Showing papers by "Min Sheng published in 2019"

UAV-Assisted Emergency Networks in Disasters

Abstract: Reliable and flexible emergency communication is a key challenge for search and rescue in the event of disasters, especially for the case when base stations are no longer functioning. Unmanned aerial vehicle (UAV)-assisted networking is emerging as a promising method to establish emergency networks. In this article, a unified framework for a UAV-assisted emergency network is established in disasters. First, the trajectory and scheduling of UAVs are jointly optimized to provide wireless service to ground devices with surviving BSs. Then the transceiver design of UAV and establishment of multihop ground device-to-device communication are studied to extend the wireless coverage of UAV. In addition, multihop UAV relaying is added to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs. Simulation results are presented to show the effectiveness of these three schemes. Finally, open research issues and challenges are discussed.

Joint Mode Selection and Resource Allocation for D2D-Enabled NOMA Cellular Networks

Abstract: The 5G cellular network employs non-orthogonal multiple access (NOMA) to enhance network connectivity and capacity, and device-to-device (D2D) communications to improve spectrum efficiency. However, the underlay D2D communications may destroy the execution condition for the successive interference cancellation (SIC) decoding of NOMA cellular networks by introducing the extra interference, which degrades the cellular transmission reliability. Thus, we develop the interlay mode as a special D2D mode for NOMA system, which enables the power domain multiplexing of the D2D pair and cellular users to eliminate the strong interference between them by the SIC decoding. When D2D pair conducts the selection between the interlay mode and underlay mode, the SIC decoding constraint should be satisfied at both D2D receiver and NOMA base station. In order to maximize the system sum rate while meeting the SIC decoding constraint, we propose a joint D2D mode selection and resource allocation scheme with interlay mode, which can be formulated as a combinatorial optimization problem. To tackle the combinatorial nature of mode selection and spectrum assignment, we first prove that the original problem can be reformulated as a maximum weight clique problem, and then propose a graph-based algorithm by applying branch-and-bound method to obtain its optimal solution. Finally, simulation results are provided to demonstrate that the interlay mode along with the proposed algorithms can coordinate D2D communications and NOMA cellular network to significantly improve the system sum rate and the D2D access rate.

Exploiting AoA Estimation Accuracy for Indoor Localization: A Weighted AoA-Based Approach

Abstract: For angle-of-arrival (AoA)-based localization methods, the AoA estimation accuracy would notably influence the localization performance In this letter, we evaluate the estimation accuracy of different AoAs in a multipath environment and propose a weighted AoA-based localization approach Specifically, the AoA estimation accuracy is quantified by deriving a closed form expression of asymptotic variance of the AoA estimation error However, in practice, the accessible information for receivers is insufficient to retrieve the variance Accordingly, we still find an approximate but effective substitution to evaluate the AoA estimation accuracy on basis of the limited information Aided by the above analysis, we design the weighted localization method, where each estimated AoA is weighted with corresponding factor related to the AoA estimation accuracy Simulations show that our proposed localization method decreases the median localization error by 20% compared with the unweighted method

Collaborative Data Scheduling With Joint Forward and Backward Induction in Small Satellite Networks

Abstract: Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.

Distributionally Robust Planning for Data Delivery in Distributed Satellite Cluster Network

Abstract: The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

Performance Analysis and Optimization of UAV Integrated Terrestrial Cellular Network

Abstract: Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

Dynamic Scheduling of Hybrid Tasks With Time Windows in Data Relay Satellite Networks

Abstract: Through the allocation of multi-antenna time blocks to spacecrafts, the data relay satellite network (DRSN) is capable of providing data relay within their visible intervals (i.e., time windows). During the relay process, the generated hybrid tasks incorporate common tasks, emergency tasks, and temporary tasks. However, higher priority unpredicted tasks (i.e., emergency tasks and temporary tasks) unpredictably preempt antenna resources, thereby resulting in more common tasks unsuccessful to relay. It is, therefore, nontrivial to investigate the dynamic hybrid task scheduling problem with time windows for the multi-antenna DRSN to efficiently and real-timely allocate multi-antenna time blocks aiming at accommodating more unpredicted tasks and reducing the number of unsuccessful common tasks. To this end, we propose a stochastic optimization framework to maximize the time average number of hybrid tasks by jointly optimizing the scheduling periods and the antenna time block allocation. For the tractability purpose, by leveraging its unique structure, we first equivalently transform it to a scheduling period adjustment (SPA) problem, embedded with a sequence of antenna time block allocation (ATBA) problems. Then, two efficient algorithms are developed to solve the SPA and ATBA problem, respectively. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the time average number of hybrid tasks.

