Search

In this paper we propose a cluster-based platoon handoff protocol (CPHP), in which the platoon is divided into clusters to minimize its handoff delay between two adjacent cells. Via the vehicle-to-vehicle (V2V) communications, multiple clusters are constructed within one platoon and only the cluster head (CH) communicates with the base station (BS) via the vehicle-to-infrastructure (V2I) communications. In this way, the number of V2I links can be greatly saved and the signaling overhead is reduced, thereby shortening the handoff delay. Specifically, we formulate a delay minimization problem based on a discrete-time Markov chain and then optimize the number of clusters and spectrum resource allocation. Simulation results demonstrate a significant decrease in the handoff delay of the platoon. The optimal expected delay under a platoon with 50 vehicles by utilizing the proposed CPHP is 15% smaller than that of the traditional handoff scheme.

In this paper, we study the harmonious coexistence of cellular and WiGig users around 60 GHz unlicensed millimeter-wave bands. To guarantee the fairness of two types of users, we propose a sensing based adaptive unlicensed channel sharing protocol. Jointly considering the frequency and spatial resource allocation, an iterative channel allocation and hybrid beamforming algorithm is designed to maximize the sum rate of all cellular users while minimizing the interference to WiGig networks. Simulation results show that our proposed scheme achieves a significant sum rate enhancement compared with that when utilizing only the licensed band.

In this paper, we focus on the reconfigurable intelligent surface (RIS) assisted single-cell uplink communication network scenario. In the network, one cellular link and multiple device-to-device (D2D) links sharing the same spectrum resources combine direct and reflective channel transmissions with the assistance of the RIS, which is adopted to alleviate the interference by fully using the beamforming capability. Subjected to qualityof-service (QoS) and total power constraints, a system sumrate maximization problem is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of all RIS elements. Since it is a mixed integer non-convex non-linear problem, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution efficiently. For the power allocation sub-problem, it is a difference of concave functions (DC) problem, which is transformed to a convex one by the multivariate Taylor expansion and then solved with the gradient descent method. For the phase shift subproblem, a local search algorithm is utilized. Simulation results verify that our proposed scheme can eliminate the interference of D2D networks better than the scheme without RIS and other benchmark schemes.

In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs’ AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm.

Given high-speed mobile Internet access today, audiences are expecting much higher video quality than before. Video service providers have deployed dynamic video bitrate adaptation services to fulfill such user demands. However, legacy video bitrate adaptation techniques are highly dependent on the estimation of dynamic bandwidth, and fail to integrate the video quality enhancement techniques, or consider the heterogeneous computing capabilities of client devices, leading to low quality of experience (QoE) for users. In this paper, we present a super-resolution based adaptive video streaming (SRAVS) framework, which applies a Reinforcement Learning (RL) model for integrating the video super-resolution (VSR) technique with the video streaming strategy. The VSR technique allows clients to download low bitrate video segments, reconstruct and enhance them to high-quality video segments while making the system less dependent on estimating dynamic bandwidth. The RL model investigates both the playback statistics and the distinguishing features related to the client-side computing capabilities. Trace-driven emulations over real-world videos and bandwidth traces verify that SRAVS can significantly improve the QoE for users compared to the state-of-the-art video streaming strategies with or without involving VSR techniques.

The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.

In this paper, we consider the cellular Internet of unmanned aerial vehicles (UAVs), where UAVs sense data for multiple tasks and transmit the data to the base station (BS). To quantify the "freshness" of the data at the BS, we bring in the concept of the age of information (AoI). The AoI is determined by the time for UAV sensing and that for UAV transmission, and gives rise to a trade-off within a given period. To minimize the AoI, we formulate a joint sensing time, transmission time, UAV trajectory, and task scheduling optimization problem. To solve this problem, we first propose an iterative algorithm to optimize the sensing time, transmission time, and UAV trajectory for completing a specific task. Afterwards, we design the order in which the UAV performs data updates for multiple sensing tasks. The convergence and complexity of the proposed algorithm, together with the trade-off between UAV sensing and UAV transmission, are analyzed. Simulation results verify the effectiveness of our proposed algorithm., 6 pages, 4 figures

In this paper, we consider the distributed energy saving for heterogeneous multi-layer mobile edge computing (HetMEC), where the edge devices (EDs) upload computing tasks to the mobile edge computing (MEC) servers and the cloud center (CC) for processing. To reduce the energy consumption, task offloading and resource allocation are performed by each device independently to distribute the computation load. However, due to the partial information, the offloading strategies of devices on different layers can hardly be aligned, which may lead to network congestion. To address this problem, we propose a smart pricing mechanism to coordinate the strategies of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. The pricing based distributed task offloading and resource allocation algorithm is designed to minimize the total energy consumption subject to latency requirements. Simulation results indicate that our algorithm achieves lower energy consumption and congestion probability compared with the traditional distributed method.

