This paper describes the multi-query multi-head attention (MQMHA) pooling and inter-topK penalty methods which were first proposed in our submitted system description for VoxCeleb speaker recognition challenge (VoxSRC) 2021. Most multi-head attention pooling mechanisms either attend to the whole feature through multiple heads or attend to several split parts of the whole feature. Our proposed MQMHA combines both these two mechanisms and gain more diversified information. The margin-based softmax loss functions are commonly adopted to obtain discriminative speaker representations. To further enhance the inter-class discriminability, we propose a method that adds an extra inter-topK penalty on some confused speakers. By adopting both the MQMHA and inter-topK penalty, we achieved state-of-the-art performance in all of the public VoxCeleb test sets.

To promote cloud computing from current pay-per-request model to truly pay-per-use, tenants are crying for automatic tools to auto-estimate the amount of resources for MapReduce jobs. Such tools call for accurately quantifying the relationship among workload, resources and completion time. Various prediction models have been proposed. However, none of these models takes virtual machines’ (VMs) performance variance during a job's execution into consideration, leading to underestimate the required resources and exceed the job's deadline. To address this problem, we propose a multi-view deep learning model to capture real-time performance variance and automatically scale out the cloud cluster whenever necessary. We implementMarVeLScaler, a prototype system including two useful modules, namely,Scale EstimatorandScale Controller. Scale Estimator preliminarily estimates the required cluster size for a MapReduce job with given a concrete workload and deadline. During the runtime, Scale Controller adjusts the scale of the cluster according to its real-time running status to guarantee the job finished on time. We evaluate the performance of MarVeLScaler based on Hadoop in Alibaba Cloud. Experiments show that MarVeLScaler can provide 98.4 percent accuracy of prediction in determining initial cluster size, and save 30.8 percent of expense while still guaranteeing similar performance compared with the state-of-the-art methods.

In this paper, we introduce a new open source toolkit for automatic speaker verification (ASV), named ASV-Subtools. Adopting PyTorch as main deep learning engine and Kaldi toolkit for data processing, ASV-Subtools allows users to develop modern speaker recognizers flexibly and efficiently. The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting the state-of-the-art technologies in speaker recognition. In addition to including the commonly used networks, such as the time delay neural networks (TDNN), factorized TDNN (F-TDNN) and ResNet, ASV-Subtools also integrates an upgraded version of SpecAugment data augmentation method, named Inverted SpecAugment, with focus on making it more appropriate for speaker recognition subtasks. Besides, for alleviating the domain mismatch between training and test data, ASV-Subtools provides multiple domain adaptation methods of Probabilistic Linear Discriminant Analysis (PLDA). Experimental results show that state-of-the-art techniques implemented on ASV-Subtools could achieve competitive performance compared to other implementations.

In order to improve the performance and robustness of text-independent speaker verification systems, various speaker embedding representation learning algorithms have been developed. Typically, exploring manifold kinds of features to describe speaker-related embeddings is a common approach, such as introducing more acoustic features or different resolution scale features. In this paper, a new multi-feature learning strategy with canonical correlation analysis (CCA) constraint is proposed to learn the instinct speaker embeddings, which maximizes the correlation between two features from the same utterance. Based on the multi-feature learning structure, the CCA constraint layer and the CCA loss are utilized to explore the correlation representation between the two kinds of features and alleviate the redundancy. Therefore, two multi-feature learning strategies are studied, using the pairwise acoustic features, and the pair of short-term and long-term features. Furthermore, we improve the long short-term feature learning structure by replacing the LSTM block with the Bidirectional-GRU (B-GRU) block and introducing more dense layers. The effectiveness of these improvements are shown on the VoxCeleb 1 evaluation set, the noisy Vox-Celeb 1 evaluation set and the SITW evaluation set.

Graph representation learning aims to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in a graph, and discriminative models that predict the probability of edge between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying the above two classes of methods, in which the generative and the discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces “fake” samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, we propose a novel graph softmax as the implementation of the generative model to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including graph reconstruction, link prediction, node classification, recommendation, and visualization, over state-of-the-art baselines.