Vehicle-Mounted Base Station for Connected and Autonomous Vehicles: Opportunities and Challenges

Abstract: The crown jewel of intelligent transportation is achieving autonomous driving that can ultimately become accident- and congestion-free through CAVs and the associated traffic management systems. Recently, this vision has sparked huge research interest, such as IoV, LTE-V2X, and 5G. However, the huge amount of traffic data generated by CAVs poses challenges for the current networks and even the upcoming 5G communication networks.In this article, we first analyze the communication requirements of CAVs and present the progress of vehicular communication networks, and on that basis propose the novel concept of VMBS and a VMBS-CCNA for CAVs. The VMBS plays multiple roles of a user node and edge computing node for external communication networks, and a base station and information caching node for CAVs, thus achieving the fusion of communication and computing for CAVs. To this end, we present both the VMBS-enabled wireless communication and computing for CAVs, and the VMBS-assisted wireless communication for other wireless devices. Several research challenges and some open research issues are highlighted and discussed. Finally, simulation results reveal that the proposed VMBS-CCNA can bring a significant improvement in terms of throughput, delay, and average number of links.

On the Interaction of Video Caching and Retrieving in Multi-Server Mobile-Edge Computing Systems

Abstract: Mobile-edge computing (MEC) could support edge caching and processing for video service, which essentially changes “cache and transmit” mechanism and provides another video retrieval mode. However, existing caching policies are usually inefficient, mainly due to lack of: 1) interaction of video caching and retrieving which happen in different timescales and 2) collaboration among servers. As motivated, we jointly consider long-term caching placement and short-term video retrieval mode selection in coordinated multi-server MEC systems to enrich user experience. Besides, we propose a sample average approximation-based two-phase algorithm which outmatches existing approaches in terms of content access delay and hit ratio.

Content Placement and User Association for Delay Minimization in Small Cell Networks

Abstract: In small cell networks, content placement can reduce the content download delay at backhaul links while imposing challenges on user association problems. Specifically, a user may be associated with a small base station (SBS) that has the desired contents but is far away, increasing the delay over radio access links. To this end, we investigate the joint content placement and user association problem to reduce the average download delay over backhaul and radio access links for content delivery. Considering both dynamic user population and natural tandem systems, we model content delivery in each SBS as a tandem queue and thus derive the average sojourn time (the average time contents stay in the tandem queue), i.e., the average download delay. On this basis, we formulate a delay minimization problem, encompassing user characteristics (traffic arrival rates, requested content lengths and content preferences) and SBS constraints (backhaul capacity and storage size). Then, this problem is decomposed into a user association subproblem and a content placement subproblem by exploiting the biconvexity. Finally, a distributed content placement and user association scheme is proposed by exploring the optimal match with the first derivative length method. Extensive simulation results reveal that the delay under the proposed content placement policy may be reduced as user characteristics become heterogeneous, which is opposite to the case under user association policies.

OpArray: Exploiting Array Orientation for Accurate Indoor Localization

Abstract: Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

Stable Throughput Region and Average Delay Analysis of Uplink NOMA Systems With Unsaturated Traffic

Abstract: This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

Effect of Interference Correlation on the Performance of Ultra-Dense Networks

Abstract: Interference serves as the most dominant factor that quantitatively and qualitatively impacts the performance of ultra-dense networks (UDN). Especially, since interference of different time slots basically comes from the same set of interfering base stations (BSs), the temporal interference correlation becomes more significant with the growing deployment of network infrastructures. In this light, we develop an analytical framework to investigate the impact of temporal interference correlation on UDN in terms of network spatial throughput (ST) in this work. In contrast to the available research, which indicates that the interference correlation is independent of BS density, we show that a growing BS density would exacerbate the influence of interference correlation on the performance of UDN. In particular, when the BS density is closer to the critical density, under which network ST could be maximized, the ST attenuation caused by temporally correlated interference is more significant. Moreover, the effect of temporal interference correlation on network ST is additive over time slots. For instance, a greater number of transmissions of hybrid automatic repeat request (HARQ) would result in a more significant effect of temporally correlated interference on network ST.

