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Abstract Multi‐agent reinforcement learning has played an increasingly important role in intelligent traffic signal control due to its self‐learning ability. However, existing algorithms only focus on signal timing mechanism design while ignoring the exponential growth of the joint action dimension as the number of intersections increases, which will ultimately face the learning difficulty. In this paper, traditional traffic methods are introduced into MARL to flexibly determine the phase and duration of each intersection. The proposed MARL algorithm based on mean field theory has the ability to convert a large number of agents to approximately binary interaction, which can effectively reduce the dimension of joint action space in multi‐agent environment and learn in a robust process. Besides, to improve the performance of traditional traffic methods, the recurrent neural network (RNN) and an improved Webster's formula with revised parameters are combined to dynamically determine the phase duration according to the historical volume of traffic flow. The simulation results indicate that the proposed algorithm shows superior scalability compared to baseline methods and has great potential to be applied in the large scale road‐networks scenario.

The Internet of Things (IoTs) has become an essential component of the 5th Generation (5G) network and beyond, accelerating the transition to digital society. The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network (5GC) control plane. To address this issue, the 3rd Generation Partnership Project (3GPP) first proposed a Service-Based Architecture (SBA), intending to create a flexible, scalable, and agile cloud-native 5GC. However, considering the coupling of protocol states and functions, there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner. We propose a Message-Level StateLess Design (ML-SLD) to provide a cloud-native 5GC from an architectural standpoint in this paper. Firstly, we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network (RAN) and the 5GC, avoiding interruptions and dropouts of large-scale user data. Furthermore, we propose an On-demand Message Forwarding (OMF) algorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC. Finally, we create a prototype that is based on the OpenAirInterface (OAI) 5G core network projects, with all Network Functions (NFs) packaged in dockers and deployed in a kubernetes-based cloud environment. Several experiments have been built with UERANSIM and Chaosblade simulation tools. The findings demonstrate the viability and efficiency of our proposed methods.

Abstract Objective: To conduct a contemporary detailed assessment of outpatient antibiotic prescribing and outcomes for positive urine cultures in a mixed-sex cohort. Design: Multicenter retrospective cohort review. Setting: The study was conducted using data from 31 Veterans’ Affairs medical centers. Patients: Outpatient adults with positive urine cultures. Methods: From 2016 to 2019, data were extracted through a nationwide database and manual chart review. Positive urine cultures were reviewed at the chart, clinician, and aggregate levels. Cases were classified as cystitis, pyelonephritis, or asymptomatic bacteriuria (ASB) based upon documented signs and symptoms. Preferred therapy definitions were applied for subdiagnoses: ASB (no antibiotics), cystitis (trimethoprim-sulfamethoxazole, nitrofurantoin, β-lactams), and pyelonephritis (trimethoprim-sulfamethoxazole, fluoroquinolone). Outcomes included 30-day clinical failure or hospitalization. Odds ratios for outcomes between treatments were estimated using logistic regression. Results: Of 3,255 cases reviewed, ASB was identified in 1,628 cases (50%), cystitis was identified in 1,156 cases (36%), and pyelonephritis was identified in 471 cases (15%). Of all 2,831 cases, 1,298 (46%) received preferred therapy selection and duration for cases where it could be defined. The most common antibiotic class prescribed was a fluoroquinolone (34%). Patients prescribed preferred therapy had lower odds of clinical failure: preferred (8%) versus nonpreferred (10%) (unadjusted OR, 0.74; 95% confidence interval [CI], 0.58–0.95; P = .018). They also had lower odds of 30-day hospitalization: preferred therapy (3%) versus nonpreferred therapy (5%) (unadjusted OR, 0.55; 95% CI, 0.37–0.81; P = .002). Odds of clinical treatment failure or hospitalization was higher for β-lactams relative to ciprofloxacin (unadjusted OR, 1.89; 95% CI, 1.23–2.90; P = .002). Conclusions: Clinicians prescribed preferred therapy 46% of the time. Those prescribed preferred therapy had lower odds of clinical failure and of being hospitalized.

