Noncoherent magnitude-based MIMO (NMB-MIMO) has been envisaged as a promising architecture for the low-cost implementation of massive machine-type communications (mMTC). Nevertheless, the existing channel estimation methods are not applicable to NMB-MIMO due to its unique magnitude-only detection mechanism. This paper introduces an innovative channel estimation framework specifically designed for NMB-MIMO systems. Initially, we deploy a local oscillator (LO) to transmit known reference signals, and the NMB-MIMO channel estimation is formulated as a compressive phase retrieval problem by leveraging the inherent angular-domain sparsity of wireless channels. To address the proposed problem, we subsequently develop an efficient universal compressive phase retrieval (UCPR) algorithm. Simulation results demonstrate the robustness of the proposed UCPR approach, validating its effectiveness in accurately reconstructing NMB-MIMO channels.

This paper investigates a dual-robust beamforming design for covert satellite communication, where covert transmission is embedded within an overt channel. While prior studies on covert communication over overt channels typically address robustness for covert user under wiretap channel (WC) uncertainty, achieving simultaneous robustness for both overt and covert users remains challenging in satellite-to-ground links with uncertain channel state information (CSI). We propose a dual-robust beamforming framework that jointly designs overt and covert beamformers to maximize the minimum covert rate, subject to the overt user’s rate outage constraints (ROCs) and covertness constraints. The resulting optimization problem is intrinsically nonconvex due to CSI and WC uncertainties, variable coupling, and the absence of closed-form expressions for the ROCs. To solve the problem, we reformulate the problem into a sequence of convex subproblems by utilizing semidefinite relaxation, the S-procedure, large deviation inequality, and fractional programming. We then develop a dual-robust beamforming algorithm (DRBA) that yields near-optimal beamforming vectors. Numerical results demonstrate that the proposed approach achieves superior covert communication performance while ensuring robust service for both overt and covert users, outperforming benchmark schemes.

The growing number of remote sensing (RS) satellites and their enhanced sensing capability lead to exponential growth in RS data, posing challenges to real-time transmission. To address this issue, this paper establishes a three-layer space-earth collaborative architecture comprising RS satellites, communication satellites, and earth stations. Specifically, we propose a joint optimization framework that jointly optimizes compression strategy selection and computing resource allocation, with the aim of minimizing long-term end-to-end delay. However, the optimization problem is a mixed integer non-convex problem with coupled variables, making it difficult to obtain the global optimal solution. To solve the problem, we firstly apply Lyapunov optimization theory to decompose the long-term problem into the deterministic sub-problem in each slot. Subsequently, the compression selection variables are decoupled from the resource allocation variables, and the resource allocation variables are solved via convex optimization to obtain a dimensionality-reduced Markov decision process (MDP) problem. An intensive reward reinforcement learning algorithm is proposed to address the reward sparsity problem in the MDP. Finally, simulation results demonstrate that the proposed algorithm significantly improves performance and reduces the average delay compared to the benchmarks.

This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy.We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

The paradigm shift from massive MIMO to extremely large MIMO (XL-MIMO) catalyzes significant improvements in the spectral efficiency and spatial resolution of MIMO systems. However, the large-scale arrays also lead to the near-field effect, which indicates the coexistence of far-field and near-field user equipments (UEs). This paper proposes a unified beam training framework applicable to both far-field and near-field scenarios, even including thosewithin the Fresnel distance. Specifically, our proposed beam training method builds upon a rigorous wavenumber-domain spectrum analysis based on the geometric propagation characteristics. By estimating the non-zero boundaries rather than the specific non-zero entries in the wavenumber-domain spectrum, we introduce an efficient and fidelity-robust algorithm to estimate the location of the UE, which is termedellipse-fitting localization (EFL). Simulation results validate the effectiveness of our beam training framework across far-field and near-field scenarios, even within the Fresnel distance.

The mutual coupling (MC) effect refers to the phenomenon where the voltage on one antenna element induces excitation currents on nearby antenna elements. This paper initially derives precise MC matrices grounded in meticulous circuit and antenna theories, unveiling the asymmetry of MC effects between the transmitter (Tx) and receiver (Rx). This asymmetry arises from the fact that the transmitted signal is the voltage on the antenna elements, whereas the received signal represents the voltage input to the low-noise amplifiers (LNAs). Upon this approach, an MC-aware hybrid beamforming methodology is introduced to mitigate distortions in radiation patterns precipitated by MC effects. Furthermore, we undertake comprehensive performance analysis across various antenna topologies, including rectangular, hexagonal, circular, and concentric circular configurations. Simulation results substantiate the necessity for addressing MC effects and demonstrate the effectiveness of our proposed MC-aware hybrid beamforming methodology. Moreover, these results indicate that MC effects can enhance the capacity under certain antenna separations and topologies, highlighting that MC effects are not merely detrimental but potentially beneficial in densely-packed MIMO.

The large antenna aperture in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems results in a hybrid far and near-field communication. Existing hybrid-field beam training works mostly treat plane waves as spherical ones with infinite distance, thereby inheriting several limitations associated with spherical wave based training, such as protocol incompatibility, high overhead, and complex hierarchical codebook design. To address these issues, we propose a unified far-field and near-field wavenumber domain beam training framework, involving a semi-codebook-based beam sweeping scheme and a hierarchical training strategy. The core idea is reinterpreting spherical wave as a superposition of plane waves, retaining accuracy while inheriting the charming protocol compatibility, low overhead, and simple codebook design provided by plane waves. Moreover, due to the linearity of plane waves, the ideal beam pattern with negligible power leakage can be easily obtained by the proposed phased-shifted alternative minimization (PS-AltMin) codeword design method. Finally, numerical results show that the proposed wavenumber domain beam training methods have a significant achievable rate gain compared to the benchmarks, which comes from the use of the semi-codebook-based transmission technique and the unified channel model for both far-field and near-field.

Task scheduling decisions for multimodal large language model (MLLM) applications in multilayer computing power networks present a significant challenge, as they simultaneously balance system delay, carbon emissions, and model accuracy requirements while adapting to network conditions and varying energy availability. Thus, in this paper, we formulate the joint optimization problem of MLLM task scheduling, resource allocation, and green energy utilization to minimize system delay and carbon emissions while meeting accuracy requirements. We propose Green-Aware MAPPO, a novel approach that integrates graph attention networks (GAT) with multi-agent proximal policy optimization (MAPPO) for distributed decision-making in multilayer computing power networks. By modeling the problem as a partially observable Markov decision process (POMDP), our algorithm enables agents to capture complex resource dependencies through relation-specific attention mechanisms while maintaining high performance with limited local observations. Experiments in various network configurations demonstrate that Green-Aware MAPPO significantly outperforms baseline algorithms.

With the development of new power systems, new services in the power data network are constantly emerging and the demand for deterministic transmission is on the rise. The transformation from "best - effort" to "punctual and accurate" has become an urgent issue to be addressed in precision load control services. This paper presents a deterministic routing approach which decouples paths and cycles for wide - area precision load control services within new power systems. The aim is to enhance the scheduling success rate and resource utilization rate of wide area deterministic load control services in new power systems. The method formulates the scheduling success rate and resource utilization rate as optimization objectives, transforms the deterministic routing problem in the wide area network into a Markov decision process (MDP), designs the action space, state space, and reward function. Additionally, a Path and Cycle Decoupled Proximal Policy Optimization Algorithm (PCDPPO) based on deep reinforcement learning (DRL) is proposed to optimize the wide-area network routing problem. Simulation results demonstrate that compared with existing methods, the Integrated Scheduling Efficiency (ISE) of this algorithm is increased by more than 4.87%, and the convergence time is less than 30.83% of the non decoupled method. This research offers an effective solution for the efficient, stable, and reliable network transmission of specific services in new power systems.

The increasing demand for computational resources from AI, blockchain, and other technologies requires efficient task scheduling within the Computing Power Network (CPN). However, data centers in CPN often face energy inefficiency and high carbon emissions due to the uneven availability of clean energy. While some research has focused on machine learning-based energy prediction, existing methods often overlook the need for integrating these predictions into large-scale, real-world computational task scheduling frameworks. Furthermore, traditional computational task scheduling methods also fall short in addressing the inherent volatility of renewable energy supplies, limiting their effectiveness in scenarios that require real-time adaptation. This paper proposes a method that integrates clean energy prediction with computational task scheduling, employing a neural network for energy forecasting and an enhanced heuristic algorithm for task scheduling. Simulation results demonstrate that the proposed approach significantly outperforms the baseline, achieving over 95 % clean energy utilization with minimal fluctuations and reduced carbon emissions. These results underscore the potential of predictive task scheduling to improve energy efficiency in data centers, aligning with global sustainability goals.

Mutual coupling is essentially the physical phenomenon where the voltage on one antenna induces excitation currents on its surrounding antennas. Mutual coupling usually occurs in holographic MIMO (HMIMO) systems, distorting the intended radiation pattern of the array. Nevertheless, the impacts of mutual coupling on wideband HMIMO have not yet been fully investigated in the literature. To fill this knowledge gap, this paper initially unveils the frequency-dependent nature of mutual coupling, which emanates from frequency-dependent antenna impedance and mutual impedance. Subsequently, this paper proposes an innovative frequency-dependent coupling-aware beamforming methodology to mitigate frequency-dependent mutual coupling (FDMC). Upon this approach, both FDMC and frequency-dependent channels (FDC) can be simultaneously mitigated for wideband HMIMO communications. Simulation results validate the necessity of mitigating FDMC and substantiate the efficacy of our proposed beamforming-based mitigation in enhancing the wideband performance of HMIMO.

This paper addresses the critical challenges of fractional delay and Doppler effects in low earth orbit (LEO) satellite orthogonal time-frequency space (OTFS) systems, where conventional channel estimation methods suffer from high complexity and inadequate prior utilization. To overcome these limitations, we propose a fast and robust framework with: 1) A subspace-constrained variational Bayesian inference (SC-VBI) mechanism that reduces complexity by decoupling high-dimensional matrix operations and 2) A statistical Delay-Doppler-prior (SD2P) integration scheme leveraging elevation angle distributions from real-world LEO orbital dynamics to enhance sparse signal recovery (SSR) accuracy. Departing from traditional sparse Bayesian learning (SBL) approaches, our algorithm mitigates the curse of dimensionality through iterative sub-space updates while embedding physical-layer Doppler characteristics into Bayesian priors. Simulations under 3GPP LEO configurations demonstrate that the proposed method achieves a ten-times compression in computational time and 60% normalized mean square error (NMSE) improvement.

In this article, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden’s precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is nonconvex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust common rate allocation and beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness.

Autonomous aerial vehicles (AAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of AAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, AAV speed variation brings challenges to achieving green communication and reliable data collection. In this article, we propose a dynamic AAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of AAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize AAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and AAV speed are jointly optimized for minimizing the long-term energy consumption of AAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed AAV speed scheme.

The digital transformation of industrial systems has been significantly influenced by the emergence of the Industrial Internet of Things. Digital twin (DT) technology plays a pivotal role in the transformation, serving as a bridge between the physical and digital realms. To support the efficient application of DT technology, the synchronization between physical entities (PEs) and DT models (DTMs) can not be ignored. Given the open nature of wireless channels, the diversity in synchronization mechanisms arising from the functional variations among PEs inevitably deteriorates the design of synchronization strategies. Moreover, a metric is required to gauge the synchronization between PEs and DTMs. In this article, a reinforcement learning (RL)-based online scheduling scheme is proposed to achieve efficient synchronization between DTMs and PEs with different mechanisms. Specifically, the Age of Information (AoI) is introduced as a metric to evaluate synchronization strategies. In addition, PEs are classified and share spectrum resources according to synchronous mechanisms, improving resource utilization efficiency. To ensure real-time scheduling, we propose a hybrid inverse RL-based scheme to support distributed time slot-level synchronization, reducing the need for manual intervention. Simulation results show that compared with other baseline RL schemes, the proposed scheme can reduce the AoI value more than 20% between the PE and the DTM.

Rydberg atomic receivers have been envisaged as a revolutionary technology for future wireless communications and sensing. In order to detect the angle of arrival (AoA), researchers have typically constructed arrays comprisingmultipleRydberg atomic receivers. This paper presents a novel finding: The AoA of an incident signal can be accurately recovered even with asingleRydberg atomic receiver, by harnessing the phenomenon of inner-vapor interference. Firstly, we apply the micro-element method to the atomic vapor and derive a closed-form expression for the laser transmission in the presence of interference between the incident and local oscillator (LO) radio frequency (RF) signals. Secondly, we propose a robust method to estimate the AoA based on the particle swarm optimization (PSO) algorithm. Simulation results substantiate the effectiveness of our proposed scheme with practical parameter settings, verifying the applicability of AoA detection using a single Rydberg atomic receiver.

This paper proposes a fast and robust graph-based wavenumber-domain approach for channel estimation in holo-graphic MIMO (HMIMO) systems. Unlike conventional angulardomain methods—prone tomutual coupling, power leakage, andsampling redundancy—our framework resolves HMIMO’s high-dimensional challenges by introducing a wavenumber-domain basis via orthogonal Fourier harmonics (FHs), eliminating dependencies on antenna density. By reformulating channel estimation as its sparse recovery counterpart, we model clustered sparsity using an elliptic Markov random field (EMRF), upon which a graph-cut swap expansion (GCSE) algorithm is developed, leveraging graph-theoretic optimizations for fast convergence and low complexity. Simulations demonstrate that our method achieves robust performance against mutual coupling, varying SNRs, and antenna density with drastically less computing time.

In the near-field context, the Fresnel approximation is typically employed to mathematically represent solvable functions of spherical waves. However, these efforts may fail to take into account the significant increase in the lower limit of the Fresnel approximation, known as the Fresnel distance. The lower bound of the Fresnel approximation imposes a constraint that becomes more pronounced as the array size grows. Beyond this constraint, the validity of the Fresnel approximation is broken. As a potential solution, the wavenumber-domain paradigm characterizes the spherical wave using a spectrum composed of a series of linear orthogonal bases. However, this approach falls short of covering the effects of the array geometry, especially when using Gaussian-mixed-model (GMM)-based von Mises-Fisher distributions to approximate all spectra. To fill this gap, this paper introduces a novel wavenumber-domain ellipse fitting (WD-EF) method to tackle these challenges. Particularly, the channel is accurately estimated in the near-field region, by maximizing the closed-form likelihood function of the wavenumber-domain spectrum conditioned on the scatterers’ geometric parameters. Simulation results are provided to demonstrate the robustness of the proposed scheme against both the distance and angles of arrival.

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially -stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence.

Segment Routing (SR) is a flexible and efficient source-routing technology. It can forward traffic along arbitrary paths in the network and has good scalability without the maintenance of routing information at intermediate nodes. These advantages make SR widely used in data centers, WANs, MANs, and other networks, particularly in the context of parallel and distributed processing. However, current SR traffic engineering has some shortcomings in terms of network load balancing, failure resiliency, and complete use of SR characteristics, which prevents better optimization of comprehensive network performance. In this paper, we propose a heuristic traffic engineering algorithm (2-SRLS) based on a two-segment routing model (2-SR), which incorporates a traffic-splitting strategy, adjacency segments and failure resiliency. The algorithm supports the technical characteristic of adjacency segments in SR with a flexible source node traffic splitting strategy and can efficiently recover from single-link failures. Experimental results show that our algorithm is close to the theoretical optimum in terms of the performance of reducing the maximum link utilization and has a 10%-16% improvement in active link coverage compared to existing algorithms. The running time is reduced by 70% compared to existing heuristics. In addition, the proposed algorithm can effectively recover from single-link failures on the basis of guaranteed the stability of the maximum link utilization (MLU).

In this paper, we investigate the employment of the extremely large-scale reconfigurable intelligent surface (XL-RIS) in dynamic near-field wireless communication systems. Given the spherical-wavefront nature in the near-field context, channel estimation becomes more challenging, in particular for time-varying channels due to the mobility of the transceivers. This implies that frequent feedback of high-dimensional cascaded channel state information (CSI) associated with XL- RIS entails substantial overhead. To tackle this challenge, we propose a channel tracking scheme for the near-field XL- RIS regime. Specifically, a Kalman filter (KF) based framework is proposed, in which two learning-based networks are employed for obtaining the channel information of the first time slot, thereby acquiring initial prior information for the KF technique. Subsequently, leveraging the known prior information and the temporal correlation of the time-varying channel, the cascaded channel matrix for the next time slot is continuously predicted. Following that, the prediction is updated and refined using the observation from the current time slot as a benchmark, enhancing the accuracy and reliability of the channel tracking process. Finally, simulation results are provided to verify the effectiveness of the proposed scheme.

The Non-geostationary orbit (NGSO) satellite constellation has gained widespread recognition owing to its exceptional low latency and seamless global coverage. However, with the increasing number of countries launching satellites, there has been a surge in the amount of satellites orbiting in low earth orbit, resulting in a growing strain on both frequency and orbital resources. Therefore, the issue of satellite coexistence has become increasingly critical. Moreover, the uneven distribution of terrestrial users imposes higher demands on beam resource management. This paper proposes a beam allocation strategy based on matching theory, which effectively mitigates harmful interference from the main constellation while optimizing the throughput of the minor constellation. The proposed strategy enables on-demand allocation, which maximizes both service quality and resource utilization, ultimately achieving the objective of constellation coexistence. Simulation results demonstrate that the performance of the proposed strategy is significantly superior compared to other link establishment schemes.

Federated learning (FL) is a paradigm that enables model training across various devices while keeping the data localized. However, for battery-powered passive devices in the satellite Internet of Things (IoT), the continuous update and transmission of the local model result in heightened energy consumption on the device side. To address this challenge, an FL framework with partial device participating is proposed. In this framework, the on-board controller strategically selects a subset of devices to upload local model parameters, effectively mitigating the overall energy consumption on the device side. Constrained by transmission power and transmission delay, a resource allocation problem is formulated. This problem jointly optimizes the uploading strategy and transmission power, aiming to minimize the utility function that combines the global model loss and energy consumption over multiple rounds of FL. Simulation results demonstrate that, compared with other benchmark schemes (DDPG, PPO), the proposed algorithm achieves energy efficiency in FL.

In this paper, we investigate a dynamic resource allocation problem for remote Internet of things (IoT) data collection in space-air-ground integrated networks (SAGIN), in which the aerial platforms are deployed to bridge the communications between IoT nodes and satellites. To obtain an efficient resource allocation strategy that accommodates the stochastic data arrivals of IoT nodes and the dynamic network topology due to the high mobility of non-geostationary orbit (NGSO) satellites, we first formulate a resource allocation problem with queue stability constraints. Our objective is to maximize the long-term network utility, ensuring a balance between throughput and fairness among the IoT nodes. The formulated long-term problem is challenging to solve due to the unknown future network states and the coupling between continuous and integer variables. Therefore, we adopt the Lyapunov optimization theory to transform the problem into a deterministic problem in each time slot. Moreover, an online resource allocation algorithm is proposed to dynamically determine data admission, subchannel assignment, and power control in each time slot based on the current network status and data backlog. In addition, theoretical analysis indicates that there is an [O(1/V ), O(V)] trade-off between network utility and data backlog with control parameter V. Numerical results demonstrate that the proposed algorithm can greatly enhance the system throughput and reduce data queue backlog as well as preserve queue stability as compared with the benchmarks.