User emotion analysis toward videos is to automatically recognize the general emotional status of viewers from the multimedia content embedded in the online video stream. Existing works fall into two categories: 1)visual-basedmethods, which focus on visual content and extract a specific set of features of videos. However, it is generally hard to learn a mapping function from low-level video pixels to high-level emotion space due to great intra-class variance. 2)textual-basedmethods, which focus on the investigation of user-generated comments associated with videos. The learned word representations by traditional linguistic approaches typically lack emotion information and the global comments usually reflect viewers’ high-level understandings rather than instantaneous emotions. To address these limitations, in this paper, we propose to jointly utilize video content and user-generated texts simultaneously for emotion analysis. In particular, we introduce exploiting a new type of user-generated texts, i.e., “danmu,” which are real-time comments floating on the video and contain rich information to convey viewers’ emotional opinions. To enhance the emotion discriminativeness of words in textual feature extraction, we proposeEmotional Word Embedding(EWE) to learn text representations by jointly considering their semantics and emotions. Afterward, we propose a novel visual-textual emotion analysis model withDeep Coupled Video and Danmu Neural networks(DCVDN), in which visual and textual features are synchronously extracted and fused to form a comprehensive representation by deep-canonically-correlated-autoencoder-based multi-view learning. Through extensive experiments on a self-crawled real-world video-danmu dataset, we prove that DCVDN significantly outperforms the state-of-the-art baselines.

How to efficiently judge whether a natural image contains texts or not is an important problem. Since text detection and recognition algorithms are usually time-consuming, and it is unnecessary to run them on images that do not contain any texts. In this paper, we investigate this problem from two perspectives: the speed and the accuracy. First, to achieve high speed for efficient filtering large number of images especially on CPU, we propose using small and shallow convolutional neural network, where the features from different layers are adaptively pooled into certain sizes to overcome difficulties caused by multiple scales and various locations. Although this can achieve high speed but its accuracy is not satisfactory due to limited capacity of small network. Therefore, our second contribution is using the knowledge distillation to improve the accuracy of the small network, by constructing a larger and deeper neural network as teacher network to instruct the learning process of the small network. With the above two strategies, we can achieve both high speed and high accuracy for filtering scene text images. Experimental results on a benchmark dataset have shown the effectiveness of our method: the teacher network yields state-of-the-art performance, and the distilled small network achieves high performance while maintaining high speed which is 176 times faster on CPU and 3.8 times faster on GPU than a compared benchmark method.

Autonomous, arbitrary pattern formation is one of the most critical applications in multi-robot systems, where robots are required to form into circles, lines, and meshes or any other desired configuration. This task is important in military applications, search and rescue operations, and visual inspection of infrastructure and equipment tasks to name a few. Most existing works are very rigid, and only able to form certain shapes, where slight target changes can cause failure in the predefined pattern-specific rules and trigger algorithm redesign. We propose a novel, deep reinforcement learning-based method that generates general-purpose pattern formation strategies, in the form of deep neural networks (DNN), for any target pattern. Our method uses the trial-and-error feedback of each round of training to gradually generate the pattern formation strategy. Thus, robots are able to query the trained DNN model to select their optimal directions and speeds in a fully distributed manner. Considering that reinforcement learning models do not perform well with large state spaces and highly variant training samples, we employ auto-encoders to learn the condensed representation for each state and compute model-free policy gradients for arbitrary pattern formation. We experimentally show that groups of robots are able to form various general target patterns while minimizing the number of completion time steps.