Recently, unmanned aerial vehicles (UAVs) as flying wireless communication platform have attracted much attention. Benefiting from the mobility, UAV aerial base stations can be deployed quickly and flexibly, and can effectively establish Line-of-Sight communication links. However, there are many challenges in UAV communication system. The first challenge is energy constraint, where the UAV battery lifetime is in the order of fraction of an hour. The second challenge is that the coverage area of UAV aerial base station is limited and the commercial UAV is usually expensive. Thus, covering a large target region all the time with sufficient UAVs is quite challenging. To solve above challenges, in this paper, we propose energy efficient and fair 3-D UAV scheduling with energy replenishment, where UAVs move around to serve users and recharge timely to replenish energy. Inspired by the success of deep reinforcement learning, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (UC-DDPG) to address the combination problem of 3-D mobility of multiple UAVs and energy replenishment scheduling, which ensures energy efficient and fair coverage of each user in a large region and maintains the persistent service. Simulation results reveal that UC-DDPG shows a good convergence and outperforms other scheduling algorithms in terms of data volume, energy efficiency and fairness.

Wireless sensors network (WSN) is widely used for environmental monitoring, surveillance, healthcare, and security services. One of the most critical challenges that WSN facing is the heavy computation requirements and limited energy storage. The recent development of radio frequency-based (RF) wireless power transfer (WPT) and mobile computation offloading to cloud provide a promising solution to overcome these shortcoming. In this paper, we consider a multi-user Mobile/Multi-Access edge computing (MEC) aided WSN powered by the WPT, where BSs are equipped with multiple antennas jointly service the whole sensors, the sensor node is powered by the WPT only. An optimal multi-user computation offloading strategy is proposed, where sensor nodes choose to operate in either the offloading or the local computing. In addition, perfect channel state information (CSI) and imperfect CSI are taken into account. We study the optimization of computation task offloading in MEC aided WSN under the conditions of perfect CSI and imperfect CSI. In particular, the optimal dynamic time division duplex (D-TDD) factor is investigated. Numerical results confirm the advantages of the proposed computation offloading strategy over the conventional local computation design in handing computationally heavy tasks.

Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs.

Unmanned aerial vehicle (UAV) data collection is a promising research direction that can be applied to many practical scenarios. Due to the limited energy of sensors in wireless sensor networks (WSN), UAV, which is considered as a mobile fusion center, can effectively prolong the lifetime of sensor via supporting communication with the sensor directly. However, since the UAV's energy constrained, it is necessary that multiple UAVs provide data collection to sensors in large areas. In this paper, we consider a scenario where multiple UAVs collect data from a set of two-dimensional distributed sensors. The UAV can communicate with sensors while flying or hovering. The goal is to minimize the total time that all UAVs from data center and return to data center after completing all collection tasks, while giving each sensor a certain amount of data and energy. The problem of minimizing the task completion time of multiple UAVs is still a big challenge. We solve the multi-UAV problem by jointly optimizing UAV-sensor association mechanism and data collection method of UAVs. The numerical results show that the proposed multi-UAV data collection scheme shortens the task completion time.

Traffic light-free intersection control is envisioned to alleviate congestion and manage vehicles intelligently. With the help of vehicle-to-infrastructure (V2I) communication and edge computing (EC), vehicles are instructed to cross the intersection with high vehicle safety and traffic efficiency without traffic lights. However, unstable channel conditions can lead to the reduction of traveling safety. In this paper, we propose a robust autonomous intersection control (AIC) approach with global optimization scheduling, which protects connected autonomous vehicles from collision under any channel conditions while achieving decent traffic efficiency. In particular, we propose an AIC model that gives vehicles certain autonomy under centralized control to ensure the traveling safety in case of some emergencies. By conducting an interference graph, we simplify the AIC problem as a weighted maximal clique problem with restriction. To improve the fairness and efficiency in terms of vehicle passage, multiple factors such as travel delay, traffic of the current lane and passengers' desired speed are considered. Furthermore, we propose a heuristic algorithm to search the solution space. For further optimization, a particle swarm optimization algorithm is proposed, achieving a near-optimal result with adjustable overhead. Finally, we build the simulation model and conduct a comparative performance evaluation. Simulation results demonstrate the superiority of our proposed scheme.

