambient backscatter communication mainly focuses on single-transmitter and single-receiver, and a few studies have multiple receivers. And backscatter system is always only allowing one transmitter-receiver pair active. However, the condition multiple transmitters communicate with multiple receivers is essential to achieve giant connection in Internet of Everything. Besides, the system with active multipair can transmit more byte and use energy more effectively. So there is an urgent need of research on multi-transmitter multi-receiver system. We propose an ambient back scatter communication system which allows multi-transmitter and multi-receiver active. This paper is devoted to studying the user association problem in such system. When studying the ambient backscatter communication system, we pay attention to the power limitation. Because the energy collected by the device is relatively small and limit communication performance. In the case of limited link budget, this paper maximized system communication capacity. A priority-based access strategy is proposed in this paper. It arranges priority to the receiver according to the power threshold and link budgets. Then, the strategy handles association problem according to receiver's priority from high to low. Simulation results show that the proposed access strategy has better convergence than the random access strategy. It achieves maximum communication capacity and suboptimal bit rate. What's more, it has low complexity.

With the rapid development of 5G technology, there is an increasing demand of high-definition (HD) video service, so that efficient content transmission is expected to guaranteed the quality of experience of users. However, traditional terrestrial networks can hardly support this kind of service due to the limited coverage and capability especially in scene of remote area or peak hours of hotspots. Under the support of High Throughput Satellite, satellite can serve as a supplement in hybrid satellite-terrestrial networks (HSTN) to provide various of services. Specifically, aggregation in packet level for collaborative transmission between satellite and terrestrial networks is in direction of development which should be reconsidered. In this paper, a classic SDN-aware HSTN architecture is adopted to capture content information of the system and make strategy dynamically for efficient distribution of content in finer scale. Key technologies, including tag method, path selection strategy and reordering scheme, are proposed to achieve collaborative transmission of HD videos. Finally, complete implementation of a prototype, a Hardware In the Loop (HIL) platform with the SITL module of OPNET, is built to illustrate the feasibility and effectiveness of the proposed solutions. Numerical results show that collaborative transmission in HSTN can effectively realize link aggregation, which has great significance for complex conditions in future networks.

Most HTTP Adaptive Streaming (HAS) video content delivery solutions today are governed by purely client-based logics. For lack of coordination among clients and awareness of the dynamic Radio Access Network (RAN) conditions, these approaches may lead to suboptimal user experience and underutilization of network resources. Recently, Mobile Edge Computing (MEC) has been studied as a new networking paradigm to play a part in the adaptive video streaming process with lower end-to-end latency and better network awareness. In this paper, we present an edge-assisted adaptive video streaming scheme with deep Q-learning techniques and bandwidth sharing policies, which performs video adaptation according to the real-time radio and network information collected at the network edge. The adaptation scheme is implemented as an edge application hosted on our own deployed MEC server in a real LTE network testbed for experimental evaluation against three popular client-based solutions, namely Buffer-Based Adaptation (BBA), Rate-Based Adaptation (RBA) and adaptation performed by dash.js with its default adaptation logic. To the best of our knowledge, it is one of the few deep Q-learning adaptive video streaming solutions that have been deployed in the MEC framework at network edge in practice. Experiment results show that our proposed scheme outperforms the other three client-based solutions with higher Quality of Experience (QoE) and fairness in the evaluated network environments.

With emerging requirement of local low-latency services, Mobile Edge Computing (MEC) is a promising solution to tackle the challenge between urgent demands for computation capability and limited battery energy of mobile devices. Moreover, the sharing property of applications costs waste as for the processing of redundant data, which derives an imperative need for the collaboration among users. In this paper, by leveraging these features, we design a D2D-assisted MEC system for energy efficiency of devices with the consideration of task delay. For sake of energy minimization, a strategy that jointly optimizes resource allocation and tasks offloading assignment is proposed. Further, a low-complexity algorithm is developed to decompose the original problem into two subproblems and get the sub-optimal solution efficiently. Simulation results present the efficient and effective performance of the proposed algorithm with different application parameters. Particularly, it is shown that our proposed algorithm gets 48.41%~90.58% and 37.33%~96.63% improvement of energy consumption than those of non-collaboratively offloading scheme and randomly offloading scheme, respectively.

Mobile edge computing (MEC) is one of the key technologies for fifth generation networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the nonuniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov-based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.

User and network behavior prediction by big data makes the traditional heterogeneous networks (HetNets) a learning and knowledgeable network. However, how much and under what conditions that prediction can benefit the upcoming 5G HetNets have not been comprehensively studied. Furthermore, how to use the quantified conditions to guide network operation is still under investigation. In term of resource allocation, this paper proposes a mobility-based proactive resource scheduling (MPRS) strategy and explores the above questions. Taking advantage of predicted information of user mobility, network residual frequency bandwidth and channel gains, MPRS aims at a) minimizing service delay and enhancing successful scheduling probability, and b) adapting to users' mobility intensity together with quality of service requirements, in long term. Comparing with the reactive strategy, fair scheduling (FS), simulation results show that with accurate prediction, MPRS achieves about 20% performance gain. And when the proportion of average residual frequency bandwidth is less than 50%, FS can be performed instead for it achieves similar performance with MPRS and its computational simplicity. With imperfect prediction, the tolerable upper bound of prediction error becomes tighter as the residual frequency bandwidth decreases.