Optimal Dynamic Multi-Resource Management in Earth Observation Oriented Space Information Networks.

Abstract: Space information network (SIN) is an innovative networking architecture to achieve near-real-time mass data observation, processing and transmission over the globe. In the SIN environment, it is essential to coordinate multi-dimensional heterogeneous resources (i.e., observation resource, computation resource and transmission resource) to improve network performance. However, the time varying property of both the observation resource and transmission resource is not fully exploited in existing studies. Dynamic resource management according to instantaneous channel conditions has a potential to enhance network performance. To this end, in this paper, we study the multi-resource dynamic management problem, considering stochastic observation and transmission channel conditions in SINs. Specifically, we develop an aggregate optimization framework for observation scheduling, compression ratio selection and transmission scheduling, and formulate a flow optimization problem based on extended time expanded graph (ETEG) to maximize the sum network utility. Then, we equivalently transform the flow optimization problem on ETEG as a queue stability-related stochastic optimization problem. An online algorithm is proposed to solve the problem in a slot-by-slot manner by exploiting the Lyapunov optimization technique. Performance analysis shows that the proposed algorithm achieves close-to-optimal network utility while guaranteeing bounded queue occupancy. Extensive simulation results further validate the efficiency of the proposed algorithm and evaluate the impacts of various network parameters on the algorithm performance.

Antenna Scheduling for Multiple User Satellites in Space Data Relay Networks

Abstract: Tracking and Data Relay Satellite System (TDRSS) is playing an important role in data relay for user satellites. Subject to the finite number of antenna, the non-negligible antenna slewing time, and the time-varying connectivity of inter-satellite links (ISLs), it is significantly challenging to improve the selection of antenna scheduling sequence to improve performances (e.g., higher throughput, shorter mean queue length, smaller mean scheduling number, etc). To overcome above challenges, we utilize the antenna slewing model, the track model, and the multi-queue single-server queuing model to calculate the satellite-specific attributes such as the practical antenna slewing time, the link availability period and the buffer state. Furthermore, we propose a Heuristic Algorithm based on Optimal Weight (HAOW) considering the obtained satellite-specific attributes to optimize the antenna scheduling sequence. With the optimized scheduling sequence, the network performances are analyzed by the proposed queuing model. Finally, we conduct numerous simulations for the performance comparisons of the proposed HAOW with classical scheduling algorithms.

Rerouting Based Congestion Control in Data Center Networks

Abstract: Data center networks (DCNs) are widely deployed to provide infrastructure backbone for various cloud services. Meanwhile, the emergence of Software-Defined Networking (SDN) makes it easy to monitor the global status of DCNs. In DCNs, traffic bursts or uneven traffic distribution may lead to network congestion, thus deteriorating the overall network performance. Rerouting is an effective method to mitigate network congestion by rerouting flows to paths with sufficient bandwidth. In this paper, we propose an efficient rerouting algorithm by leveraging the SDN technique. We firstly formulate the rerouting problem as a Multi-Commodity Flow (MCF) problem with the objective to minimize the maximum link utilization in the DCN. Since the problem is a NP-hard problem, we propose a heuristic algorithm that jointly considers the current path utilization and the path criticality to approach the optimal solution. Our simulation results demonstrate the superiority of the proposed algorithm in mitigating the network congestion and guaranteeing the performance of the network, over the classical Equal-Cost Multi-Path (ECMP) algorithm and Global First Fit (GFF) algorithm.

Computation Offloading in C-RAN: A Sequential Computation Model

Abstract: In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.

Energy-Efficient Flow Routing and Scheduling in Hybrid Data Center Networks

Abstract: Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.