This paper considers a hybrid beamforming massive multiple-input multiple-output (MIMO) system operating in time-division duplex (TDD) mode. We investigate the combined impacts of quantized phase shifters (QPSs), channel non-reciprocity and channel estimation errors on the achievable spectral efficiency (SE) of the considered system. We first introduce an equivalent hybrid beamforming structure, based on which, a generic and practical channel non-reciprocity model is built for massive MIMO systems with hybrid structure. This model jointly considers the mismatch between transceiver and receiver circuits, as well as the mutual coupling between antennas. Then we derive the generic expression of achievable SE for both the ideal phase shifters (IPSs) and QPSs cases. Furthermore, the closed-form expressions of SE are derived for IPSs and QPSs cases, respectively. The asymptotic performance in large antenna regime is also analyzed based on these expressions. Simulation results demonstrate the validity and accuracy of the derived analytical expressions. In addition, results show that the phase quantization and channel non-reciprocity both contributes to the SE degradation and the detrimental effects cannot be canceled completely even in the large-antenna regime.

Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving.

The future mobile communication networks (beyond 5th generation (5G)) are evolving toward the service-based architecture where network functions are fine-grained, thereby meeting the dynamic requirements of diverse and differentiated vertical applications. Consequently, the complexity of network management becomes higher, and artificial intelligence (AI) technologies can improve AI-native network automation with their ability to solve complex problems. Specifically, deep reinforcement learning (DRL) technologies are considered the key to intelligent network automation with a feedback mechanism similar to that of online closed-loop architecture. However, the 0-delay assumptions of the standard Markov decision process (MDP) of traditional DRL algorithms cannot directly be adopted into real-world networks because there exist random delays between the agent and the environment that will affect the performance significantly. To address this problem, this paper proposes a random-delay-corrected framework. We first abstract the scenario and model it as a partial history-dependent MDP (PH-MDP), and prove that it can be transformed to be the standard MDP solved by the traditional DRL algorithms. Then, we propose a random-delay-corrected DRL framework with a forward model and a delay-corrected trajectory sampling to obtain samples by continuous interactions to train the agent. Finally, we propose a delayed-deep-Q-network (delayed-DQN) algorithm based on the framework. For the evaluation, we develop a real-world cloud-native 5G core network prototype whose management architecture follows an online closed-loop mechanism. A use case on the top of the prototype namely delayed-DQN-enabled access and mobility management function (AMF) scaling is implemented for specific evaluations. Several experiments are designed and the results show that our proposed methodologies perform better in the random-delayed networks than other methods (e.g., the standard DQN algorithm).

So far, the handoff management involved in the wireless local area network (WLAN) has mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff mechanism generates noticeable delays during the handoff process, resulting in discontinuity of service, which is more evident in dense WLANs. Inspired by software-defined networking (SDN), prior works put forward many seamless handoff mechanisms to ensure service continuity. With respect to the handoff decision algorithm, when to trigger handoff and which access point to reconnect to, however, are still tricky problems. In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN frameworks. Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep Q-network. The proposed scheme enables the network to learn from actual users' behaviors and network status from scratch, adapting its learning in time-varying dense WLANs. Due to the temporal correlation property, the handoff decision is modeled as the Markov decision process (MDP). In the modeled MDP, the proposed scheme depends on the real-time network statistics at the time of decisions. Moreover, the convolutional neural network and the recurrent neural network are leveraged to extract fine-grained discriminative features. The numerical results through simulation demonstrate that DCRQN can effectively improve the data rate during the handoff process, outperforming the traditional handoff scheme.

