As more autonomous vehicles enter traffic, it is critical for autonomous vehicles to behave like human drivers, allowing humans and autonomous vehicles to share traffic environments harmoniously and safely, especially in lane-changing scenarios where collisions often occur. To model human lane change behavior and make human-like decisions, we propose a tailored incremental model predictive control (TIMPC) model. Building upon the improved incremental model predictive control, we illustrate the relationship between the vehicle states and controls. For each modeled vehicle, we tailor an optimal decision-making problem, which takes into account personalized driving characteristics and vehicle interaction safety. By solving the problem, we obtain a personalized control sequence and the lane-changing trajectory. A naturalistic highway driving dataset is applied to calibrate and validate the proposed model. Experimental results demonstrate that the TIMPC model enables efficient, personalized human-like decision-making while generating controls and trajectories that closely align with human behaviors in complex scenarios.

Autonomous exploration in unknown environments is a fundamental capability for intelligent mobile IoT systems, especially in scenarios where prior environmental information is unavailable. In such settings, mobile IoT devices are required to achieve safe and efficient full-area coverage based solely on onboard sensing and limited computational resources. However, the unpredictable and continuously changing nature of unknown environments poses significant challenges to adaptive and collision-free exploration, particularly for resource-constrained mobile IoT devices. To address these challenges, we propose an autonomous full-area exploration algorithm for mobile IoT devices based on reward strategy cloning. Specifically, a state representation method using color mapping is designed to improve the information intensity of input state in full-area coverage exploration missions. Simultaneously, to address the issue of sparse rewards in full-area exploration missions, we construct an intensive reward shaping function that integrates exploration rewards, collision penalties, and incentives for exploring frontier trends. Furthermore, a lightweight exploration model that maps state to action reward is designed for mobile IoT devices with limited computing power and storage resources. Moreover, we propose a reward-sensitive dynamic ϵ-greedy strategy that adaptively balances exploration and exploitation based on real-time performance trends. Finally, empirical results demonstrate the robustness of the proposed algorithm in exploring various complexities and dynamic environments. In particular, the computational complexity of the proposed exploration model is significantly reduced compared to other models.

The advent of the 5G RedCap, the upcoming 6G and the proliferation of the Internet of Things (IoT) have catalyzed the rapid advancement of unmanned aerial vehicle (UAV) technology while also promoting UAVs' widespread application. In IoT-enabled environments where the global positioning system (GPS) signals are compromised, visual simultaneous localization and mapping (V-SLAM) technology has emerged as an effective positioning solution, valued for its reliability. However, the presence of dynamic elements in complex environments, such as pedestrians and vehicles, poses challenges to the positioning accuracy of UAVs employing V-SLAM for navigation. This paper proposes a dynamic feature filtering-based SLAM (DFF-SLAM) approach to eliminate the impact of dynamic factors in dynamic environments, thereby enhancing the positioning accuracy of UAVs in IoT-enabled complex environments. Firstly, a semantic detection thread is designed to identify semantic information in the scene and acquire prior dynamic targets, facilitating the filtering of prior dynamic feature points. Secondly, optical flow tracking conducted at each level of the image pyramid facilitates feature point matching across consecutive images. Finally, the epipolar geometry constraint is utilized to determine the motion status of remaining feature points, further filtering out dynamic feature points. Simulation results demonstrate that compared to traditional visual SLAM systems, the UAV equipped with the DFF-SLAM system achieves more accurate positioning and meets real-time positioning requirements when navigating through IoT enabled complex environments

As a focal area within computer vision algorithms, human pose estimation algorithms find applications in diverse fields such as security and virtual reality. Achieving a balance between speed and accuracy is imperative for practical applications. Existing methods often present a trade-off between high accuracy and real-time performance. In response, this paper introduces the Fast Coordinate Classification (FastCC) detection head. It employs a shared fully connected Transformer for global self-attention operations on feature layers from the backbone network. The spatial attention coordinate encoder then outputs the coordinates of the horizontal and vertical axes of the keypoints, which are subsequently combined to derive the actual keypoint positions. Experimental evaluations conducted on the COCO and MPII datasets demonstrate that our detection head enhances the accuracy of human pose estimation algorithms while maintaining a lightweight design, outperforming the traditional heatmap method.

The autonomous scheduling at unsignalized intersections faces a great challenge for ensuring collision-free passing and improving traffic efficiency under the complex intersection environment and heavy traffic density. In this article, we first design an autonomous management model for unsignalized intersections which uses the intersection control center to manage the passage of vehicles instead of traffic light. To avoid vehicles collision, the intersection is divided into multiple collision subzones, and each collision subzone needs to satisfy that the occupancy time of different vehicles is not overlapped. Second, a conflict graph tree search (CGTS) algorithm is developed to attain the optimal passing priority, which has the highest traffic efficiency. The CGTS algorithm reduces the computational complexity by compressing the solution space and reducing repeated calculations. Then, a heuristic threshold-based motion control strategy is proposed, which supports vehicles to reach the intersection at the assigned time by controlling their acceleration. Finally, we perform simulation experiments to show that the effectiveness of our algorithm outperforms comparison algorithms at various traffic densities.

Understanding lane changing intention on highways is a challenging task for satisfying the safety requirements and supporting the decision-making of autonomous vehicles in dense traffic environments. We propose a peril set aided lane changing intention inference model (PS-LCIIM) to accurately and timely recognize a driver’s lane changing intention before the vehicle crosses the line. To save computational resources of the onboard processor, a peril set-based vehicle selection strategy is designed to select the target vehicles based on collision risk and perception salience. Then, a dynamic time series Bayesian network is introduced to model time-varying vehicle interactions. The real-time intention classification is conducted based on the outputs of a newly developed population evolutionary particle filter algorithm. A naturalistic vehicle trajectories dataset is applied to train and validate the proposed model. The results demonstrate that the proposed model predicts the lane changing intention earlier with higher accuracy while maintaining a low computation cost.

With the emergence of parallel computing systems and distributed time-sensitive applications, it is urgent to provide statistical guarantees for age of information (AoI) in wireless cyber-physical systems (WCPS) with diverse parallelisms. However, most of the existing research on AoI have tended to focus on serial transmission, and the AoI performance of multi-stage parallel systems remains unclear. To help address these research gaps, in this work, we set out to investigate the age of event (AoE) violation probability in a three-stage WCPS with diverse parallelisms such as fork-join and split-merge. We analyze both transient and steady-state characteristics of AoE violation probability (AoEVP). Using these characteristics, we transform the AoEVP minimization problem into an equivalent minimization problem. Moreover, we develop a queuing model to capture the queue dynamics under the max-plus theory of stochastic network calculus (SNC) approach. Based on the max-plus model, we derive a closed-form Chernoff upper bound for the equivalent problem by applying the union bound and the Chernoff inequality. Furthermore, we characterize the service process for different parallelisms applicable to each stage. By solving the Chernoff upper bound with the service moment generation functions (MGFs), we obtain heuristic update period solutions for minimizing the AoEVP of three-stage WCPS. Simulation results validate our analysis and demonstrate that our heuristic update period solutions are near optimal for minimizing the AoEVP of three-stage WCPS with diverse parallelisms. Through the theoretical framework, we further provide insights into how such networks can be designed for better age performance.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

The ability of predicting state of a Network Slice (NS) is indispensable for the run-time management of NSs for providing proactively adjustment and reconfiguration of the NS to avoid Service Level Agreement (SLA) violation and ameliorate network resource utilization. In the literature, NS state prediction methods neglect the spatio-temporal correlation among NS entities. Moreover, NS state involves both Virtual Network Function (VNF) state and transmission link state in the virtual network of the NS. In this paper, we propose an end-to-end model by integrating Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) for the dynamic NS state prediction. Typically, we apply two types of GNN models, Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN), for aggregating the spatial features for VNFs and transmission links. Then, LSTM is utilized for sequential NS state prediction. Finally, we conducted intensive simulation to validate the effectiveness of the proposed model by comparing to several baselines.