With the evolution of network function virtualization (NFV), diverse network services can be flexibly offered as service function chains (SFCs) consisted of different virtual network functions (VNFs). However, network state and traffic typically exhibit unpredictable variations due to stochastically arriving requests with different quality of service (QoS) requirements. Thus, an adaptive online SFC deployment approach is needed to handle the real-time network variations and various service requests. In this paper, we firstly introduce a Markov decision process (MDP) model to capture the dynamic network state transitions. In order to jointly minimize the operation cost of NFV providers and maximize the total throughput of requests, we propose NFVdeep, an adaptive, online, deep reinforcement learning approach to automatically deploy SFCs for requests with different QoS requirements. Specifically, we use a serialization-and-backtracking method to effectively deal with large discrete action space. We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality. Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large, frequently transferred network state space. Consequently, NFVdeep surpasses the state-of-the-art methods by 32.59% higher accepted throughput and 33.29% lower operation cost on average.

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end-to-end training. Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings. To provide better inductive bias, we rely on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over the edge weights and we prove that it is equivalent to a label propagation scheme on a graph. We also develop an efficient implementation that shows strong scalability with respect to the knowledge graph size. Experiments on four datasets show that our method outperforms state of the art baselines. KGNN-LS also achieves strong performance in cold-start scenarios where user-item interactions are sparse.

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by crosscompress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that crosscompress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.

Radio frequency (RF) estimation plays a significant role in cellular network's planning and optimization. The conventional methods for RF estimation are mainly based on radio propagation models, which suffer from low accuracy and coarse granularity. Distinguished from existing methods, we propose sparsely self-supervised generative adversarial nets (SS-GAN), a novel data-driven model to generate the RF maps of an area from irregularly distributed measurement samples. SS-GAN meticulously adopts the standard GAN framework, where the generator learns the distribution of true observations under the guidance of the discriminator that discriminates whether the input data is from real samples or from generated outputs. Competition in the minmax game between generator and discriminator drives them to improve their capability, until the generator is indistinguishable from the true RF distribution. Specifically, on top of the GAN framework, SS-GAN carries out a variety of operations to enhance the estimation: (1) In addition to observations of the measured RF coverage and RF interference, SS-GAN also employs a collection of crucial auxiliary information (e.g., geographic data) as additional input features to the GAN framework so as to precisely characterize the RF environment; (2) To dampen the training instability, a new lightweight reconstruction loss is introduced to the objective function of SS-GAN rather than solely using the adversarial loss, which aims to impose a weak supervision on the generated RF maps according to estimation accuracy; (3) Moreover, SS-GAN designs an innovative sparsely self-supervised (SS) learning mechanism that facilitates the validation of the estimated results for a model lacking direct ground truth knowledge. Extensive experiments on a real-world 4G LTE dataset demonstrate that SS-GAN can substantially improve the estimation accuracy over the state-of-the-art baselines. Comparison results are presented through visualized case studies and quantitative statistics.

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems.

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose RippleNet , an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The outward propagation version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user’s potential interests along links in the KG. The multiple “ripples” activated by a user’s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The inward aggregation version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users’ long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.

Monaural source separation (MSS) aims to extract and reconstruct different sources from a single-channel mixture, which could facilitate a variety of applications such as chord recognition, pitch estimation and automatic transcription. In this paper, we study the problem of separating vocals and instruments from monaural music mixture. Existing works for monaural source separation either utilize linear and shallow models (e.g., non-negative matrix factorization), or do not explicitly address the coupling and tangling of multiple sources in original input signals, hence they do not perform satisfactorily in real-world scenarios. To overcome the above limitations, we propose a novel end-to-end framework for monaural music mixture separation called Deep Representation-Decoupling Neural Networks (DRDNN). DRDNN takes advantages of both traditional signal processing methods and popular deep learning models. For each input of music mixture, DRDNN converts it to a two-dimensional time-frequency spectrogram using short-time Fourier transform (STFT), followed by stacked convolutional neural networks (CNN) layers and long-short term memory (LSTM) layers to extract more condensed features. Afterwards, DRDNN utilizes a decoupling component, which consists of a group of multi-layer perceptrons (MLP), to decouple the features further into different separated sources. The design of decoupling component in DRDNN produces purified single-source signals for subsequent full-size restoration, and can significantly improve the performance of final separation. Through extensive experiments on real-world dataset, we prove that DRDNN outperforms state-of-the-art baselines in the task of monaural music mixture separation and reconstruction.