The mobile edge caching is a promising way to reduce the user-perceived delay and improve the transmission data rates for the wireless networks. However, the cache capacities of base stations (BSs) tend to be limited and users' interests for the contents are diverse, which makes the content placement decision critical for the network performance optimization. Besides, due to the flexible user-BS association, it is more complicated to optimize the content delivery and placement which are coupled with each other. This paper investigates the content placement and content delivery strategies in the cache-enabled wireless networks. In particular, the effective capacity, which can characterize the end-to-end user-perceived delay and data rates simultaneously, is introduced as the user's utility metric. As the content caching and content delivery operate in different time-scales, they are investigated separately. For the content caching, a content placement problem is formulated, where both users' different active levels and diverse content preferences are considered. Due to the NP-hard nature, the problem is decomposed into two sub-problems, and an iterative association-aware content placement algorithm is proposed. For the content delivery, the user-BS association problem is formulated, and a cache-aware user-BS association algorithm is designed. The performance of the proposed algorithms is evaluated based on the simulations. The numerical results show that the proposed algorithms have a better capability to cope with users' diverse active levels, and achieve a better performance in terms of effective capacity and fairness level, compared with the existing algorithms.

The growing physical (PHY) layer capabilities of Wi-Fi have made it possible to use Wi-Fi signals for both communication and human sensing. Wi-Fi channel state information (CSI) in PHY layer can be obtained from commodity Wi-Fi devices. As CSI can detect the minute environment changes that alter signal propagation, it is thus capable of capturing the subtle human activities to provide cost-effective and easy-to-use human sensing. However, the limited bandwidth of each individual Wi-Fi channel fundamentally constrains the resolution of signals, resulting in poor performance of human sensing. In this paper, we present WiRIM, a resolution improving mechanism for Wi-Fi based human sensing. We design a channel switching and aggregation algorithm to extend the effective bandwidth of commodity Wi-Fi signals and improve the performance and efficiency of human sensing applications. With aggregated CSI, WiRIM constructs feature images which contain rich frequency, temporal and spatial characteristics, and then uses a deep learning method to process the extracted features. We propose a cross-location human activity recognition (CLHAR) scenario as a case study. The CLHAR scenario requires a high enough resolution of the Wi-Fi signals to accurately recognize different activities under the interference of tiny changes in human location. The experiments demonstrate the generality and effectiveness of the proposed mechanism.

For fifth-generation wireless communication systems, network slicing has emerged as a key concept to meet the diverse requirements of various use cases. By slicing an infrastructure network into multiple dedicated logical networks, wireless networks can support a wide range of services. However, how to fast deploy the end-to-end slices is the main issue in a multi-domain wireless network infrastructure. In this paper, a mathematical model is used to construct network slice requests and map them to the infrastructure network. The mapping process consists of two steps: the placement of virtual network functions and the selection of link paths chaining them. To efficiently utilize the limited physical resources, we pay attention to the service-oriented deployment by offering different deployment policies for three typical slices: eMBB slices, mMTC slices, and uRLLC slices. Furthermore, we adopt complex network theory to obtain the topological information of slices and infrastructure network. With the topological information, we define a node importance metric to rank the nodes in node mapping. To evaluate the performance of deployment policy we proposed, extensive simulations have been conducted. The results have shown that our algorithm performed better in terms of resource efficiency and acceptance ratio. In addition, the average execute time of our algorithm is in a linear growth with the increase of infrastructure network size.

Network function virtualization (NFV) has become an emerging issue in both academia and industry. By outsourcing network functions from dedicated hardware to virtualization platform, NFV promises to significantly improve the scalability and flexibility of network management and orchestration. One of the main challenges for NFV deployment is to realize coordinated service function chaining on NFV-based infrastructures. This challenge is referred to as the coordinated NFV resource allocation (coordinated NFV-RA) problem which is proved to be NP-hard. In order to response timely to the service variation, many heuristic or meta-heuristic algorithms are proposed to reduce the computing complexity. However, it is very difficult to evaluate the approach degree between obtained sub-optimal solutions and the optima, since finding the optimal solution is a non-trivial task. In this paper, a novel modeling approach called homogeneous link mapping is proposed to find the optimal solutions of a typical threestage coordinated NFV-RA model with CPLEX. Then we further establish a service function chain (SFC) deployment database with optimal solutions and the results in the database can be used as a criterion to evaluate other SFC algorithms. In order to imitate different practical networks, the SFC deployments are conducted on three type network topologies. And we also analyze the SFC deploying performance on different topologies. At Last, we make the optimal modeling approach open source, and upload the database on http: //www.opensource5g.org/database.