The main requirement of the computation offloading service is the low service delay, which would correspond to a high Quality of Service (QoS). Recently, many works show that placing virtual resources (e.g., computing resource) in the Mobile Edge Network (MEN) to form a Mobile Edge Computing (MEC) system contributes to a lower service delay. However, due to the limited virtual resource in the MEN and the constraints of the wireless channel condition, only part of users can be served with low enough service delay. Moreover, considering the independent virtual machine (VM) environment on the MEC server in MEN for each user, efficient virtual resources management is needed. In this paper, we propose two computation offloading strategies combined with the virtual resources management to minimize the average service delay, where the virtual resources consists of the computing resources and the storage resources. One of the proposed strategies guarantees the fairness of users while the other one not. Then the optimal deployments of VMs with different strategies are obtained through the simulations. We also find and calculate the optimal proportion of these two kinds of virtual resource when there is only a limited budget for them.

Caching at the user devices is a key technology to alleviate backhaul load and improve users' quality of experience in mobile edge networks. However, due to the concern of privacy and limited battery life, users are not willing to share their resources. In this study, the authors jointly design the caching strategy and incentive scheme to encourage the users to share their storage resources and improve the social welfare of the cellular network. First, the user equipments (UEs) are classified into different types according to their preference to share cache resources. Then, based on contract theory, an optimisation problem with necessary constraints is formulated aiming at maximising the base station (BS)'s utility. The reward the BS pays for a UE is based on the total amount of contents it shares with other UEs, which is related to the caching policy of UEs. A heuristic caching strategy is proposed and the optimal contract is obtained. Finally, the system performance of the proposed solution is analysed, simulation results show that the incentive scheme can improve the willingness of users to share contents, and the proposed caching strategy contributes more to the social welfare compared with two baseline caching schemes: fair caching and random caching.

The dramatic development of Internet of Things (IoT) is not only leading to the spectrum crunch, but is also resulting in exorbitant energy consumption. It is thus desirable to liberate IoT from the constraint of the spectrum scarcity and to rein in the growing energy consumption. Wireless energy harvesting relay-assisted underlay cognitive networks (WEH-CRNs), which combine cognitive radio and wireless energy harvesting techniques to alleviate the spectrum and energy constraints by reusing spare spectrum for data transmissions and harvesting ambient energy for power supplies, are conceived as an efficient solution for massive IoT deployments. However, all existing works about WEH-CRNs did not take into account of the spatial location distribution of nodes, which is very important for energy harvesting and information transmission. Therefore, in this paper, we develop a framework for the design and analysis of WEHCRNs with spatial randomly distributed nodes (WEH-RCRNs). We first propose an efficient relay selection strategy in WEHRCRNs to determine when and which relay should be selected to assist transmission. Then, based on the proposed relay selection strategy, we derive the expression for outage probability to measure the outage performance of WEH-RCRNs. Finally, the impacts of related network parameters on the outage probability is also explored on the basis of our analysis results.

With the ever-increasing spectrum requirements for transmitting explosively growing mobile data, spectrum-efficient solutions need to be integrated into future mobile networks. Spectrum sharing enables the primary system to share licensed spectrum with the secondary system. Thus, it is conceived as an appealing solution for improving spectrum usage to eliminate the spectrum supply-demand gap. In this paper, we develop an efficient transmit power control strategy for underlay spectrum sharing networks with spatially Poisson-distributed primary users. A distinguishing feature of the proposed strategy is that it only requires channel state information and location information of a few primary users close to the secondary transmitter, rather than those for all primary users. Furthermore, we evaluate the outage performance of the secondary system and the interference situation of the primary system under this kind of transmit power control strategy. Numerical results demonstrate that the proposed transmit power control strategy can achieve near-optimal outage performance compared to the perfect power control strategy, while reducing the control complexity and the feedback burden significantly at the same time.

In this paper, we propose to leverage the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. First, we perform a preliminary analysis for a big dataset from China Mobile, and use traffic load as an example to show non-zero temporal autocorrelation and non-zero spatial correlation among neighboring Base Stations (BSs), which motivate us to discover both temporal and spatial dependencies in our study. Then we present a hybrid deep learning model for spatiotemporal prediction, which includes a novel autoencoder-based deep model for spatial modeling and Long Short-Term Memory units (LSTMs) for temporal modeling. The autoencoder-based model consists of a Global Stacked AutoEncoder (GSAE) and multiple Local SAEs (LSAEs), which can offer good representations for input data, reduced model size, and support for parallel and application-aware training. Moreover, we present a new algorithm for training the proposed spatial model. We conducted extensive experiments to evaluate the performance of the proposed model using the China Mobile dataset. The results show that the proposed deep model significantly improves prediction accuracy compared to two commonly used baseline methods, ARIMA and SVR. We also present some results to justify effectiveness of the autoencoder-based spatial model.

Device-to-device (D2D) caching network is proposed to cope with the explosively growing traffic of next-generation mobile networks. Given the limited mobile users' (MU) caching capacity, it is significant to decide which contents and how much proportion of each content should be cached. Based on the selfish nature and varied content preference of MUs, we propose a mobility aware caching incentive scheme, in which base stations (BS) reward MUs for sharing contents with others through D2D communication. By solving the problem of maximizing MU utility and minimizing BS cost at the same time, we obtain each MU's caching strategy and optimal unit reward provided by BS with adapted gradient projection algorithm. Comparisons are deliberated with several baseline caching schemes without incentive, in order to measure the performance of the proposed scheme. Simulation results show that under our settings, BS serving cost can be reduced by up to 45.6% when the optimal reward is selected compared with the fair caching scheme, the advantage is more obvious when MUs' mobility speed gets higher. Furthermore, the proposed scheme outperforms greedy caching, fair caching and random caching scheme in terms of MU utility under different distributions of content popularity.