In this paper, we consider an uplink ultra-dense LEO-based multi-terminal satellite system where each ground terminal station connects to multiple satellites for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the spatial diversity of multiple satellites. To evaluate the multi-satellite channel capacity performance, we first derive the lower bound and upper bound of the channel capacity in uplink LEO-based multi-terminal systems with multiple single-antenna satellites. Based on the capacity bounds, we theoretically prove that the capacity grows almost linearly with the number of satellites, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. We then illustrate that such statements also hold for the multi-antenna satellite case where the upper bound of the channel capacity and the lower bound of the achievable rate after receive beamforming are derived separately. Simulation results verify our theoretical analysis.

User profiles are not always visible in E-commerce scenarios, in which case the recommender systems can only summarize users’ preferences through sessions of historical records. However, the items in a session might be irrelevant to users’ preferences or become the disturbances for modelling the users’ portraits, and thus degrade the performance of the recommender systems. In this paper, we propose the preference-aware mask to capture user preferences over the items within the sessions, which adapts to the preference-irrelevant items within the sessions and provides explainable evidence for the recommendation. Evaluation over three real-world datasets verifies that MBTREC performs well on the new-item recommendation task, and outperforms several state-of-the-art recommender systems on the general metrics.

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

In this paper, we consider a cellular Internet of unmanned aerial vehicles (UAVs) where the sensory data can be transmitted to the mobile devices directly by overlaying UAV-to-Device (U2D) communications, or through the base station (BS) by cellular communications. Since the transmission modes of UAVs may influence their trajectories, we study the trajectory design problem for UAVs aiming to maximize the total utility in consideration of their transmission modes. This problem is a Markov decision problem (MDP) with a large state-action space, and thus, we propose a multi-UAV trajectory design algorithm using multi- agent deep reinforcement learning (DRL) to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than the single-agent method.

In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing user fairness, we formulate a joint mode selection, subchannel allocation, trajectory optimization, and power allocation problem, which is NP-hard. To solve the problem efficiently, we first decompose it into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation. Then we propose a joint mode selection and subchannel allocation, trajectory optimization, and power allocation (JMS-T-P) algorithm where these subproblems are solved iteratively. Simulation results show that the JMS- T-P algorithm outperforms the random algorithm and the cellular scheme.

In this paper, we consider an ultra-dense LEO-based satellite-terrestrial network where the traditional operator (TO) cannot satisfy the increasing data demand of its subscribed users due to the limited backhaul capacity of traditional small cells. Therefore, the TO offloads its users to LEO-based small cells owned by the satellite operator (SO) for satellite-backhauled network access. To motivate both operators for data offloading and maximize their revenues, we propose a pricing mechanism for data offloading based on the Stackelberg game. An iterative optimization algorithm framework is developed by jointly considering user association, Ka-band spectrum allocation and pricing to achieve the Stackelberg equilibrium. The user association scheme and pricing scheme to maximize the TO and the SO's revenues are designed separately. The closed-form optimal solution for user association problem is derived, and the iterative pricing mechanism is also designed. Simulation results show that our proposed pricing scheme can motivate two operators for offloading efficiently. The influence of frequency resources and the number of LEO satellites is also discussed.

As distributed energy resources (DERs) are widely deployed, the DC packetized power microgrid is a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC microgrid, where the energy is dispatched by a power router in the form of power packets. Since energy subscribers (ESs) with DERs in the microgrid can generate surplus electricity, the peer-to-peer (P2P) energy trading is an effective way in order to balance the energy. Different from the P2P trading in AC smart grids, the dispatching capability of the router in the DC microgrid needs to be considered, which will make the trading problem more complicated. To tackle this challenge, we formulate the P2P trading problem as an auction game, where the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. The effectiveness of the proposed scheme is validated through simulations.