With the spurt progress of the industrial Internet of Things (IIoT), enhancing the IIoT’s ability to deal with burst traffic is essential. User perceived throughput (UPT) is an appropriate metric for evaluating the real user experience of burst traffic since it takes buffer state into account, which is overlooked by traditional throughput calculations. However, the inherent strong randomness coupled with the emphasis on long-term performance, poses significant challenges to UPT optimization in such a stochastic industrial environment with time-varying characteristics. In this paper, we focus on the UPT to measure the quality of experience (QoE) for burst IIoT, formulating a long-term UPT-maximization problem in the system. To address this, joint user scheduling at the medium access control (MAC) layer and beamforming design at the physical (PHY) layer optimization is considered. By applying Lyapunov optimization theory, the long-term optimization problem is decoupled into more manageable short-term problems. Subsequently, we propose both centralized and decentralized algorithms, based on fractional programming (FP) and successive convex approximation (SCA), respectively. Simulation results verify the effectiveness of the proposed algorithms compared to the baselines, and FP-based collaborative QoE-driven cross-layer co-design algorithm (FPCQA) demonstrates superior UPT gain.

With the rapid development of the Internet of Things (IoT), more and more IoT traffic is generated in the data network. Accurate perception of IoT traffic changes will facilitate traffic engineering decisions, thus ensuring the performance of IoT applications. However, current traffic prediction methods ignore the limitations of actual application environment. In this article, we propose an IoT traffic prediction method based on horizontal federated learning to predict traffic trends under the cooperation of the cloud and the edge side. In order to improve the accuracy of IoT traffic prediction, a traffic prediction model SMN3-CIFGA is proposed to predict IoT traffic based on traffic feature extraction in a limited hardware environment. In addition, in order to improve the communication efficiency in the distributed training process of the traffic prediction model, we propose a gradient compression algorithm based on dynamic threshold (GCADT). The experimental results demonstrate that compared with current methods, the average training time of the GCADT algorithm is reduced by about 6.21%, the transmission gradient size of the GCADT is reduced by about 66.71%, the average training time of the classification model SMN3 is reduced by about 40%, and the testing set prediction accuracy of SMN3-CIFGA can reach 97.61%.

In industrial edge caching networks, to ensure long-term accurate decision making of industrial applications, it is critical to obtain fresh sensing contents with low sensor energy consumption. The acquisition of sensing contents consists of cache update and content delivery, jointly determining the Age of Information (AoI) of applications. However, cache update suffers from the large sensor energy consumption and the mismatch between content offerings and demands. Content delivery suffers from the limited fronthaul capacity. Furthermore, contents from multiple sensors typically need to be aggregated, allowing the AoI of applications to be determined by the co-AoI of all correlated sensors. It is challenging to make the tradeoff between the energy efficiency of each sensor and the co-AoI performance of all correlated sensors. In our work, the weighted sum of application AoI and sensor energy consumption is minimized by jointly optimizing cache update and content delivery, which is formulated as a long-term stochastic optimization problem. Next, two caching schemes, access point centric scheme (APCS) and request adaptive caching scheme (RACS), are presented. In APCS, we fully decouple cache update and content delivery by applying statistical probability of application requests to control update. In RACS, cached contents are updated along with content delivery according to real-time requests. Thus, we introduce the concept of decision reward to transform the stochastic problem into the per-time slot reward maximization problem and propose online algorithms to solve it. Simulation results show that proposed schemes can reduce the sensor energy consumption by 40% while guaranteeing the application AoI.

Leveraging the concept of the electromagnetic signal and information theory, holographic multiple-input multiple-output (MIMO) technology opens the door to an intelligent and endogenously holography-capable wireless propagation environment, with their unparalleled capabilities for achieving high spectral and energy efficiency. Less examined are the important issues such as the acquisition of accurate channel information by accounting for holographic MIMO’s peculiarities. To fill this knowledge gap, this paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO’s electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.

In the era of 5G and beyond, different service scenarios have put forward rich and differentiated requirements for the carrier network. The emergence of flexible Ethernet technology has met the needs of high-speed transmission and flexible bandwidth configuration. However, the current FlexE transmission mechanism based on the 5Gbit/s granularity creates a massive waste of resources in multi-granularity hard isolation services. To optimize the utilization of slot resources, we propose a new FlexE calendar slot allocation mechanism based on a novel hybrid-granularity model. This mechanism encompasses traffic prediction and a calendar slot allocation method based on a FlexE hybrid-granularity model. The former adapts the slot allocation process to the fluctuations in client flows through accurate traffic prediction. The latter adopts a hybrid-granularity slot allocation algorithm based on dynamic programming to ensure a high isolation of the service transmissions and to improve the utilization of slots. A comparison with the existing schemes shows that under experiments with different periodic regularities, the proposed method can increase the slot utilization by 71.5%-77.9%, and under experiments with diverse client granularity distributions, the proposed method can increase the slot utilization by 59.2%-76.7%.

Time sensitive networking (TSN) has been widely adopted and applied in many fields. The scheduling problem of TSN requires that the gate control list (GCL) is calculated according to the flow information in a given topology network. Conventional flow scheduling schemes are usually based on the given routing scheme, which limits the scheduling performance. Besides, current works mostly focus on the time trigger flows (TT). However, AVB flows exist as aperiodic flows in the industrial Internet. The integrated scheduling of these two types of flows is required to improve the overall schedulability. In this paper, a problem model of joint routing and GCL scheduling is proposed. An algorithm based on Tabu search (Tabu-RG) is proposed to solve the problem with specific design of neighborhood movement policy, neighborhood selection policy, as well as diversified function. Experimental results show that compared with the solver method, the proposed algorithm can save 75% of the time cost on the premise of ensuring the solution performance.

In industrial control system, applications perform time-critical operations based on the observations of multiple processes. We consider a request-based scenario, where a cache-enabled base station (BS) stores the most recent status observed by energy harvesting (EH) sensors, and delivers the cached status to applications upon request. Due to the time-varying nature of processes, cached status may be outdated which affects the accuracy of operations. Frequent cache update improves the status freshness but leads to high energy consumption of EH sensors. Furthermore, the freshness on the application side is simultaneously determined by multiple status, which requires joint updating of multiple sensors to improve update efficiency. Age of Information (AoI) is employed to measure the freshness of status. We adopt the maximum AoI among the responded status as age of response (AoR). A long-term average AoR minimization problem is formulated, subject to the number of wireless channels and the energy constraint of each EH sensor. The problem is challenging due to the random arrivals of requests and energy harvesting as well as the random association between requests and sensors. By introducing AoR reduction as the reward of each schedule, the problem is decomposed into a per-slot reward maximization problem, and then transformed into a knapsack problem. Then, an online correlated cache update algorithm is proposed. Numerical experiments illustrate that our solution outperforms the traditional greedy policy and achieves 16% performance gains.

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.

To both take the advantage of wired and wireless networks, the burgeoning mobile edge computing (MEC) technology is integrated into fiber-wireless (FiWi) network to support the cost-effective deployment of Internet of Things (IoT). However, the trusted model training, efficient task computing, reasonable comprehensive energy consumption and different quality of services, are still the key problems to be solved. Thus, we introduce the federated reinforcement learning (FRL) to the framework to jointly optimize the accessing mode selection, computation offloading decision and transmission power allocation without the leakage of users’ privacy. Then, we further design a twolayer FRL algorithm based on reputation value to respectively realize the protection of user privacy and efficient optimization of the global model. The simulation results demonstrate that our proposed method outperforms others in balancing energy consumption, reducing service delay, as well as providing differentiated services.

To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay.

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatial-temporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair of critical issues: 1) the high-dimensional cascaded channel to be estimated, which always entails substantial pilot overhead typically proportional to RIS elements and 2) the high possibilities of false alarms and unfaithful angle detection during cascaded channel estimates, referred to as the angular distortion, particularly in the case of conventional fully-passive RIS systems. To circumvent these issues, we investigate in this paper a multi-user multiple-input multiple-output (MIMO) system assisted by a wireless beacon (WB) enabled RIS over millimeter wave (mmWave) frequency. Specifically, WB is a functionally independent hardware that attaches parasitically to the RIS using only one radio frequency chain for broadcasting pilots, allowing for the accurate acquisition of partial cascaded channel information, e.g., some of angles of arrival/departure (AoA/AoDs) and complex gains. This efficiently eliminates the angular distortion effect and thus facilitates high precision recovery of sparse cascaded mmWave channels. Then, a hybrid structured sparsity (HSS) model is presented to capture hybrid sparse priors and approximate exact posterior distributions associated with RIS-aided twin-hop channels. We conceive of channel estimation as a compressive sensing problem in contrast to its conventional copy due to the presence of an unknown sensing matrix. To tackle this problem, an expectation-maximization (EM)-based algorithm, i.e., HSS-EM, is developed in order to fully employ the sparse priors encapsulated by the proposed HSS model for a robust and accurate recovery of the cascaded channels. Numerical results validate our analytical claims and demonstrate the significant improvement in terms of the normalized mean square error (NMSE) performance over conventional designs.

5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.

Although reconfigurable intelligent surface (RIS) has been touted as a technology star for future wireless networks, the critical bottleneck still lies in the accurate acquisition of channel state information (CSI). The vast majority of state-of-the-art cascaded channel estimates entail the pilot overhead typically proportional to the number of RIS elements, which results in a long training time and thus may not be tolerable in high-mobility scenarios. In this paper, we investigate the channel tracking for RIS-aided high-mobility millimeter wave (mmWave) communications. By leveraging the angular domain representation of cascaded channels, we initially demonstrate the dynamic dual-structured sparsity (DDS), i.e., i) the angular cascaded channel matrices associated with different users share the identical non-zero rows while differ in their non-zero columns and ii) the cascaded channel support exhibits temporal correlation inherent to the dynamic nature of mobile channels. Then, a layered processing with dynamic dual-structured sparsity (LP-DDS) framework is customized to provide sparse priors for the exact distributions of cascaded channels. In this case, the joint estimate of the angular cascaded channel and Doppler shift is formulated as a compressive sensing (CS) problem while taking into account the temporal correlation of dynamic channels, which, however, is highly intractable due to the ill-conditioned sensing matrix. To tackle this issue, we propose an efficient algorithm, namely DDS-VBIMP, where in particular, both variational Bayesian inference (VBI) and message passing techniques are complemented each other to achieve parameter updates by taking full advantage of the sparse priors as captured by LP-DDS. We demonstrate through our analyses that the proposed DDS-VBIMP can significantly reduce pilot overhead. Simulation results reveal the superiority and robustness of the proposed DDS-VBIMP as compared to various benchmark schemes.

The deep flow inspection (DFI) can identify abnormal traffic to avoid the data congestion caused by the sudden increase of traffic, which can maintain the stability of 6G network. However, the traditional DFI cannot protect the privacy of clients. This paper proposes a deep flow inspection method based on the horizontal federated learning to achieve the traffic identification locally, which reduces the risk of data leakage. Besides, a lightweight CNN model, Simplified-MobileNet, is proposed to realize the effective traffic identification under the limited hardware environment. Federated aggregation algorithm FedAvg is also applied to promote the communication efficiency during model training. The experimental results demonstrate that the Simplified-MobileNet decreases the training time per round by about 15%, and compared with the standalone mode, the FedAvg algorithm can achieve a higher training accuracy with a limited communication time.

Network delay is a crucial indicator for realizing delay-sensitive task offloading, network management, and optimization in B5G/6G edge computing networks. However, the delay prediction for edge networks becomes complicated due to diverse access strategies and heterogeneous services’ storage, computing, and communication resource requirements. Current GNN-based delay prediction models such as RouteNet and PLNet lack the ability to express the complex associations between links and paths, so the predicted delay is not accurate. In this paper, we propose a novel end-to-end delay prediction model named MixerNet for edge computing, which is based on the mixed multi-layer perceptron (MLP). In this model, a mixed MLP architecture is applied to represent the association between links in the network topology and various paths. Observing that each link may have different effects on various paths, a weight matrix is then defined and multiplied by the path matrix to express it. Thus, a complete mapping frame from network characteristics (e.g., traffic intensity and routing schemes) to delay indicator is constructed. Finally, we perform extensive experiments on NSFNET and GEANT2 datasets and regard RouteNet as the baseline model. Experimental results show that MixerNet can accurately predict end-to-end delay results on various network topologies and the mean absolute error is merely about 0.36%. MixerNet also outperforms the baseline model in most evaluation indicators, especially the mean square error has a 3-fold decrease in NSFNET.

The demand for satellite relay service is increasing, while the satellite network resources are limited and unevenly distributed, which pose a great challenge to task scheduling of tracking and data relay satellites. Most existing relay scheduling models are based on static antenna setup time, which has limitations in practical applications and leads to ineffective utilization of satellite resources. This paper models the task scheduling problem based on dynamic antenna setup time and splittable tasks, which maximizes the total scheduled task number and minimizes the total antenna setup time. We also propose a two-stage insertion heuristic to solve the problem. The experimental results show that the proposed algorithm can significantly improve the total scheduled task number, total antenna setup time and effective time window utilization compared with traditional methods.

Knowledge graph completion (KGC) aims to perform link prediction to fill lost relations between entities by knowledge graph embedding (KGE). Translating embedding, as an efficient embedding method in KGE, is widely applied in numerous recent KGC models. However, these translating models may lack the ability to express various relation patterns and mapping properties for knowledge graphs (KGs). In this paper, a simple and well-performed translating model named TransC is proposed to express different relations. A multi-channel mechanism is defined firstly to constrain translating embeddings. Then a relation-aware transfer function is designed to break the expressive restriction and map triplets involving the same relation into a corresponding plane. We also mathematically prove that TransC is capable of expressing four popular relation patterns and all mapping properties. Finally, experimental results illustrate that TransC can efficiently represent the different relation patterns and properties and achieve better performance than state-of-the-art translating models.

Doppler-shift-induced channel aging effect significantly erodes the system performance due to the channel mismatch that evolves with time. This paper aims to investigate the joint beamforming strategy in reconfigurable intelligent surface (RIS)-aided high-mobility communications with channel aging effect. Specifically, a novel frame structure is first proposed for alleviating the heavy signaling overhead in the RIS-aided high-mobility scenario. Furthermore, a deep reinforcement learning (DRL)-based algorithm is devised for rapidly co-designing the precoder at the base station (BS) and the passive beamforming at the RIS relying exclusively upon partial channel state information (CSI) and real-time environment feedback, instead of only employing the outdated estimated CSI. The proposed joint beamforming scheme is capable of adapting to the dynamic propagation environment by exploiting the channel correlation between consecutive instants. Finally, simulation results demonstrate that the proposed algorithm can effectively mitigate the performance degradation caused by channel aging while being computationally efficient and outperforms several benchmark schemes.

In Industrial Internet of Things (IIoT), it is essential to acquire timely device status information to ensure efficient operation. In this article, we consider a wireless monitoring system in IIoT and employ the concept of Age of Information (AoI) to characterize the timeliness of device status information in the system. Considering the impact of resource constraints on information acquisition, we apply a pull-based model to control the entire process of sampling, transmission, and processing associated with device status updates, which constitutes a system-wide AoI minimization problem. The formulated problem is a mixed-integer nonconvex problem, due to the temporal correlation of AoI and the intractability of the implicit AoI-associated objective function. We introduce the concept of average AoI earnings to equivalently substitute the optimization objective. The original problem in consecutive time slots is decomposed into the per-time slot average AoI earnings maximization problem to deal with the temporal correlation of AoI. Then, an online slot-by-slot optimization algorithm (SBSA) is proposed to control device status updates without long-term system state information. Simulation results show that SBSA can significantly improve the AoI performance of the system. However, the problem decomposition in SBSA inevitably brings approximation error. Hence, based on the actual transmission and processing in the system, we get the lower bound of the system total AoI by designing a multislot optimization algorithm (MSA) and analyze the approximate error caused by SBSA. Through simulation results, SBSA has a substantially lower computational complexity, while maintaining acceptable approximation error in comparison to MSA.

Two key impediments to reconfigurable intelligent surface (RIS)-aided vehicular communications are, respectively, the double fading experienced by the signal on RIS-aided cascaded links and the high-mobility-induced intractability of acquiring channel state information (CSI). To overcome these challenges, a novel kind of RIS is presented in this paper, namely active reconfigurable intelligent omni-surface (RIOS), each element of which is supported by active loads, that concurrently transmits and reflects the incident signal amplified rather than just reflecting it as compared to the case of a passive reflecting-only RIS. We consider the use of an active RIOS to a vehicular communication system for mitigating double fading effect. Specifically, the active RIOS is mounted on the vehicle window to enhance transmission for users in the vehicle and for adjacent vehicles. We aim to jointly optimize the transmit precoding matrix at the base station (BS) and RIOS coefficient matrices to minimize the BS’s transmit power relying exclusively upon the imperfect knowledge of the large-scale CSI. To significantly relax the frequency of channel information updates, initially an efficient transmission protocol is put forward to reap the high active RIOS beamforming gain with low channel training overhead by appropriately tailoring the time-scale of CSI acquisition. Then, two algorithms, namely an alternating optimization (AO)-based algorithm and a constrained stochastic successive convex approximation (CSSCA)-based algorithm, are developed to tackle with the investigated resource allocation problem, whose pros and cons are elaborated, respectively. Simulation results substantiate the significant performance improvement of active RIOS as well as determine the validity and robustness of our proposed algorithms over various benchmark schemes.

Recently, mobile social cloud (MSC), formed by mobile users with social ties, has been advocated to allocate tasks of big data applications instead of relying on the conventional cloud systems. However, due to the dynamic topology of networks and social features of users, how to optimally allocate tasks to mobile users based on the trust becomes a new challenge. Therefore, this paper proposes a novel incentive scheme based on the trust of mobile users in the MSC to allocate the tasks of big data. First, a social trust degree is defined according to the social tie among users, the importance of task, and the available resources of networks. With the social trust degree, the task owner can select a group of mobile users as the candidates for task allocation. Second, a reverse auction game model is developed to study the interactions among the task owner and the candidates. With the reverse auction game model, the optimal strategy of task allocation can be obtained with a low cost for the task owner where the selected candidate of mobile users can also obtain the high profit. Finally, simulation experiments are carried out to prove that the proposal can outperform other existing methods with a low delay and a high efficiency to allocate tasks in the MSC.

Energy consumption is becoming a key issue in the research of future network. In practice, network traffic has a periodic time distribution that occurs most often at a low level. This feature provides the possibility of achieving network energy savings through topology switching. By considering the deficiencies in existing studies, such as the low adaptability between network working topology and traffic load, the abnormal topology switching caused by abnormal and unbalanced traffic, and the low reliability of energy-saving topology, this paper proposes an energy-efficient routing method for software-defined networks based on topology switching and reliability. The method involves two parts: a topology-switching method and a failure recovery method. The former adapts the network working topology to the network traffic demands through dynamic topology switching to decrease the network energy consumption. The latter adopts an active strategy for fast fault recovery to ensure network reliability in the energy-efficient topology. Two network typologies and their traffic data are used to experimentally verify the method. The results show that, compared with the static topology switching method TLS, the energy saving of the proposed method can be improved at most 2.07 times and 4.63 times in two typical typologies, respectively, while ensuring network reliability.

The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, specifically, a multi-antenna base station (BS) serves vehicle user equipments (VUEs) with the help of RIS at the mmWave frequency. The uplink average achievable rate is maximized relying only upon the imperfect knowledge of statistical CSI. Considering the time-varying characteristics, we first propose an effective transmission protocol by reasonably configuring the time-scale of CSI acquisition in order to significantly relax the frequency of channel information updates, which constitutes one of the most critical issues in RIS-aided vehicular communications. Then, the formulated resource allocation problem is discussed in the single- and multi-VUE case, respectively. To be specific, for the single-VUE case, a closed-form expression of the average rate is derived by extracting the statistical characteristics of mmWave channels, and an alternating optimization (AO)-based algorithm is proposed. For the multi-VUE case, we develop an efficient algorithm, called JAPMC, to circumvent the unavailability of the closed-form of the objective function and probabilistic constraint by constructing quadratic surrogates of that. Simulation results confirm the effectiveness and robustness of our proposed algorithms as compared to benchmark schemes.