Service Function Chain (SFC) is composed of an ordered set of service functions, which are also noted as virtual network function (VNF) instances, to adaptively form a composite network service with increased flexibility and agility. One fundamental challenge in SFC run-time management is the accurate prediction of the resource load of SFC VNFs for proactive VNF resource provisioning. In the literature, existing works largely neglect the application level relationship for the fine-grained atom-VNFs in microservice architecture. In this work, we propose an end-to-end deep-learning-based prediction model, namely granularity-captured Graph Attention LSTM Network (GGAL), for accurate load prediction at the fine-grained atom-VNF level. By integrating granularity-captured graph attention layers, LSTM and MLP, the model is able to extract the application level spatio-temporal relationship among atom-VNFs and perform accurate load prediction. The effectiveness of the proposed GGAL model is demonstrated through intensive simulations by comparing to several baselines.

Distributed edge clouds (ECs) have been recently shown with remarkable advantages in enhancing customized service provisioning by leveraging user proximity and edge resources. However, operating a massive EC network would inevitably incur a huge amount of energy cost to EC providers, which would offset their operating revenue without proper energy cost management. In this paper, we focus on conserving energy cost of ECs by taking advantage of both electricity price-aware geographical task dispatching and dynamic central processing unit (CPU) provisioning according to the spatiotemporal diversities of electricity prices and user task demands. Due to the significant switching cost of turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates both large-timescale CPU provisioning and service placement, and small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to deal with the spatio-temporal variable couplings. A distributed and online mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal Lagrange multipliers. Simulation results show the outstanding performance of the MBL algorithm.

In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.

This paper investigates the joint resource allocation and group formation for platoon in vehicle-to-everything (V2X) networks under vehicular channel uncertainty. To achieve the high spectrum efficiency and overcome the platoon head communication range limitation, an adaptive multicast-based group cooperation communication model is developed for the platoon with dynamic topology. Considering the heterogeneous characteristics of different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra-reliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the V2I capacity whilst satisfying a probability constraint for ultra-reliable V2V-supported intra-platoon communication. To handle the intractable probability constraint, a support vector clustering (SVC) based method is developed to capture the distributional geometry of massive uncertain channel samples as a sphere in high-dimensional feature space with asymmetric structure. Based on it, the probability constraint is transformed into a tractable linear convex set. After that, an exploration-selection-alternating-iterative algorithm is developed to solve the formulated problem with coupled optimization variables. Specifically, in the exploration process, a two-stage algorithm is proposed for the resource allocation problem under fixed group formation decision, which includes power control and spectrum allocation. During the selection process, a performance difference-based decision transition rate is designed to optimize group formation solution. Simulation results demonstrate the proposed data-driven approach can overcome the over-conservatism of the traditional symmetric-geometry-based uncertainty sets, and the multicast-based group cooperation communication model corresponds to a higher performance on V2I capacity than other traditional schemes.

The emergence of Industrial Internet of Things along with fog computing (FC) has brought great benefits in the industry field through real-time monitoring, resource optimization configuration, and intelligent cloud control. The deployment of fog nodes in industrial plants is the essential precondition for FC implementation. Existing works are mainly based on pregiven sensor traffic in the deployment of fog nodes, which largely ignores the stochastic uncertainty imposed by the dynamic industrial environment. In this article, without requiringa prioriknowledge of the sensor traffic pattern, we establish a mathematical model based on uncertain programming to formulate the fog node location determination and the sensor association problem. Owing to the complexity of the model, we introduce a learning-based algorithm to effectively solve the problem with an acceleration mechanism. Finally, intensive simulations are implemented to verify the performance of the algorithm. The results indicate that our approach outperforms other benchmarks in terms of transmission energy on planning fog nodes in industrial plants with stochastic sensor traffic.

Bluetooth Low Energy (BLE) has become one of the enabling wireless technologies to facilitate the Internet of Things. Neighbor discovery is critical in BLE communications. BLE uses multiple (three) channels in neighbor discovery. It is challenging to achieve low-latency and low-energy-consumption BLE neighbor discovery due to the lack of analytical models for multichannel neighbor discovery. In this paper, we study BLE multichannel neighbor discovery for two advertising modes specified by BLE: periodic deterministic advertising (PDA) and pseudo-random delay advertising (RDA). We build two generic models, BLE 3-Circle model and BLE 1-Circle model, for characterizing BLE multichannel neighbor discovery. For PDA mode, we present a necessary and sufficient condition for BLE multichannel neighbor discovery, and provide a guideline for parameter setting. With the guideline, we derive the expected discovery latency in closed form, and demonstrate that the expected discovery latency is very close to a theoretical lower bound. For RDA mode, we build an analytical model based on Markov chain to accurately compute the expected discovery latency. Simulation and experimental results show accuracy of our analytical works. Interestingly, our parameter setting guideline works well for both PDA and RDA modes.

On one hand, the congestion notification mechanism Explicit Congestion Notification (ECN) can only provide coarse-grained congestion signal, which is not sufficient to indicate accurate network and congestion status. On the other hand, the emerging learning-based intelligent congestion control and route selection mechanisms require fine-grained network states information to take accurate actions. This calls for the development of enhanced ECN mechanism to provide precise congestion information and network states. In this work, we design Accurate-ECN, an enhancement of ECN with Inband Network Telemetry (INT) to collect and report detailed network congestion states by attaching network state metadata to the data packets and send back to the sender through TCP ACK by the packet receiver. We designed the Accurate-ECN frame format and the data packet parsing process, and implement the mechanism through the P4 language. Finally, through evaluation, Accurate-ECN is demonstrated to provide various precise network states under different congestion levels.

In the era of Internet of Things (IoT), various computation-intensive applications emerge and bring great challenges to IoT devices with limited computation capability. Mobile edge computing (MEC) provides rich computing resources for IoT devices and improves applications’ execution efficiency. In this paper, we model applications as directed acyclic graphs (DAG) and target to minimize applications’ execution latency in heterogeneous MEC networks. To solve this problem, a Deep Q-Network (DQN)-based task scheduling (DQNTS) algorithm is proposed by utilizing deep reinforcement learning (DRL). By modeling the task scheduling process as a Markov decision process (MDP) and designing its critical elements, satisfying scheduling decisions are obtained. Simulation results show that the proposed algorithm achieves lower execution latency than the compared algorithms and it is adaptable to different MEC network environments.