In this paper, we consider a cellular Internet of UAVs, where UAVs execute multiple sensing tasks continuously and cooperatively through sensing and transmission with the objective to minimize the age of information (AoI). However, the cooperative sensing and transmission is coupled with the trajectories of the UAVs, which makes the trajectory design a challenging problem. To tackle this challenge, we first propose a distributed sense-and-send protocol to coordinate the UAVs. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a deep reinforcement learning algorithm to solve it, which we refer to as the compound-action actor-critic (CA2C) algorithm. Simulation results show that the CA2C algorithm outperforms two baseline algorithms for AoI minimization.

Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing~(MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages of limited computing capacity, preventing computation-intensive tasks to be processed on time. To relief this issue, we propose a heterogeneous multi-layer MEC (HetMEC) network where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We design the latency minimization algorithm by jointly coordinating the task assignment, computing and transmission resources among the EDs, multi-layer MEC servers, and the CC. Simulation results indicate that our proposed algorithm can achieve a lower latency and stronger robustness than the conventional MEC schemes.

In this work, we consider a heterogeneous multi-cloud mobile edge computing (Het-MEC) network, where multiple independent cloud centers (CCs) publish tasks to edge devices (EDs) and MEC servers, and compete for their computing and transmission resources. To minimize the system latency, we propose a distributed scheme for each CC to determine its data offloading and resource allocation strategy independently. Competition among multiple CCs in this distributed scheme is depicted by our designed multi-agent reinforcement learning (MARL) based algorithm, where each CC is self-motivated to learn the explicit models of other CCs and adjusts their behaviors. Simulation results indicate that multiple clouds are self-organized to take full advantage of computing and transmission resources to minimize their own task latency, while a lower system latency can be achieved compared with the cloud computing and local computing schemes.

In this paper, the energy efficient virtual machine (VM) placement and virtual resource allocation problem for the mobile edge computing (MEC) system is explored. Particularly, we develop an optimization framework of energy consumption minimization for computing and offloading by jointly optimizing the VM placement matrix and the number of physical machines (PMs). To resolve this problem, we transform the optimization problem into a non-uniform weighted hypergraph model. In this model, the weight of hyperedge is defined as the negative of the accumulated energy consumption for computing at VM instances hosted by one PM. Based on the hypergraph model, a hypergraph matching algorithm by utilizing the local search policy is proposed for finding the maximum-weight subset of vertex-disjoint hyperedges, aiming to obtain an optimal VM placement, i.e., (M*)-perfect matching. Furthermore, the virtualized resources are further allocated to user equipments (UEs) in the form of multiple VM instances via the optimal VM placement to meet the requirement of workloads. Simulation results are presented to demonstrate the effectiveness of the proposed hypergraph matching algorithm over the alternative benchmark algorithm.

In this paper, the optimum design of the downlink sparse code multiple access (SCMA) based user-centric ultra-dense networks (UUDNs) is studied, in which lots of access points (APs) are deployed to provide service to many user equipments (UEs). In UUDNs, the network architecture is shifted from traditional cell-centric to user-centric, where many APs can serve for one single UE, with the density of APs higher than that of UEs. One main challenge faced by UUDN design is the large interference due to the dense AP/UE deployments. The objective of this paper is to find the optimum SCMA codebooks allocation scheme to minimize the network interference that can maximize the system throughput, subject to the quality of service (QoS) constraints for each UE. The design is formulated as a mixed-integer nonlinear program (MINLP) problem by using different codebooks allocation among different APs serving the same UE, and it is a well known NP-hard problem. To tackle this problem, we use a weighted hypergraph model to transform it into a clustering problem, where machine learning (ML) algorithms are proposed to solve this MINLP problem efficiently. Simulation results show that the proposed hypergraph based ML algorithm outperforms the existing algorithm.