Network slicing is a promising technology for wireless networks to provide various services using limited resources of infrastructure networks. In order to decrease capital expenditure and operational expenditure, multi-tenant slicing provides resource sharing mechanisms among multiple infrastructure providers (InPs). However, one of the main challenges for cooperation among InPs is to meet the diverse demands of various services considering the different structural characteristics of each InP. In this paper, we investigate on-demand cooperation among multiple infrastructure networks from a perspective of the complex network. Four typical network topologies are used to imitate different practical infrastructure networks. The proposed cooperation strategy contains two stages, the selection of connectors and the creation of interconnections. We adopt the complex network theory to obtain the topological information of infrastructure networks. With the topological information, connectors are selected with selection fitness, and interconnections between connectors are created using the two-sided matching. Finally, extensive simulations are conducted, and the effects of variables in the two stages are analyzed. The simulation results illustrate that the proposed strategy performs better in terms of meeting the demand.

Machine-type communications (MTCs) have been envisaged to play a key role within the future 5G cellular network. To handle the massive MTCs (mMTCs) and alleviate the congestion of the radio access network, the group paging (GP) scheme was proposed by the third-generation partnership project. However, its performance quickly decreases in the face of massive simultaneous channel accesses. In this paper, we propose a two-phase cluster-based GP (CBGP) scheme. First, owing to the advantages of low-cost, high-access capacity and handy deployment, IEEE 802.11ah is introduced to increase the capability of coping with massive access attempts. The separation of inner cluster data collection and header-based data transmission phases greatly alleviates the access congestion of cellular networks, reducing the access delay and increasing the successful access probability for mMTC devices. Besides, we also derive mathematical models of the CBGP scheme in terms of the successful access probability and average access delay. Moreover, effects from different numbers of clusters on the performance of the CBGP scheme are investigated and the optimal number of clusters is also derived, adaptive to different access scales. At last, numerical results are presented to validate the accuracy of our analytical models, demonstrate the effectiveness of the proposed CBGP scheme, and verify the optimal number of clusters, providing insights for the coming 5G cellular system design.

The proposition of network function virtualization (NFV) aims to solve the difficulty and ossification in current network's management and service provision caused by ever-growing NFs with dedicated hardware. By decoupling the NFs from dedicated hardware to virtualized platform, NFV promises flexible deployment and management of service function chains (SFCs). However, an optimal resource allocation for requested SFC in NFV-based infrastructures should coordinately consider following three stages: virtual network functions (VNFs) chain composing, VNF forwarding graph embedding, and VNFs scheduling, which is a tough task as the decision of these three phases is mutually dependent. In this paper, staring from the challenges in solving coordinated NFV resource allocation (NFV-RA), we first formulate a typical three-stage coordinated NFV-RA model as a mixed integer programming (MIP) and, then, propose a heuristic solution called merge-split viterbi (MSV). MSV can automatically determine the appropriate number of VNF instances without given maximum number threshold, and it does not take the iterative deployment strategy, which is commonly used in current solutions. The main idea of MSV is to first find a global basic solution and, then, to further optimize the basic solution through some improvement procedures, and this makes it not be easily trapped in local optimality and avoid complex anti-local-optimal measures as well. Extensive experiments demonstrate that MSV can get solutions in global range with reasonable execution time and achieves total cost ratio within 115% compared to the MIP implement.

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.

A large-scale deployment of femtocell BSs (FBSs) causes substantial energy consumption. This paper proposes a hybrid BS-cooperative power management (HBCPM) scheme with self-organized sleep mode in virtual cell-based femto networks. Firstly, HBCPM builds a leader-member virtual cell framework, in which only one FBS is a FBS leader (FL) and others are FBS members (FMs). Then, the FL acts as an autonomous entity and is responsible for detecting active calls in the virtual cell coverage, while the FMs without active connections can entirely shut down pilot transmissions and the related processing all the time. So it is suited for self-organizing networks (SONs). Based on the proposed scheme, the state transition model is established by the semi-Markov stochastic process, and the analytic formulas of average cumulative delay and interference time as well as the energy consumption are derived. Meanwhile, three prominent-related schemes are also studied by the proposed model. With the practical long-term evolution (LTE) system parameters and three-dimensional femtocell network model, the numerical simulation and theoretical analysis match pretty well, and the tradeoff between the energy consumption and average cumulative delay is also manifested. Moreover, simulation results show that the proposed algorithm outperforms other three schemes in terms of average cumulative interference time and the energy consumption. [ABSTRACT FROM AUTHOR]