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.

Wireless coverage is the received signal strength of a particular region, which is a key prerequisite to provide high quality mobile communication service. In this paper, we aim to estimate the wireless coverage of an area based on the randomly distributed samples of received signal strength collected within the area. Specifically, we propose a weakly-supervised generative adversarial nets with auxiliary information (WS-GAN), which is a data-driven model to make estimation on the wireless coverage. Different from conventional methods (e.g., k-NN) that predict the missing values by simply replacing them with the average of nearby observations, WS-GAN adopts a GAN-based structure, in which the generator approximates the real distribution of observations under the supervision of the discriminator, while the discriminator tries to distinguish the ground truth from "faked" data produced by the generator. WS-GAN differs from the literature in two aspects: (1) WS-GAN is able to use the auxiliary information of local radio environment, e.g., geographical information as terrain and buildings, to improve the estimation performance, since such auxiliary information has significant impact on the variation of received signal strength. (2) The objective function of WS-GAN combines adversarial loss and a new light-weight reconstruction error term for weakly supervision, which are jointly optimized during training. The experiments on a real dataset show that WS-GAN can generally achieve more accurate results than baselines. Moreover, through a case study, we demonstrate that the wireless coverage maps generated by WS-GAN are more rational and practical than those obtained by baselines.

Multi-view anomaly detection is a challenging issue due to diverse data generation mechanisms and inconsistent cluster structures of different views. Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal, but their collective behavior as a group is abnormal. In this paper, we formalize this group anomaly detection issue, and propose a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD). By representing the multi-view data with different latent group and topic structures, MGAD first discovers the distribution of groups or topics in each view, then detects group anomalies effectively. In order to solve the proposed model, we conduct the collapsed Gibbs sampling algorithm for model inference. We evaluate our model on both synthetic and real-world datasets with different anomaly settings. The experimental results demonstrate the effectiveness of the proposed approach on detecting multi-view group anomalies.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

Recent years have witnessed the emergence and ongoing proliferation of dynamic adaptive streaming over HTTP (DASH1), which reuses web servers with HTTP communication instead of relying on RTSP/RTP/RTCP-based media server and promises to be capable of automatically tuning to bandwidth dynamics. Aware of its excellent performance, the third generation partnership project (3GPP) long term evolution (LTE) has adopted DASH (with specific codecs and operating modes) for use over mobile wireless networks in order to realize ubiquitous multimedia delivery. In a multi-user multiple-input-multiple-output (MU-MIMO) LTE system, spatial multiplexing gain can be achieved by making sure the transmitter to deliver distinct data streams to multiple receivers simultaneously, which provides the choices to opportunistically schedule the preferred receivers each time for a common time-frequency resource. In such a system, one of the major challenges to enhance DASH performance is to design an effective scheduler that can fully enjoy the benefit of spatial reuse as well as guaranteeing satisfactory video services for all users. To this end, in this paper, we propose a utility maximization framework (UMF) for DASH application delivered over MU-MIMO LTE downlinks. In particular, we characterize DASH performance by a combined utility function in terms of average video rate, playback buffer status, and battery energy state. Correspondingly, we develop a utility-based scheduler that selects multiple user equipments (UEs) to share each common network resource under the consideration of precoding-based MU-MIMO links in order to maximize system-wide DASH performance. We prove the NP-hardness of the scheduling problem and propose a priority search algorithm to provide time-efficient solution. We further incorporate novel rate adaptation on the application layer for the scheduled UEs to dynamically set the requested encoding bitrates to explore the balance between agile responsiveness and shifting smoothness. Extensive system-level simulations with realistic video trace validate the effectiveness of our framework in terms of rate adaptability, playback buffer depletion percentage, and battery energy saving.