In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

5G realizes flexible networking by building network slices, and its realization depends on network function virtualization (NFV) technology, which combines different types of virtual network functions (VNFs) to provide network services. The reliability of VNFs is lower than that of traditional hardware due to the risk of both software and hardware failure, and redundant backup is an effective solution. Meanwhile, from the security point of view, because the 5G network is based on the unified and standardized hardware of the industry, the need for isolation is put forward. Current research on VNF reliability assurance has not considered the special isolation requirements of 5G. In this paper, aiming to guarantee the safety demand as well as minimize backup resource to meet the reliability target, we formalize the safety-oriented backup problem for 5G core network slices and propose a backup algorithm based on isolation (BABI). Simulation results show that the introduction of isolation can double the security of slices. The comparison with the existing backup methods shows that under the same isolation constraint, the proposed approach can achieve a less resource consumption by 60% - 80% and a improvement of the proportion of effective resources by 40% - 80%.

5G enables many industries, and each industry's application requirements vary greatly. Today's “one-size-fits-all” network architecture approach no longer meets the needs at the same time. The introduction of network slicing brings great flexibility to the network, so that the network can be customized, deployed and dynamically guaranteed. However, how to orchestrate the functions of network slices according to the needs of business scenarios is an important challenge for slice operation. In order to solve the problem that the existing methods can hardly distinguish the differential requirements of different slices for delay, bandwidth and node load balancing, this paper proposes an exclusive orchestration optimization goal to match the business requirements, and establishes a business demand-oriented network slice orchestration problem model. Then, aiming at the typical slices, a business-oriented slicing algorithm based on DQN (BOSAD) is proposed. In this algorithm, we propose the strategy of cooperating the core data center and the edge data center, which can effectively save bandwidth and reduce network delay. The experimental results show that the proposed BOSAD saves the consumption of bandwidth resources, reduces the average slice delay and optimizes node load balancing.

5G is a new generation of broadband mobile communication technology featuring high speed, low delay and large connection. It is a network infrastructure to realize the interconnection between man and machine and things. Before 5G terminals are put into commercial use, they need to be tested to verify their RF performance. In view of the lack of polymorphic support in 5G RF test applications, this paper designs an automatic terminal RF test method supporting polymorphic adaptation, The implementation of the system shows that the proposed method can effectively complete the RF testing of 5G terminals, and the introduction of polymorphic adaptation technology can effectively improve the testing efficiency of the existing tests.

With the continuous construction and development of smart grid, the continuous introduction of new services, largescale access of new energy sources, energy storage and charging piles, and continuous growth of large-bandwidth services, traditional communication networks have been unable to meet the requirements of smart grid. FlexE technology adds FlexShim layer in MAC layer and PCS layer to achieve network flexibility, sub-rate, rigid interface and other characteristics, which can be well connected with IP/Ethernet technology to meet the higher requirements of network bandwidth, delay, slicing, reliability and other aspects. Therefore, it is necessary to develop a slicing network framework based on flexible Ethernet technology oriented to the communication requirements and trends of smart grid. Based on the analysis of the differentiated (deterministic time delay, large bandwidth, security isolation, etc.) requirements analysis of power communication services, we proposed a slice network framework based on flexible Ethernet technology and studied typical use cases based on FlexE technology for power services.

With the advent of the 5G era and the smart grid era, in order to achieve high reliability of grid power supply efficiency, the combination of power Internet of Things with artificial intelligence, edge computing, and advanced communication technologies is the basis for the interconnection of everything in the smart grid era. As an important tool for edge computing-oriented perceptual access implementation, edge IoT agents can not only have gateway functions such as protocol conversion and data collection, but also carry applications including edge-side data storage and stream data processing, intelligent reasoning decision-making, etc. service. Traditional edge IoT agents mostly use heavyweight virtual machines as the implementation technology, and the applications provided are tightly coupled, and they cannot achieve mutual isolation and independent deployment between applications. Therefore, this paper uses lightweight virtualized Docker container technology to deploy services and build an edge IoT agent platform based on Docker containers. At the same time, facing real-time changing access requirements, edge IoT agent clusters may have the problem of limited container load. We propose a dynamic container scheduling method to improve the access carrying capacity of container clusters and ensure the high availability of edge IoT agents.

With the rapid development of 5G commercialization on a large scale and edge computing, cloud-edge collaboration technology has been widely used in various industries, and how to achieve high efficiency cloud-edge network environment has become a research hotspot. In this paper, we propose a network efficiency evaluation model based on analytic hierarchy process (AHP) and logistic regression (LR) algorithm in cloud-edge collaborative environment. Using AHP to calculate the weight matrix of indicators and formulating multiple discrete parameter measures into the same dimensional area to obtain the final comprehensive efficiency value of the network. It describes the efficiency value in cloud-edge collaborative environment as a qualitative concept and realizes the qualitative evaluation in cloud-edge collaborative environment. On this basis, comparative experiments are carried out to evaluate the comprehensive efficiency of the network by using the traditional method and the method proposed in this paper, which verifies the practicability and effectiveness of the efficiency evaluation method.

Recently, ultrareliable and low-latency communication (URLLC) has attracted a significant interest for mission-critical applications in future wireless communication systems. Achieving strict requirements of latency and reliability for URLLC with a fixed infrastructure is challenging, and unmanned aerial vehicles (UAVs) have been deemed as promising enablers to handle this issue due to its salient attributes, such as high maneuverability, flexible deployment, and high probability of line-of-sight links. This article investigates a novel UAV-assisted URLLC service system, where the blocklength of channel codes is finite in Internet-of-Things (IoT) networks. Considering the limited energy of IoT devices, the average uplink transmit power of the IoT devices are minimized by jointly optimizing the device scheduling and association, power control and resource allocation, as well as UAV deployment. The formulated problem is a mixed-integer nonconvex optimization problem because of the finite blocklength regime. To tackle the problem, we derive the approximation of the achievable rate and propose an effective iteration algorithm by applying the block coordinate descent (BCD) and Lagrange dual decomposition techniques. Furthermore, the convergence of our proposed algorithm is analyzed and illustrated. The minimum average transmit power of IoT devices is calculated with a different resource allocation scheme. Simulation results demonstrate that our proposed iterative algorithm can obtain a performance gain of 15%-20% in terms of the average transmit power for URLLC. Moreover, compared with the average bandwidth allocation scheme, our proposed algorithm can get a stable minimum as the total bandwidth increases.

In the network function virtualization (NFV) environment, network services are usually provided in the form of service function chains (SFCs), which defines the link order of virtual network functions required in service requests and are mapped to the physical network. Although NFV facilitates the flexible provision of network services, service interruptions may occur as a result of software and hardware failures. Current solutions mostly use the backup method to ensure the reliability of SFCs. However, these methods ignore the SFC construction phase that has an impact on reliability. Besides, the resource efficiency still requires improvement. To address these issues, reliability-oriented SFC construction and backup problems are investigated in this work. First, an instance-sharing and reliable construction algorithm (ISRCA) is proposed to aggregate multiple SFCs into a service function graph (SFG), and perform reliability screening for the SFG set. After mapping the SFG to the physical network, a node-ranking algorithm with centrality and reliability (NRCR) is proposed for backup node selection and backup instance deployment to improve the reliability of SFCs that have not met the requirements. Experimental results demonstrate that under the premise of ensuring reliability, the proposed backup method can reduce the consumption of bandwidth resources by about 11.7%, when combined with the proposed construction method, it can further reduce the backup resources by 13.9%.

Reconfigurable intelligent surfaces (RISs) have the capability of reconfiguring the wireless environment in a favorable way to improve the quality-of-service (QoS) of wireless communications. This makes RISs a promising candidate to enhance vehicle-to-everything (V2X) applications. This paper investigates the spectrum sharing problem in RIS-aided vehicular networks, in which multiple vehicle-to-vehicle (V2V) links reuse the spectrum already occupied by vehicle-to-infrastructure (V2I) links. To overcome the difficulty of acquiring instantaneous channel state information (CSI) due to the fast varying nature of some V2X channels, we rely upon large-scale (slowly varying) CSI in order to fulfill the QoS requirements of V2I and V2V communications. Particularly, we aim to maximize the sum capacity of V2I links that are used for high-rate content delivery and to guarantee the reliability of V2V links that are used for the exchange of safety information. The transmit power of vehicles, the multi-user detection (MUD) matrix, the spectrum reuse of V2V links, and the RIS reflection coefficients are jointly optimized, which results in a mixed-integer and non-convex optimization problem. To tackle this problem, the outage probability of each V2V link is first approximated by introducing an analytical expression to simplify the formulated problem. By leveraging the block coordinate descent (BCD) method, the considered optimization problem is decomposed into three sub-problems, whose optimal solutions are provided independently and updated alternately to obtain a near-optimal solution. Simulation results verify the theoretical analysis and the effectiveness of the proposed algorithm, as well as unveil the benefits of introducing RISs for enhancing the QoS performance of vehicular communications.

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software‐defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate‐variable flow scheduling scheme to realize energy‐saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG‐EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG‐EEFS algorithm only trains part of the states and gets a good energy‐saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

Traffic engineering (TE) can balance the traffic in the network to reduce network congestion and improve network resource utilization. The emergence of Software Defined Network (SDN) provides a more flexible and effective way to control traffic in the network. Existing TE solutions mainly focus on routing traffic via the shortest path or evenly distributing the traffic among multiple available paths, but these methods are not flexible since this static mapping of traffic to paths does not consider either the current network utilization or traffic load. Heuristics-based TE methods depends on operators' understanding of the workload and environment. Designing and implementing those methods thus take at least weeks. Furthermore, it usually takes minutes to output the solution. Inspired by recent successes in applying Deep Reinforcement Learning (DRL) techniques to solve complex control problems, we leverage DRL to control traffic in SDN. We start by building a framework which integrates the DRL algorithm into SDN. Based on this framework, we propose a modified DRL algorithm to control the traffic split ratio to multiple paths. Simulation results show that the proposed approach performs better than three baseline methods when the traffic load is dynamically changing.

Smart grid deploys a large number of smart terminals and sensing devices to form an edge network, as well as a virtual network of information space and the power Internet of Things. As a key component of 5G and future network, the latency of end-to-end and the traffic of backhaul link could be reduced by edge network. Nevertheless, the function of storage and computing are moved down to the edge nodes in mobile edge network which increases the complexity of resource management. So it is an important issue to find out a more effectively resources allocation mechanism as well as meeting the requirements of each user. Edge computing refers to the processing of large amounts of edge data in the edge space in the edge network, thereby reducing dependence on the data center, achieving limited self-governance of the edge network, and reducing off-line threats. Although Deep Reinforcement Learning (DRL) has been applied to many of the work related to edge networks, there lacks the applications for green resource allocation. A Deep Reinforcement Learning (DRL) based green resource allocation mechanism is proposed in this paper which aims at efficiently allocating the resources while satisfying the needs of mobile users. The value of energy efficiency can be obtained when the algorithm achieves convergence according to the simulation results. The efficiency of the DRL-based mechanism and its effectiveness in meeting user requirements and implementing green resource allocation are validated.

The cloud computing market is dynamic, distributed, and lacks central authorization. In this environment, cloud resource providers are vulnerable to deception and cloud resources may be abused. How to implement efficient and feasible trusted negotiations with users to expand Benefits is an urgent issue. Based on SLA (Service Level Agreement), this paper proposes a trusted negotiation method to optimize cloud resource allocation from the perspective of cloud resource providers. In a nutshell, it firstly quantifies each indicator based on the total amount of cloud resources requested by the user and the corresponding price, the user's comprehensive credit, and the total amount of resources corresponding to each SLA level, then filters the users who meet the requirements. Next knapsack algorithm and the greedy algorithm based on dynamic programming are used to predict the allocation of cloud resources respectively. Finally, the allocated users are negotiated to reach a transaction. This article takes the resource allocation price, negotiated price, and negotiated success rate as the evaluation index. The simulation results show that compared with the greedy algorithm, the algorithm in this paper has higher resource allocation price, negotiated price and negotiated success rate under different numbers of users, and can effectively realize the optimal allocation of cloud resources.

Network Function Virtualization (NFV) is an important shift in telecommunication service provisioning. It enables the decoupling of network element functions and dedicated hardware devices. How to economically place and chain Virtual Network Functions (VNFs) according to the requirements of Service Functions Chains (SFCs) are the challenges for NFV orchestration. In this paper, we consider the offline deployment issue from the perspective of sharing VNF instance to improve resource utilization and reduce total placement costs. Firstly, we generalize the problem as a Facility Location Problem and propose a Mixed Integer Linear Programming (MILP) model. Besides, our model can be dynamically configured according to the different deployment preferences. Then we propose a heuristic algorithm based on the Steiner Tree Problem and Markov Decision Process (MDP). We evaluate our heuristic algorithm by comparing with the optimal solution of MILP and a classic graph based algorithm. The results show that the difference of the deployment costs between our algorithm and the optimal solution is less than 3%. However, the execution time can be significantly reduced by 57.4%.

Cloud computing technologies can not satisfy the requirements of applications on the mobile terminals because of their disadvantages in delay, link load and energy. So Mobile Edge Computing (MEC) is proposed as a kind of novel computing technology. As an important research direction of MEC, service migration methods still have limitations that they cannot learn migration paths and be adaptive in dynamic situation and user movement. In this paper, we propose a novel service migration policy method based on reinforcement learning. We firstly investigate user movement, four different edge network situations and traditional migration policies. Then we formulate the system requirements by Satisfiability Modulo Theory (SMT) logic to acquire the migration policy space. We further propose a dynamic-awareness deep Q-learning algorithm to select paths from the policy space iteratively and conduct dynamic awareness to adjust learning rate adaptively. Meanwhile, the optimal convergence of our algorithm is proved theoretically. Finally, the experimental results highlight the effectiveness as migration successful rate, service interruption time and load balance of our method compared to the other solutions.

This paper studies a joint communication, computing and caching resource allocation problem in vehicular networks to improve user satisfaction and reduce costs. We propose a double-scale deep reinforcement learning (DSDRL) framework that combines on-policy strategy and off-policy strategy to enable dynamic resources allocation, which considers not only the diversity and difference of services but also the costs of network operators. Simulation results show that the proposed scheme can effectively improve the long-term revenue of network operators.

Ultra-reliable and low-latency communication (URLLC) is proposed as one of the three key services of 5G for Internet of Things (IoT), especially for mission-critical applications. This paper investigates the minimum power of devices in uplink in IoT networks for URLLC. Unmanned aerial vehicles (UAVs) are utilized to assistant the IoT system because they have flexible deployment and high probability to establish line-of-sight (LoS) communication links. First, we formulate a minimum average transmit power problem under the constraints of latency and reliability in modern industry. The deployment of UAVs and device association need to be jointly optimized, making the problem non-linear and non-convex. Then the block error probability which characterizes the reliability is derived under finite blocklength regime and an iteration algorithm is proposed. Additionally, the minimum average transmit power of IoT devices in URLLC is also calculated by deploying different number of UAVs. Simulation results are presented to show that the transmit power can be greatly reduced by appropriately deploying more UAVs or relaxing the tolerance of latency.

Summary The development of the Internet of Things (IoT) and wearable technology provides an opportunity for the development of maintenance of communication. The use of wearable technology and instant messaging technology of IoT can improve the support capabilities and data interaction ability of on‐site maintenance of the communication network. Existing communication maintenance systems lack real‐time operation and maintenance of data interaction. In the field operation decision‐making and execution process, there are problems of lack of field links and inconvenient information interaction. On‐site maintenance mainly relies on maintenance personnel to actively search for information, and the retrieval results are lacking of personalization, which makes it difficult to meet the needs of on‐site maintenance of the communication network. Edge computing and information push technology can solve these problems to some extent. In this paper, we focus on the current low level in information, complicated scenes, and various information of on‐site maintenance and propose a dynamic context‐aware information push algorithm. The simulation results demonstrate that the algorithm delivers good performance in terms of precision, recall, and F1. Besides, we present a smart wearable maintenance system, an edge computing–assisted IoT platform for the real‐time guidance of technical experts and systems for on‐site maintenance personnel, aiming to improve the efficiency and quality of on‐site maintenance.

With the development of Internet of Vehicles, vehicles establish the social relationships with other vehicles and road side units for exchanging information, which is called Social Internet of Vehicles (SIoV). Making use of the relationships, we propose a QoS-based opportunistic routing mechanism to guarantee the QoS requirement and route reliable of information transmission in this paper. First, we establish a mathematical model for QoS evaluation considering the transmission correct ratio and delay, which can accurately estimate the QoS of road section. Then, we propose the QoS-based opportunistic routing mechanism, which aims to form a reliable and robust route path. Finally, the obtained simulation results validate the accuracy and correctness of our approach.

The scheduling of Virtual Network Functions (VN-Fs) is an important problem for Network Function Virtualization (NFV) resource allocation. In this paper, we investigate how to manage the Network Functions (NFs) efficiently to enhance the utilization of network resources. In the system model, we take into account the VNF transmission delay and processing delay at the same time. Our objective is to minimize the total end-to-end delay for all network services. To reduce the complexity of this issue, we propose a novel algorithm based on genetic algorithms by improving the method of crossover and mutation. The simulation results show that the proposed algorithm can reduce the total end-to-end delay at most 16.74%.

In order to ensure users enjoying the services continuously and steadily, we need an efficient service recovery strategy to quickly recover the failed links and reconstruct the device set. In this paper, we introduce a service recovery strategy based on recovery node which can save and maintain service data flexibly. First, we give the definition of recovery node and the selection mechanism for it. Then we describe our recovery strategy in detail. At last, we make a simulation by NS-3. The effectiveness of the proposed methods is demonstrated by simulation results.

Fog computing can aggregate the computing resources to handle the unprecedented amounts of data and becomes a promising technology in the future 5G smart Internet of Things (IoT) networks. This paper considers an IoT video data analysis system where smart IoT cameras can transmit all data to the base station or analyze the data locally. After receiving smart cameras offloading data, the base station can partially redistribute the analyzing task to the smart user equipment. The smart cameras and base station task offloading scheme and the uplink-downlink bandwidth allocation are jointly optimized to minimize the system level delay. The problem is a mixed integer non-linear problem, and the objective function contains the sum of several segmented maximum, which makes it very challenging to solve. Firstly, the smart device 0-1 binary task offloading is relaxed into a continuous form, with adding an upper bound to guarantee the solution can be as close as possible to the integer. Then introduced by a change of variables in handling the segmented maximum, all non-convex constraints are transformed with slack variables and successive convex approximation. To further ensure the iteration algorithm convergence, the disciplined iteration algorithm is proposed to prevent the iteration from getting stuck. The simulation results verify that the assisted smart user equipment can reduce the system delay combining with the proposed resource allocation algorithm.

To meet the exponentially increasing mobile services and applications, heterogenous networks (HetNets) have been envisioned as a promising technology. In HetNets, multiple caching-enabled small-cell based stations (SBSs) are deployed within the coverage of a macro-cell base station (MBS) to cache multimedia contents for mobile users. However, due to security threats of untrusted SBSs, the cached contents may be illegally accessed by owners of these untrusted SBSs, resulting in the content privacy leakage. To tackle this problem, we propose a secure edge caching scheme for layered multimedia contents in HetNets. Specifically, considering the layered features of contents, we first develop a secure edge caching framework based on the cooperations of SBSs and MBS. In this framework, the critical base layer subfile of the content are directly delivered by the trusted MBS, whereas the enhancement layer subfiles are cached on untrusted SBSs. Furthermore, according to the limited caching capacities of SBSs and dynamic content demands of mobile users, we formulate the enhancement layer subfile caching problem as a non-convex 0-1 integer programming problem. To solve this problem, we devise a distributed alternating direction method of multipliers (ADMM) and secure the edge caching for each SBS to iteratively search the optimal caching strategy. Simulation results show that the proposed scheme provides secure and efficient multimedia content caching for mobile users.