Mobile data offloading through Wi-Fi is a promising solution to alleviate the explosive data increase in cellular network. While extensive attempts have been made at mobile data offloading, previous studies have rarely paid attention to network characteristics (e.g. Wi-Fi deployment density) and its effect on overall mobile offloading performance. In this paper, we propose a stochastic geometry model for the performance analysis of delay-tolerant offloading with mobility prediction. We aim to understand how the network parameters, such as network size and channel condition, affect the long-term offloading potential in the dense Wi-Fi sitting. The deployment of Wi-Fi is modeled as an independent Poisson point process (PPP) to take the effect of interference and CSMA/CA-based medium access control protocol into account. Then the semi-Markov process is used to model the user's movement taking the sojourn time into account. Based on the PPP deployment and semi-Markov process, we can obtain the potential offloading traffic. Through the above proposed analytical studies, the network providers can easily obtain a rough estimation on the average offloading performance from a given dense network.

A load balancing mechanism can adjust the load distribution among access points (APs) and improve resource utilization for dense wireless local area networks (WLANs). In this paper, we propose a semi-matching-based load balancing scheme for the IEEE 802.11 dense WLANs. The proposed scheme runs in a centralized controller. The controller judges whether the load is unevenly distributed according to the collected channel busy time ratio information of the entire network, and triggers the load balancing mechanism accordingly. In order to realize load balancing among APs and maximize the overall network throughput, we model the station to AP association problem as a weighted bipartite graph matching problem and find the optimal semi-matching using the Kuhn-Munkres (K-M) algorithm. Simulation results show that the proposed scheme achieves performance improvement comparing with traditional schemes.

Machine-type communications have suffered serious congestion, overload, and poor quality of service problems in cellular networks, since the cellular networks are designed for human-to-human communications. Moreover, current solutions just focus on the congestion for single base station and cannot adaptively work in the dynamic and complex conditions. In this article, we provide an autonomous and adaptive attractor-selection-based congestion control scheme for massive access from machine-type communication devices based on the resource separation scheme. First, we introduce a feasible and self-adaptive extended attractor-selection mechanism to decide which base station to be chosen. Simultaneously, an effective estimation algorithm for the traffic load of base stations is also designed to represent the network traffic load without frequent information exchanges among devices or base stations. With the available access resources and estimated traffic load taken into consideration, massive access attempts can receive the decisions via the broadcast and adaptively choose proper stations for alleviation of the congestion and overload. Finally, simulation results show that the proposed attractor-selection-based congestion control scheme achieves better performance in terms of average access delay, collision probability, and throughput of the whole system, adaptively accommodating to unpredictable environments under cellular networks.

Demand-side management is an important method to improve the stability of power consumption and the reliability of power grid. In Energy Internet, the power supplier can obtain more detailed data about users’ behaviour with numerous smart devices and advanced information network. Here, the utility function of power supplier is established by using the utility function of different users at different times, explicitly taking into account the economic operation's impact. The authors propose a Stackelberg game model to formulate the gaming process between users and power supplier. The authors convert the model into a convex problem to maximum power supplier's benefit, and then calculate the corresponding optimal time-of-use prices, and optimal economic operation power. The simulation results show that the model is effective and has a better result than the traditional profit-only model and non-time-of-use electricity pricing model.