Pointwise and pairwise collaborative ranking are two major classes of algorithms for personalized item ranking. This paper proposes a novel joint learning method named alternating pointwise-pairwise learning (APPL) to improve ranking performance. APPL combines the ideas of both pointwise and pairwise learning, and is able to produce a more effective prediction model. The extensive experiments with both explicit and implicit feedback settings on four real-world datasets demonstrate that APPL performs significantly better than the state-of-the-art methods.

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.

Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users' interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.

Single Carrier-Frequency Division Multiple Access (SC-FDM) and Tone Reservation (TR) are used independently to reduce Peak to Average Power Ratio (PAPR) in wireless communications. In this paper, we jointly consider TR and SC-FDM to achieve much lower PAPR. We first present the PAPR of SC-FDM system with different DFT/IDFT size, and then theoretically analyze the PAPR gain of TR in SC-FDM. By considering the impact of TR on transmission power, we propose a novel metric, the effective signal power, to measure the performance of TR in SC-FDM. Afterwards, the TR optimization problem is formulated and solved by TR gradient algorithm. Finally, the performance of TR with SC-FDM is evaluated.

Recent studies have shown that significant benefit can be achieved in wireless sensor networks (WSNs) by employing mobile collectors for data gathering via short-range communications. A typical scenario for such a scheme is that a mobile collector roams over the sensing field and pauses at some anchor points on its moving tour such that it can traverse the transmission range of all the sensors in the field and directly collect data from each sensor. In this paper, we study the performance optimization of such mobile data gathering by formulating it into a cost minimization problem constrained by channel capacity, required minimum data uploads from each sensor and bound of total sojourn time at all anchor points. In order to provide an efficient and distributed algorithm, we decompose this global optimization problem into two subproblems that can be solved by each sensor and the mobile collector, respectively. We show that such decomposition can be characterized as a pricing mechanism, in which each sensor independently adjusts its payment for the data uploading opportunity based on the shadow prices of different anchor points. Correspondingly, we give an efficient algorithm to jointly solve the two subproblems. Our theoretical analysis demonstrates that the proposed algorithm can achieve the optimal data control for each sensor and the optimal sojourn time allocation for the mobile collector, which minimizes the overall network cost. Finally, extensive simulation results further validate that our algorithm achieves lower cost than the compared data gathering strategy.

In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as LBC-DDU. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to SenCar for its moving trajectory planning. At the mobile collector layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual data uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-DDU scheme. The results show that when each cluster has at most two cluster heads, LBC-DDU achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.

Providing well-satisfactory multi-party video conferencing service is remarkably challenging, which demands not only high bandwidth but also low latency. Current commercial implementations typically use one or multiple multipoint control units (MCUs) as the central points for distributing video bit-streams to all participants in the conferencing session. Such MCU-based solution has limited control on quality of service (QoS), which may cause large delay, single-point malfunction and communication bottleneck for entire system. Recent years have witnessed the emergence of a new paradigm in networking, software defined networking (SDN), which advocates separating data plane and control plane, making network switches in the data plane simple packet forwarding devices and leaving a logically centralized controller to manipulate network behaviors. SDN provides the flexibility of changing underlying infrastructure and makes it possible to radically provide service/flow-aware QoS guarantee. In this paper, we propose a novel architecture for multi-party video conferencing by utilizing SDN-enabled multicasting, where SDN controller helps media controller to buildup multicast trees for video flows originated at video parties. To realize the architecture, we correspondingly propose a novel multicast construction and packing method, in which multiple source-based multicast trees are constructed and integrated to maximize system-wide utility while guaranteeing an end-to-end delay bound. Extensive simulations demonstrate that it could provide better video delivery compared to the conventional MCU-based solution in terms of video rate and delay in both sparsely and densely distributed networks.