In this paper, we focus on the resource management in a heterogeneous small cell network implemented in a Content Centric Network (CCN), where a hierarchical innetwork caching framework is formed with small cell base stations (SBSs) and macro base station (MBS) taking the role of the CCN router. In the hierarchical network structure, SBSs in the same hot spot cooperate and serve as the first hop routers, handling the Interest packets issued by the users. While the MBS works as the next hop router when a cache miss occurs at the small cell layer. Taken into consideration the feature of content request and content retrieving in CCN, we establish the link between SBSs and users on the request level rather than adopt the fixed connection regime. This means different SBSs may come to serve the same user in terms the content request it generates. In this context, we model a cooperative game among SBSs with transferable utility, in which the SBSs in a hot spot collaborate to maximize their overall revenue by selecting the best one to respond to each content request. In the meantime, a certain amount of spectrum resource is bought from the operator to satisfy the user's QoS requirement. Finally, the payoff is divided among SBSs via a bankruptcy game, and the solution is found exploiting the Nucleolus fairness concept. A decentralized Nucleolus calculation algorithm is adopted here so that the network can work in a distributed manner.

In this paper, we propose a destination-aided jamming scheme for secrecy simultaneous wireless information and power transfer (SWIPT) in cognitive relay networks. In which, an energy-constrained secondary transmitter (ST) assists to forward the traffic from a primary transmitter (PT) to a primary receiver (PR) and collaborates with PR-aided jamming to prevent the eavesdropper around PR from eavesdropping, in exchange for communicating with its own receiver in the same frequency. To maximize the rate of ST, we jointly design the relay processing matrix, beamforming vector and power split ratio under the constraint of PT secrecy rate demand. For tackling the non-convex problem, the semi-definite relaxation technique and Charnes-Cooper transformation are adopted. Simulation results demonstrate that our proposed scheme can significantly improve the performance of communications.

To bridge the gap between the resource‐constrained mobile devices and the resource‐demanding applications, mobile cloud computing (MCC) emerges for offloading complex tasks to a cloud server. Based on this concept, cloudlets, which move available resource to the vicinity of the mobile network, enhance further the system accessibility and performance. Moreover, to strengthen the network capacity in traffic intensive area, dense small cell network (DSCN) is proposed as one of the promising solutions. In this study, the operation of cloudlets and DSCN is collaboratively studied in order to further improve the system performance. On the one hand, users can select a cloudlet and dynamically adapt the connection according to the performance and the cost, which is referred to as a user‐essential dynamic cloudlet selection problem. On the other hand, a cloudlet needs to set the optimal selling price and the size of resource for the users, which is considered as a cloudlet resource allocation problem. To jointly address the problems of dynamic cloudlet selection and resource allocation, the authors propose a hierarchical evolutionary game to maximise the utilities. Simulation studies are carried out to demonstrate the effectiveness of the proposed algorithms, which, indeed, improve the entire system performance significantly.

With the development of the maritime networks, the data of vessel users is growing exponentially, and more and more resource intensive tasks, such as multimedia applications, high-definition video playback and games, appear in the daily demands. These changes have greatly increased the energy consumption and bandwidth requirements of vessel terminals and networks. In order to meet the requirements of high bandwidth and low delay for the high-speed development of mobile network, and reduce the network load, the concept of mobile edge computing (MEC) is proposed and has been widely supported by the academia and industry. It is considered to be one of the key technologies of the next generation networks. Inspired by this idea, this paper introduces computing offloading technology to maritime mobile cloud networks. Maritime mobile cloud network is the product of the continuous development of cloud computing technology and mobile Internet technology. In this paper, we studied the issue of computation task offloading for vessel terminals, focusing on minimizing the energy consumption of vessel terminals and the execution delay of computation task. First, it determines that whether if it should be offloaded to the cloud server. Second, the server should be selected to run the computation task. The goal of the optimization is to minimize the energy consumption of vessel terminals and the execution delay of computation task taking into account of different weights. To reduce the execution latency and device energy consumption, we proposed a multivessel computation offloading algorithm based on improved Hungarian algorithm in maritime MEC network. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

In recent years, the joint configuration of communication, computing and popular content resources in wireless networks has been gaining an increasing amount of attention to efficiently handle the gigantic data traffic. To effectively manage resources, the network virtualization is deemed as a promising technique in which mobile virtual network operators (MVNOs) create virtual slices to serve the requests issued by their subscribed users via obtaining contents and computing abilities from content providers and fog nodes. In this paper, the above MVNO optimization is formulated as an assignment game employing the supplier-firm-buyer game model, which gives the optimal solution of matchings among the contents, computation nodes, MVNOs, and users. Moreover, the existence of the non-empty core of such game is proved, indicating that the proposed framework is stable. In order to obtain the simple practical solution, a distributed suboptimal algorithm of reduced version of three-sided matching with size and cyclic preference (RTMSC) is adopted. Furthermore, a greedy strategy is proposed to improve the convergence speed as well as performance of the R-TMSC scheme. The simulation results show that compared to the random allocation, a 12.97% increase in average revenue can be reached by solving the supplier-firm-buyer problem, and that the greedy R-TMSC algorithm is able to reach the similar point of the optimal value with a faster speed.

With the continuous development of emerging information technologies, the preciousness and scarcity of wireless spectrum resources are becoming increasingly prominent. With the rapid development of wireless mobile communications, Long Term Evolution (LTE) system is actively studied and widely deployed. Meanwhile, the satellite communication system is also one of the research hotspots in recent years. The compatibility analysis of the two systems in the adjacent frequency and the same frequency has far-reaching significance. In this paper, we study the coexistence of TD-LTE system and mobile satellite services (MSS). By the deterministic analysis based on the link budget criteria, the coexistence conditions between TD-LTE and the typical MSS system in the worst case are studied. In addition, the system-level simulation based on Monte Carlo is used to study the interference coexistence between the two systems in different scenarios. For the analysis results, the conditions for coexistence of the TD-LTE system and the typical MSS system are given and practical suggestions are made. Finally, some issues that need to be considered in the coexistence problem between 5G and the typical mobile satellite service system, as well as preliminary assessments, are proposed.

Indoor navigation system provides customized path planning for requesters who are unfamiliar with the indoor environment, such as shopping mall and airport. Spatial crowd-sourcing technology can be applied to indoor navigation to offer fundamental services related to location. However, spatial crowdsourcing-based indoor navigation is vulnerable to the intrusion of injected anomalous paths from attackers. In this paper, we propose an anomalous path detection (APD) scheme to classify attackers according to their reputation management and abnormal trajectory sequence. Specifically, we first develop a crowdsourcing system to support the indoor location service using the fog as the spatial crowdsourcing server. Then, we identify two levels of attackers, i.e., the malicious responders and the semi-honest responders in the indoor environment according to their attacking purposes. Through the responders' historical records from the fog server, we analyze a series of trajectory sequences consisting of the distance between the current position and the destination to distinguish the semi-honest responders from the normal. In addition, we propose a semi-supervised learning with hidden Markov model (HMM) to detect the semi-honest responders. Finally, the extensive simulations show that the APD scheme can achieve higher accuracy with the acceptable false rate.

We investigate the max-min weighted downlink signal- to-interference ratio (SINR) problem under uplink SINR constraints and practical per-antenna constraints in full- duplex systems. The successive convex approximation (SCA) method is adopted to iteratively deal with this non-convex problem. Within each SCA iteration, to lower the complexity, a parallel beamforming algorithm based on alternating direction method of multipliers (ADMM) is proposed. Specifically, local variables are introduced to decompose the problem to multiple independent subproblems with closed-form solutions. Numerical results show that our proposed algorithm can achieve the similar performance with existing algorithms, but runs much faster especially in large-scale systems.

Pre-caching popular videos at the local storage of base stations (BSs) can significantly alleviate the tremendous backhaul burden. In this paper, we consider a cache-enabled cloud radio access network (C-RAN) scenario, where multiple BSs cooperatively serve multiple users. Each BS has a local storage and connects to the central processor (CP) via a backhaul link. Since multiple users may simultaneously submit the same request, the multicasting is also exploited to further offload the wireless traffic. The joint BS clustering and beamforming are optimized to maximize the weighted sum quality of experience (QoE) subject to the transmission power constraint and the backhaul capacity constraint. To solve this mixed-integer nonlinear programming, we first equivalently reformulate it as a sparse beamforming problem. Then, the reweighted ℓ1-norm technique is adopted to approximate the non-convex backhaul constraint and the successive convex approximation (SCA) method is applied to deal with the non-convex QoE objective. Simulation results show that cache strategies have great impact on the QoE performance and our proposed scheme significantly outperforms the traditional rate maximization scheme.

For the recent emerging applications such as augmented reality (AR), delay is a key performance evaluating the quality of user experience (QoE). Caching the execution results of the popular AR applications' computational tasks can significantly reduce the execution delay. In this paper, we consider the mobile edge computing (MEC) server and the cloud can proactively cache the execution results of computational tasks. Then, in our proposed scenario, there are four optional ways to process a task. They are, respectively, computing tasks locally, offloading tasks to the MEC server to computing, returning the task's computation results directly from the MEC server's cache, and returning the task's computation results from the cloud's cache. The computation offloading, resource allocation and task proactive caching are jointly optimized to minimize the execution latency subject to the constraints of the radio, computation and storage resources. To solve this complex mixed-integer nonlinear programming (MINLP) problem, we first propose a proactive caching algorithm for collaboration between the cloud and the MEC server to determine the task's caching status. Then, we propose a heuristic algorithm based on greedy strategy to solve the remaining problem, which includes resource allocation and the selections of task's execution mode. By analyzing the simulation results and comparing with an exhaustive algorithm, effectiveness and optimality of our proposed schemes are verified.

The construction of energy-efficient network and achievement of green communication have garnered great attention as a promising way to reduce network operating costs and greenhouse gas emissions. Link sleeping and rate adaptation are proposed to reduce energy consumption when the traffic demands are at low levels. It has been observed that many networks (include ISP backbone network) exhibit regular diurnal traffic patterns, which offers the opportunity to apply link sleeping for energy saving. In this paper, we propose an online scheme called Multiple Topology Switching with Data Plane Forwarding Path Rerouting (MTSDPFPR) for energy saving. Based on the dynamic network traffic demands, MTSDPFPR switches the links to sleep mode to save energy. Then we use the GEANT network and the real traffic matrix to evaluate proposed scheme. The results show that up to 30% energy savings can be achieved.

Satellite communication network (SCN) has the capability to provide long-distance and high-quality communication services. It could play a significant role in the future networks for its high reliability and large capacity. However, SCN still needs more efficient resources allocation and dynamical traffic scheduling. As a new design paradigm, network functions virtualization (NFV) is potential to facilitate the performance of traditional networks, including SCN. Therefore, the applicability of NFV in SCN has attracted many people's attention, especially the study on service function chains (SFC). In this paper, we try to explain the problem of SFC deployment in NFV-enabled SCN and deal with it. Our main goal is to minimize the end-to-end service delay and then achieve flexible service orchestration. Based on the general NFV-enabled architectures, we build a time-varying SCN model and novel forms of SFC requests. Then we formulize this problem and propose an effective approach named SFC deployment in satellite network (SDSN). The solution is conducive to promoting the development of SCN. The simulation results show that SDSN could not only take much shorter execution time and minimize the total delay, but also has a good performance in the resource utilization and acceptance ratio.

Software-defined networking (SDN) enables a network to be programmable, which makes it easy for the network to recover from failures. Upon failure, network can revert to operational state through preprogrammed recovery strategies. However, most of existing recovery approaches do not consider storage resource consumption. Nowadays the network scale and the number of flows increase greatly, numerous flow entries are required in case of failures, but the Ternary Content Addressable Memory (TCAM) that stores flow entries is very expensive and capacity-limited. Therefore, it is significant to reduce the consumption of backup resource. In this paper, we propose a ring-based single-link failure recovery approach (RSFR) to achieve failure recovery with less flow entries. A ring is selected from the network to act as a shared backup path, based on the ring, we plan all backup paths and design switches' flow tables to improve the utilization of flow entries required for failure recovery, thus network can recover from failures with less flow entries. Simulation results show that the proposed approach has a better performance in backup resource consumption, and recovery delay is less than 50ms.

Software-defined networking (SDN) develops a logically centralized control plane from the data plane, which makes the network management more intelligent. As the network becomes larger, the control plane with one controller can no longer manage the network efficiently. Therefore, multiple controllers are needed to manage the network. However, the survivability has been a key challenge in multi-controller SDN, which is sensitive to the controller failures. Once the controller breaks down, the switches will lose connections to the controller. This leads to severe consequences. In this regard, we propose an approach to improve fault tolerance of network in face of controller failures. In our work, we also attach great importance to the survivability of connections between controller and switch under random-link failures. Propagation delay is considered in our approach. Simulation results show that our approach guarantees that the controller failures can be effectively recovered. Moreover, after the controller failure is recovered, the survivability of multi-controller SDN in face of random-link failures can be improved.

Network function virtualization is a new network architecture, where the dedicated hardware network functions can be implemented in network function instances running on general purpose hardware such as high volume servers in data centers. End-to-end services require the traffic flow go through a list of NFs in sequence, which is defined by service function chain (SFC). Multiple NFs in a SFC are often orchestrated across multiple DCs to satisfy their position or performance requirements. However, different orchestration strategies of the SFC will lead to different deployment cost, including VNF instance cost and inter-DC bandwidth cost. Besides, large number of NFV instances are deployed in micro-DCs which have limited physical resource. Therefore, in this paper we investigate a costaware strategy to orchestrate the SFCs across multiple DCs, while considering the loads of DCs. An Integer Linear Programming (ILP) model is formulated to minimize the total deployment cost. Then, we prove that the problem is NP-hard and provide a heuristic Cost-Aware SFC Orchestration algorithm (CASO) to solve it. The simulation results show that CASO orchestrates SFCs in a cost-efficient way.

In this paper, we investigate the security for cognitive radio networks with simultaneous wireless information and power transfer (SWIPT). In such a system, an energy-limited secondary user (SU) helps relay the traffic from a primary user (PU) to the primary receiver (PR) and assists PU secure communication using beamforming technology, in return to serve its own secondary receiver in the same spectrum. In order to further enhance the security of PU traffic performance and increase the energy harvested by SU, we propose a destination-assisted scheme in which the PR transmits jamming signal to confuse the eavesdropper, while jamming signal can also be used to power SU. The beamforming vectors and power split ratio are jointly designed to maximize the secrecy rate of PU while satisfying the rate demand of SU. It boils down to a challenging non-convex problem. We resolve this issue by a general two-stage procedure. First, by fixing the power split ratio, we obtain the optimal beamforming vectors by applying the semi-definite relaxation (SDR) technique and the Charnes Cooper transformation. Then, the problem is solved by a one-dimension search to obtain the optimal power split ratio. Extensive simulations are provided and the results demonstrate that our proposed scheme has good performance.

Device‐to‐device (D2D) communication can increase network coverage, spectrum efficiency and energy efficiency (EE) within the existing cellular infrastructure, which makes it a promising architecture for the future networks. Due to the diversification of services, heterogeneous statistics quality‐of‐service constraints are considered in this study, where cellular users are concerned about the delay constraint and D2D user groups pay more attention to the outage probability of data transmission. The power allocation problem of cellular users can be solved by optimising the capacity‐payoff power‐loss game model. Upon exploiting Lagrange dual decomposition and the Newton iteration method, the power optimisation problem of cellular users is transformed into a parameter optimisation problem. Due to the limited energy resource of D2D users, EE is the focus of D2D user groups. Using fractional programming and convex optimisation techniques, energy‐efficient optimal power allocation algorithms of D2D users are proposed subject to the outage probability constraint. As a result, a power allocation algorithm based on hierarchical game is conceived for efficiently solving the power optimisation problem. The simulation results show that the proposed algorithm can obtain a performance improvement compared with other algorithm and converge within a certain number of iterations.

Network function virtualization (NFV) is a powerful emerging technique with widespread applicability. It provides Network Functions (NFs) through software virtualization techniques that decouple software and hardware. Some connected network functions constitute a service function chain (SFC). Therefore, the deployment of SFCs is much agile and simple. However, this leads to more service failure. The service failure includes service availability failure and service quality degradation. Aiming at the problem that the existing service function chain detection methods have high detection cost and cannot locate the failure accurately. This paper presents a method based on minimum detection cost. The method consists of failure detection and failure localization. In failure detection, we calculate detection paths according to the topology of network functions to avoid duplicate probing of links between network functions. In failure localization, we locate service availability failure and service quality degradation respectively and add timestamp fields to network service header to analyze locations of service quality degradation. Experiments show that the method reduces active detection cost and improves the recall and false-positive of service failure localization.

Software-defined networking (SDN) develops a logically centralized control plane by abstracting the underlying network forwarding devices, which makes the control of network traffic more flexible and more intelligent. In SDN, a switch can only work according to the rule of the flow tables received from its controller. Once the controller breaks down, the switch cannot transmit the incoming data packet which cannot be matched in the flow table. The SDN network can be severely affected by the controller failure. In this regard, we are committed to design a proper backup approach for SDN controllers to reduce the loss brought by controller failures. Besides, we attach great importance to the survivability of the control network under network failures. In this paper, we first formulate the survivability of control network. Then we propose a backup approach for controllers based on the survivability model. The network delay is considered in the backup approach. Simulation is conducted to verify the validity and efficiency of our approach. Results show our backup approach guarantees that the controller failures can be effectively recovered. Comparison results between our approach and other existing approaches prove that the approach can effectively reduce the link loss brought by network failures when the backup controller replaces the failed controller to manage the network.

In network virtualization environment, multiple virtual networks share the same resource of a physical network. Since the physical resources of a substrate network is limited, it is necessary to improve the utilization of physical resources. Considering the resource requirement of a virtual network may change over its lifetime, we propose a prediction-based resource management mechanism. To increase the utilization of the substrate network, we can adjust the resource allocated to the virtual network based on the result of prediction. Additionally, in order to avoid the result of prediction deviates from the real requirement, we compare our prediction result with the collection of the resource utilization at real time to ensure the correctness of our result. The simulation results show that our approach can increase the utilization of the physical resource and improve the virtual network acceptance ratio while ensuring the requirement of the virtual networks.

The construction of energy-efficient network and achievement of green communication have garnered great attention as a promising a way to reduce network operating costs and C emissions. Moreover, recently the deadline-aware and energy-efficient routing and scheduling algorithms in data center network have been attracting a broad attention. However, the dynamic scheduling for flows has not been explicitly studied by the existing research. In this paper, we investigated the dynamic flow scheduling in data center network, and propose a deadline-aware and energy-efficient dynamic flow scheduling (DEDFS) algorithm, assuming the path of the flow could be calculated in advance and pre-stored. In addition, the number of mouse flows in data center network accounts for main proportion, but consumption is very small. In order to achieve the balance of energy-saved and efficiency, mouse flows will be directly transferred, while elephant flows will be scheduled by the Most-Critical-First static strategy based dynamic scheduling algorithm. It selects the interval of largest energy consumption density as the critical interval, and all of the flows in this critical interval will be preferentially scheduled. Finally, the feasibility and validity of the algorithm are verified by simulation.

In this paper, we studied the transmission performance of underwater wireless networks, where underwater network users (UNUs) can transmit their data through wireless optical and acoustic communication in a certain range to improve the overall underwater networks. By jointly considering UNUs' volume of data transferred and overall network transmission performance, we introduced an auction game based optical and acoustic communication mechanism (AGOC). With AGOC mechanism, the base transceiver station (BTS) sells wireless optical communication chances through auctions. The users will decide whether to bid according to their own situation, and then the winner could use wireless optical to transmit finally. The simulation results verified the effectiveness of our proposed algorithm. It also be concluded that AGOC mechanism could improve the overall underwater wireless network performance through reducing the number of UNUs contending for the wireless optical channel.