In this paper, we investigate the joint user association and resource allocation problem in the downlink of cloud radio access networks (CRAN). The central cloud performs the scheduling of spectrum resource across different base stations (BSs) to user equipments (UEs) for the maximal network utility under practical network constraints. Nevertheless, the problem is a combinatorial optimization problem, which is intractable by the traditional exhaustive search when the network size is large. We propose a new scheduling selection method by introducing Markov approximation, which synthesizes algorithm to achieve approximate optimization by forming a reversible continuous-time Markov chain. This method adapts to not only the static network situation but also the dynamic network situation caused by users joining or departure. Besides, this method solves the two network subproblems of user association and resource allocation concurrently, which reduces the computing complexity. Simulation results demonstrate the convergence of Markov approximation and the validity of the proposed algorithm.

Cooperative task offloading has emerged as a compelling computing paradigm for balancing spatially uneven task workloads and computational resources in distributed mobile edge computing (MEC) systems. However, enabling cooperation among multiple MEC nodes inevitably requires extra communication and computational energy overheads which might counteract the cooperation gain without energy-efficient offloading mechanisms. This paper presents an adaptive cooperative task offloading algorithm aiming at maximizing the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. With the considered network dynamics, the proposed algorithm makes a good tradeoff between the harvested cooperation utility and the total energy consumption in the long term. In addition, our algorithm ensures the network stability and fulfills the task admission rate requirement of each individual user equipment, by making slot-based decisions over time without requiring a-priori knowledge of the network dynamics. Simulation results verify the outstanding performance of the proposed algorithm by comparing with the static cooperative and adaptive non-cooperative schemes.

Digital immersion via Virtual Reality (VR) and Augmented Reality (AR) applications is expected to be a key driver of growth for the 5G mobile network. The immersive requirement imposes many technical challenges. On one hand, Mobile Edge Computing (MEC) is an effective network paradigm to provide low transmission latency and massive computation for 360° videos. On the other hand, viewport adaptive streaming also provides a bandwidth efficient solution. Accordingly, in this paper, we investigate the caching policy for tile-based 360° videos in an MEC caching system. Our goal is to find the optimal caching policy to co-optimize users’ quality of experience (QoE) and MEC energy consumption with no a-priori knowledge on video content popularity. We apply the combinatorial multi-armed bandit (CMAB) theory to solve the above problem which is a sequential decision making problem. On the basis of the combinatorial UCB (CUCB), an improved algorithm is proposed to speed up learning process. The outcome of the algorithm is the caching decision for each time slot. The effectiveness of the proposed learning based caching policy is confirmed by simulation results in terms of the learning speed, hit rate, energy consumption and request latency.

Identification of transmit-antenna number is of importance in cognitive Internet of Things (IoT) with multiple-input–multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access, and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable ($S\alpha S$), which cause the performance degradation of conventional algorithms based on the Gaussian model. In this article, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive IoT, assuming that signals are corrupted by both$S\alpha S$interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix (GCM), and provide its bound with$S\alpha S$interference. Then, the discriminating feature vector is constructed by utilizing the higher order moments (HOMs) of eigenvalues of the GCM. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need fora prioriinformation about the transmitted signals, such as coding mode, modulation type, and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and$S\alpha S$interference.

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

In the edge computing Internet of Things network, we minimize the offloading overhead (caused by the bandwidth cost for data transmission and computation resource consumption for task remote processing) while providing the end-to-end (E2E) delay provisioning. Under the scenario, a tandem queue consisting of a transmission queue and a computing process queue is formed by the tasks offloaded to the edge server via wireless link and then processed through the computing resource. Due to the tandem queue, the offloading decision and computing resource allocation are coupled over the tandem queue. To make the problem tractable, we decouple the above two operations and propose a two-stage offloading filtering and computing resource allocation policy. After decouple, we then investigate the delay bound violation probability of the tandem queue by leveraging large deviation analysis. Further, we reveal that under the same E2E delay provisioning, the offloading overhead under the proposed decoupled policy can approach to the non-decoupled optimum by selecting an appropriate value of control parameter. Simulation results verify the theoretical analysis and show the efficiency of the proposed policy.

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Estimation of communication parameters, a major task of intelligent receivers, has important applications in adaptive wireless systems. Multiple antennas make the identification problem more challenging. In this paper, we focus on the problem of estimating the number of transmit antennas in multiple-input-multiple-output (MIMO) communication systems. A novel estimation algorithm is proposed to determine the number of transmit antennas for MIMO with alpha-stable noise. We first introduce the correlation matrix based on the fractional lower order statistics (FLOS) and provide a particular structure of FLOS-based correlation matrix. Then, the eigenvalues of the FLOS-based correlation matrix are employed to construct a test statistic and the central limit theorem is exploited to obtain the decision threshold. Finally, the transmit-antenna number is estimated using a serial binary hypothesis test. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number estimation algorithm for MIMO with alpha-stable noise.

In this paper, we propose a novel detection algorithm of the number of transmit antennas in multiple-input multiple-output (MIMO) systems, assuming that signals corrupted by non-Gaussian interference and Gaussian noise. We first introduce generalized correlation matrix. Then, the discriminating feature vector is constructed by exploiting the higher-order moment of the eigenvalues. Finally, an advanced clustering algorithm is employed for decision the number of transmit antennas, which is determined by the dimension of the cluster where the minimum eigenvalue is located. The proposed algorithm does not require a priori information about the transmitted signals, such as coding scheme, modulation type, and pilot patterns. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number detection algorithm in MIMO systems with Gaussian Noise and non-Gaussian interference.

Unmanned aerial vehicles (UAV) wireless communications have attracted great interests in 5G networks due to its high mobility, on-demand deployment and low cost. However, it arises new serious concerns about the malicious eavesdropping attacks against UAV communications. In this paper, we study the UAV transmission with a wiretap Rayleigh fading channel, over which UAVs transmit data to a target receiver in an unsafe environment with multiple eavesdroppers. A secure transmission scheme is proposed for satisfying various performance requirements including secrecy and transmission latency, considering the unavailability of wiretappers' instantaneous channel state information (CSI). In particular, secrecy performance is measured by the derivation of secure transmission probability (STP) by physical layer security (PLS) technique. A novel stochastic-network-calculus (SNC) approach is proposed to analyze the service capability of the wiretap channel and as well calculate the latency bounds, whilst obtaining the internal relationship between secrecy and latency. Simulation results verify the theoretical performance bounds, which provide a guidance for designing secure transmission strategies with various performance requirements.