Recent years have witnessed the emergence and ongoing proliferation of dynamic adaptive streaming over HTTP (DASH1), which reuses web servers with HTTP communication instead of relying on RTP-based media server and promises to be capable of automatically tuning to bandwidth dynamics. The third generation partnership project (3GPP) long term evolution (LTE) has adopted DASH for use in order to realize ubiquitous multimedia delivery. In a multi-user multiple-input-multiple-output (MU-MIMO) LTE system, spatial multiplexing gain can be achieved by making the transmitter deliver distinct data streams to multiple receivers simultaneously, which provides choices to schedule preferred receivers for a common resource. In such a system, one of the major challenges to enhance adaptive HTTP streaming performance is to design an effective scheduler that can fully enjoy the benefit of spatial reuse as well as guaranteeing satisfactory video services for all users. To this end, we propose a utility maximization framework (UMF) for DASH application over MU-MIMO LTE downlinks. In particular, we characterize DASH performance by a combined utility function in terms of average video rate, playback buffer status, and battery energy state. Correspondingly, we develop a utility-based scheduler that selects multiple user equipments (UEs) to share each common network resource under the consideration of precoding-based MU-MIMO in order to maximize system-wide DASH performance. Specifically, we propose a priority search algorithm to provide time-efficient solution to the scheduling problem. We further incorporate novel rate adaptation on the application layer for scheduled UEs to dynamically set the requested video encoding bitrates. Extensive system-level simulations validate the effectiveness of UMF in terms of rate adaptability, playback buffer depletion percentage and battery energy consumption.

Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless s`ensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCar in this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar’s controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, the tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

As the latest IEEE 802.11 standard, 802.11n allows a maximum physical data rate as high as 600Mbps, making it a desirable candidate for wireless local area network (WLAN) deployment. In WLANs, access points (APs) are often densely deployed, and thus neighboring APs should be assigned with orthogonal channels to avoid performance degradation caused by interference. It is challenging to find the optimal channel assignment strategy, as the number of channels is very limited. Many channel assignment schemes have been proposed for WLANs in the literature. However, most of them were not designed for 802.11n WLANs, and did not consider the challenges from the new channel bonding and frame aggregation mechanisms. Moreover, the impact of multi-rate clients on channel assignment is not fully investigated yet. In this paper, we study channel assignment in multi-rate 802.11n WLANs, aiming at maximizing the network throughput. We first present a network model and an interference model, and estimate the client throughput based on them. We then formulate the channel assignment problem into a throughput optimization problem. As the formulated problem is NP-hard, we propose a distributed channel assignment algorithm to provide practical solutions. We have conducted extensive simulations to evaluate the proposed algorithm and the results show that the network throughput can be significantly improved compared with existing schemes.

Current study on prolonging lifetime for wireless sensor networks (WSNs) mainly focuses on two techniques. The first technique is to reduce energy consumption of sensor nodes, while the second technique is to recharge sensor nodes by harvesting energy from the ambient environment or RF based energy transmission. However, neither of these two techniques are able to guarantee network lifetime and network performance. In order to achieve perpetual operation for WSNs while providing high network utility, in this paper we propose an optimal wireless energy replenishment and mobile data gathering mechanism (OWER-MDG) which charges sensor nodes effectively and collects data from the network using a mobile vehicle (SenCar). We study the application of OWER-MDG in WSNs and provide an efficient algorithm which maximizes network utility. Our numerical results demonstrate the performance advantage of OWER-MDG and provide a guidance on parameter selection for system design.

Recent study reveals that great benefit can be achieved for data gathering in wireless sensor networks by employing mobile collectors that gather data via short-range communications. To pursue maximum energy saving at sensor nodes, intuitively, a mobile collector should traverse the transmission range of each sensor in the field such that each data packet can be directly transmitted to the mobile collector without any relay. However, this approach may lead to significantly increased data gathering latency due to the low moving velocity of the mobile collector. Fortunately, it is observed that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. In such a scheme, the number of local transmission hops should not be arbitrarily large as it may increase the energy consumption on packet relays, which would adversely affect the overall efficiency of mobile data gathering. Based on these observations, in this paper, we study the tradeoff between energy saving and data gathering latency in mobile data gathering by exploring a balance between the relay hop count of local data aggregation and the moving tour length of the mobile collector. We first propose a polling-based mobile gathering approach and formulate it into an optimization problem, named bounded relay hop mobile data gathering (BRH-MDG). Specifically, a subset of sensors will be selected as polling points that buffer locally aggregated data and upload the data to the mobile collector when it arrives. In the meanwhile, when sensors are affiliated with these polling points, it is guaranteed that any packet relay is bounded within a given number of hops. We then give two efficient algorithms for selecting polling points among sensors. The effectiveness of our approach is validated through extensive simulations.