With the rapid development of wireless communication and mobile devices, heterogeneous networks have emerged as a promising paradigm to enable users' data services. However, it lacks an experience blocking theory to optimize data services. Furthermore, due to the limited resources of spectrum, the spectrum sharing based on the quality of experience (QoE) in heterogeneous networks becomes a new challenge. Therefore, to tackle the above challenge, we present an experience blocking (EB) ratio based game-theoretic approach for spectrum sharing in heterogeneous networks where the small cell can lease the spare spectrum from macro cell. Specifically, firstly, a novel EB ratio based model is proposed to evaluate the efficiency of spectrum usage in a cell. Then a Stackelberg game is employed to formulate the interaction between macro cell and small cell according to the EB ratio. Finally, an EB table is given to evaluate the blocking status of a cell and simulation results show that the proposed scheme can improve the efficiency of spectrum sharing better than other schemes.

The delivery of services typically requires packets to be steered through a sequence of middleboxes to improve network security and performance. One constraint on the deployment of services is that middleboxes are tightly coupled to the physical network topology. As a result, ensuring successful deployment requires error-prone and complex low-level configurations. Software-Defined Networking (SDN) can eliminate the need to configure network devices manually to deploy services. However, in terms of steering middlebox-specific traffic in data plane, applying the existing capabilities supported by OpenFlow protocol may lead to incorrect forwarding decisions when there is a loop in the route used to steer traffic. In this paper, we present an implementation using tagging to discriminate different instances of the same packet arriving at the same ingress port on the same switch (i.e. the existence of the loop). Moreover, we propose an algorithm to judge the existence of the loop in a physical sequence of switches and decide which switches are responsible for adding tags. The experimental result demonstrates that our implementation can properly steer traffic through a specific sequence of middleboxes even when there are loops in forwarding path.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to divide it into multiple domains. Since the network scale becomes larger, the probability of failure occurrences is higher. Therefore, it is important to guarantee the control plane resilience in multi-domain SDN. However, the existing approaches cannot store the network state in real time, and do not consider the backup controllers placement problem in multi-domain SDN. In order to ensure the resilience of the control plane in multi-domain SDN, we propose a sharing data store and backup controllers based approach. Sharing data store is used to ensure that each master controller has a view of the whole network and data store can save the network state during the failure time. The sharing backup controllers are used to guarantee the resilience of control plane with minimum cost. Simulations show that our approach can use as less backup controllers as possible to ensure the resilience of control plane.

Emerging ultra-dense networks (UDN) can increase the network coverage and improve the overall throughput which makes it a promising network technology. However, the massive deployment of low power, small coverage micro base stations makes the traditional cell selection algorithm more complex and resource allocation less efficient. To solve these problems, this paper proposes a joint cell selection and hierarchical resource allocation algorithm. To improve the overall system performance, the proposed cell selection algorithm is executed according to the throughput of users. Meanwhile, a heuristic sub-channel allocation algorithm is proposed to improve the resource utilization. In addition, the different service requirements of mobile devices significantly increase the burden of power consumption. So the power allocation process takes into account the balance between the throughput and power consumption. Simulations demonstrate that the proposed hierarchical algorithm achieves a large performance improvement compared with the other algorithm in system throughput and energy efficiency (EE).

Cloud Radio Access Network (C-RAN) architecture with optical fiber fronthaul has been confirmed as a promising solution to achieve high capacity and low latency signal transmission, which has been a key technology and trend of the evolving fifth generation (5G) cellular networks. However, with the fiber fronthaul increasing, the complexity and cost of the CRAN fronthaul networks will grow exponentially. Accordingly, the hybrid fronthaul network of wireless and optical will be the direction of C-RAN architecture design in the future. In this paper, we study the wireless fronthaul C-RAN system in downlink and propose a robust precoder design. The channel state information (CSI) at the baseband unit (BBU) pool and remote radio head (RRH) cluster is assumed to be imperfect, where the additive channel state information error is modeled as Gaussian distributed. Based on this model, we propose a robust C-RAN precoder design that minimizes the total transmit power under a signal-to-interference-plus-noise ratio (SINR) constraint at each user terminal. The original goal is to establish SINR constrained power allocation formulations in the form of convex conic optimization problem. The analysis results reveal that the original problem formulation is non-convex, in general. We develop a novel conservative approximation scheme for handling the non-convex constraint. Furthermore, we solve the optimization problem by transforming it into a semidefinite program with relaxation, which can be efficiently solved. Simulation results show the advantage of using the proposed power-conserving robust precoding algorithm.

Hadoop has become a popular platform for the management of big data. To provide a healthy Hadoop platform for big data application, an HMM-based approach for performance diagnosis in Hadoop clusters is proposed. We use metrics which are collected under the normal situation to train HMM (Hidden Markov Model), then use this model to detect anomaly based on the probability, which is more accurate than other methods. Through evaluation in a controlled environment running Hadoop clusters, we find our approach can find out the real cause of performance problems in an average 84% precision and 83% recall, which is better than the method based on ARIMA and KNN (k-Nearest Neighbor).

Software Defined Networking (SDN) enables the underlying infrastructure to be abstracted from the network services and controlled by one or more controllers. If a link or a node fails, the switches that can detect the failure have to either inform controller to update flow tables or transform the data to pre-configured paths to recover the failure. However, existing failure recovery approaches mainly consider the recovery delay and packet loss, and ignore the storage resources consumption for backup paths in case of link or node failure. Moreover, the Ternary Content Addressable Memory (TCAM) that stores flow entries is expensive and limited with high-energy consumption. Thus in order to minimize the consumption of backup resources and meet the required failure recovery delay, a backup-resource based failure recovery approach is proposed. Two metrics are proposed to grade physical links, and three kinds of strategies for different graded links are provided, based on which the approach tries to use less flow entries to recover link failure and meets the required failure recovery delay, while guaranteeing the reliability of the network. Simulations show that backup-resource based approach can use as less flow entries as possible to ensure the performance of failure recovery and satisfy the required delay of important traffic at the same time. Moreover, the approach has good and steady performance in networks of different scales and connectivity.

Software defined network (SDN) develops a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to deploy multiple distributed controllers. In SDN, a switch can only work by relying on flow tables received from its controller. Therefore, controller placement is an important problem to keep the switches working efficiently and improve the reliability of the control network, which consists of controllers, switches and the communication paths between them. However, the existing controller placement approaches are not effective or do not consider the network reliability and the required delay between switches and controllers at the same time. In order to ensure the reliability of the control network and meet the required propagation delay, a min-cover based controller placement approach is proposed. Two metrics are proposed to measure the reliability of a control network, and the definitions of neighborhood and min-cover are provided, based on which the approach try to use less controllers to achieve the reliability and low delay of the control network while guaranteeing the manageability of the network. Simulations show that min-cover based approach can use as less controllers as possible to ensure the reliability of control network and satisfy the required delay at the same time. Moreover, the approach has steadily good performance in networks of different scales and connectivity.

Ultra-dense networks (UDN) can increase the network coverage and improve the overall throughput which make it a promising network technology. However, traditional resource allocation algorithms are concerned with the improvement of the overall performance of the network. This paper considers the quality of service (QoS) and energy consumption of each femtocell and proposes a game-theoretic hierarchical resource allocation algorithm in UDN. Firstly, a modified clustering algorithm is performed. Then we transform this resource allocation problem to a two-stage Stackelberg game. In sub-channel resource allocation, we aim to maximize the throughput of the whole system by cluster heads (CHs). The power allocation takes account of the balance between QoS requirement and transmit power consumption. Simulation results show that the proposed algorithm can obtain a performance improvement compared with other algorithms.

A hybrid satellite-aerial-terrestrial system has been recently studied as a promising candidate to meet the urgent communication needs of emergency relief, and a good resource allocation policy is important to solve the contradiction between the sudden growth of victims' demand and the shortage of wireless resource in emergency situation. This paper addresses the joint relay selection and power allocation for an OFDMA-based hybrid satellite-aerial-terrestrial cooperative network, aiming at maximizing the energy efficiency (EE) with power constraints, quality of service (QoS) requirements and backhaul capacity. The optimization problem not only is a mixed 0-1 nonlinear program, but also contains a fractional objective function and a non-convex constraint condition. To tackle this complicated optimization problem, we firstly relax the binary variables and then transform the fractional objective function into a subtractive one. In each iteration, the power allocation solution and relay selection policy are approached via dual decomposition method. Simulation results illustrate the impact of total transmit power and backhaul capacity on EE and system capacity. What is more, relay selections highly enhance the system performance.

Proactive caching at the mobile network edge has been considered as a promising technology for enhancing users' Quality of Experience (QoE) and reducing redundant transmissions over the already overburdened cellular networks. The problem of video file caching in wireless Device-to-Device (D2D) communication networks, in which mobile users designated as helper users store popular video files and serve other requesting users via D2D localized transmissions, is studied in this paper. As personalized video recommendation systems are widely applied in video sites such as YouTube and Netflix, they cause mobile users' diversification and individuation in file preferences and users may make selfish caching decisions. Moreover, designing the file placement in caches is a task of hugely computational complexity due to the vast number of involved files and users. In this paper, we simultaneously cluster users and files into different interest groups and then propose a greedy intra-cluster caching scheme to greatly reduce its complexity. And we also compare the performance of each clustering algorithm while the file preferences matrix becomes high dimensional, sparse and highly asymmetric. Simulation results confirm that, with markedly reduced complexity, our proposed greedy caching scheme with spectral clustering using cosine similarity as the distance measure achieves near-optimal delay performance.

This paper proposes a new energy efficiency (EE) model with circuit power consumption in downlink massive multiple-input multiple-output (MIMO) systems, and analyzes how the number of transmit antennas and the transmit power affect the EE. A concise model of the distribution of the mutual information and a new realistic power consumption model are used to draw a closed-form expression for the EE model. Mathematical analysis proves that the EE is a concave function of the number of transmit antennas and the transmit power, and the EE increases first and then decreases as the transmit power increases, indicating the existence of the optimal number of transmit antennas and the transmit power. An iterative algorithm is given to compute jointly the optimal number of transmit antennas and the transmit power. Simulation results show that when the circuit power consumption is comparable to the transmit power, there exists an optimal number of transmit antennas to maximize the EE.

Heterogeneous cloud radio access networks (H-CRANs), which take a separation of the remote radio units (RRHs) from the baseband units (BBUs), is considered to be a promising architecture for the future network due to its competitive advantages in both spectral efficiency and energy efficiency (EE). The RRH is mainly used to provide high data rate for mobile users (MTs) with high quality of service (QoS) requirements, while the evolved Node B (eNB) is deployed to guarantee the seamless coverage. To further enhance these benefits, in this paper, energy efficient uplink communications are investigated for H-CRANs with user cooperation and QoS guarantees. We formulate a joint optimization problem of relay selection, power allocation and network selection to maximize the EE of MTs with high QoS requirements. The optimization problem is a mixed-integer non-linear non-convex program and solved by Dinkelbach method and dual decomposition method. Moreover, a relay region selection algorithm is applied to reduce the computational complexity at the beginning of the optimization process. Numerical results show that the proposed scheme has improvement on the EE as compared with the scheme that joint power allocation and network selection and the scheme that joint relay selection and power allocation in H-CRANs.

In this study, an energy harvesting (EH)‐based cooperative non‐orthogonal multiple access (NOMA) system is considered, where node S simultaneously sends independent signals to a stronger node R and a weaker node D. The authors focus on the scenario that the direct link between S and D is too weak to meet the quality of service (QoS) of D. Based on the NOMA principle, R, the stronger user, has prior knowledge about the information of the weaker user, D. To satisfy the targeted rate of D, R also serves as an EH decode‐and‐forward relay to forward the traffic from S to D. In the sense of equivalent cognitive radio concept, R viewed as a secondary user assists to boost D’s performance, in exchange for receiving its own information from S. Specifically, transmitter beamforming, power splitter and receiver filter are jointly designed to maximise R’s rate with the predefined QoS constraint of D and the power constraint of S. Since the problem is non‐convex, they propose an iterative approach to solve it. Moreover, a zero‐forcing based low‐complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission.

This study considers a multi‐pair massive multiple‐input multiple‐output two‐way relay network where the M ‐antenna relay simultaneously serves K pairs of single‐antenna users in the same time–frequency resource. For a more general case of Ricean fading channel, the authors propose a fixed‐gain maximum ratio combing/maximum ratio transmission relay scheme. The approximate expressions on the ergodic sum rate with such scheme are derived in two cases: (i) M , which is much larger than the users’ Ricean factors, is large enough; and (ii) if M is large enough but bounded, the Ricean factors of all the users go to infinity. Simulation results show that the approximate results are very tight. Based on the result for the first case, they further discuss the power‐scaling laws, which reveal that despite the Ricean fading channel and the interference at the relay, the achievable sum rate can still remain unchanged if the transmitted power at each user or at the relay is or both are made inversely proportional to M .

This letter considers secrecy simultaneous wireless information and power transfer (SWIPT) in full-duplex (FD) systems. In such a system, FD capable base station (FD-BS) is designed to transmit data to one downlink user and concurrently receive data from one uplink user, while one idle user harvests the radio-frequency (RF) signals' energy to extend its lifetime. Moreover, to prevent eavesdropping, artificial noise (AN) is exploited by FD-BS to degrade the channel of the idle user, as well as to provide energy supply to the idle user. To maximize the weighted sum of downlink secrecy rate and uplink secrecy rate, we jointly optimize the information covariance matrix, AN covariance matrix, and receiver vector, under the constraints of the sum transmission power of FD-BS and the minimum harvested energy of the idle user. Since the problem is nonconvex, the log-exponential reformulation and sequential parametric convex approximation (SPCA) method are used. Extensive simulation results are provided and demonstrate that our proposed FD scheme extremely outperforms the half-duplex scheme.

Network virtualization has been considered as an enabling technology for future network, through which multiple heterogeneous virtual networks can run on a shared infrastructure. In order to study and test the network management mechanism of future network, we develop and implement a network operation simulation platform of the network virtualization environment. The platform mainly simulates the double-layer network topology and the virtual network embedding in the network virtualization environment. In addition, the running status and the fault of networks can also be simulated. The validation results show that our platform can effectively emulate the network virtualization environment. It has three advantages: (i) Simulating Double-layer network model. (ii) Running virtual network embedding experiments graphically and supporting comparison among different embedding algorithms. (iii) Simulating the faults of both substrate and virtual networks and emulating the detection results based on the simulated faults.

In network virtualization environment, if a virtual network (VN) needs to be deployed across multiple infrastructure domains, a splitting scheme of the VN should be found. With the goal of minimizing embedding cost, the existing methods solve VN splitting by linear programing. However, since the VN splitting problem is NP-Hard, these methods will take a lot of computing time when the problem scale gets bigger. In this paper, a max-flow/min-cut theory based VN splitting mechanism is proposed. The proposed method first creates a binary tree of the InPs by system clustering method, based on which the multidomain VN splitting problem is decomposed into several two-domain VN splitting problems. Then the method transforms each two-domain splitting problem into a max-flow/min-cut problem, and solves it by the shortest augmenting path algorithm efficiently. Simulations show that the proposed mechanism can improve the efficiency of VN splitting steadily and save the embedding cost.

Future small cells are expected to be energy-efficient and utilize green technologies. To this end, a promising solution is to employ automatic energy harvesting techniques, such as power splitting (PS) which harvests energy from ambient radio frequency (RF) signals in modern communication systems. In this paper, optimization on PS ratio design is investigated to maximize the average harvested energy in the context of general largescale K-tier heterogeneous cellular networks (HCNs). Specifically, coverage probabilities and average energy harvesting expressions are derived with the stochastic geometry treatment to elucidate the performance of future green networks. Then optimal fixed PS ratios for each tier are obtained under coverage performance constraints. Moreover, with receivers' position information effortlessly provided in future networks, a dynamic location-based PS ratio design (DLPS) is proposed to further enhance the energy harvesting performance. Simulation results are given to demonstrate that the average harvested energy is effectively increased by more than 30% when coverage probability requirement is greater than 0.7 by our proposed DLPS compared with the optimal fixed PS ratio while maintaining the coverage performance. Furthermore, rather than drawing the conclusions about the merits of our PS strategy, this work is to provide a tractable analytical framework for addressing the energy harvesting issues in such HCNs.

Multi-domain virtual network embedding (MVNE) aims to embed a virtual network (VN) across multiple physical domains while minimizing the embedding cost. A key phrase of MVNE is VN partitioning which partitions a VN into multiple physical domains. Since the MVNE problem is NP-hard, we provide a heuristic VN partitioning approach named VNP-PSO based on the Particle Swarm Optimization (PSO) to increase the efficiency of VN partitioning. The VNP-PSO algorithm generates a near-optimal solution of VN partitioning through the evolution process of the particles. The simulation results show that our proposal can increase the efficiency of VN partitioning and decrease the embedding cost of MVNE.

Survivable virtual network mapping (SVNM) has been extensively investigated to guarantee that the mapped virtual network (VN) works normally against substrate failures. The existing studies of SVNM mainly focus on single node or single link failure. Since natural disasters usually cause severe substrate failures in geographic regions, some work addressing SVNM against regional failures has been studied. However, the current approaches only solve the mapping problem against single regional failure. When there are multiple regional failures aroused by natural disasters, such approaches are not effective. In this paper, we first design a regional failure model with the knowledge of risk assessment. Then we propose two effective mapping algorithms based on the disaster-prediction scheme with the regional failure model. One is the minimum link risk prior selection algorithm and the other is the asymmetric parallel flow allocation algorithm. Simulation results show that both approaches can reduce the capacity loss of virtual networks caused by regional failures and can effectively increase the average VN acceptance ratio.

Network virtualization has been regarded as a core attribute of Future Internet. To improve the quality of virtual network, it is important to diagnose the faulty components quickly and accurately. Recently more and more researches focus on end-user fault diagnosis, which can fit incomplete knowledge and dynamic challenges. In this paper, we present a fault diagnosis system called DiaEO in virtual network. It improves the present end-user fault diagnosis methods by screening evidences before analyzing to reduce the time-consuming. Besides that, DiaEO also improves the anti-noise ability of the system. The simulation results show that the proposed method can keep high accuracy and ameliorate time performance.

As cells are getting smaller, more random and chaotic in future heterogeneous cellular networks (HCNs), mitigating interference to enhance coverage performance has become one of the key challenges. To elucidate the coverage and throughput performance of downlink HCNs, a tractable framework with frequency division (FD) scenario is provided. Coverage probability and average cell throughput expressions are carried out to further understand how FD effects system performance with the stochastic geometry treatment. Furthermore, with some plausible assumptions, we derive the specific closed-form expressions in some special cases. Simulation results show that the coverage performance is effectively improved by using FD comparing with conventional frequency sharing (FS) scenario, which are firmly consistent with our theoretical derivation. The analytical results of the present work also demonstrate that although some throughput will be lost compared to FS, it is still acceptable since UEs could choose any tier which offers greatest performance under FD scenario, hence this work further provides mobile operators a guideline for resource allocation schemes in future chaotic multi-tier networks.

Consider an underlay cognitive relay network with multiple source and destination pairs and that a decode-and-forward scheme is adopted at the relay. We propose a multi-phase transmission protocol in this paper which involves cooperation among source, relay and destination nodes. Given perfect interference elimination before decoding at the destination nodes, we derive closed-form expressions for outage probability calculation over Rayleigh fading channels. Furthermore, the optimal relay power allocation factor which leads to minimal outage probability is determined. Numerical results reveal the conditions under which satisfactory performance can be achieved based on the proposed scheme.