In this paper, we investigate an energy harvesting and cooperation protocol in hybrid green cellular networks, where power beacons (PBs) overlaying with an uplink cellular network. PBs harvest renewable energy from the nature and then charge mobile terminals (MTs) with microwave power transfer in energy beamforming. The network is powered by green energy only. Owing to the space-time instability and non-uniformity of renewable energy, some PBs' energy may not be enough to support the communication. We propose a bargaining-based Green Energy Allocation Game (GEAG) and introduce a dual-level control architecture for implement green energy cooperation to improve the performance of the system. The optimal power allocation problem is solved by the Nash bargaining solution (NBS). Simulation results show that the proposed GEAG algorithm can achieve 84% gains of MT's average rate and 75% reductions of the outage probability over the conventional green energy cooperation scheme.

Base station (BS) sleeping operation in transient process has been widely analyzed in current studies to save cellular network energy consumption. In this paper, we propose a hysteretic BS sleeping strategy in 5G cellular network. The BS adjusts its transmitting power to adapt to the traffic load in active state, and frequently goes to sleep state and keeps sleeping until N requests assembled. When the BS wakes up or shuts off, it will experience a state transition delay, which means the BS actions somehow lag behind the BS decisions. This paper explored the impact of state transition delay on the power consumption and traffic delay performance, and discussed the tradeoff between power-saving and traffic delay requirements. Numerical results show that the longer transition delay is, the worse system performance the BS is suffered. And the BS sleeping operation is energy-efficient only under a short transition delay.

The emerging 5G wireless networks will pose extreme requirements such as high throughput and low latency. Caching as a promising technology can effectively decrease latency and provide customized services based on group users behaviour (GUB). In this paper, we carry out the energy efficiency analysis in the cache-enabled hyper cellular networks (HCNs), where the macro cells and small cells (SCs) are deployed heterogeneously with the control and user plane (C/U) split. Benefiting from the assistance of macro cells, a novel access scheme is proposed according to both user interest and fairness of service, where the SCs can turn into semi- sleep mode. Expressions of coverage probability, throughput and energy efficiency (EE) are derived analytically as the functions of key parameters, including the cache ability, search radius and backhaul limitation. Numerical results show that the proposed scheme in HCNs can increase the network coverage probability by more than 200% compared with the single- tier networks. The network EE can be improved by 54% than the nearest access scheme, with larger research radius and higher SC cache capacity under lower traffic load. Our performance study provides insights into the efficient use of cache in the 5G software defined networking (SDN).

Jamming emits white Gaussian noise that causes interference to both the legitimate receiver and eavesdropper. It has been an effective method for improving physical-layer security in wireless communication. In this paper, cautious jamming strategy based on optimal jammer selection method is proposed. More specifically, by using perfect channel state information, cautious jamming strategy selects an optimal jammer to improve the secrecy performance. For the purpose of comparison, we consider the direct jamming strategy and the adaptive jamming strategy as benchmark schemes. Closed-form expressions of the exact intercept probability for three jamming strategies are derived over Rayleigh fading channels and the theoretical analysis is validated by Monte-Carlo simulation. Both the analytical and the simulation results show that: 1) increasing the power of jammer is an effective approach to mitigate the intercept probability; 2) the intercept probability can be decreased and the wiretapped range of eavesdropper can be reduced by increasing the number of jammer; 3) by using perfect CSI and selecting optimal jammer, cautious jamming strategy outperforms direct jamming strategy and adaptive jamming strategy.

This paper studies the physical layer security (PLS) in an underlay cognitive radio (CR) network. Under an interference constraint set by the primary user, we propose a secure transmission scheme from a secondary source to a secondary destination in the presence of spatially random eavesdroppers, the distribution of which is modeled as a homogeneous Possion point process (PPP). In addition to transmitting the message signal, the secondary source is allowed to use part of its power to transmit jamming signals in order to interfere the eavesdroppers. Assuming that the secondary destination has the knowledge of the jamming signals, we derive the closed-form expression for the connection outage probability and an easy-to-compute expression for the secrecy outage probability to characterize the reliability and the security performance, respectively. Moreover, we determine the optimal power allocation factor and wiretap code rates which maximize the secrecy throughput of the wiretap channel under secrecy outage probability constraint.

With the worldwide evolution of 4G generation and revolution in the information and communications technology(ICT) field to meet the exponential increase of mobile data traffic in the 2020 era, the hybrid satellite and terrestrial network based on the soft defined features is proposed from a perspective of 5G. In this paper, an end-to-end architecture of hybrid satellite and terrestrial network under the control and user Plane (C/U) split concept is studied and the performances are analysed based on stochastic geometry. The relationship between spectral efficiency (SE) and energy efficiency (EE) is investigated, taking consideration of overhead costs, transmission and circuit power, backhaul of gateway (GW), and density of small cells. Numerical results show that, by optimizing the key parameters, the hybrid satellite and terrestrial network can achieve nearly 90% EE gain with only 3% SE loss in relative dense networks, and achieve both higher EE and SE gain (20% and 5% respectively) in sparse networks toward the future 5G green communication networks.