Abstract Energy harvesting (EH) aided Internet of Things (IoT) network is a promising paradigm to librate IoT network from energy deficiency. Dynamic energy and traffic scheduling in such a scenario is challenging due to temporal correlation of energy constraints and delay requirements of IoT applications. In this paper, joint energy management and sampling rate control to explore the tradeoff between network utility and delay performance are studied while maintaining the energy causality constraint. Taking into account the dynamic characteristics of EH process, channel fading and traffic arrivals, a stochastic optimisation problem is formulated to maximise the network utility. Leveraging the Lyapunov optimisation approach, combined with the idea of weight perturbation, a framework is proposed to decompose the stochastic problem into several deterministic sub‐problems that can be solved separately. Based on the framework, an online resource allocation algorithm is developed to achieve two major goals: first, balancing energy consumption and energy harvesting to stabilise their data and energy queues; second, deriving the utility‐delay tradeoff by adjusting the control parameter. The stability of data buffer and energy buffer in the proposed network is theoretical verified with performance analysis.

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

In this article, we investigate control-aware energy-efficient transmission strategies for wireless control systems with short packets (WCSs), in which remote state estimation error, system stability, transmission energy consumption, and communication packets with finite-length coding are all taken into account. Specifically, we formulate the transmission strategy design problem as a multiobjective optimization problem, which minimizes the remote state estimation error and transmission energy consumption simultaneously under the constraints of system stability and short packet communications. To solve the multiobjective optimization problem, we first introduce a novel objective function that encapsulates two different objective functions into a single one by using weight parameters, and further prove that the solution of the new stochastic optimization problem is a nondominated solution of the original one. Moreover, to solve the new stochastic optimization, we propose a dynamic control-aware energy-efficient transmission (DCET) algorithm that pushes the objective cost close to the optimal with a tradeoff in virtual queue backlogs for constraints. In particular, to tackle the nonconvexity constraint due to short packet communications, we introduce an additional constraint, with which the optimization problem is convex. Finally, simulation results verified the superiority of our proposed transmission strategy as compared with schemes of TDMA and Aloha-RAM multi access.

The softwarization and virtualization based Network Slicing (NS) technology provides the momentum for integrating the Information-Centric Networking (ICN) systems into the 5G infrastructure, such that ICN can be virtualized as a NS to co-exist with other IP-based vertical services slices. The implementation of the ICN network slice (ICN-NS) is essentially a 5G end-to-end (E2E) NSs embedding problem, which is normally solved by embedding the given virtual network (VN) of the slice instance. However, determining the details of the VN is non-trivial and largely ignored in the literature. In this work, we formulate the ICN network slices determination and embedding (ICN-NS-DE) problem through an Integer Linear Program (ILP) formulation, such that the ICN-NS determination and embedding problems are jointly solved for a hierarchical ICN system without requiring a-priori knowledge on the VN's topology and resource provisioning information. Due to the complexity of the model, we design an heuristic algorithm for solving the problem in practical large scale network. Finally, we demonstrate the performance of the ICN-NS-DE model and the algorithm through intensive simulations.

The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.

To capture the ubiquitous randomness such as time-varying wireless channel and unpredictable task arrivals in the non-orthogonal multiple access aided multi-access edge computing networks, we formulate a stochastic optimization problem aiming to minimize the time-average cost for Internet of Things devices in this paper. Due to the absence of distribution of random network information, we develop a stochastic gradient descent (SGD) based method to learn the randomness online and minimize the cost asymptotically. The proposed SGD method makes decisions only depending on the observed network information in each time-slot and achieves an [O(ε),O(1/ε)]-tradeoff between the cost-optimality and task queue backlog. To polish this tradeoff, we further propose a momentum-based SGD method by amending SGD iterations with momentum terms, which can efficiently accelerate algorithm convergence while reducing the task queue backlog without loss of cost-optimality. Finally, simulation results confirm the outstanding performance of the proposed methods.

Non-orthogonal multiple access (NOMA) is considered as a potential technique to relieve the congestion due to concurrent access from massive devices in machine-to-machine (M2M) communication system. However, the cochannel interference caused by NOMA, and the energy budget of machine-type devices (MTDs), become the bottleneck to further improve the system performance. Given above issues, we formulate the joint user scheduling and energy management problem as a stochastic optimization problem. Specifically, the goal of the problem is to maximize the long-term average sum rate under the constraint of all MTDs’ quality-of-service (QoS) requirements. For tractability, the stochastic problem is firstly transformed into two static subproblems based on Lyapunov optimization. Then, using successive convex approximation (SCA) method, we design an effective algorithm to deal with the joint user scheduling and power allocation subproblems, which is a mixed integer and non-convex programming (MINCP). Simulation results demonstrate that our proposed algorithm has a good performance in convergence and outperforms other schemes in terms of user satisfaction.

The future 5G wireless network is largely driven by the increasing heavy traffic and spectrum scarcity. Cognitive Radio (CR) techniques provide a potential solution for improving the spectrum efficiency. In this paper, we study the stochastic framework for the CR networks, considering different quality of service (QoS) requirements. To analyze the performance of the CR network, we adopt a poisson point process (PPP) to capture the mobility and randomness of user location. A stochastic-network-calculus (SNC) based approach is proposed to model the wireless transmission and evaluate the network performance. In order to achieve the performance metrics of end-to-end (E2E) delay and backlog in the entire network, we propose a new conception named as effective service process (ESP) which is able to capture the QoS requirements of users. Furthermore, we evaluate the performance in the exponential domain, which can present the E2E analysis more directly. The simulation results verify the theoretical analysis and show that the performance in the CR networks can be derived perfectly with the proposed approach, considering the stochastic traffic arrival and designed service model in our schedule.

The emerging cognitive radio-based Internet-of-Things (CR-IoT) network provides a novel paradigm solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in the CR-IoT network which has been investigated extensively under the Gaussian noise/interference. Since most of the interference in an IoT network is non-Gaussian, in this article, we introduce a novel spectrum sensing method for CR-IoT with additive Gaussian mixture noise/interference. The introduced method maps the observation signal matrix from the original input space to a high-dimensional feature space by a nonlinear Gaussian kernel function and then constructs a kernelized test statistic in the feature space. The approximate analytical expressions of the false alarm and detection probability of the proposed scheme are derived under Gaussian mixture noise, and the decision threshold can be determined according to false alarm probability. The simulation results show that the introduced multiple-input-multiple-output (MIMO) spectrum sensing method achieves good performance under Gaussian mixture noise/interference and significantly outperforms existing detectors.

In this paper, we consider a mission-critical control system, where a dynamic plant is monitored by a mobile device (MD), and the monitored signal is transmitted to a remote controller via heterogeneous cellular and Wi-Fi networks. We propose an optimal control-aware machine-to-machine (M2M) transmission strategy for mission-critical control applications, in which control performance is measured by remote estimation error and system stability while limited by bandwidth cost. Specifically, the problem of minimizing estimation error, subject to the constraints of cellular usage costs and system stability, is formulated as an infinite-horizon constrained Markov decision process (CMDP), where the MD has options to transmit through Wi-Fi or cellular, or to stay idle. We solve the problem by utilizing the Lagrange multiplier approach, and prove that the optimal strategy is a randomized mixture of two threshold structure strategies. Furthermore, to estimate the structured optimal strategy, we present an algorithm called simultaneous perturbation stochastic approximation (SPSA), in which the complexity is O(IAI) lower than a non-structured one with IAI being the number of the actions.

