Performance Enhancement of IEEE 802.11AX in Ultra-Dense Wireless Networks

IEEE 802.11ax, which is one emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in dense deployment scenarios and diverse services to be supported, there are many technical challenges to be overcome. Firstly, the potential high packet collision rate significantly degrades the network efficiency of WLAN. In this thesis, we propose an adaptive station (STA) grouping scheme to overcome this challenge in IEEE 802.11ax using Uplink OFDMA Random Access (UORA). In order to achieve optimal utilization efficiency of resource units (RUs), we first analyze the relationship between group size and RU efficiency. Based on this result, an adaptive STA grouping algorithm is proposed to cope with the performance fluctuation of 802.11ax due to remainder stations after grouping. The analysis and simulation results demonstrate that our adaptive grouping algorithm dramatically improves the performance of both the overall system and each STA in the ultra-dense wireless network. Meanwhile, due to the limited RU efficiency of UORA, we adopt the proposed grouping scheme in the Buffer State Report (BSR) based two-stage mechanism (BTM) to enhance the Uplink (UL) Multi-user (MU) access in 802.11ax. Then we propose an adaptive BTM grouping scheme. The analysis results of average RU for each STA, average throughput of the whole system and each STA are derived. The numerical results show that the proposed adaptive grouping scheme provides 2.55, 413.02 and 3712.04 times gains in throughput compared with the UORA grouping, conventional BTM, and conventional UORA, respectively. Furthermore, in order to provide better QoS experience in the ultra-dense network with diverse IoT services, we propose a Hybrid BTM Grouping algorithm to guarantee the QoS requirement from high priority STAs. The concept of ``QoS Utility is introduced to evaluate the satisfaction of transmission. The numerical results demonstrate that the proposed Hybrid BTM grouping scheme has better performance in BSR delivery rate as well as QoS utility than the conventional BTM grouping

LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Abstract Background Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. Methods In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart. Results The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments. Conclusion Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weight

Multi-Dimensional QoS and Collaborative MAC Layer Design for Dense, Diverse, and Dynamic IoT Network

With the ubiquitous proliferation of Internet of Thing (IoT) devices, Access Points (APs) of future Wireless Fidelity (Wi-Fi) networks are expected to support dense Stations (STAs) with diverse Quality-of-Service (QoS) requirements under dynamic channel conditions. On account of high access collision and optimization problem complexity, the performance degradation brings new challenges to the existing Media Access Control (MAC) layer design in Wi-Fi. This thesis proposes novel technologies to enable low-latency high-performance MAC layer designs, which support dense access of STAs with real-time solutions of resource allocation and link adaptation problems. Both grouping and collaborative architectures are utilized based on game and graph theory to enable service provisioning and efficient network management. When supporting hybrid and dense access of STAs with both guaranteed and non-guaranteed QoS requirements, the performance of Non-guaranteed STAs (NG-STAs) often suffers more due to the exacerbated random-access congestion and the increased random-access collision. To overcome these challenges, we propose a Joint Traffic and Access Management (JTAM) mechanism to reshape the access traffic of Guaranteed-STA (G-STAs) and arrange the access opportunities of NG-STAs. The reshaped traffic smooths the varying Resource Units (RUs) for random access, and the grouping strategy optimizes the access efficiency of NG-STAs. Both analytical and simulation results show that JTAM improves the average throughput and reduces the average access latency of NG-STAs in the dense access scenario without impacting the performance of G-STAs. To precisely satisfy the diverse service requirements at the MAC layer, we propose an integrated evaluation scheme based on Deep Neural Networks (DNN) that takes into account performance parameters in end-to-end transmission along with access network multi-dimensional QoS performance. Based on users\u27 different requirements and the characteristics of the end-to-end network, we optimize the resource allocation strategy in the access network. However, the complex functional forms and multi-dimensional variables contribute to an exceedingly high time complexity in the optimization problem of resource allocation. To implement resource allocation within limited processing time, a Distributed Optimization with Centralized Refining (DO-CR) mechanism is proposed to support effective and real-time resource allocation. Specifically, the new DO-CR mechanism utilizes the distributed processing capacity of each STA in the first stage, allowing them to optimize their resource allocation schemes. AP generates the graph based on individual optimization results to indicate the topology of RU trading among devices and utilizes the graph to find Pareto Optimal in the second stage. Consequently, the resource allocation problem at the AP is simplified based on Pareto Optimality with a smaller feasible region compared to conventional optimization approaches. To meet diverse QoS metrics for future wireless applications, this thesis proposes a multi-dimensional QoS Provisioning Link Adaptation (QPLA) to enable more flexible Link Adaptation strategies. However, with multi-dimensional variables in the objective function, the overall optimization problem of power allocation and link adaptation becomes an NP-hard Mixed-Integer Nonlinear Programming (MINLP) problem. To enable the timely adjustment of Link Adaptation, this thesis also proposes a Collaborative Link Adaptation (CLA), which decomposes the optimization problem and utilizes distributed processing capacity to facilitate the convergence rate of optimization. Considering the differences between devices in processing capacity and potential overhead, CLA utilizes game theory to coordinate AP and STAs. By broadcasting the average optimization performance at the AP, CLA encourages STAs with advanced processing capabilities to process locally, while also ensuring that STAs with limited processing capabilities could be assisted by AP. The analysis proves that CLA can achieve a Pareto-optimal solution, and simulations demonstrate that our proposed CLA performs faster convergence rate and better performance than traditional centralized schemes under time-limited conditions

Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against <i>m</i>DT-STAP System

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulation shows that numerous false targets with different amplitude and intervals can be generated by changing the retransmitted parameters. The independent identical distribution (IID) of system sample data can be destroyed after these false targets are received by the radar system, and the main lobe will be distorted when the system’s adaptive weight vector is formed. The processing performance of the mDT-STAP system is seriously degraded. The jamming method proposed based on interrupted-sampling and the non-uniform periodic repeater offers great potential for the interference research on STAP in real conditions

Occluded Video Instance Segmentation: A Benchmark

Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, that is, to simultaneously detect, segment, and track instances in occluded scenes. OVIS consists of 296k high-quality instance masks from 25 semantic categories, where object occlusions usually occur. While our human vision systems can understand those occluded instances by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16.3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario. We also present a simple plug-and-play module that performs temporal feature calibration to complement missing object cues caused by occlusion. Built upon MaskTrack R-CNN and SipMask, we obtain a remarkable AP improvement on the OVIS dataset. The OVIS dataset and project code are available at http://songbai.site/ovis .Comment: project page at https://songbai.site/ovi