This study investigates the cross‐tier base station (BS) cooperation in non‐uniform heterogeneous networks where the distribution of pico BSs (PBSs) is modelled as Neyman–Scott cluster process. The authors propose an edge aware cross‐tier cooperation scheme to improve the performance of edge hotspot users that have weaker signal‐to‐interference‐plus‐noise ratio (SINR). Taking consideration of various user behaviours, non‐hotspot users are only served by the nearest macro BS whilst the hotspot users with better SINR are only served by their serving PBSs. The edge hotspot users who suffer from high cross‐tier interference operate in the cooperation mode. Stochastic geometry is utilised to derive the SINR and energy efficiency performance of the proposed scheme, which is compared with other classical schemes such as full cooperation (FC) and traditional non‐cooperation scheme. Numerical results show that compared with the FC scheme, the proposed scheme can maximise the energy efficiency of the network by an optimal cooperation threshold, when SINR coverage is larger than a threshold. The authors also find that user behaviour have little effect on the tradeoff between SINR Coverage and energy efficiency, unless spatial aggregation coefficient is very small.

This paper studies the physical layer security in cognitive relay network (CRN) with multiple antennas in the presence of multiple eavesdroppers. Under spectrum sharing scenario and orthogonal space-time block code (OSTBC) transmission, we derive both the exact and asymptotic expressions of secrecy outage probability over Rayleigh fading channels and give the reliability-security tradeoff analysis. The results which have been verified by Monte Carlo simulation show that the loss of the secrecy outage performance caused by an increase of eavesdroppers number can be totally overcome by multiple antenna diversity. It's illustrated that increasing the number of antennas can also improve both reliability and security of the system. Besides, the asymptotic analysis indicates the secrecy diversity order is only determined by number of antennas and is independent with number of eavesdroppers. The secrecy array gain is relevant with both number of antennas and eavesdroppers. In other words, the existence of eavesdroppers will degrade the secrecy outage performance but will not affect the diversity order of the system.

Existing cooperative file downloading schemes have some blindness on the selection of collaborative nodes. In this paper we present a cooperative file downloading scheme with genetic algorithm to resolve the collaborative nodes selection problem, especially in the case that we cannot have a prior knowledge about the data rate and position of the mobile nodes. A simple and efficient extended on-demand proxy discovery algorithm will be used to find the potential cooperative nodes. Character of the genetic algorithm (GA) makes it suitable for the selection of cooperative nodes. After this, client uses multiple parallel paths for file downloading. The simulation results show that the cooperative file downloading scheme with genetic algorithm can successfully select collaborative nodes with better performance and effectively reduce file download latency.

One of the main purposes for heterogeneous wireless networks is to promote the network energy efficiency. Cell Range Expansion (CRE), as a promising solution to offload macro BS traffic and improve the network capacity, will help to improve the network energy efficiency. Users that offloaded to micro Base Stations (BSs) will always have much lower downlink (DL) signal-to-interference-and-noise ratio (SINR) but higher uplink (UL) SINR. Therefore, introducing CRE can improve UL transmission performance while decrease DL performance slightly. In this paper, exploiting stochastic geometry, we focus on the performance of downlink and uplink joint transmission in two-tier heterogeneous network where micro BS employs CRE. We derive the expression of whole network transmission success probability (TSP) and energy efficiency (EE) which considering joint transmission of DL and UL. Both theoretical and simulation results show that there is an optimal CRE bias and an optimal transmit power of macro BS to maximize TSP and EE. When the optimal value of CRE bias is selected, the increase of micro BS density has little influence on EE but can improve TSP considerably.

This paper investigates the effect of imperfect channel state information (CSI) on the performance of radio resource management for the uplink OFDMA system. First, we prove that the imperfect CSI introduces a constellation-point dependent and error-floor symbol error rate (SER), which correspondingly yields the nonconvex and nondifferentiable throughput function in real communication systems. Then, in the uplink OFDMA system, a bisection algorithm and BA-based algorithm are proposed for single-user and multi-user cases, respectively, for the adaptive resource allocation to minimize the total transmit power.

Future wireless network will address the explosive increase demand of high-data-rate video services as well as massive-access machine type communication (MTC) requests, so that increasing number of small cells are conceived to be densely deployed in hot spots, resulting in an Ultra-dense Network (UDN). As a main issue for the future network, UDN is a step further towards a low-cost, self-configuring and self-optimizing network, while also leading to high-frequent measurement, intolerable handover failure (HOF), as well as huge power consumption in both the terminal and access network. Thus, mobility enhancement in ultra-dense scenario has become a critical problem for the next generation wireless systems. To solve this problem, the split of control plane and user plane (C/U) has become one of the most promising way, as it allows more flexibility and better service control schemes. Inspired by this, a set of macro assisted small cell enhancement schemes is proposed contributing to a novel Data-only Carrier (DoC) system in our previous work. In this paper, for improving the handover (HO) performance, new mobility-enhanced schemes are designed and analyzed in detail in DoC network, taking into consideration of mobility, flexibility and various typical handover scenarios. Simulations are conducted by a system-level platform to illustrate the fundamental relationship between key handover parameters and mobility performance. Numerical results show that the gain of system HOF rises by 53.6% via optimizing and reconfiguring the handover parameters in DoC network. In addition, the DoC network has an excellent performance gain in UDN with 82% HO improvement and 44.34% energy efficiency promotion compared with the current LTE network, which may be a promising mobility enhancement strategy for future 5G networks.