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high dynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter V and the decrease of grid power consumption cost is at the speed of 1/V. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

In mobile edge computing (MEC), the computation offloading of massive users could cause the task uploading congestion to deteriorate the users' offloading delay. The non-orthogonal multiple access (NOMA) enabled MEC is envisioned to address this issue by allowing multiple users to simultaneously upload their tasks on one subchannel. However, the differentiated uploading delay of users may make task uploading completion inconsistent with NOMA decoding order, which complicates the co-channel interference and restricts NOMA to reducing the uploading delay. In this paper, we characterize the interaction between the differentiated uploading delay and co-channel interference for a pair of NOMA users. Furthermore, we propose a computation offloading scheme to reduce the users' average offloading delay by jointly optimizing offloading decision and resource allocation. Specifically, the proposed scheme first obtains the optimal power allocation based on the characterized interaction and the closed-form solution of computation resource allocation by convex programming. Then, the NOMA user pairing and offloading decision are iteratively determined by semidefinite relaxation and convex-concave procedure. Simulation results show that the proposed scheme effectively mitigates co-channel interference under differentiated uploading delay of users and outperforms in reducing the users' average offloading delay and increasing the number of users to offload tasks.

This paper investigates the resource allocation and user association for time-varying heterogeneous wireless network (HetNet), where the multi-homed user equipment (UE) utilizes multiple access options (AOs) simultaneously. Firstly, a stochastic optimization model is proposed to maximize the long-term energy efficiency (EE), which is defined as the ratio of long-term total throughput to the long-term corresponding energy consumption. A modified fractional programming method is proposed to convert the long-term EE maximize problem into an instance throughput-minus- energy optimization problem, which is proved as a mixed integer nonlinear optimization (MINO). The continuity relaxation and Lagrange dual method are proposed to solve the MINO problem. Then, a utility-based selection algorithm is developed to determine the optimal associated AO sets for each UE. After that, the dynamic EE-based resource allocation algorithm is developed to allocate the AOs radio resource energy efficiently, which depends only on the current network state information. Our simulation results show that the proposed algorithm achieves EE performance improvement compared to other general algorithms.

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named M time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other M - 1 time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.

In 5G networks, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, especially in ultra- dense small cell networks. However, diverse SON functions have different time scales and inconsistent targets, which leads to the operation conflicts and network performance degradation. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) to guarantee efficient and stable network operations for densely deployed SONs, where different SON functions have their own specific time scales. Specifically, we propose a novel analysis model , named M time-scale Markov decision process (MMDP), where SON decisions made in each time-scale considers the impacts of SON decisions in other M-1 time scales on the network. Then the proposed scheme with two functions of mobility load balancing (MLB) and energy saving management (ESM) is evaluated in terms of the designed network utility. Simulation results demonstrate that the proposed SON function coordination scheme significantly improves the network utility, while guaranteeing the stability of cooperative operations in wireless networks.

Small cell networks (SCNs) are envisioned as a promising solution to increase the network capacity and coverage. The densely deployments of SCNs in 5G networks pose new challenges for energy-efficient network management. Energy harvesting technique is put forward as a relatively new energy saving concept. However, due to the opportunistic nature of energy harvesting, the uncertainty and complexity will be introduced in energy harvesting SCNs (EH-SCNs) network management. In this paper, we study the self- organized cell operation management problem with different quality of service (QoS) requirements of users, in which the EH-SCNs needs to perform cell activation operation in a distributed manner with the uncertainty of harvested energy. With the assumption of Markovian energy harvesting process, multi-armed bandit game (MAB) based Thompson Sampling algorithm is developed to solve the small cell activation problem with a self-organized manner in EH-SCNs. Simulation results show that our proposed approach is particularly suitable to manage the large-scale EH-SCNs more efficiently under uncertain environment with incomplete information.

As a key technology, it is expected that the Ultra Dense network (UDN) architecture will play a key role in supporting the fifth generation (5G) of mobile communication technologies, especially for hotspot and blind wireless areas. Energy Efficiency (EE) and Spectrum Efficiency (SE) are two important metrics in the 5G UDN. Generally, they can not obtain optimal results simultaneously. To balance the tradeoff of them, in this paper a multi-objective optimization problem (MOOP) is formulated and an improved version of nondominated sorting genetic algorithm II (NSGA-II) based intelligent approach is proposed which enables small cell users to optimize their downlink performance of EE and SE by jointly allocating transmission power and resource blocks. Simulation results show that the proposed algorithm yields significant performance gains when compared with existing exhaustive search and weighted sum method. Furthermore, the convergence and computational complexity of our proposed algorithm are studied.

Recently, millimetre‐wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C‐RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time‐frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C‐RANs is investigated. The TDMA‐based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed‐integer non‐linear fractional programming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes.

To enable sustainable wireless networks, though new technologies have been proposed to improve the system spectrum efficiency, the energy efficiency (EE) is also of vital importance due to the increasing users and devices. Active array system (AAS) and heterogeneous networks (HetNets) have been reckoned as an enormous enhancement in spectrum efficiency and EE. In this study, the energy efficient user association and resource allocation problem for preference‐aware multicast service in AAS aided HetNets is investigated, formulated as a mixed‐integer non‐linear fractional programming. By generalised fractional programming theory and Lagrangian dual decomposition, an iterative algorithm is devised to determine user association and resource allocation. Further, an efficient solution is proposed to perform quality of service‐guaranteed user association and resource allocation to maximise the EE. Simulation results demonstrate the convergence performance and potential gain of the proposed algorithms in terms of EE.

In this letter, we investigate the dynamic rate allocation and forwarding strategy adaption scheme for wireless networks with potential selfish nodes. Aided by an incentive mechanism, we develop a stochastic differential equation (SDE) to portray the dynamic node selfishness in terms of node's energy resource and incentives. Then, a stochastic optimization model is employed to maximize the average network utility while bounding the node selfishness. Based on the continuous-time Lyapunov optimization theory, we solve the optimization problem and propose a dynamic rate allocation and forwarding strategy update (DRAF) algorithm to accommodate the dynamic network state. We further analyze the tracking errors between the output of DRFA algorithm and the optimal solution. Then, an adaptive-compensation rate allocation and forwarding strategy update (ACRAF) algorithm is designed, which iterates only once when network state changes. Finally, we provide a sufficient condition that the ACRAF algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis.

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC's limitations, such as limited coverage, strictly line-of-sight transmission, and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closed-form solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC's joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While at the small timescale, we obtain the optimal solution for RF's joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes.