The demand of content delivery (for video streaming, photos, mobile web pages) has explosively grown in recent years, leading to an excessive volume of network traffic at the edge of mobile networks. Mobile social networking (MSN) is widely recognized as the most popular mobile application, as it attracts the largest number of users, and consumes the highest daily time of each user on average. However, mobile users can be distantly located, and their preferences to content may vary over time, leading to highly dynamic distribution of network traffic. Hence, it is necessary to allocate network edge resources for meeting the growing user demand to different types of contents spatially and temporally. To study this problem, we establish a machine learning framework for predicting how users' location and their consumed content can affect the traffic distribution in the underlying content distribution network. Given the predicted distribution of content demands, the proposed smart content delivery scheme is able to instruct the edge servers to dynamically deliver the content to edge users. We evaluate the performance of the conventional regression models and the proposed content delivery scheme over a real-world content distribution network for MSNs. Results reveal that the neural network model outper-forms other approaches by increasing the hit ratio by 30%, and saving the network cost by 15%.

Due to the significant air pollution problem, monitoring and prediction for air quality have become increasingly necessary. To provide real-time fine-grained air quality monitoring and prediction in urban areas, we have established our own Internet-of-Things-based sensing system in Peking University. Due to the energy constraint of the sensors, it is preferred that the sensors wake up alternatively in an asynchronous pattern, which leads to a sparse sensing dataset. In this paper, we propose a novel approach to predict the real-time fine-grained air quality based on asynchronous sensing. The sparse dataset and the spatial-temporal-meteorological relations are modeled into the correlation graph, in which way the prediction procedures are carefully designed. The advantage of the proposed solution over existing ones is evaluated over the dataset collected by our air quality monitoring system., 5 pages, 3 figures, submitted to ICASSP 2019

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

Mobile edge computing has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in a mobile edge computing network is computation offloading in which computing nodes with limited capabilities and performance can offload a computation-intensive task to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable and have a non-negligible effect on the total utility of the network. Since mobile edge computing networks have a finite number of computing nodes where the computing capability of a computing node can affect the state (i.e., the amount of computation task) of the network, we propose a MFTG approach to formulate and solve a computation offloading problem. In this scenario, the goal of each computing node is to compute the portion of the aggregate computation task it can offload from the network that minimizes its cost. Then, we utilize a direct approach to solve for the optimal portion of the aggregate computation task that minimizes the cost incurred by a computing node. Finally, we conclude the paper with simulations to show the significance of the approach.

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.

In this paper, we study a multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything communication. The platoon formation is important and difficult in multi-platoon scenario, for the platoon size, power and resource allocation of each platoon interact with each other and influence those of other platoons. To this end, we propose a two-step resource allocation strategy in consideration of platoon formation, including the resource allocation at the base station and within each platoon, respectively. A branch and bound algorithm is utilized for the resource allocation at the BS. We design a distributed dynamic programming based subchannel allocation and power control algorithm for the joint optimization of platoon size, power and intra-platoon resource allocation. Simulation results evaluate the influence of the penalty factor and latency constraint on the system performance.

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. The objective of our optimization problem is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy a limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%–30% on average given different QoE objectives.

In this paper, we consider a cooperative autonomous driving system where a vehicle overtakes the one in front based on collective perception. To avoid collisions with vehicles on the other lane, we propose a V2X-based cooperative collision avoidance scheme. The overtaking vehicle estimates its distance with the neighbors via V2V communications and decides whether to overtake or not. Two cases where the distance information is obtained independently and cooperatively are taken into account. We derive the probability of collision avoidance, and analyze the influence of different factors such as speed and density of vehicles on the system performance. Simulation results verify our analysis and show the improvement brought by the cooperative case compared to the independent case.

In this paper, we consider a cellular controlled unmanned aerial vehicle (UAV) sensing network in which multiple UAVs cooperatively complete each sensing task. We formulate a joint trajectory, sensing location, and UAV scheduling optimization problem that minimizes the completion time for all the sensing tasks in the network. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: trajectory optimization, sensing location optimization, and UAV scheduling. An iterative trajectory, sensing, and scheduling optimization (ITSSO) algorithm is proposed to solve these sub-problems jointly. The convergence of the proposed algorithm and the dominated factors on the system performance are analysed. Simulation results show that the task completion time obtained by the proposed ITSSO algorithm is 15% less than that by non- cooperative scheme.

In this paper, we consider a three-level hierarchical structure for resource allocation in the radio access network (RAN) slicing. The infrastructure provider (InP) allocates the RAN slices to the mobile virtual network operators (MVNOs), and the MVNOs then allocate the radio resources to the users. It is challenging for the InP to determine resource allocation strategy efficiently due to the selfish strategic responses of both the MVNOs and the users. To handle this issue, we propose a tri-level Stackelberg game to jointly solve the frequency and power allocation and payment negotiation problem among the three levels. Simulation results verify a general market principle that the more the MVNOs focus on revenue collecting, the lower payoff the InP and the users will obtain.