In this paper, we present performance analysis for underlay cognitive decode-and-forward relay networks with the Nth best relay selection scheme over Nakagami-m fading channels. Both the maximum tolerated interference power constraint and the maximum transmit power limit are considered. Specifically, exact and asymptotic closed-form expressions are derived for the outage probability of the secondary system with the Nth best relay selection scheme. The selection probability of the Nth best relay under limited feedback is discussed. In addition, we also obtain the closed-form expression for the ergodic capacity of the secondary system with a single relay. These expressions facilitate in effectively evaluating the network performance in key operation parameters and in optimizing system parameters. The theoretical derivations are extensively validated through Monte Carlo simulations. Both theoretical and simulation results show that the fading severity of the secondary transmission links has more impact on the outage performance and the capacity than that of the interference links does. Through asymptotic analysis, we show that the diversity order for the Nth best relay selection scheme is min(m1, m3) × (M - N) + m3, where M denotes the number of cognitive relays, and m1and m3represent the fading severity parameters of the first-hop transmission link and the second-hop transmission link, respectively.

In underlay cognitive radio networks, imperfect channel knowledge will have an adverse effect on the performance of both the primary users (PU) and the secondary users (SU) owing to the interference power constraint of PU. This paper investigates the performance of a cognitive relay network (CRN) with imperfect channel state information (CSI) of the interference links over Nakagami-m fading channels. Interference probability is defined to characterize the interference caused by SU to PU due to CSI imperfection. Closed-form expressions for both the interference probability of the primary user and the outage probability of the secondary system are derived and validated by simulations. It is proved that the interference probability of the primary user is always 0.5 under the condition of imperfect CSI when no power control mechanism is adopted. The effects of CSI imperfection, channel fading severity, interference probability of PU and transmit power of PU on the outage performance of the secondary system are also analyzed.

Large-scale user behavior can be used as the guidance for deployment, configuration, and service control in heterogeneous cellular networks (HCNs). However, in wireless networks, large-scale user behavior (in terms of traffic fluctuation in spatial domain) follows inhomogeneous distribution, which brings enormous challenges to energy-efficient design of HCNs. In this paper, the heterogeneity of large-scale user behavior is quantitatively characterized and exploited to study the energy efficiency (EE) in HCNs. An optimization problem is formulated for energy-efficient two-tier deployment and configuration, where the base station (BS) density, BS transmit power, BS static power, and quality of service are taken into account. We present closed-form formulas that establish the quantitative relationship between large-scale user behavior and energy-efficient HCN configuration. These results can be used to determine BS density and BS transmit power with the objective of achieving optimal EE. Furthermore, we present three energy-efficient control strategies of micro BSs, including micro BS sleep control, coverage expansion control, and coverage shrinking control. Simulation results validate our theoretical analysis and demonstrate that the proposed control strategies can potentially lead to significant power savings.

Cooperative spectrum sensing is employed in Cognitive Radio (CR) networks to reliably detect Primary User (PU) transmissions by fusing the sensed data of multiple Secondary Users (SUs). The local detection reliability of an individual SU is closely related to its channel condition. In this paper, we propose a scheme that uses SNR to evaluate the reliability of each individual SU's local decision. We optimize the number of SUs for the sensing based on their channel conditions to achieve the optimal global detection probability at the fusion centre. Simulation results show that the proposed algorithm is robust against noise uncertainty with the optimal number of SUs and better receiver operating characteristic (ROC) performance is obtained in comparison to conventional schemes.

Interference mitigation between femtocells and the surrounding macrocells is one of the major challenges in femtocell deployment. This paper proposes a system architecture of using TV White Space (TVWS) in LTE femtocell networks, which includes: (i) a Geo-location database to obtain locally available TVWS information, and (ii) a new resource allocation scheme using the locally available TVWS to mitigate the downlink cross-tier interference between macrocell users and nearby femtocells. A two-tier multi-femtocell simulator is established to demonstrate the system performance. Simulations at different scenarios are conducted to compare the performance of the traditional all-shared resource allocation scheme, dynamic resource partitioning scheme and the proposed scheme. Simulation results show that the proposed scheme has better downlink interference mitigation performance in comparison with the other two schemes.

The widespread requirement of multimedia application, especially Video-on-Demand (VoD), has lead to rapidly rising energy consumption. User behavior brings a great effect on the energy consumption of traffic transmissions. In wireless networks user behavior in terms of user request frequency follows power-law distribution, which indicates that some media streams are requested more frequently than the others. There-fore, exploiting multicast transmission technique for multimedia can significantly reduce the transmission power consumption. However, due to user requests occurs at different moment, in multicast transmission a subsequent user will miss the pervious part of the media stream that has been already transmitted for the early-arriving user. In order to guarantee the quality of service (QoS), the transmitter can deliver the missing fraction of the media stream through initiating separate patching stream. In this paper, we propose an energy efficient multicast scheme with patching stream which enable the transmitter to deliver both patching stream through unicast transmission and shared stream through multicast transmission with dynamic bandwidth allocation. Closed-form formula for power consumption of this scheme is established, in which the impact of the time window length, the user requests distribution are considered. We also derive a tight upper bound on the minimum power consumption under the optimal time window length. Compared to the traditional unicast transmission power consumption which grows like O(eλ), power consumption in the proposed scheme grows like O(e√λ), where λ represents the user request rate. Simulation results validate the theoretical analysis and demonstrate that our scheme can lead to 73% and 20% power deduction compared with the traditional unicast transmission scheme and traditional patching multicast transmission scheme.