Distributed Content Replacement in Small Cell Networks using Continuous-Time Markov Chain

Abstract: Content caching is a promising way to overcome backhaul limitations in small cell networks However, in such type of networks, small base stations (SBSs) are always deployed with limited cache storages Thus, it is necessary for SBSs to adjust their contents for better caching efficiency, so as to reduce backhaul traffic In this paper, we study the content replacement problem to minimize the traffic flowing into the costly backhaul links However, in small cell networks where SBSs make up backhaul mesh networks, the effectiveness of reducing backhaul traffic depends on the hop distance from the content location to the requesting user On this basis, we formulate a hop minimization problem that is inherently combinatorial Through log-sum-exp approximation, we can solve the problem and arrive at a close-form solution with guaranteed performance gap to the optimal solution By exploiting the properties of continuous-time Markov chain (CTMC), the solution can be implemented by designing a CTMC that can instruct the content replacement process As a consequence, a concise, efficient, and flexible content replacement strategy is proposed Simulation results verify our analysis and show that our proposed strategy outperforms the conventional strategies

Wireless Backhaul: Intrinsic Bottleneck of Ultra-Dense Networks?

Abstract: With the growing deployment of small cell base stations (BSs), the impact of backhaul congestion on the performance of small cell networks becomes dominant. To capture this effect, we present a conjoint framework integrating backhaul architecture with the access network. In particular, we evaluate the performance of ultra-dense networks (UDNs) in terms of network spatial throughput (ST), supposing that backhaul is conveyed to BSs through millimeter wave (mmWave) links by gateways. Notably, in contrast to diminishing to zero in previous work, network ST is shown to converge into a saturation under the given gateway density since the limited backhaul capability of gateways would stabilize the interference distribution of access network. Moreover, despite the benefit of enhancing backhaul capacity, over-deployed gateways would result in the potential intercell interference, which degrades network ST of UDN. On this account, we further study the optimization of gateway density to balance the tradeoff between enhancing backhaul capacity and mitigating intercell interference. It indicates that the application of mmWave beamforming at gateways leads to an increase in optimal gateway density, under which network ST could be further improved.

Fingerprint Similarity Indoor Localization: An AP Selection Based Approach

Abstract: With the flexible collection of received signal strengths (RSSs) by wide and ubiquitous indoor Wi-Fi infrastructures, the indoor fingerprint-based localization systems based on RSSs are gaining popularity. However, the device heterogeneity significantly degrades the performance of the fingerprint-based localization. Since different devices have different reception sensitivity, the RSSs collected by different devices at the same location for the same access point (AP) may be significantly different. To handle the device heterogeneity problem, we propose an AP selection based calibration-free method, which includes offline phase and online phase. In particular, an AP selection method is designed in the offline phase and selects the APs whose RSSs are of great difference for constructing fingerprint database. Moreover, a signal strength difference ranking method is designed in the online phase, which alleviates the impact of heterogeneous devices. Especially, experiments results validate that the proposed method could improve the region localization accuracy up to 23%, compared with the existing calibration-free methods.

Exploring on the Critical Link Sequence of Satellite Networks

Abstract: Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.

Delay-Efficient Offloading for NOMA-MEC with Asynchronous Uploading Completion Awareness

Abstract: Non-orthogonal multiple access mobile edge computing (NOMA-MEC) is proposed to enhance the connectivity between the edge node and users for low- latency computation offloading. However, it is asynchronous for users to complete the task uploading, which complicates the co-channel interference between NOMA users to affect overall offloading delay. In this paper, we first characterize the impact of this asynchronism in task uploading on interference management in NOMA enabled computation offloading. The optimal power allocation is proposed to coordinate the co-channel interference between both NOMA users. Then, we propose a multi- user offloading scheme to jointly optimize offloading decision and NOMA user pairing, aiming to minimize the users' average delay on executing their tasks. The proposed offloading scheme is designed by formulating a binary nonlinear problem, which is solved by the proposed relaxation method and heuristic algorithm. Simulation results demonstrate that compared with other NOMA based schemes, our proposed scheme can effectively reduce the average delay of users and increase the number of users to perform computation offloading.