Software defined networking (SDN) and network function virtualization have emerged as a promising solution for elastic, dynamic, and scalable network management. A collection of works have investigated how virtualization and cloud technology can ameliorate the infrastructure management for the fifth generation mobile network. However, employing these techniques in mobile network for live streaming has not received enough attention. By leveraging the development of SDN and virtualization techniques, mobile live streaming service providers can efficiently manage their system to cope with network dynamics incurred by the variation of the environment. Specifically, we dynamically instantiate network entities at appropriate locations in response to the user live streaming demands. These network entities are in charge of transcoding and transmitting the live videos to the mobile end users, which we name as virtual live streaming agent (vLA). In this paper, we investigate the dynamic virtualization and migration of vLAs for mobile live streaming services. Typically, rate adaptive streaming is adopted to improve spectrum efficiency and user quality of experience. We formulate an integer linear program for the optimal vLA placement problem. Furthermore, we design a practical SDN-based live vLA migration process. Then, by designing and integrating migration cost functions, we develop a dynamic vLA placement mechanism for dynamic environments with the consideration of migration cost. Both heuristic and on-line algorithms are designed for the vLA placement and migration problems. Finally, a large scale real trace-based simulation is conducted to demonstrate the performance of our vLA placement and migration algorithms.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

In a Mobile-CDN, Base Stations (BSs) are equipped with storages for replicating content, and they are allowed to cooperate in replying user requests through backhaul links. In this paper, we investigate the joint optimization problem of content placement and user request redirection for such a BS-based mobile CDN system. Specifically, each BS maintains a transmission queue for replying user requests issued from other BSs. Due to the limited link capacity and the dynamic network environment, the optimization problem should be jointly considered with the transmission queue states. We employ the Stochastic optimization model to minimize the long-term time-average transmission cost under content availability and network stability constraints. By applying the Lyapunov optimization technique, we transform the long-term problem into a set of linear programming (LP) problems, which are solved in each short time duration. Further, we propose a semi-distributed online algorithm to jointly decide content placement and user request redirection. The evaluation confirms that our solution guarantees network stability comparing to the traditional user request redirection scheme.

We consider a mobile-CDN system where base stations (BSs) are equipped with storage for replicating and distributing content. In such a system, BSs cooperation in replying user requests is a widely adopted mechanism. For such cooperative caching, a key issue is the joint optimization of content placement and request redirection, which has been intensively investigated in the literature. However, optimizing this problem to guarantee delay has not received enough attention. Practically, each BS maintains a transmission queue for replying requests issued from other BSs. We investigated the management of such transmission queues to guarantee the queuing delay. By solving an admission rate determination problem and a typical content placement and request redirection problem, the throughput of the system is optimized with no violation on the queuing delay. Finally, a real trace based evaluation demonstrates the benefits of managing transmission queues in improving the mobile-CDN system performance.

In this study, the authors investigate the problem of maximising the mean rate in a multi‐cell coordinated beamforming system considering practical delayed limited feedback and constrained backhaul. To tackle this hard problem, they first derive the analytical expression of the upper bound on the mean rate loss due to the clustered coordination and the imperfect channel feedback. Particularly, this upper bound quantitatively reveals a key tradeoff between decreasing inter‐cell interference with larger base station (BS) cluster and decreasing inter‐user interference with smaller BS cluster but improved channel state information accuracy. In light of this, they jointly optimise the BSs clustering and feedback bits allocation (BCFA) with the objective of minimising the upper bound on mean rate loss, which is equivalent to the problem of maximising the mean rate. Utilising the relaxation‐and‐rounding approach, a BSs clustering and feedback bits allocation algorithm, referred to as BCFA, is designed, which has good performance with low complexity. Finally, extensive simulation results are provided to demonstrate the advantages of the proposed algorithm.

In this study, the authors investigate the delay‐guaranteed resource optimisation in orthogonal frequency division multiple access networks under time‐varying channels and bursty data arrivals. Stochastic optimisation model is employed to minimise the long‐time‐average transmit power consumption (PC) of base station under the constraints of network stability and individual user's delay requirement. They develop a delay‐guaranteed power‐optimal algorithm (DPOA) to obtain the optimal decisions of stochastic optimisation problem. Without prior knowledge of channel statistics and data arrivals, DPOA yields a time‐averaged transmit PC that can arbitrarily approach the theoretical optimum attained by the network with complete knowledge of statistics. Simulations results verify the theoretical analysis on the network performance and show the effectiveness of DPOA.

In this study, the authors investigate the resource allocation for orthogonal frequency‐division multiple access (OFDMA) wireless networks, where the base station is powered by renewable energy and electric grid. To fully exploit the renewable energy, the authors propose an energy‐aware resource allocation (EARA) algorithm to maximise the network utility, which captures the tradeoff between the system throughput and the grid energy consumption. Specifically, the EARA algorithm only has to track the current system states (e.g. channel and queueing conditions) without requiring a relevant priori distribution knowledge, making it applicable for practical OFDMA wireless networks with unpredictable channel dynamics, renewable energy arrivals and stochastic traffics. Moreover, the performance achieved by the EARA algorithm is theoretically characterised. Most importantly, the authors develop an implementation architecture to take the EARA algorithm into practice, and also analyse the low implementation costs (e.g. low computational complexity, trivial signalling overhead etc.). Finally, simulation results verify the theoretical analysis and also demonstrate the advantages of the EARA algorithm.

In this paper, dynamic resource management is investigated for serving on-demand video streaming users in heterogeneous wireless networks (HWNs) with time varying channel conditions. The HWN is equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. In order to take advantage of the time varying nature of wireless channels, we utilize the joint quality selection associated with quality adjustment at application layer and resource allocation associated with power allocation, subcarrier assignment, and time fraction determination at physical layer to perform the dynamic resource management. By using Lyapunov optimization technique, we develop a quality-aware streaming (QAS) algorithm to maximize the network utility, which is the difference of time-averaged users' perceived video quality and time-averaged HWN's transmit power. Simulation results exhibit that the proposed QAS algorithm can significantly improve network utility compared with the state-of-art baselines, which are not specific for on-demand video streaming.

Dynamic adaptive bitrate (ABR) streaming has recently been widely deployed in wireless networks. It, however, does not impose adaptation logic for selecting the quality of video chunks for mobile users. In this paper, we propose a two time-scale resource optimization scheme for ABR streaming over wireless networks under time-varying channels. Our proposed resource optimization scheme takes into account three key factors that make a critical impact on quality of experience (QoE) of ABR streaming, including video quality, quality variation, and video rebuffer. Lyapunov optimization technique is employed to maximize the QoE of users by dynamically adapting the video quality at the application layer and allocating bandwidth at the physical layer. Without the prior knowledge of channel statistics, we develop a video streaming algorithm (VSA) to obtain the video quality adaptation and bandwidth allocation decisions. For the arbitrary sample path of channel states, we compare the QoE achieved by VSA with that achieved by an optimal T-slot lookahead algorithm, i.e., knowledge of the future channel path over an interval of length T time slots. Simulation results demonstrate the effectiveness of the proposed VSA for ABR streaming over time-varying wireless networks.