Conventional study of green communication mainly focuses on the transmission power adjustment to minimize the total power consumption while guaranteeing a target system capacity. However, for the energy efficient design the dynamic transmission mode is an effective way to reduce total transmission power in multiuser networks. In this paper, an energy efficient traffic transmission scheme based on user convergence behavior (UCB) is proposed which characterizes the phenomenon of similar/convergent users' traffic requests during a certain timewindow. First a system model is built to study the relations of user convergence, length of time-window and transmission power consumption. Specifically in each time-window the transmitter analyzes the similarity of users' traffic requests and the similar traffics will be transmitted by multicast mode while the other traffics will be transmitted using unicast mode. To analyze the performance of our scheme, we establish a simple stochastic model in which locations and density of users, wireless channel conditions and transmitting mode are considered. Analytical results, such as power reduction ratio and energy efficiency (EE) of the proposed scheme, are developed, from which the quantitative relationship between UCB and the energy conservation can be obtained. Simulation results validate the theoretical analysis and demonstrate that our scheme can potentially lead to 35% power consumption deduction compared with the conventional transmission scheme.

Many research results show that in real world, networks are neither regular nor random but ones with the feature of scale-free characteristic. Applying this feature in telephone network, traffic behavior has been proved to have significant relationship with the user network. However, in a particular scenario, objective existence of user network cannot be changed and each user can only be occupied in one call, so the traffic behavior under this scenario is determined, which means no improvement strategies can be proposed. Based on this point, base station network is proposed: its relation can be changed by adding some new base stations; what's more, base station can support many calls simultaneously, then the limitation factor is subject to the number of capacity at each base station node, which can also be controlled. In this situation, some configurations can be proposed to improve the system performance. In this paper, we establish the base station network with weighted scale-free property. Through comparing with fully-connected base station network which was widely used in the previous model, we research on the effects of network relation on traffic behavior. Then a channel capacity distribution strategy is proposed to improve the system performance, which also verifies the advantage of base station network establishment.

In this paper, considering a relay assisted hybrid overlay/underlay cognitive radio system with peak interference power constraints for protecting the primary system, the upper bound of outage probability of secondary system with power limit is derived. The results show that the outage performance of secondary system improves with the increase of maximum tolerated interference power before a specific threshold, but it keeps unchanged after the threshold. The results also show that more relays can provide higher diversity gain to get better outage performance in this kind of cognitive radio system. It is also indicated that the outage performance of secondary system is affected by the activity behavior of primary network, and relay cooperation can decrease this kind of affection.

Wireless cooperative relay is an efficient way in obtaining diversity in distributed manner, and it can also be used as a way in extending the cell coverage in cellular systems. In this paper, assuming receiver channel state information (CSI) only, the ergodic and outage capacity analysis are presented for amplify-and-forward (AF) multiple-input multiple-output(MIMO) relay channels deploying orthogonal space-time block codes (OSTBCs). Under flat Rayleigh fading environment, we derive closed-form analytical expressions for the calculation of the ergodic capacity and outage capacity for both the OSTBC transmission with and without the direct link. Numerical results show that all the derived analytical expressions are very tight and are valid for evaluations of the ergodic and outage capacity under various antenna configurations. The results also show how the number of transmit antennas and receives antennas affect the overall channel capacity under fading channel environments.

In this paper, capacity performance analysis is presented for multiple-input multiple-output (MIMO) relay channels with transmit antenna selection and maximal-ratio combining receive (TAS/MRC) in amplify-and-forward (AF) relay networks operating over flat Rayleigh fading channels. Assuming that the source and destination are equipped with Ns and Nj antennas, respectively, and communicate with each other with the help of a single-antenna relay, we derive the cumulative distribution function (CDF), probability density function (PDF) and moment generation function (MGF) for the system end-to-end SNR. Based on these, we then present closed-form analytical expressions for the calculation of the ergodic capacity and outage capacity for the AF MIMO relay channels with TAS/MRC. Numerical results show that all the derived analytical expressions are very tight and are valid for evaluations of the ergodic and outage capacity under various antenna configurations. The results also show how the number of transmit antennas and receives antennas affect the overall channel capacity under fading channel environments.

Selection cooperation has been proposed as a promising way of realizing cooperative diversity for its simplicity and good performance. In this paper, a framework to analyze the multiuser diversity and selection diversity in multiple-relay networks is presented. Based on this framework, we derive closed-form asymptotic expressions of outage probability and symbol error rate (SER) for both amplify-and-forward (AF) and decode-and-forward (DF) based multiple-relay networks. Both the theoretical analysis and simulations show that a multiuser diversity order of K and a selection diversity order of M + 1 can be achieved simultaneously for both AF and DF protocols (where K is the number of accessing users and M is the number of available relays). These show that the multiuser diversity can be readily combined with the selection diversity in multiple-relay networks.