By exploiting the spatial correlation in spatial domain, a three-dimensional (3D) pilot aided channel estimation (PACE) has been proposed to improve the mean-square error (MSE) performance in 3D multiple- input multiple-output (MIMO) systems. However, with the development of 3D MIMO technique, there are increasing number antenna ports in a limited space. The pilot overhead in 3D PACE method which increase linearly with the antenna number becomes unacceptable. Since compressed sensing (CS) technique ignoring the theoretic upper limit in the pilot spacing derived by the sampling theorem has been successfully applied to pilot aided 2D sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems, we introduce the CS technique to 3D pilot aided channel estimation to reduce pilot overhead in 3D MIMO systems. Moreover, a random search method based non-uniform pilot allocation algorithm with low computational complexity is proposed to further improve the CS performance. Simulation results demonstrate that compared to the traditional evenly pilot for 3D PACE, our proposed non-uniform pilot for CS-based channel estimation its average gain can improves about 3.58dB with the same pilot overhead. The result also shows that by employing the CS-based channel estimation, pilot overhead can be sharply reduced without estimation accuracy loss.

In this paper, we look at the raising spatial antenna correlations in massive antenna arrays and leverage spatial correlation combined with Compressive Sensing (CS) theory in the process of channel estimation. According to CS, the success probability of recovery is highly dependent on the restricted isometry property (RIP) of dictionary matrix. Recent advances in CS suggest that minimizing the coherence of dictionary matrix is an alternative efficient and effective way to test RIP. In this basis, this paper addresses the pilot pattern design problem in spatial domain aiming at minimizing the averaged coherence of the dictionary matrix. We first formulate an optimization problem with regard to pilot power distribution (PPD) and pilot antenna indexes set (PAIS) in CS-based channel estimation. Then two algorithms are proposed to separately design PPD and PAIS. Moreover, a jointly optimizing algorithm is presented. Simulation results demonstrate that the designed CS-based spatial pilot pattern outperforms random pilots and equal pilots, which significantly reduce pilot overhead and improve channel estimation quality compared with linear square (LS) estimation in spatial domain for massive antenna arrays.

The performance and reliability of services relies on the network virtualization environment's capabilities to effectively detect and diagnose faults in both substrate and virtual network. However, Network Virtualization Environment (NVE) brings to fault diagnosis new challenges such as inaccessible substrate network information and multi-layer faults. To solve the above issues, a Multi-layer Fault Diagnosis Method (MFDM) is proposed. A layer-by-layer strategy is used to resolve the problem of inaccessible substrate network information. And a filtering algorithm is proposed to distinguish the multi-layer faults in the network virtualization environment. At last, a contribution-based hypothesis selection algorithm is proposed to infer the most possible faults. Simulations and experimental results show that MFDM has a higher performance in the accuracy ratio, false-positive ratio.

Survivable virtual network embedding (SVNE) aims at embedding a virtual network (VN) in a way, that after being affected by substrate failures, the VN is still operating. Based on the single node failure assumption, that at any time there can be at most one failed substrate node, the existing studies for the SVNE against substrate node failures back up VNs with a maximum resource sharing. However, multiple node failures do happen in reality, thus those methods are not always effective. In this paper, we propose a topology-aware VN embedding approach to enhancing the survivability against multiple node failures. We make use of the topology attributes to provide each substrate node with multiple potential failover choices, based on which a recoverability-based VN embedding algorithm and a profit-driven VN remapping algorithm are presented. Simulation results show that the proposed approach can achieve rational resource allocation and effectively increase the long term business profit to the infrastructure provider.

This paper investigates coordinated resource allocation for 3-dimension (3D) antenna array systems in mul-ticell multiple-input multiple-output (MIMO) and orthogonal frequency division multiple-access (OFDMA) wireless networks. Cell-center user and cell-edge user specific downtilts are accordingly partitioned through dynamic vertical beamforming in the 3D MIMO-OFDM communication systems. Taking these user specific downtilts into consideration, the objective of our proposed coordinated resource allocation scheme is to maximize both the cell-edge users' and cell-center users' throughput, subject to per base-station (BS) power, cell-center user and cell-edge user specific downtilt constraints. To solve the coordinated resource allocation problem, resource blocks (RBs) are accordingly partitioned for cell-center users and cell-edge users, by referring to the fractional frequency reuse (FFR) scheme. Based on such RB partitioning, FFR-based dual decomposition method (FDDM) are proposed, where RB assignment, power allocation (RAPA) and downtilts adjustment are jointly optimized. Simulation results demonstrate the efficacy of our proposed coordinated resource allocation scheme.

We address the problem of inferring the network link loss rates using end-to-end measurements, which can also be formulated as network tomography. As we have known that most tomography problems are rank-deficit. One kind of method uses multiple probe measurements to acquire more information about the system that may generate much additional overhead; the other method imposes unrealistic assumption on the system. To address the issue that most network tomography methods cannot take into account both accuracy and efficiency, a novel link loss rate inference algorithm is proposed. In this paper, we get all identifiable links and then we utilize the information of these determined links to acquire the global distribution of the system. Moreover we partition all links in the network into several subsets. For each group, nonlinear programming is used to get the optimization solution of link loss rate. Finally, we evaluate our method and two former representative methods by the simulation. The results demonstrate that our method not only reduces the probe costs and the running time to a low level, but also makes a great improvement on the accuracy. Furthermore, our method can also perform well in more congested and large networks.

Network virtualization has been regarded as a core attribute of the Future Internet. In a Network Virtualization Environment (NVE), heterogeneous virtual networks can share the same physical infrastructure regardless of their different topologies, demands, protocols and so on. In this case, the Survivable Virtual Network Embedding (SVNE) problem becomes increasingly critical to overcome the failure of physical infrastructure. Backup resources needed to provide survivability of virtual network undoubtedly increase the challenge of resources efficiency of SVNE. In this paper, we study the SVNE problem and propose a method of allocating bandwidth resources based on load balancing of the physical resources and a strategy of reconfiguring backup resources. Simulation experiments show that load balancing based method has a higher performance in the long term acceptance ratio, revenues and utilization of substrate links. And the reconfiguration of backup resources is cost-efficient and also helpful to increase the acceptance ratio.

In this paper, we study the radio resource allocation for femtocell networks in orthogonal frequency division multiple access (OFDMA) systems from the aspect of users' quality of experience (QoE). Three types of services are considered including audio stream, data stream and video stream. With the metric being mean opinion score (MOS), a two-tier network is modeled and optimization problem is formulated for maximizing the system performance with lower consumed power. Given the Weber-Fechner Law, we investigate the logarithmic nature of QoE. Afterward, a two-stage iterative algorithm is proposed to resolve such optimization problem. Simulation results show that the overall MOS achieved is significantly higher than the traditional algorithm with a lower power consumption.

Energy efficiency (EE) has become an increasingly important issue in Device-to-Device (D2D) communications since wireless terminals are hand-held equipments with limited battery life. In this paper, an energy-efficient channel reusing scheme for multi-D2D links is proposed. We first analyze the EE of a single D2D link in both non-cooperative mode (NCM) and cooperative mode (CM), and prove that the EE of the D2D link is mainly determined by the location of the cellular user equipment (CUE) that shares resource with the D2D pair. On this basis, a location-based algorithm (LBA) is proposed to select the optimal CUE for each of the D2D pairs, aiming to maximize the sum EE of all D2D links. Numerical results show that the proposed LBA could effectively improve the overall EE of the D2D system while guaranteeing the target rate of each D2D link. Moreover, the proposed LBA does not require the channel state information (CSI) of all the involved links, which could significantly reduce the feedback overhead and computational complexity.

This paper investigates coordinated resource allocation for cell-center and cell-edge users in multicell orthogonal frequency division multiple-access (OFDMA) wireless networks. Here, we assume the data symbols of each user is only known by one base station (BS). Referring to the fractional frequency reuse (FFR) scheme, the resources for cell-center and cell-edge users are partitioned. Based on such resource planning, the objective of our resource allocation scheme is to maximize both of cell-edge and cell-center users' throughput, subject to per base-station power constraints. Moreover, the proposed resource allocation scheme also considers the cell-center users' performance requirement. Note that the ratio of transmit powers for cell-center users to cell-edge users can be adjusted in FFR scheme, which is introduced in the so-called soft frequency reuse (SFR) scheme. We propose a modified dual decomposition method (MDDM) to solve the above-mentioned resource allocation problem, where the joint subcarrier assignment and power allocation are performed. To simplify the computation complexity, a suboptimal algorithm (SOA) is presented to decouple the optimization problem into three sub-problems. Simulation results demonstrate the efficacy of MDDM and SOA.

Adaptive active diagnosis method is widely adopted for fault diagnosis in networks. In active diagnosis, appropriate probes are selected sequentially and fault diagnosis is made by inference from results of selected probes. It is very important to select active probes with low cost and less impact on network performance. However, the selection of the most informative set of probes with limited cost is an NP-hard problem. The computational complexities of existing probe selection algorithms are still too high for large scale networks. In this paper, a lemma about mutual information provided by probes is proved based on the property of conditional entropy. Then an approximate method derived from this lemma is introduced to compute mutual information of probe. With this approximate method an efficient probe selection algorithm for active diagnosis is proposed. At last, the efficiency and effectiveness of the proposed algorithm is verified through simulation.

The paper investigates cluster-based Cooperative Spectrum Sensing (CSS) issues in two-layer hierarchical Cognitive Radio Networks (CRNs). Most previous studies focus on hard decision fusion under the assumption of perfect reporting channels. However, wireless channels are not error-free in reality. Therefore, we propose a novel cluster-based CSS scheme with imperfect reporting channels between SUs and cluster heads (CHs) to optimize cluster strategy. A weighted reporting channel method is adopted in our analysis. To further reduce the complexity, 1storder Taylor series expansion approximation scheme is proposed. Numerical results show that the proposed scheme achieves a satisfying performance.

Traditional two-dimensional (2D) multi-input multioutput (MIMO) beamforming technologies can only adjust the beamformers in the horizontal dimension according to horizontal channel information. However, due to the three-dimensional (3D) character of the real channel, 2D MIMO beamforming technologies can not achieve the optimal system throughput. In this paper, a 3D MIMO beamforming scheme is proposed, which takes into account vertical beamforming. In the proposed scheme, we first design a codebook for Vertical dimension based on 3D MIMO channel model; then a 3D MIMO beamforming scheme is proposed combining the proposed Vertical beamforming codebook and legacy horizontal dimension beamforming codebook. Through simulation, we evaluate the proposed 3D MIMO beamforming scheme and compare it with former 2D beamforming technology. Owing to the additional spatial degrees of freedom in Vertical dimension, our 3D MIMO beamforming scheme can effectively improve the overall system performance.

This paper proposes a scheme combining multi-cell downlink joint transmission(JT) with 3- Dimensional(3D) beamforming. The proposed scheme is based on the following two observations: 1) The performance of multi-cell joint transmission is highly dependent on the received signal strength from the coordinated cells, 2) Interference may become more severe at the cell boundary due to the extremely flat beams serving the cell edge users with 3D beamforming. In the proposed scheme, users at the cell boundary are served with JT and 3D beamforming simultaneously, and those at the cell center are served with 3D beamforming. Simulation results based on LTE-Advanced systems show that significant gains can be achieved by the scheme, with about 14% gain on cell average spectral efficiency and about 58% gain on edge user spectral efficiency compared to traditional single cell transmission with 2-Dimensional(2D) beamforming.

Cellular structures are to be enhanced by heterogeneous deployments. One type of such deployments is to add outdoor pico evolved NodeBs (PeNBs), formulating a hierarchical cell structure with macro eNBs (MeNBs). The cell selection in such system is facing challenges, including problems of coverage and cross interference. One method is called cell range expansion (RE), typically in which a common fixed bias value is added to the users' receiving signal power from PeNBs to adjust their coverage. However, it might not be appropriate to use the same bias for all the PeNBs. This paper focus on the specific bias design. By defining the interfered region, it proposes a mechanism to obtain the average interference suffered by each PeNB. The interference is from MeNBs with sector antennas and other randomly located PeNBs. In the discussion, it is further pointed out that its implementation follows a self-organized manner, with sustainable signalling overhead. Simulation results show that the proposed method achieves much better cell edge performances than those with fixed bias values.

In long term evolution (LTE) downlink indoor scenarios, we study an inter-cell interference coordination (ICIC) by interacting the relative narrow-band transmit power (RNTP) indicators among evolved NodeBs (eNBs). Assuming the data symbols of each user is only known by one eNB, a novel RNTP-based resource block (RB) allocation scheme is presented and carried out in two steps: 1) Each eNB partitions RBs for celledge and cell-center users, and then transmits such information to other eNBs within RNTP bit maps; 2) Based on the received RNTP bit maps, a novel scheduling algorithm is proposed to allocate RBs for cell-edge and cell-center users in each eNB. Simulation results show that the proposed RNTP-based RB allocation scheme improves the cell-edge users' performance, and yields up a certain overall cell throughput enhancement.

In cognitive radio (CR) systems, efficient spectrum sensing ensures secondary users (SUs) to successfully access the spectrum hole. Typically, the detection problem has been studied separately from the optimization of throughput of secondary network. However, due to non-zero probabilities of miss detection and false alarm, the sensing phase has an impact on the throughput of CR networks as well as on the transmission of primary users (PUs). In this paper, using energy detection, we maximize the total throughput of all SUs by jointly optimizing the detection threshold and resource allocation in multiband CR systems. Efficient algorithms including online and offline solutions are proposed to solve the proposed mix-integer programming problem, which show better performance compared with traditional uniform detection threshold selection algorithm.

Spectrum sensing, as a key technology of cognitive radio (CR), needs to reliably and efficiently detect spectrum holes in wireless environments, which challenges the traditional spectral estimation methods typically operating at or above Nyquist rates. This paper develops a novel compression ratio allocation (CRA) method for wideband spectrum sensing in CR networks. In our scheme, each CR terminal performs compressed sensing with sub-Nyquist rate samples to scan a wide spectrum range at practical signal-acquisition complexity. It can greatly reduce the sensing measurements through fewer sample numbers. Meanwhile, the cognitive base station optimizes the compression ratio at each CR terminal according to their local signal-to-noise ratio (SNR), so the total sample number can be further cut down. Simulation results show that the CRA algorithm provides an optimal performance while requiring a relatively low complexity of sensing process.

In Long Term Evolution (LTE) systems, the evolved Node B (eNodeB) performs link adaptation to improve the system performance on the basis of channel quality information estimated and fedback by the user equipments (UEs). However, the channel estimation error (CEE) can not be avoided due to hardware constraint. To make up the performance loss caused by CEE, two types of link adaptation algorithms are proposed: weighted average (WA) algorithm based on three different parameters and an enhanced adaptive time window (ATW) algorithm. Unlike pervious work, this paper not only presents the specific process of all the algorithms but also takes the theoretical derivation of performance comparison into consideration. Moreover, simulation results are demonstrated using an LTE system-level simulator, which show that the WA algorithm based on channel estimation value can effectively mitigate CEE and achieve a significant gain. Subsequently, a better system performance can be obtained using ATW algorithm by adjusting the window size dynamically.

In this paper, we study joint spectrum sensing and resource allocation for heterogeneous services in multi-band cognitive radio systems. Two types of services are considered: delay-sensitive (DS) services and delay-tolerant (DT) services. Considering the influence of the probabilities of miss detection and false alarm, the detection threshold, power and sub-channel allocation are jointly optimized to maximize the total data rate of DT services while satisfying the delay requirement of DS services. For the protection of primary transmission, a new criterion referred to as rate loss constraint is introduced. With the queue theory, the delay requirements of DS services are transformed into constant rate requirements. The optimization problem is formulated as a three-variable non-convex problem under constraints. Moreover, by dividing the optimization problem into two stages, an iterative dual decomposition method is proposed to solve it. The effectiveness of our proposed algorithm is evaluated by extensive simulations and compared with existing algorithms.

This paper addresses overhead-throughput tradeoff issues for the centralized cooperative cognitive network. Taking the reporting overhead into consideration, a novel frame structure consisting of M + 1 subframes is proposed to maximize the achievable throughput of the cognitive network. According to the channel condition between primary user (PU) and each secondary user (SU), the overhead-throughput tradeoff problem is investigated in two scenarios. For scenario I, we focus on optimizing the number of reporting secondary users (SUs) to achieve overhead-throughput tradeoff. For scenario II, we not only optimize the number of reporting SUs, but also design reporting SUs' selection methods. Numerical results show that under the proposed frame structure, there exists an optimal number of reporting SUs to achieve overhead-throughput tradeoff, and maximize the achievable throughput of the cognitive network.

In this paper, we investigate the power saving issue in cognitive radio (CR) multi-hop relay network. Due to the dynamic property of the wireless channel, the quality of service (QoS) guarantee for multi-hop transmission is quite challenging. To deal with these problems, automatic repeat-request (ARQ) protocol in an end-to-end manner is incorporated. For multi-hop transmission evaluation purpose, the end-to-end packet delivery probability is put forward as a QoS indicator in this paper. Besides, by underlay spectrum sharing, each relay is possessed of a power budget (i.e., maximum transmit power) to protect primary user from suffering intolerable interference. This paper addresses the power saving problem under each relay's power budget constraint, which means the end-to-end QoS constraints can be satisfied with the minimum total power consumption for relays along the optimal path. Motivated by this, we propose a joint Lagrange dual method based power allocation and exhaustive search based relay selection algorithm to obtain the solution. Numerical simulations are presented to validate the theoretical analysis. The results show that the proposed algorithm achieves a good performance in power saving.

As one of the essential characteristics for CRN, the cognitive reconfiguration can automatically adjust the cross-layer parameters to meet the user requirements, realize interoperability between heterogeneous networks and adapt to the time-varying environment. However, the cross-layer parameters reconfiguration implementation is still challenging due to its need for complex environment cognition and multi-objects optimization. In this direction, ant colony optimization (ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the adaption. The aim of this paper is to present a generic cross-layer parameters reconfiguration framework including indispensable function entities for autonomous reconfiguration decision making with regard to the multiple and complex objectives. Finally, numerous results prove the effective performance improvements of ACO based reconfiguration solution in CRN.

This paper studies a spectrum sharing cognitive radio (CR) network coexisting with a primary network. In particular, the channel state information (CSI) between the secondary transmitter (STx) and the primary receiver (PRx) is assumed to be outdated due to channel feedback latency. We assume that the secondary user (SU) shall satisfy a given delay quality-of-service (QoS) constraint as well as the average interference power constraint. Our aim is to obtain the maximum arrival rate of the SU under aforementioned constraints with the outdated CSI. In this respect, we derive the optimal power allocation scheme to achieve the maximum effective capacity, and further derive the effective capacity. The closed-form expressions for the lower and upper bounds on the effective capacity are also provided. Numerical and simulation results are presented to show the effects of the outdated CSI. It is shown that the effective capacity of the SU is insensitive to the channel correlation coefficient especially under low channel correlation coefficient.

This paper addresses the evaluation of channel quality indicator (CQI) delay compensation schemes considering the impact of signaling interaction delay between evolved node B (eNodeB) and user equipments (UEs). The Jakes' model and International Telecommunication Union (ITU) channel models are introduced, which are the representatives of simple and complex simulation environments, respectively. The channel characteristics and the variation of CQIs of four typical scenarios are analyzed in detail. We then propose two types of CQI delay compensation schemes, namely weighted mean scheme (WMS) and prediction scheme based on normalized least mean square (NLMS-PS). Evaluation results show that both of these schemes do not work well on ITU channel models because system performance of complex simulation environment depends on not only the accuracy of CQI, but also the combined effects of the scheduling and other factors. However, these proposed schemes can boost the system performance effectively based on Jakes' model when the CQI delay is less than 5 ms.