The development of Network Function Virtualization (NFV) and Software Defined Networks (SDN) standards is an opportunity for Mobile Network Operators (MNOs) to deploy Content Delivery Network (CDN) functionalities into the mobile network edge, such as Base Stations (BSs). In this paper, we investigated the content placement problem for the BS-based wireless CDN system. We call the storage resources implemented on BSs as storage helpers. Due to the limited helper storage capacity and the limited user population served per BS, helpers exhibit low hit ratio comparing to traditional CDN edge servers serving a wide area. Then, cooperation is a suitable means to enhance the performance of the wireless CDN system. We propose that BSs close to each other cooperate in replicating content and replying user requests. We formulate the optimum content placement problem to minimize the traffic pressure on mobile network gateways, and show the problem complexity is NP-Hard. We then transform the problem into a multiple-Maximum Weighted Independent Set problem, and propose a heuristic algorithm. The evaluation shows that the hit ratio is improved by our algorithm comparing to the traditional Least Frequently Used (LFU) policy without cooperation.

In this study, the authors investigate the energy‐aware quality of information (QoI) maximisation problem by jointly optimising the sensor selection, sampling rate, packet‐dropped rate, and transmit power in wireless sensor networks. By introducing the weight parameters, the authors first present a revenue‐cost (RC) function which combines the optimisation objectives of the QoI and energy expenditure into a single objective to capture the tradeoff between them. Then, a stochastic optimisation programming is formulated to maximise the long‐term average RC value subject to the network stability constraint. Using the Lyapunov drift theory, the authors develop a collaborative sensing and transmit power control algorithm that can guarantee the worst‐case delay for each packet. Simulation results demonstrate the advantages of the proposed algorithm.

This work is motivated by the following observation: modern wireless applications such as content prefetching, allow different extent of delay tolerance, thus in practice users may have different time‐averaged data rates during a certain period of time. In this study, the authors consider resource allocation in small cell networks under time‐varying channels while each user has an individual time‐averaged rate requirement. Stochastic optimisation model is employed to minimise the time‐averaged power consumption of small base stations subject to individual user's time‐averaged rate constraint. They develop an online power optimal resource allocation (PORA) algorithm to achieve the optimal power allocation and subcarrier assignment decisions without prior knowledge of channel statistics. Furthermore, considering that the power allocation and subcarrier assignment problem is a nonconvex combinatorial problem, they further develop an iterative heuristic algorithm with polynomial complexity. Simulation results show the effectiveness of PORA and verify the theoretical analysis on the network performance.

In this study, the authors investigate subcarrier and power allocation for point‐to‐point data transmission under imperfect channel state information in multi‐user orthogonal frequency‐division multiple‐access (OFDMA) networks. The resource optimisation problem is formulated to handle inter competition (the resource competition among different users) and intra competition (the resource assignment between channel estimation and data transmission for each individual user). The authors employ the non‐cooperative game with pricing approach to solve the optimisation problem. By analysing optimal relationship between channel estimation cost and total occupied resource for each user, the two‐level resource competition is simplified to the problem including only inter competition, which is modelled as a non‐cooperative subcarrier and power allocation game with subcarrier pricing. In this game, the utility function is defined as the number of received data bits per joule of energy, which is associated with the estimate error and cost, the data transmit power and the number of data‐subcarriers. The authors prove the existence, uniqueness and Pareto efficiency of Nash equilibrium (NE) for the proposed resource allocation game. Furthermore, a distributed resource allocation algorithm is designed to achieve the NE.

In this letter, we investigate the optimal EH-ratio and beamwidth selection in millimeter wave (mmWave) communications. Specifically, users in mmWave communications operate in slotted mode, where each frame consists of three continuous segments-energy harvesting, beam-searching, and data transmissions. In this sense, we propose a joint optimal EH-ratio and beamwidth selection scheme by maximizing the achievable throughput. Simulation results validate the performance superiority of the proposed scheme.

In this paper, we investigate the multiservice delivery between the source-destination pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose their selfish behaviors, i.e., forwarding or dropping multiservices. Owing to the effect of the RNs' node-selfishness on the multiservices, a distributed framework of the node-selfishness management is constructed to manage the RN's node-selfishness information (NSI) in terms of its available resources, the employed incentive mechanism and the quality-of-service (QoS) requirements, and the other RNs' NSI in terms of their historical behaviors. In this framework, the RNs' NSI includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES). Under the distributed node-selfishness management, a path selection criterion is designed to select the most reliable and shortest path in terms of RNs' DeISs affected by their available resources, and the optimal incentives are determined by the source to stimulate forwarding multiservices of the RNs in the selected path. Our simulation results demonstrate that this framework effectively manages the RNs' NSI, and the optimal strategies of both the path selection and the incentives are determined.

In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in V and the increase of network utility is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, we investigate the transmission strategy for cognitive radios (CRs), which opportunistically operate on various primary users' channels with the aid of proactive spectrum handoff. In particular, a secondary user (SU) in the CR network proactively predicts the future spectrum status and decides whether to keep idle, or stay in the current channel, or switch to a new channel to resume its transmission. A problem of completing a target data packet size of V bits within a predefined deadline D time slots is formulated as a discrete-time Markov decision process, in which the SU aims at minimizing its expected total cost, i.e., transmission cost, handoff cost, and overtime penalty. We solve the problem using dynamic programming, and propose a general optimal transmission with the proactive spectrum handoff (OTPH) algorithm whose complexity is 2 x |V| x D. Furthermore, we prove that for a convex penalty function, the optimal handoff-aided transmission exhibits a threshold structure. A monotone OTPH algorithm with a complexity of max(|V |, D) is used in this case. Simulation results verify that our proposed scheme achieves both the minimal total cost and the highest data transmission efficiency as compared with the traditional always staying and always changing schemes.

We consider the dynamic resource management and allocation with respect to users' personalized quality requirements in dynamic orthogonal frequency division multiple access (OFDMA) networks with time varying wireless channels and bursty traffic. We first propose a new performance metric for evaluating the degree of user's satisfaction with respective to its personalized quality requirement. We then develop a quality-oriented joint rate control and resource allocation algorithm (QO_JRCRA) with the aim to maximize the time average satisfaction of all users (SAU). QO_JRCRA can be adaptive to the time varying wireless channels and bursty traffic without knowledge of external data arrivals and channel statistics, yet yields an SAU that can arbitrarily approach the optimal SAU achieved by an algorithm with complete knowledge of future events. Simulations results verify the theoretical analysis on the system performance and show the adaptiveness of the QO_JRCRA.

In this paper, we design an energy-efficient cross-layer optimization framework for media streaming over time-varying wireless network. The energy efficiency (EE) is characterized by the stochastic optimization model subject to the network stability, which is also used to depict the average media delivery delay. In harmony with the hierarchical architecture of the wireless network, the problem of stochastic optimization of media streaming is decomposed by the Lyapunov drift theory into two subproblems, associated with the flow control in transport layer and the power allocation in physical (PHY) layer. Specifically, the dynamic cross-layer control algorithm for media streaming is developed for adapting to the time-varying network state information, i.e. time-varying channel state information (CSI) of mobile terminal (MT)-access points (AP) links and dynamic queue state information (QSI) at APs. We derive a tradeoff between EE and media streaming delay, where the increase of average delay is approximately linear in V and the increase of EE is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, a fast content distribution scheme is proposed with QoS provisions and user cooperation in cellular networks. A group of mobile terminals (MTs), which are interested in the same content, can cooperate to reduce the content distribution time (CDT). The Nash bargaining solution was derived to formulate the content distribution problem, where each player of the game can maximize its individual payoff. Simulation results demonstrate that the MTs endowed with cooperative scheme achieve a significant gain in terms of decreasing the CDT compared with those without cooperation.