Network function virtualization (NFV) has already been a new paradigm for network architectures. By migrating NFs from dedicated hardware to virtualization platform, NFV can effectively improve the flexibility to deploy and manage service function chains (SFCs). However, resource allocation for requested SFC in NFV-based infrastructures is not trivial as it mainly consists of three phases: virtual network functions (VNFs) chain composition, VNFs forwarding graph embedding, and VNFs scheduling. The decision of these three phases can be mutually dependent, which also makes it a tough task. Therefore, a coordinated approach is studied in this paper to jointly optimize NFV resource allocation in these three phases. We apply a general cost model to consider both network costs and service performance. The coordinate NFV-RA is formulated as a mixed-integer linear programming, and a heuristic-based algorithm (JoraNFV) is proposed to get the near optimal solution. To make the coordinated NFV-RA more tractable, JoraNFV is divided into two sub-algorithms, one-hop optimal traffic scheduling and a multi-path greedy algorithm for VNF chain composition and VNF forwarding graph embedding. Last, extensive simulations are performed to evaluate the performance of JoraNFV, and results have shown that JoraNFV can get a solution within 1.25 times of the optimal solution with reasonable execution time, which indicates that JoraNFV can be used for online NFV planning.

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.

To support distributed energy generators and improve energy utilization, energy Internet has attracted global research focus. In China, energy Internet has been proposed as an important issue of government and institutes. However, managing a large amount of distributed generators requires smart, low-latency, reliable, and safe networking infrastructure, which cannot be supported by traditional networks in power grids. In order to design and construct smart and flexible energy Internet, we proposed a software defined network framework with both microgrid cluster level and global grid level designed by a hierarchical manner, which will bring flexibility, efficiency, and reliability for power grid networks. Finally, we evaluate and verify the performance of this framework in terms of latency, reliability, and security by both theoretical analysis and real-world experiments.

Mobile edge caching is regarded as a promising way to reduce the backhaul load of the base stations (BSs). However, the capacity of BSs’ cache tends to be small, while mobile users’ content preferences are diverse. Furthermore, both the locations of users and user-BS association are uncertain in wireless networks. All of these pose great challenges on the content caching and content delivery. This paper studies the joint optimization of the content placement and content delivery schemes in the cache-enabled ultra-dense small-cell network (UDN) with constrained-backhaul link. Considering the differences in decision time-scales, the content placement and content delivery are investigated separately, but their interplay is taken into consideration. Firstly, a content placement problem is formulated, where the uncertainty of user-BS association is considered. Specifically, different from the existing works, the specific multi-location request pattern is considered that users tend to send content requests from more than one but limited locations during one day. Secondly, a user-BS association and wireless resources allocation problem is formulated, with the objective of maximizing users’ data rates under the backhaul bandwidth constraint. Due to the non-convex nature of these two problems, the problem transformation and variables relaxation are adopted, which convert the original problems into more tractable forms. Then, based on the convex optimization methods, a content placement algorithm, and a cache-aware user association and resources allocation algorithm are proposed, respectively. Finally, simulation results are given, which validate that the proposed algorithms have obvious performance advantages in terms of the network utility, the hit ratio of the cache, and the quality of service guarantee, and are suitable for the cache-enabled UDN with constrained-backhaul link.

With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

Because of the limited resources on radio access channels of third generation partnership projection (3GPP) network, one of the most challenging tasks posted by 3GPP cellular-based machine type communications (MTC) is congestion due to massive requests for connection to radio access network (RAN). In this paper, an overload control algorithm in 3GPP RAN is proposed, which proactively disperses the simultaneous access attempts in evenly distributed time window. Through periodic reservation strategy, massive access requests of MTC devices are dispersed in time, which reduces the probability of confliction of signaling. By the compensation and prediction mechanism, each device can communicate with MTC server with dynamic load of air interface. Numerical results prove that proposed method makes MTC applications friendly to 3GPP cellular network.

Over the last couple of years, video service distribution among smart phones and other mobile video devices is becoming increasingly popular in sensor networks. However, the huge energy consumption caused by video encoding and transmitting and the slowly evolving battery technologies become the major bottlenecks that hinder the development of video streaming services. Hence energy efficient video coding and transmitting solutions are required to be investigated. Yet, energy consumption reduction of mobile video devices will be accompanied with Quality of Experience (QoE) degradation of video applications. Such diverse tendency makes it difficult to encode and transmit video streams with less energy as well as better QoE. This paper analyzes the major energy consuming factors in mobile video devices. A specific energy consumption model concerning encoding bitrates and transmitting power level is built. Further, a noninvasive QoE perceptive model is adopted so that the energy efficiency problem becomes a cross-layer optimization problem. Chaos particle swarm optimization is used to solve this cross-layer optimization problem with fast convergence. By this method, energy consumption of mobile video device is minimized with acceptable QoE for video users. At last, Pareto front of energy and QoE is analyzed to certify the performance of our method.