Recent advance in Internet-of-Things (IoT) brings consumer- level smart mobile robot to our life. Indoor navigation is one of the most critical challenges for mobile robots. Existing approaches using wireless signal fingerprinting (e.g., WiFi fingerprint), computer vision techniques, require extensive war-driving of the indoor environment to collect sufficient environmental data. In this paper, we present SIGN, a lightweight, visual-tag based, indoor navigation approach that is free of indoor war-driving. The approach deploys a set of coded visual tags in the environment, and allows the robot to autonomously decide the moving direction by recognizing nearby tags and leveraging the geometry information. The proposed approach is robust to the change of the environment such as unexpected obstacles. Experiments in two indoor spaces under various scenarios show that SIGN helps the mobile robot using the off-the- shelf camera to self-navigate in indoor environment with the deployment of coded visual tags.

With the large penetration of distributed energy resources, DC power packet transmission is a potential technique to achieve efficient peer to peer (P2P) power dispatching. In this paper, we consider a multi-router local area packetized power network (LAPPN), where multiple power routers are employed to dispatch power packets among demander and supplier energy subscribers (ESs) connected to different routers. To achieve the efficient P2P power transmission in the LAPPN, we develop a power packet dispatching protocol in which the routes of power packets are optimized first to maximize the power packets uti⌉lization efficiency. We then determine the transmission schedule by allowing different power packets to transmit on different power channels concurrently to meet the variety of urgency requirements of demander ESs. Simulation results demonstrate the effectiveness of the proposed LAPPN power dispatching protocols in achieving high power packet utilization.

Mobile sensing is challenging in 3D space, as there are many inaccessible places where people rarely venture. Unmanned aerial vehicle (UAV), commonly known as drone, has greatly extended the scope of mobile sensing in 3D space, and pushed forward a variety of 3D mobile sensing applications, such as aerial photo- or video-graphy, 3D wireless signal survey, and air quality monitoring. However, the short battery life of drones has largely restricted the wide adoption of these applications. In this paper, we study the trajectory planning problem for optimizing the flight route in a given sensing space. We first divide the 3D space into an infinite three-dimensional network of observation locations (OLs), and model the sensing scope as a finite subgraph of 3D OL network. We formulate the problem as finding the optimal trajectory in the sensing scope. We propose an algorithm that finds trajectory in each divided 3D grid of the sensing scope by generating a nearly optimal dominating path, and finding the minimum dominating set in the dominating path. Then, we concatenate obtained trajectories in 3D grids to a nearly optimal trajectory in the sensing scope. Experimental results show that the proposed algorithm takes 24% less time to complete sensing the given space, and during the battery life it can cover 19% more sensing scope, than existing solutions.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra- dense low earth orbit (LEO) networks and the terrestrial networks for data offloading. In TSN, each user can access the network over C-band via a macro cell, a traditional small cell, or a LEO- backhauled small cell (LSC). Each LSC is scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then solved by our proposed matching algorithm. Simulation results show that the integrated network significantly outperforms the non-integrated one in terms of the sum data rate.

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two communication modes are considered to support the multi-UAV communications, i.e., UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. We then formulate the subcarrier allocation and trajectory design problem to maximize the uplink sum-rate taking the delay of sensing tasks into consideration. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2I and cellular user (CU) subcarrier allocation, U2U subcarrier allocation, and UAV trajectory design. An iterative subcarrier allocation and trajectory design algorithm (ISATCA) is proposed to solve these sub-problems jointly. Simulation results show that the proposed ISATCA can upload 20% more data than the one without U2U communication.