A window based channel allocation scheme is conceived for two-way AF relay aided OFDMA systems. Two users communicating with each other via a specific relay constitute a user-pair. At most one channel index with its channel gain lying within a window, given the left and the right thresholds, is reported to the relay by each user at a time. If two users of a user-pair send non-identical channel indices, one of them makes a concession to match the channel index sent by its partner. Furthermore, all user-pairs with different QoS requirements can employ the concession process with the aid of scheduling scheme parallelly to attain optimal channel allocation. Simulation results demonstrate that the proposed algorithm with limited feedback of channel state information (CSI) achieves asymptotic system performance compared with the full CSI feedback based scheme.

In this paper, a cell zooming based dynamic base stations (BS) switching scheme is conceived for energy saving in cellular networks. A cell zooming algorithm is developed for leveraging the BS's coverage with inter-cell cooperation and downlink power control. Meanwhile, we design a feasible working strategy for the cell zooming scheme to maximizing the energy saving. Simulations are provided to demonstrate the advantages of the proposed scheme.

We conceive an interference mitigation scheme, for twin-layer networks for protecting the macrocell-users, from the interference imposed by the femtocells as well as for mitigating the interference amongst femtocells. Femtocells are capable of finding the available sub-bands using cognitive radio techniques, where the lowest interference is observed by the nearby macrocell-users. A sub-channel allocation algorithm is developed with the aid of graph-theoretic approaches for optimizing the femtocell throughput in dense femtocells deployment scenarios. The femto-users are grouped into different clusters for suppressing the interference amongst them. Each cluster is assigned a unique sub-channel by using the classic cluster-coloring approach. Adaptive power allocation is performed among the femtocells for further enhancing the system throughput and the attainable performance is quantified.

An over-booking based dynamic spectrum management (DSM) scheme is conceived for improving the attainable spectral efficiency. All secondary users (SU) will be categorized into different classes and they borrow spectral resource from the primary users (PU) before data transmission. Under the risk-based policy model, the effects of both booking cancellations and 'no-show' reservations are analyzed. Assuming that the booking demands obey an inhomogeneous Poisson process, we derive the optimal number of excess reservations, while minimizing the total compensation costs. Algorithms are developed for determining the capacity allocation dedicated to each class, whilst denying allocation, which would lead to congested bookings.

This paper presents the behavior design and multimodal locomotion control of a biomimetic amphibious robot based on a bio-inspired CPG (central pattern generator). A set of four key parameters are introduced serving as external stimuli to shape the CPG rhythmic activities where necessary speed and orientation modulation as well as 3-D locomotion can be obtained. In terms of the built parameter set, a library of movement primitives based on finite state machine is established to facilitate rapid and smooth gait transitions. To enhance adaptive behaviors, well-integrated sensory feedback by means of two liquid-level detectors enables the gait transition between ground and water autonomously. Simulations and experiments are also conducted to demonstrate the feasibility of a behavior based control architecture governed by CPGs.

In this paper, we propose a distributed adaptive interference coordination algorithm for a practical orthogonal frequency division multiple access (OFDMA)-based mobile cellular systems. The designed algorithm can achieve an efficient frequency reuse for any user distribution and traffic load. Since no a priori frequency planning is required, the minimal coordination between base stations is also achieved. Moreover, the proposed algorithm can adapt to different network interference conditions and is power-saving in some degree. We also develop a way to decompose a multi-cell optimization problem into distributed single-cell optimization problems, which greatly reduces the computational complexity.

This paper presents a control architecture for the underwater locomotion control of a biomimetic amphibious robot with multi-mobility mechanism. In view of both hydrodynamic problem and engineering approach, we develop a robotic prototype capable of multi-mode motion. A robust gait control for steady swimming using the central pattern generator (CPG) is proposed and has been successfully applied to the robot. The CPG can produce coordinated patterns of rhythmic activity while being simply modulated by control parameters including input drive, frequency, amplitude, threshold, etc., which will be suitable for manually interactive modulation. Using the CPG model, the robot is capable of performing and switching between various locomotion modes such as swimming forwards and backwards, turning and pitching, with the speed, direction and gait types modulated accordingly. A test-bed is provided and results are presented demonstrating interesting properties of the CPG-based control approach and feasibility of the CPG control for efficient propulsion.

The authors investigate the symbol error rate (SER) performance of the cooperative decode-and-forward (DF) relaying strategy with orthogonal space–time block coding (OSTBC) applied at all links of source-relay, source-destination and relay-destination. Only when one relay node is able to correctly decode the OSTBC codeword of the source, it will forward source information to the destination with the same OSTBC codeword. The exact SER expressions of DF relaying with OSTBC are presented for M-PSK and M-QAM modulations, respectively, over dissimilar Rayleigh fading channels. By virtue of the multinomial theorem and the law of total probability, the derived expressions are further deduced in closed form. Simulations demonstrate the proposed closed-form analytical results. It is pointed out that such results have seldom appeared in literatures before.

This paper addresses the performance analysis for orthogonal space-time block coded (STBC) multiband orthogonal frequency division multiplexing (MB-OFDM) ultra-wideband (UWB) systems. As the channel model considers the log-normal fading, the resultant signal to noise ratio (SNR) will be the product of a log-normal distributed variable and a variable of sum of several Gamma- distributed variants. Based on this, the exact expressions for the bit error probability and the outage probability are derived theoretically. And their approximate expressions can be obtained via the Gauss-Hermite and the Gauss- Laguerre quadratures, respectively. Simulation results verify the theoretical solutions.

We study the outage performance for the decode-and-forward (DF) relaying in the Nakagami fading under unequal Nakagami interference. A closed-form expression for the outage probability is derived. Simulations are provided to verify the analytical results.

The authors investigate the outage performance for decode-and-forward relaying schemes in the presence of dissimilar Nakagami-m interferers under non-identical Nakagami-m fading channels. A closed-form expression for the outage probability is derived under this scenario. Simulation results demonstrate our theoretical solutions.

Coexisting with many concurrent narrowband services, the performance of UWB systems will be affected considerably by them due to the high power of these narrow-band signals with respect to the UWB signals. Specifically, IEEE 802.11a systems which operate around 5 GHz and overlap the band of UWB signals will interfere with UWB systems significantly. In this paper, a novel narrow-band interferences (NBI) suppression technique based on singular value decomposition (SVD) algorithm for two UWB systems is presented. The two systems are direct sequence ultra-wideband (DS-UWB) system and timing hopping (TH) binary pulse amplitude modulation (PAM) UWB system. SVD is used to approximate the interferences which then are subtracted from the received signals. The proposed technique is simple and robust. Simulation results show that the proposed new technique is very effective.