This paper presents a resource allocation framework for the transmission of heterogeneous services in two-hop orthogonal frequency division multiplexing (OFDM) cooperative relay systems. An optimal adaptive power and subcarrier allocation scheme is proposed to maximize the system throughput while satisfying the quality of service (QoS) requirements of both the real-time (RT) and nonreal-time (NRT) service for point-to-point transmission, and a sub-optimal fast search algorithm is proposed for practical implementation. Then, the idea is extended to point-to-multipoint transmission of heterogeneous services. Optimal solution is provided, and a fastest-power-descending based power and subcarrier allocation algorithm is proposed. Simulation results show that the proposed schemes improve the spectral efficiency while guaranteeing the QoS of each user.

Performance analysis is presented for multiple-input multiple-output (MIMO) relay channels with transmit antenna selection and maximal-ratio combining receive (TAS/MRC) in two-hop amplify-and-forward (AF) relay networks operating over flat Rayleigh fading channels, where the source and destination are equipped with Ns and Nj antennas, respectively, and communicate with each other with the help of a single-antenna relay. We derive closed form expressions for the cumulative distribution function (CDF) and probability density function (PDF) of the overall system SNR, based on which we present exact symbol error rate (SER) and outage performance analysis. The theoretical analysis is validated by Monte Carlo simulations, which show an exact match between them. Our analysis shows that full spatial diversity order, which corresponds to the minimum number of antennas at the source and destination, i.e., min{Ns,Nd}, can be achieved for the two-hop AF MIMO relay channel with TAS/MRC.

This paper analyzes the problem of multi-service transmission in multiuser cooperative networks. The objective is to maximize the best effort (BE) service utility while guaranteing the rate constraint of QoS service. In this paper, we first deduce the optimal power ratio of the source obtained power to total power. Based on this deduction result, the optimal solution of multi-service transmission is investigated. To further reduce the algorithm complexity, a suboptimal resource allocation algorithm is also proposed. Simulation results are provided to evaluate the utility and outage performance of these two algorithms when different value of power ratio is chosen.

In multiuser cooperative relay networks, cooperative diversity can be obtained with the help of relays, while multiuser diversity is an inherent diversity in multiuser systems. In this letter, the performance analysis of multiuser diversity in cooperative multirelay networks is presented. Both the case of all relay participating and the case of relay selection are considered. We first derive asymptotic expressions of outage probability and symbol error probability for amplify-and-forward (AF) and decode-and-forward (DF) protocols with joint multiuser and cooperative diversity. Then, the theoretical analysis are validated by Monte Carlo simulations. Both the theoretical analysis and simulations show that a multiuser diversity order of K and a cooperative diversity order of M+ 1 can be achieved simultaneously for both AF and DF protocols (where K is the number of accessing users and M is the number of available relays). These demonstrate that the multiuser diversity can be readily combined with the cooperative diversity in multiuser cooperative relay networks.

We consider a two-hop relaying network in which orthogonal frequency division multiplexing (OFDM) is employed for the source-to-destination, the source-to-relay and the relay- to-destination links. Amplify-and-forward (AF) and decode-and- forward (DF) policies are both discussed with or without two-hop diversity, respectively, for the relaying network with a sum-power constraint. An unified approach is used for optimal power allocation in the four different relaying scenarios. First, equivalent channel gains are developed for any given subcarrier pair in each scenario, and then optimal power allocation can be obtained by applying the classic water-filling method. Moreover, we provide the proof to the optimality of sorted subcarrier pairing for AF and DF relaying without diversity, which, combined with optimal power allocation, can offer further performance gain.

In this paper a novel Weighted Proportional Fair (WPF) scheduling algorithm is proposed. Traditional Proportional Fair (PF) algorithm cannot exploit the inherent near-far diversity given by pathloss and cannot achieve maximum best-effort service utility under multicell scenario. WPF overcomes this deficiency. The weight value and weight factor are introduced to WPF which enable WPF utilizing near-far diversity to reduce the inter-cell interference. All the asymptotic analysis and numerical results show when weight factor chooses a small value, WPF scheduling can enhance both the best-effort service utility performance and throughput performance compared to PF scheduling in multicell network.

We consider a multi-hop wireless sensor network in which a large number of sensor nodes are grouped into cooperative clusters. Multi-hop transmission is carried out between the source and the destination nodes by concatenating consecutive cluster-to-cluster hops. For each hop, both the transmit and the receive clusters can exploit node cooperation such that cooperative distributed multiple-input multiple-output (MIMO) channels can be formed. As proposed in [1], a time-division protocol is employed for transmissions within a cluster and between clusters, i.e., the intra-cluster slot is used for broadcasting within the transmit cluster, and the inter-cluster slot is used for transmissions between clusters. Distinguished from the scheme in [1] that space-time codes (STC) are utilized for inter-cluster transmissions, random beamforming is proposed in this paper, which is shown to outperform STC in term of energy efficiency provided that the number of nodes within receive cluster is adequately large. We demonstrate that even with moderate number of nodes within receive cluster, e.g., 10 nodes, random beamforming can offer higher energy efficiency for inter-cluster links, at 5% outage rate. The "random" nature incorporated in random beamforming scheme assigns the clusterhead node within receive cluster in an alternate manner, thus balancing energy consumption within receive cluster.