Rapid development of online social networks further facilitates the expansion of information over the Internet. In many scenarios, several pieces of different or even opposite information could diffuse competitively at the same time. Recently, some messenger APPs such as WeChat arose, where users can send links in their "WeChat Moments (WM)", which is called the messenger-based social network (Msg-SN). The unique characteristics in Msg-SN may lead to great difference on the network topology compared to conventional social networks. It is impossible to find the key opinion leaders with millions of followers to help block the rumors or to locate the source of rumors. Thus, the business often tries to select and inject a set of users in the network with the truth to block negative influence diffusion. We call this problem as the competitive influence blocking (CIB) problem. In this paper, we study the CIB problem in Msg-SN under the competitive linear threshold (CLT) model. We propose a fast heuristic algorithm based on eigenvector centrality to optimize the negative influence reduction by selecting a positive seed set. The experimental results using real-world WeChat Moments data show that our algorithm performs better than the effective CLDAG algorithm and also runs faster.

The downlink coordinated multiple points transmission (CoMP) has addressed a greet number of attentions in recent years. However, an open problem is still remaining that the QoS requirements from the edge users are not guaranteed to be fully achieved due to the low data rate and the high frequency handoff between cells. In this paper, we discuss the spectrum allocation in the downlink CoMP with incomplete QoS requirements. Based on a real world dataset, China Family Panel Studies (CFPS) dataset, we use a number of learning algorithms to anticipate the users’ QoS requirements based on their profiles before the CoMP implementation. Based on the learning results, we further propose a learning embedded three-side matching to do the spectrum allocation. Simulation results show that the human behavior based learning framework can achieve a larger coverage ratio than other learning approaches. Simulation results also show that the learning embedded three-side matching can obtain a larger coverage ratio than the naive matching and random allocation.

In this paper, we consider the 3 dimensional (3D) small cell basestations (SBSs) placement problem for hetergeneous users with incomplete QoS requirements. We propose a learning embedded hypergraph construction algorithm to jointly predict the heterogeneous users’ QoS requirements and deploy the SBSs to cover as many users as possible. Specifically, we predict and leverage users’ QoS requirements with a real-world dataset, China Family Panel Studies (CFPS), and design a human behavior based learning framework (HBLF) to anticipate users’ QoS requirements with users’ profiles. Then, based on HBLF, we propose a hypergraph construction algorithm to deploy the SBSs. Simulation results indicate that the learning embedded hypergraph construction can use the least number of SBSs to cover all users compared with a number of algorithms, such as K-means. The HBLF can achieve a higher coverage ratio of users compared with other learning algorithms, such as decision tree. Simulation results also corroborate that the more accurately we can predict the users’ QoS requirements, the more users we can satisfy their QoS requirements.

Multiple wireless standards, e.g., IEEE 802.11af and 802.22, have been developed for enabling opportunistic access in TV white space (TVWS) using cognitive radio (CR) technology. When heterogeneous CR networks that are based on different wireless standards operate in the same TVWS, coexistence issues can potentially cause major problems. Enabling collaborative coexistence via direct coordination between heterogeneous CR networks is very challenging, due to incompatible MAC/PHY designs of coexisting networks, requirement of an over-the-air common control channel for inter-network communications, and time synchronization across devices from different networks. In this paper, we propose an architecture for enabling collaborative coexistence of heterogeneous CR networks, called SYMbiotic heterogeneous Coexistence ARchitecturE (SymCare). By leveraging the symbiotic relationships between heterogeneous organisms in a stable ecosystem, SymCare establishes an indirect coordination mechanism between heterogeneous CR networks via a mediator system, which avoids the drawbacks of direct coordination. SymCare includes a spectrum sharing algorithm inspired by theories in theoretical ecology, viz, the optimal foraging theory and the ideal free distribution model.

Unmanned aerial vehicle (UAV) has greatly extended the scope of wireless broadband access by serving as a relay between the base station and user devices. In the paradigm of dynamic spectrum access (DSA), a UAV needs to traverse the flight area without interfering with incumbent users, and takes tune to serve the secondary users in the user area. It is important to minimize the service period by planning the UAV’s trajectory. In this paper, we study the trajectory planning problem of optimizing the flight route of the UAV, given the incumbent protection zone where the UAV cannot enter, for DSA. We divide the user area and the flight area into multiple 2D grids. Then, we formulate the problem as finding the optimal trajectory in the flight area to cover the user area. We propose an algorithm that first finds the trajectory in each divided 2D grid by generating the minimum dominating path and selecting necessary critical flight locations within the path, and then concatenates the obtained trajectories to the trajectory in the flight area. Experimental results show that the UAV takes 32% less time to finish trajectory under proposed algorithm, and as the user area and the flight area become larger, the gap among algorithms increases.