In cooperative cognitive radio networks (CCRNs), the reporting time is consumed by all secondary users (SUs) for reporting the sensing results to the central node or fusion center. Intuitively, the performance of CCRNs will be degradation, since the more SUs for cooperation, the more reporting time for reporting sensing results. However, most previous studies have not considered this point. In this paper, a cluster-based cooperative spectrum sensing model is proposed in order to reduce the reporting time. Based on a novel frame structure, a sensing-throughput tradeoff problem considering the reporting time is formulated for two scenarios. The optimal clustering rule is obtained by maximizing the transmission time of all SUs. Then, a low-complexity solution is proposed to solve the tradeoff problem, which shows close-to-optimal performance by simulation.

In this paper, we consider the power saving issue in the cluster based multi-hop cognitive radio (CR) network with one pair of primary user (PU) in presence. By underlay spectrum sharing, the transmit power of CR nodes are strictly restricted to protect PU from suffering severe interference. Moreover, the end-to-end outage probability is put forward as an essential QoS indicator for multi-hop transmission and has been carefully studied in the article. The objective of this paper is to minimize the total power consumption of CR transmitters along relay path. Both the end-to-end outage requirement and power budget of relays are incorporated as constraints. To solve the formulated problem and obtain the optimal solution, we propose a joint Lagrange dual method based power allocation and objective oriented optimal relay selection algorithm, and give thorough evidences and illustrations as well. Finally, numerical simulations are made and results demonstrate that the proposed algorithm has a good performance in power saving.

This letter addresses the joint linear processing issues for 3-phase two way cooperative MIMO relay systems, aiming to minimize the mean squared error (MMSE). Considering the difficulty to acquire the channel state information (CSI) of the direct link at relay node, we first derive an iterative linear processing scheme for the relay node ignoring the direct link between two source nodes. Then the receive processing matrix combining the signal from direct link and relay link can be easily given according to MMSE orthogonality principle. Finally by comparing other schemes that can accomplish bidirectional data exchange, simulation results show that our proposed 3-phase cooperative scheme can achieve better tradeoff of link reliability and spectrum efficiency especially when the direct link is in medium quality.

This paper takes into account the uncertainty of the primary users' locations and transmission power in designing an optimal downlink scheduling scheme for cognitive radio cellular networks (CogCells). Localization technique is exploited to estimate the position and transmission power of the primary user (PU) transmitting on specific channel. The objective of our scheduling scheme is to maximize the downlink average throughput for CogCells without causing harmful interference to PUs. This paper models the problem as a mixed integer nonlinear programming (MINLP) problem, which has exponential complexity by traditional direct search method. An efficient joint channel assignment and power control scheme based on dual decomposition method is proposed. Firstly, the dual optimization problem is decomposed into K independent subproblems of channel assignment. Karush-Kuhn-Tucker (KKT) conditions are then applied to find the optimal user for a specific channel. Secondly, ellipsoid method is applied to update the dual variables and find the optimal solution for the primal problem. Numerical results demonstrate the effectiveness of the proposed scheme.

In this paper, a novel scheme for MSOT scenario (multi-service on one terminal)using link sharing to guarantee service continuity is proposed. Fuzzy control theory is applied for designing the sharing strategy. The functionalities of mobile terminal for link sharing are modular design considering easy implementation. The performance of the proposed algorithm is analyzed and compared with fixed percentage allocation algorithm (FPA).

In this paper, we study the cross-layer resource allocation for the secondary users (SUs) supporting heterogeneous services in interference-limited spectrum sharing system. Two classes of SUs are considered: delay-tolerant SUs (DT-SUs) and delay- sensitive SUs (DS-SUs). With the queue theory, the delay requirements of DS-SUs are transformed into constant rate requirements. Unlike most previous works, this paper formulates the optimization problem by taking heterogeneous Quality of Service (QoS) of both primary users (PUs) and SUs into consideration. Moreover, based on the convex optimization theory, we propose the dual decomposition method in which the joint subcarrier assignment and power allocation are performed to achieve the optimal solution. To simplify the computation complexity, a suboptimal algorithm is proposed to decouple the optimization problem into two sub-problems. Simulation results show that the system performance achieved by using the proposed suboptimal algorithm is close to that achieved by the dual decomposition method.

This paper addresses the linear processing issues for two way non-regenerative MIMO relay systems with multiple antennas at each node. Based on theoretical derivation, we first propose an optimal joint iterative linear processing scheme for the relay node and receiving source nodes, aiming to minimize the total mean squared error (MSE). The convergence of this iterative algorithm is proved in terms of analysis and simulation. Then in order to reduce the practical complexity in real systems, a closed form suboptimal solution is also derived. Finally, numerical results are provided to show the performance gain of the proposed schemes.

For wireless sensor networks (WSNs), which have crucial limitation on energy as all the nodes rely on nonrenewable batteries and are often inconvenient to be replaced or recharged, the energy efficiency that has a close relationship with system longevity becomes a significant issue. In this paper, a cross-layer strategy in terms of physical layer and network layer is proposed for maximizing equilibrium lifetime of WSNs. In the proposed strategy, the residual energy ratio (RER) of individual node is taken into account and regarded as a key factor in power control and routing selection. Utilized to identify the relay capability of an intermediate node, the RER is quantized as a specific access probability, which is highly related to the contention window of each node in the end-to-end transmission link. By way of intelligent and efficient allocation of transmission power, and reasonable routing, the proposed cross-layer tactic can achieve preferable performance with energy efficiency. Simulation results prove that the cross-layer based equilibrium power control and routing (EPCR) strategy could greatly prolong the system lifetime of WSNs.

This paper addresses power allocation issues for amplify-and-forward (AF) two-way relay systems. Based on theoretical derivation, we first propose an energy-efficient power allocation (PA) strategy for 2-phase and 3-phase two-way systems with single relay or multi-relay, under a certain transmission data rate of source nodes, aiming to minimize the system energy consumption. Numerical results show that this energy-efficient PA strategy can balance the system performance and achieve about 3dBw power gains compared to the uniform PA.

One of the most important features of the evolving Fourth Generation (4G) wireless communication system is heterogeneous wireless access in which users could connect to several wireless access networks simultaneously. The new feature brings new challenges for the bandwidth allocation (BA) among heterogeneous networks. A non-cooperative bandwidth allocation game (NCBAG) algorithm for heterogeneous wireless networks is proposed in this paper. A BA problem is modeled as a non-cooperative game, and formulated to maximize the total utility of different networks. The existence of Nash equilibrium is verified for the proposed game model. The utility functions are developed for different applications to avoid assigning too much bandwidth to single user. Simulation results show that our proposed scheme can not only achieve high utility, but also reduce the blocking probability within a few steps of iteration.

This paper propose an iterative inter-cell interference coordination strategy to meet the requirements of the next generation mobile communication systems, which are higher data rate, broader coverage, especially more stringent demand on cell edge spectral efficiency. Sector cooperation is introduced in MU MIMO (multi-user multiple input multiple output) systems. In the aspect of user pairing, both the system capacity and fairness among users are taken into consideration through pairing center and edge users. As for the inter-cell interference coordination, iterative sector cooperation on selecting pre-coders from code books is implemented, in order to avoid inter-cell interference and improve cell edge performance. Theoretical analysis and simulation results demonstrate that the proposed strategy can significantly improve cell edge throughput while guaranteeing the performance of average cell spectrum efficiency.

This paper addresses the filter design and user selection issues for the downlink of a multi-user MIMO-relay system. To eliminate interuser interference, the filter design is based on the singular value decomposition (SVD) of the first hop link and block-diagonalization (BD) for the second hop link. Then the problem is converted to the power allocation problem at the relay station (RS). It can be determined in a closed- form by the water-filling policy. In a practical system with a large number of users, the RS need to select a subset of best users to serve. The exhaustive search for the optimal userset is, however, computationally prohibitive. Therefore, we propose a low-complexity algorithm that is based on the MIMO-relay channel capacity. Simulation results show that the proposed linear filter design scheme achieves significant system performance improvement compared with the equal power allocation scheme and the user selection scheme allows for a reasonable tradeoff between the complexity and performance.

This paper studies the power allocation (PA) and distributed antenna (DA) selection in the downlink orthogonal frequency division multiplexing distributed antenna systems (OFDM-DAS). Two schemes are analyzed, with the objective of maximizing the downlink received signal power. In the first scheme, the DA selection is based on large-scale fading only and the related PA is simple. It is proved that at most three DA ports are enough for the transmission. In the later scheme, the DA selection is based on both large-scale and small-scale fading, and an optimal power partition result to the utilized DA ports is shown for each individual subcarrier. Then a novel resource allocation problem with suboptimal objective is formulated, which is modified to an integer program for complete solution. Finally, the advantages of the two schemes are combined and a suboptimal algorithm is proposed. Simulation compares various transmission strategies of OFDM-DAS and the proposed algorithm shows promising performance.

In this paper, heterogeneous services in multiuser multiple-input multiple-output (MIMO)-Relay systems are investigated, where the relays work in amplify-and-forward mode. A utility based adaptive scheduling algorithm is proposed, which aims to maximize user satisfaction as well as system spectral efficiency. Joint optimal carriers and spatial subchannels allocation is considered based on the proposed utility function, and two factors ratio and w are introduced in order to differentiate QoS and users. Moreover, a suboptimal solution is also presented for the sake of decreasing computational complexity and processing delay. Simulation results show that the proposed strategy can guarantee QoS of multiple services with a tolerable decline in terms of spectral efficiency compared with traditional maximum carrier/interference (MCI) algorithm. Besides, the complexity can be reduced obviously by utilizing the suboptimal scheme, which achieves nearly the same performance compared with the optimal solution.

In this paper, we focus on the development of effective and practical scheduling algorithm for heterogeneous services per user of orthogonal frequency division multiplexing distributed antenna systems (OFDM-DAS). We take two scenarios into account: 1)Fixed distributed antenna connection (FDAC), in which each user can connect to only one distributed antenna (DA) port; 2)Dynamic DA connection (DDAC). In this scenario the power constraint of single DA port is considered and users can dynamically connect to the DA ports. Firstly, the optimization problem of heterogeneous services based on utility function is formulated and a algorithm namely joint dynamic subcarrier assignment and sharing (JDSAS) is proposed. Then, in FDAC scenario, a suboptimal algorithm namely FDAC-JDSAS is proposed. In DDAC scenario, a DDAC-JDSAS algorithm is evolved from FDAC-JDSAS by converting the power constraint to subcarrier constraint. The design goal is to fully exploit multiuser diversity gain and DA selection diversity gain while guaranteeing the quality of service (QoS) for delay sensitive services. Numeric results show that, in both FDAC and DDAC, our algorithms outperform traditional M-LWDF algorithm in terms of FTP throughput and fairness criterion.

This paper presents a robust linear processing scheme for the downlink of MIMO-relay systems with imperfect channel state information (CSI) aiming to minimize the mean squared error (MSE). The imperfect CSI consists of the channel estimate and the estimation error covariance matrix. The robust linear processing is based on the associated Karush-Kuhn-Tucker conditions. After obtaining the solution to the optimization problem, we investigate the effects of the channel estimation errors and antenna correlation on system performance. Simulation results show that the robust linear filter design scheme is capable of alleviating the effects of CSI errors and improving the robustness of system.

This paper investigates proportional fairness-oriented scheduling issues for multihop OFDMA systems with multiple relays. Based on the idealized L-hop linear network model, three adaptive proportional fair scheduling (PFS) algorithms, namely optimal PFS, iterative user pairing (IUP) PFS and successive distributed (SD) PFS, are proposed for multihop OFDMA systems. Different with the existing scheme presented in former work, these three algorithms could be applied to OFDMA based multihop (more than two-hop) systems. The optimal PFS is presented as an upper bound in terms of proportional fairness, which involves exponential times of calculations. Thus we propose other two simpler algorithms for practical issue. Given the feature of multihop networks, they try to balance the aggregate data rates of each hop. Simulation results show that both IUP PFS and SD PFS achieves a good tradeoff between performance and complexity.

We consider relay selection technique in a cooperative cellular network where user terminals act as mobile relays to help the communications between base station (BS) and mobile station (MS). A novel relay selection scheme, called Joint Uplink and Downlink Relay Selection (JUDRS), is proposed in this paper. Specifically, we generalize JUDRS in two key aspects: (i) relay is selected jointly for uplink and downlink, so that the relay selection overhead can be reduced, and (ii) we consider to minimize the weighted total energy consumption of MS, relay and BS by taking into account channel quality and traffic load condition of uplink and downlink. Information theoretic analysis of the diversity-multiplexing tradeoff demonstrates that the proposed scheme achieves full spatial diversity in the quantity of cooperating terminals in this network. And numerical results are provided to further confirm a significant energy efficiency gain of the proposed algorithm comparing to the previous best worse channel selection and best harmonic mean selection algorithms.

The scenario that different RATs are used to satisfy mobile user's two or more application requirements simultaneously in heterogeneous network is called multi-service environment. Recently, powerful multi-mode wireless terminal, which is suitable for multi-service situation, has become the test product. The mobility management (MM) issues, especially handover, of different interfaces in the test product are resolved independently with a network layer solution FMIPv6. The packet loss of FMIPv6 could become an MM bottleneck. A potential approach of improving packet loss performance jointly between interfaces in multi-service scenario exists in our research. Therefore, this paper proposes a novel scheme using IEEE 802.21 MIH services to improve packet loss performance by utilizing the active links to maintain the data flow. The MIH services, Link_Action and MIH_Link_Action, are extended and an MIH event named Link_PDU_Receive_Status is added. A complete message exchange in handover procedure is provided. Numerical analysis shows that the proposed scheme performs better in terms of packet loss comparing with the traditional independent FMIPv6 scheme.

In cellular network, dropping probability of handoff calls (Pd) and blocking probability of new calls (Pb) are two important QoS measures. In this paper, focusing on the problem of how to minimize blocking probability of new calls with guaranteed dropping probability of handoff calls, a new priority queueing policy with finite queue size is proposed. And a two-steps algorithm is developed for optimizing queue size of the proposed policy and other parameters, so as to achieve the minimum value of Pb. In addition, recursive formulas are derived to deal with complexity of computing Pd and Pb. Both theoretical analysis and numerical results show that proposed policy could effectively minimize blocking probability of new calls with guaranteed dropping probability of handoff calls.

This paper investigates joint power and subcarrier allocation issues for cellular OFDM-relay networks. Two novel decentralized schemes, namely semi-distributed method and distributed method based on multihop equilibrium are proposed, which aims to exploit the radio resource management (RRM) function for relay nodes (RNs) and decrease the amount of feedback information for RN-MS links. In decentralized mechanism, the base station (BS) distributes resources to direct users and RNs roughly first with partial CSI feedback or without CSI feedback for RN-MS link, and then the RNs allocate resources for each relay user by striking an efficient balance for the multihop transmission. Simulation results show that the proposed decentralized methods can achieve good performances in terms of average throughput and fraction of satisfied users, especially for relay users with multihop equilibrium mechanism. Moreover, the semi-distributed method is better choice for future LTE-A system due to excellent tradeoff between performance and complexity.

This paper addresses the filter-design issues for multi-user non-regenerative MIMO-relay systems. Based on the perfect channel state information (CSI), optimal joint linear filter schemes at the base station and the relay are derived, aiming to minimize the mean squared error (MSE). We first propose the joint optimal filter scheme in the downlink scenario along with a more practical suboptimal scheme, and then a closed-form optimal solution in the uplink scenario is exploited. Numerical results show that the proposed joint schemes can reduce the bit error rate (BER) significantly, especially for the high SNR case.

In distributed antenna systems (DAS), the scenario that each antenna port is a multi-antenna array with multi-user is far beyond thoroughly studied. In this paper, the performance of four extended methods for DAS downlink transmission are analyzed and compared in such a scenario, two of which are based on the block diagonalization (BD) algorithm, namely joint BD and intra BD method. The other two methods are joint time division multiplexing (TDM) method and central antenna system (CAS) method. Both theoretic analysis and insightful simulations are utilized to evaluate these four methods. Theoretic analysis shows that the intra BD method requires less channel state information at transmitter (CSIT) and has lower computational complexity and process latency. Simulation results show that the intra BD method suffers only a little performance loss compared with the joint BD method. Overall, the intra BD method is proved to be a best tradeoff which achieves high capacity with relatively low complexity when power constraints are considered.

In heterogeneous wireless networks, rank aggregation based on multiple decision factors has recently been proposed as a useful approach for network selection. For each decision factor, a rank of the candidate networks is derived according to the value of this decision factor. Then these decision factor dependent ranks are aggregated into a single rank and the top one is considered as a favorite network. However in the realistic scenario, the measurement on decision factor is often inaccurate so that the candidate networks are possibly not ranked appropriately. As a result, if there only exists the slight differences between the decision factor values of several candidate networks, same rank is preferable to these networks. The set of networks is therefore separated into several groups and these groups are ranked accordingly. Moreover the networks tied in the same group have the same rank. In this paper, a new approach namelymedianbasednetworkselectionmethodis therefore proposed to handle such partial tied rank aggregation problem.

This paper focuses on adaptive resource scheduling for multiple services per user at the downlink of multiple input multiple output (MIMO) - orthogonal frequency division multiplexing (OFDM) system. In future wireless networks, one user will simultaneously require multiple homogeneous or heterogeneous services. Then, the scheduling algorithm is responsible for not only assigning resource blocks to different users but also distributing the assigned resource blocks among multiple services for one user. This paper firstly formulates this integrated optimization problem based on utility function in homogeneous service system. As the solution to the optimization problem requires high computational complexity, a sub-optimal and low-complexity algorithm is proposed with two theorems for practical implementation. Moreover, the algorithm is extended to heterogeneous services system by classifying delay sensitive services according to the head-of-line packets delay. The design goal of the algorithm is to fully exploit multiuser diversity gain while guaranteeing the quality of service (QoS) for delay sensitive services. Numeric results show that the algorithm outperforms traditional algorithm in terms of system spectral efficiency and fairness criterion.

Multiple input multiple output (MIMO) system has been recognized as a promising candidate for future wireless communication. The adaptive modulation which adjusts the transmitter parameters, such as modulation order, transmit power or coding rate, to time-varying channel conditions has been applied to MIMO system and shown a good average spectral efficiency performance. In this paper, the channel estimation error (CEE)'s effect on the effective spectral efficiency of the MIMO system with zero-forcing receiver is investigated, when the transmitter adopts adaptive modulation. To reduce CEE's negative effect, a dynamic adaptive modulation scheme is proposed. This scheme can dynamically adjust the signal to noise ratio (SNR) thresholds for the different modulation orders according to the feedbacks from the receiver. The numerical results show that the system performance of the proposed scheme is near optimal with acceptable implementation complexity.

To meet the increasing demand of wireless services associated with the scarcity of the radio spectrum and the trend to provide end to end quality of service (QoS), on the one hand advanced technologies that harness the available resource efficiently should be developed, on the other hand the collaboration of different layers such as physical (PHY) layer and medium access control (MAC) layer is needed. In this paper, we propose a joint space-frequency-power scheduling algorithm (JSFP) for real time service in multiuser cellular MIMO-OFDM system which jointly optimizes the subcarrier, bit and power allocation in the PHY layer along with the scheduling in the MAC layer to exploit the multiuser diversity. This algorithm considers both the user equipments (UE)' QoS requirements (such as packet delay and packet loss ratio) and the UEs' channel conditions and includes three parts: packet scheduling, subcarrier allocation and antenna selection, power allocation. Numerical results show that the proposed algorithm achieves significant system performance improvement compared with the conventional methods.

In this paper, a radio resource allocation and scheduling algorithm for multi-user MISO-OFDMA systems with downlink zero-forcing beamforming is proposed to efficiently support the diverse quality of service (QoS) requirements of heterogeneous services. According to the channel state information (CSI) and the queue state information (QSI), the proposed algorithm dynamically assigns subcarriers and selects the users on the same subcarrier. The goal of the algorithm is to maximize the system throughput by fully exploiting multiuser diversity gain in space, time, and frequency domain while guaranteeing the QoS for real time (NT) services and non-real time (NRT) services with minimum data rate requirement. From the system level simulation, it shows that the proposed algorithm significantly improves the system performances.