In this paper, a framework is presented to analyze the performance of multiuser diversity (MUD) in multiuser point-to-multipoint (PMP) MIMO systems with antenna selection. Based on this framework, the tight closed-form expressions of outage capacity and average symbol error rate are derived for the multiuser transmit antenna selection with maximal-ratio combining (TAS/MRC) system, by which we show how and with what characteristics antenna selection gains, MIMO antenna configurations and fading gains impact on the system performance, with an emphasis on the study of multiuser diversity influence. From both theoretical and simulation results, our study shows that in multiuser PMP TAS/MRC systems an diversity order equals to the product of the number of transmit antennas, number of receive antennas and number of users can be achieved; what's more, users plays a key role in the system performance and can be viewed as equivalent "virtual" transmit antennas, which is the source of the multiuser diversity inherent exists in the multiuser system. This kind of diversity can be efficiently extracted in the design of multiantenna systems.

In this paper, a framework to study the error performance of multiuser diversity in point-to-multipoint (PMP) MIMO transmit antenna selection networks is presented. Based on this framework, we derive the exact closed-form symbol error rate formula in terms of antenna selection gain, MIMO configurations, multiuser diversity and amount of fading gain to study the insight interaction between these factors. Our analytical results indicate that, (1) in PMP MIMO antenna selection system with large number of receive antenna, a diversity order equals to the product of the number of transmit antennas, receive antennas and users can be extracted; (2) the number of users provides the same effect on the error performance as that of the transmit antenna, and users can be viewed as "virtual" transmit antennas which is the inherent multiuser diversity; and (3) largest performance gain is achieved by equally distributing antennas over transmit and receive ends.

This paper develops a framework to analyze the outage capacity of multiuser diversity in a multiuser MIMO transmit antenna selection with maximal-ratio combining (TAS/MRC) system. A closed-form expression of the system outage capacity is derived, through which we show how and with what characteristics the MIMO configurations, the number of users and the multiuser diversity affect the outage capacity performance. Both the analytical and simulation results show that (1) the outage capacity increases with the increase of mean of effective average SNR and decreases with the increase of variance of the effective average SNR; (2) to obtain a higher outage capacity, the number of receive antennas should be no more than that of transmit antennas. This paper has presented some conclusions which can be used for the efficient design of multiuser MIMO systems.

This letter develops a framework to analyze the multiuser diversity (MUD) gain in multiple-input multiple-output (MIMO) transmit antenna selection with a maximal-ratio combining (TAS/MRC) system, through which the transmitter selects the optimal antenna out of the total transmit antennas based on the channel state information (CSI) feedbacks. Over a flat-fading Rayleigh channel, a tight analytical closed-form expression of the MUD gain in terms of outage probability is derived. The analytical results show that 1) the MUD system can achieve an order of diversity equal to the product of the number of users, number of transmit antenna, and number of receive antennas; 2) users can be viewed as equivalent "virtual" transmit antennas, and the number of users has a great impact on the system outage probability

In this paper, we present the adaptive multiuser frequency-time domain radio resource allocation model which adaptively allocates the radio resource jointly in the frequency and time domain to exploit the frequency diversity and time diversity as well as multiuser diversity. Then we give an in-depth capacity analysis of the proposed radio resource allocation with frequency-time domain power adaptation in the downlink OFDMA systems and draw a comparison with the conventional frequency domain resource allocation method. Simulation results show that: 1) the proposed resource allocation method achieves much higher spectral efficiency than the conventional frequency-domain resource allocation; and 2) for the proposed two-dimensional allocation method, the performance of water-filling power allocation is almost the same as that of the equal power allocation, especially when the number of user is large

Efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, we propose an adaptive multiuser radio resource allocation model for the downlink of OFDMA system, which efficiently exploits the time diversity, frequency diversity as well as multiuser diversity in the time, frequency and user domain, respectively. According to the allocation algorithm's computational complexity, two different-complexity adaptive resource allocation algorithms algorithm A and algorithm B - are proposed which adopt a two-step allocation method to reduce the scheduling complexity and meanwhile improve the scheduling performance. Simulation results show that while the performance of algorithm A and B is slightly different, both of the proposed algorithms yield much higher spectral efficiency and much lower outage probability, which are flexible and efficient for the downlink of OFDMA systems

In this paper, we present the theoretical work (model) of the radio resource allocation (RRA) of DS-CDMA reverse link (uplink) and forward link (downlink) based on QoS constraint models. Our goal is to optimally allocate the users' data rate and power levels to minimize the total transmission power under each accessing user's QoS constraint model, i.e., taking into account user's application BER and data rate requirements. We show that through strict theory deduction, the proposed model provides the optimal joint data rate and power allocation for both the reverse link and the forward link in multimedia DS-CDMA systems.

This paper proposes a time and frequency synchronization solution for MIMO OFDM systems. The synchronization is achieved using one preamble which is simultaneously transmitted from all transmit antennas in the same OFDM time instant. The synchronization is accomplished sequentially by coarse time synchronization, fractional frequency offset estimation, integral frequency offset estimation and fine time synchronization. Simulation results demonstrate that the proposed synchronization algorithms have a satisfactory performance even at a low SNR in a rich multipath environment. And the synchronization performance in a MIMO channel are superior to those in a SISO channel due to its larger diversity gain.

We propose an efficient frame-based two-dimensional frequency-time grid (FTG) allocation algorithm to maximize the system throughput while satisfying users' rate requirements in a multiuser and multirate service environment. The FTG scheduling algorithm is done per OFDM frame, allocating each frequency-time grid to different users according to both channel state information (CSI) and users' QoS constraints. The performance of the frame-based FTG algorithm is obtained in a multiuser frequency selective fading environment. The results show that the system using the proposed FTG algorithm has significant lower outage probability and higher capacity which is very near the upper bound (maximum allocation method).