Information security has been received more and more attention for next-generation wireless sensor networks. In this paper, we consider the problem of resource management based on security satisfaction ratio with fairness-aware in two-way relay networks. Multiple source nodes exchange information with the help of relay node in the presence of an eavesdropper, and diverse security requirements are taken into account with coexistence of security users and normal users. The joint problem of power allocation, and subchannel pairing and allocation aims to maximize the security satisfaction ratio for legitimate users subject to limited power and subchannel constraints. We model the security resource management problem as a mixed integer programming problem, which is decomposed into three subproblems, distributed power allocation, distributed subchannel allocation, and distributed subchannel pairing, and then solved it in constraint particle swarm optimization (CPSO), binary CPSO (B_CPSO), and classic Hungarian algorithm (CHA) method, respectively. Moreover, a suboptimal subchannel pairing algorithm is proposed to reduce the computational complexity compared with the CHA. Simulations are conducted to evaluate the effectiveness of the proposed algorithms.

Femtocell is a promising technique not only in operator networks but also in having potential applications for industrial wireless sensor networks. In this paper, we investigate energy efficient uplink power control and subchannel allocation in two-tier femtocell networks. Taking transmit power and circuit power into account, we model the power control and subchannel allocation problem as a supermodular game to maximize energy efficiency of femtocell users. To reduce the cochannel interference from femtousers to neighboring femtocells and macrocells, we introduce a convex pricing scheme to curb their selfish behavior. We decompose the resource allocation problem into two subproblems, that is, a distributed subchannel allocation scheme and a distributed power control scheme to reduce costs and complexity. Simulation results show that the proposed algorithm can improve user utilities significantly, compared with existing power control and subchannel allocation algorithms.

BACKGROUND: The timeliness of HIV diagnosis and the initiation of antiretroviral treatment are major determinants of survival for HIV-infected people. Injection drug users (IDUs) are less likely than persons in other transmission categories to seek early HIV counseling, testing, and treatment. Our objective was to estimate the proportion of IDUs with a late HIV diagnosis (AIDS diagnosis within 12 months of HIV diagnosis) and determine the factors associated with disease progression after HIV diagnosis. METHODOLOGY/PRINCIPAL FINDINGS: Using data from 33 states with confidential name-based HIV reporting, we determined the proportion of IDUs aged >or=13 years who received a late HIV diagnosis during 1996-2004. We used standardized Kaplan-Meier survival methods to determine differences in time of progression from HIV to AIDS and death, by race/ethnicity, sex, age group, CD4(+) T-cell count, metropolitan residence, and diagnosis year. We compared the survival of IDUs with the survival of persons in other transmission categories. During 1996-2004, 42.2% (11,635) of 27,572 IDUs were diagnosed late. For IDUs, the risk for progression from HIV to AIDS 3 years after HIV diagnosis was greater for nonwhites, males and older persons. Three-year survival after HIV diagnosis was lower for IDU males (87.3%, 95% confidence interval (CI), 87.1-87.4) compared with males exposed through male-to-male sexual contact (91.6%, 95% CI, 91.6-91.7) and males exposed through high-risk heterosexual contact (HRHC) (91.9%, 95% CI, 91.8-91.9). Survival was also lower for IDU females (89.5%, 95% CI, 89.4-89.6) compared to HRHC females (93.3%, 95% CI, 93.3-93.4). CONCLUSIONS/SIGNIFICANCE: A substantial proportion of IDUs living with HIV received their HIV diagnosis late. To improve survival of IDUs, HIV prevention efforts must ensure early access to HIV testing and care, as well as encourage adherence to antiretroviral treatment to slow disease progression.