The cooperative cellular relaying network is expected to achieve the higher capacity and enlarge the coverage. In this paper, a novel cost-aware handover decision algorithm (CHDA) for cooperative cellular relaying networks is proposed. Two cost functions, namely the triggering and priority decision cost functions are exploited, which involves the signal transmission quality, the handover signaling cost, the handover latency and the interference estimation. Simulation results show that the signaling overhead and the handover delay decrease significantly by utilizing the CHDA scheme. It is also proved that the CHDA strategy is an efficient method to achieve the tradeoff among the QoS requirements and the system overheads, which can remarkably enhance the system performance.

In this paper, three centralized packet scheduling schemes with fairness-oriented feature are proposed for OFDMA multihop relaying networks, namely Greedy polling with novel starvation restrained (SR-GP), enhanced proportional fairness with SR (SR-EPF) and novel subcarrier pairing with hop balance (HB-SP) scheduling algorithms. SR-GP can bring better fairness than traditional throughput-oriented algorithms by sacrificing a little complexity. SR-EPF with novel priority function can guarantee the quality-of-service (QoS) of the cell edge users. HB-SP can enhance the system throughput significantly by utilizing the relay link efficiently with multihop equilibrium. Simulation results of the throughput's deviation of different users indicate that HB-SP and SR-EPF are applicable to realtime services and SR-GP is suitable for non-realtime services. Generally, HB-SP algorithm is a good achievement for both capacity enhancement and resource fairness.

This paper proposes maximum utility principle slide handover strategy for multi-antenna cellular architecture. Based on generalized distributed cellular architecture-group cell, slide handover strategy is illustrated and its merits are presented. Slide antenna window is applied by slide handover in the handover process, which makes users always in the cell centre and eliminates cell-edge effect. But for traditional slide handover, the handover rules of adding new antenna elements and replacing or releasing existing antenna elements are only by the pilot strength of each antenna element. This will constrain the performance of slide handover. Therefore, the rules need to be enhanced. Maximum utility principle slide handover strategy, proposed by this paper, can effectively solve this problem. The utility function in the slide handover and steps for handover are described in this paper and system-level performance evaluation is provided with comparison of traditional slide handover to verify the merits of maximum utility principle slide handover strategy.

Seamless mobility in heterogeneous networks is difficult to be achieved because of various QoS requirements and complex heterogeneous network environments. Media independent handover (MIH) is used to handle such problem in IEEE 802.21 standard. However, only link layer dependent information is involved for the mobility decision. In this paper, an enhanced media independent handover (EMIH) framework and mobility management mechanism are proposed, in which new function entities (FEs) and modules are defined and used to provide link layer and application layer information from client side and network side to mobility decision engine. Compared with MIH, the EMIH provides more sufficient and comprehensive trigger events. The static and dynamic information are collected flexibly at mobile node (MN) and within the network infrastructure. Various handover types are designed to make use of such information to optimize the handover, which is illustrated by an example in this paper. The proposed EMIH architecture can benefit not only mobile users, but also network operators.

This paper presents a distributed power allocation (PA) algorithm for the downlink of relay enhanced cellular networks. The PA problem is built into a non-cooperative game where a utility function is formulated and maximized. The utility function is comprised of two parts considering the interests of the node B and the relay node respectively, which facilitates the distributed PA on the nodes. Since the relay user's data rate is constrained by the minimum capacity of the two hops, a novel pricing function is developed to avoid allocating excess power to the second hop when the capacity is inferior in the first hop. The proposed game theoretic approach is compared with the uniform power allocation and the pure iterative water-filling method. Simulation results show that within a few steps of iteration, the proposed scheme can not only achieve the highest system capacity, but also efficiently balance the capacities of relay users in the two hops.

This paper considers zero-forcing beamforming (ZFBF) at the transmitter for a downlink multi-user multiple-input single-output (MU-MISO) system. Transmit beamforming as a simple yet efficient technique can exploit the benefits of multiple transmit antennas provided that the instantaneous channel state information (CSI) is known at both sides of transmission link. In order to have such improvements, the CSI at both link ends must be updated timely. However, the updating process is always subject to non-ideality such as feedback delay and estimation error, which destroy the orthogonality of the parallel channels and cause the mismatch between the actual channel characteristic and the modulation matrices used. By analyzing the effect of feedback delay on the performance of capacity with spectral efficiency and outage probability as benchmarks, we design a cross-layer scheduler combining semi-orthogonal user selection (SUS) algorithm at the medium access control (MAC) layer to reduce the inter-user interferences and an adaptive proportional weighted modulation (APWM) algorithm at the physical (PHY) layer to address the problem of such mismatch, and compare the proposed scheduler (APWM-SUS) with the naive scheduler (the rate modulation matrices designed for the perfect CSI). Finally, simulation and numerical results reveal significant gains and high feasibility.

This paper investigates the antenna selection and relay node selection issues for multi-antenna relay system. Based on the channel statistics, optimal selection criteria for antenna and relay node are derived respectively, aiming to maximize the ergodic capacity. We first discuss the statistical antenna selection in the single-relay scenario, and then the joint antenna and node selection in the multi-relay scenario is exploited. Simulation results show that the proposed statistical optimal selection provides significant selection gains. For the antenna selection in high signal-to-noise ratio regime, the achieved capacity based on the derived statistical criterion is close to that based on the instantaneous channel knowledge. In addition, the system can benefit from a moderate increase of relay number with node selection, compared to the transmission with all nodes used.

Wireless broadcast systems provide the users with high bandwidth while 3G cellular systems provide complementary service to support personality and interactivity. In this paper, we develop a novel scheduling algorithm for the integrated Wireless Broadcast/3G system. The proposed algorithm combines Analytic hierarchy process (AHP) and Grey relational analysis (GRA). Simulation results are presented to demonstrate that the proposed algorithm could effectively support data dissemination with low response time, request drop rate, and the unfairness of request drop.

Vertical handover decision making is one of key problems in heterogeneous network environment. In IEEE 802.21 standard, a Media Independent Handover (MIH) framework is presented to facilitate handover with measurements and triggers from link layers. However, vertical handover decision making can benefit from the information more than link layers. In this paper, an Enhanced Media Independent Handover (EMIH) framework is proposed by integrating more information from application layers and user context information. Given such information, the issue becomes how to select a favorite network. In this paper, two novel weighted Markov chain (WMC) approaches based on rank aggregation are proposed, in which a favorite network is selected as top one of rank aggregation result fused from multiple ranking lists based on decision factors. The proposed approaches can easily integrate a priori knowledge and/or human experiences into vertical handover. Simulation results demonstrate the effectiveness of the proposed approaches.

Mobile IPv6 (MIPv6) standardized by IETF, is expected to support the global IP mobility. Fast Handovers for Mobile IPv6 (FMIPv6) is further proposed to improve the performance of MIPv6. However, FMIPv6 only concentrates on the protocol operation while it does not address other critical issues, such as the L2 and L3 identifiers mapping problem and precise L2 triggers generation. This paper proposes an efficient service-oriented framework for FMIPv6. In this framework, FMIPv6 is integrated with candidate access router discovery (CARD) mechanism, specified L2 triggers and different handover processes will be performed for different service types according to QoS requirements. The analytical and simulation results both show that the proposed scheme could achieve better performance in terms of handover latency.

Orthogonal frequency division multiplexing (OFDM) is a very promising transmission technology for the beyond 3G (B3G) wireless communication system with orthogonal frequency division multiple access (OFDMA) as its major multiple access technology. And in the multi-cell scenario, the management of inter-cell interference has significant impact on the performance of the wireless network. This paper proposes an optimal and a sub-optimal inter-cell scheduling strategy both of which coordinate the transmission of interfering cells. In addition, the coordinated scheduling strategies are proposed to be implemented by an distributed and coordinated network architecture such as the radio on fiber (RoF) system. The utility function is used to balance the efficiency and fairness of wireless resource allocation. Simulation results show that, the proposed inter-cell scheduling strategy provides significant gain in fairness over the single-cell scheduling strategy which doesn't involve multi-cell coordination of transmission.

In this paper, a QoS differentiation adaptive retransmission limits ARQ (QDARL-ARQ) is proposed to improve the efficiency of retransmission in conventional SR-ARQ for the IEEE 802.16e BWA systems. With a simple algorithm implemented based on the conventional SR-ARQ, QDARL-ARQ scheme is able to dynamically adjust the retransmission limits for services with different characteristics by considering their QoS requirements as well as the current system states simultaneously. This scheme aims to achieve lower packet error rate with restrained end-to-end delay in the time-variable and error prone wireless environment in comparison with conventional SR-ARQ. Several performance metrics of QDARL-ARQ are compared with conventional SR-ARQ in both single service scenarios and multiple services scenarios. The performance improvement due to QDARL-ARQ is evaluated through the IEEE 802.16e system level simulation, and the results clearly show that it can improve the performance of mean end-to-end delay, packet error rate and throughput, especially the retransmission efficiency. It can also be found that the conventional SR-ARQ is in fact a special instance of the QDARL-ARQ designed here.

Relaying transmission is a candidate way to combat wireless channel fading and enlarge the coverage, and efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, an adaptive multiuser radio resource allocation model is proposed for the downlink of OFDM-relay system, which exploits performance gain in both frequency domain and time domain. According to the different transmission modes, two QoS-oriented scheduling algorithms based on the feedback of the channel state information (CSI) of two hops are investigated. One is enhanced proportional fairness (EPF) algorithm, and the other is improved priority (IPRI) algorithm. Both of them can achieve high system throughput and better resource fairness due to the adaptive allocation, especially in the QoS-guarantee aspect for cell edgy users compared with conventional scheduling schemes. The priority strategy is a novel scheme, because of considering resource fairness with artificial starve (AS) state in IPRI, which yields higher spectral efficiency and achieve better data rate requirements for the users.

Through exploiting the multiuser diversity, the capacity of MIMO-OFDM system can increase dramatically. In multiuser singular value decomposition (MU-SVD) based MIMO-OFDM system, the scheduler can allow multiple users to simultaneously transmit independent data to base station (BS) on the same subcarrier and the data can be separated in space domain at the BS. In this paper the effect of the correlations between the singular vectors in a MU-SVD based system was investigated, and propose a greedy scheduling algorithm based on MU-SVD. Given a set of users, the algorithm finds the best and most orthogonal spatial subchannels, in order to exploit the multiuser spatial diversity. The simulation results show that if the user data are transmitted on the maximum singular mode (MSM), the amplified noises on the spatial subchannels may lead to the degradation of the system performance, however the proposed algorithm can greatly increase the system capacity.

This driven by the need to promote a more efficient use of radio resources and improve the operators' profits, resource allocation has turned into a joint technical and economical problem. At the same time, as a possible enabling solution, game theory has been applied to either dynamic spectrum access (DSA) or joint radio resource management (JRRM) in wireless communication research recently. In this paper, we propose a novel DSA and JRRM combined approach to resource allocation in cooperative networks. With the scenario that distributed reconfigurable radio access networks (RAN) are controlled by different operators, the emerging concept of resource trading is introduced and new entities, such as trading agents (TA), are described. Meanwhile, Shapley value in cooperative game as well as its economic model is exploited to share the profits among the trading RANs. Numerical results show that comparing with existing DSA or JRRM methods, our scheme has better effect in maximizing the individual operator's profits and improving the efficiency of radio resources utilization.

Abstract With current research focusing on beyond 3G (B3G)/4G mobile systems, many advanced techniques are investigated by world‐wide research institutes and standard organization, such as multi input multi output (MIMO), orthogonal frequency division multiplex (OFDM), and multi‐antenna distributed cellular network architecture. Based on these novel techniques, the radio resource management (RRM) strategies, such as access control, also need to be developed. This paper proposes the maximum utility principle access control (MUPAC) basing on Dijkstra's Shortest Path Algorithm for multi‐antenna cellular network architectures. In the accessing process of the proposed algorithm, the shortest path in Dijkstra's Algorithm is replaced by the cost of accessing process, which is represented by utility function. Taking Generalized Distributed Cellular Architecture—Group Cell as an example, MUPAC is described in details with the utility function, maximum utility principle, flow chart of accessing process. Performance evaluation and analyses verify the merits of MUPAC algorithm in improving system capacity, accessing success probability, and efficiency of system resources usage. Copyright © 2007 John Wiley & Sons, Ltd.

The integration of the third generation (3G) wireless wide-area networks (WWAN) and the IEEE 802.11 wireless local-area networks (WLAN) has drawn much attention from both industry and academia. To achieve an effective and efficient integration between the two networks with very different characteristics, nonetheless, is still an open issue. One of the challenges is to provide an integrated strategy for achieving a seamless vertical handoff of mobile users roaming between the two network domains where the delay, delay jitter, and packet loss probability can be well controlled. This paper is committed to study a two-step vertical handoff mechanism based on linear regression which is further modeled through an analytical approach. The proposed vertical handoff scheme is characterized by its adaptability to different quality of service (QoS) requirements by manipulating a threshold on the expected handoff instant. A new approach of mobility analysis is introduced to facilitate modeling of vertical handoff delay by taking advantage of Markov chain techniques. We have seen merits gained in our scheme in achieving a good trade-off between the average handoff delay and the multi-tunnel time by manipulating a threshold value, where both analytical and simulation results prove the effectiveness.

As the evolution of 3G, long term evolution (LTE) standardization activity is issued in 3GPP and 3GPP2. In previous paper OFDMA is proposed for downlink of LTE. As the channel reciprocity can be obtained in TD-SCDMA LTE system, the channel status information (CSI) can be exploited at the transmitter to obtain the spatial-frequency multiuser diversity by joint spatial-frequency subcarrier and antenna assignment of MIMO OFDMA for the independent fading of different user in spatial and frequency domain. To guarantee the user fairness, a joint spatial-frequency proportional fairness (PF) scheduling is proposed in this paper. Further, no inter-cell interference mitigation capability can be observed from the current MIMO OFDMA schemes in downlink, the soft frequency reuse is adopted in this paper to avoid the inter-cell interference. Finally the downlink performance of the TD-SCDMA LTE with no-real time service is evaluated with spatial frequency PF scheduling and soft frequency reuse in multi-cell scenario

OFDMA will be the dominant multi-access method in 3GPP long term evolution (LTE) system. Multiuser scheduler plays an important role in optimizing its shared data channel (SDCH). This paper proposes and investigates a multi-carrier gradient scheduling algorithm with minimum/maximum rate constraints (MC-GMR) for downlink SDCH. The objective of MC-GMR scheduler is to maximize the system utility as well as provide quality of service (QoS) guarantee for data service. Performance is evaluated through system level simulations.

The inter-cell interference of orthogonal frequency division multiple access (OFDMA) based multi-cell distributed network architecture is analyzed. Based on generalized distributed cellular architecture-group cell, the interference condition without power control and with power control is analyzed respectively, and the system outage probability compared to traditional cellular structure is evaluated. Analyses and simulation results indicate that the inter-cell interference of group cell architecture does not increase more than traditional cellular structure. Moreover, the system resources of group cell architecture are centralized scheduled and allocated by the access point (AP), which makes it flexible to apply centralized SRA power control algorithm to improve the system performance further.

Orthogonal frequency division multiplexing access (OFDMA) which can make full use of frequency resources by using adaptive modulation and coding (AMC) and multi-user diversity, is a promising technology for the next generation wireless communication system. The flexibility of OFDMA also makes the radio resource management (RRM) more complicated. This paper proposes a modified largest weighted delay first (M-LWDF) packet scheduling algorithm with subcarrier allocation for the real time service in the multiuser OFDMA systems. The simulation result shows that it can maximize the system throughput and guarantee the QoS (quality of service) of different users.

Resource allocation schemes for traditional MIMO system have been widely studied. However, when the MIMO technique is applied to relaying systems, where several separated terminals form a virtual receive array (VAA) and relay the signal from the transmitter to the receiver, these issues, which have a close relationship with the system performance, should be solved before a MIMO relaying system is deployed on a large scale. The channel capacity for a 2-hop MIMO relaying system is derived from the perspective of information theory. An optimal resource allocation under an aggregate power constraint between relaying nodes is proposed in closed form for the case with two relaying nodes. Numerical results indicate that a MIMO relaying system can achieve a higher channel capacity than traditional MIMO does and that the positions of the relaying nodes affect the system performance directly.

The average rate updating mechanism was first promoted for the starvation problem emerging when some user experiences a sudden drop in channel quality or just keeps on moving backward from the base station under the proportional fair scheduler in the HDR system. Although proportional fair scheduling has received much theoretic research attention recently for its attractive capability on the tradeoff between system utility and fairness, the influence of average rate updating mechanism to the performance of scheduler has been neglected. We point out here that the previous rate update mechanism based on fixed time window is insufficient in keeping the users from starvation. Furthermore, updating the average rate to users with no data to send may not get the overall maximized system utility. We also promote a new average update mechanism as the basis of the proportional fair scheduling algorithm. Simulation and analysis show that this novel design with starvation supervision has better performance over the traditional design.

The paper discusses the key technologies of an all IP architecture for a B3G mobile communication system. The aim is to contribute to the technical innovation of 3G systems by exploiting the potential of an IP-based B3G wireless communication system. The discussion focuses on the realization of an all IP core network. An all IP network architecture, improvement of end-to-end QoS, and flexible service provision are among the major challenges toward the B3G communication system. However, an all IP network architecture is the goal of the evolution of wireless networks on the way to the B3G system. The paper first reviews the development of the 3G core network, and proposes technologies for the all IP architecture for communication between 3G systems and the Internet, such as the assumed all IP network architecture and the design of protocol stacks. The paper describes the architecture of a protocol stack based B3G system, and then elaborates on the functionality of the MPPP, RLC, LLC and MAC layers. Finally, for fully supporting an all IP solution, the paper suggests further research work required based on the IPv6 core network.

A cooperative relaying system is presented to allow the application of multiple-input-multiple-output (MIMO) capacity enhancement techniques, such as space-time block codes (STBC), to mobile terminals (MT) with a limited number of antenna elements. The adaptive optimal power allocation (PA) scheme among relay stations (RS) is an important issue to achieve power efficiency and maximum performance improvement. This paper analyzes the non-regenerative (NR) relaying system based on the STBC technique, and investigates the optimal PA scheme between two non-regenerative RS under power constraint. Two receiving methods, with or without direct path signal, are also discussed in this paper. Numerical results indicate that compared with the uniform PA scheme, the proposed PA scheme can obtain the maximum instantaneous signal to noise ratio (SNR) and improve the system performance despite the RS's position.

In interference limited CDMA systems, call admission control plays a very important role because it directly controls the number of users. We introduce a interference-based CAC algorithm according to the characteristics of HCS. This algorithm adopts a global strategy in macro layer, while using a local strategy in the micro layer. Meanwhile, in order to make full use of resources in the macro layer, different threshold values in the two layers are used. Simulation results show that the performance has been greatly improved.

In the HCS system when different frequencies are applied, compressed mode is used so that interfrequency measurement can be obtained. When and how to trigger compressed mode is a crucial problem because the compressed mode may have a bad effect on the system performance: The performance of the periodic and the event-triggered compressed mode is compared with the help of system simulations. Moreover, the impact of some of the parameters is evaluated in this paper.

This paper compares two different handover criteria in WCDMA through simulations. The simulations are carried out in a WCDMA HCS system where the hexagonal macro and the Manhattan-like micro layer use different frequencies. Two possible handover triggering schemes i.e. CPICH RSCP and CPICH Ec/No, are compared under the same simulation environments and various cell loads. The handover performance is observed in terms of system efficiency including signalling and QoS such as call dropping probability and call blocking probability.