Recent advances have shown a great potential of mobile data gathering in wireless sensor networks, where one or more mobile collectors are employed to collect data from sensors via short-range communications. Among a variety of data gathering approaches, one typical scheme is called anchor-based mobile data gathering. In such a scheme, during each periodic data gathering tour, the mobile collector stays at each anchor point for a period of sojourn time, and in the meanwhile the nearby sensors transmit data to the collector in a multihop fashion. In this paper, we focus on such a data gathering scheme and provide distributed algorithms to achieve its optimal performance. We consider two different cases depending on whether the mobile collector has fixed or variable sojourn time at each anchor point. We adopt network utility, which is a properly defined function, to characterize the data gathering performance, and formalize the problems as network utility maximization problems under the constraints of guaranteed network lifetime and data gathering latency. To efficiently solve these problems, we decompose each of them into several subproblems and solve them in a distributed manner, which facilitates the scalable implementation of the optimization algorithms. Finally, we provide extensive numerical results to demonstrate the usage and efficiency of the proposed algorithms and complement our theoretical analysis.

In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and MIMO uploading techniques, which is referred to as LBC-MU. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate MIMO data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to the SenCar for its moving trajectory planning. At the mobile collector layer, the SenCar is equipped with two antennas, which enables multiple cluster heads to simultaneously upload data to the SenCar. The trajectory planning for the SenCar is optimized to fully utilize MIMO uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, the SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-MU scheme. The results show that when each cluster has at most two cluster heads, LBC-MU can reduce the maximum number of transmissions a sensor performs by 90% and the average number of transmissions by 88% compared with the enhanced relay routing scheme. It also results in 25% shorter average data latency compared with the mobile collection scheme with single-head clustering.

Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this issue by adopting mobility and space-division multiple access (SDMA) technique. Specifically, mobile collectors, called SenCars in this paper, work like mobile base stations and collect data from associated sensors via single-hop transmissions so as to achieve uniform energy consumption. We also apply SDMA technique to data gathering by equipping each SenCar with multiple antennas such that distinct compatible sensors may successfully make concurrent data uploading to a SenCar. To investigate the utility of the joint design of controlled mobility and SDMA technique, we consider two cases, where a single SenCar and multiple SenCars are deployed in a WSN, respectively. For the single SenCar case, we aim to minimize the total data gathering time, which consists of the moving time of the SenCar and the data uploading time of sensors, by exploring the trade-off between the shortest moving tour and the full utilization of SDMA. We refer to this problem as mobile data gathering with SDMA, or MDG-SDMA for short. We formalize it into an integer linear program (ILP) and propose three heuristic algorithms for it. In the multi-SenCar case, the sensing field is divided into several regions, each having a SenCar. We focus on minimizing the maximum data gathering time among different regions and refer to it as mobile data gathering with multiple SenCars and SDMA (MDG-MS) problem. Accordingly, we propose a region-division and tour-planning (RDTP) algorithm in which data gathering time is balanced among different regions. We carry out extensive simulations and the results demonstrate that our proposed algorithms significantly outperform single SenCar and non-SDMA schemes.

In a multiparty video conference, multiple users simultaneously distribute video streams to their receivers. As the traditional server-based solutions incur high infrastructure and bandwidth cost, conventional peer-to-peer (P2P) solutions only leveraging end-users' upload bandwidth are normally not self-sustainable: The video streaming workload increases quadratically with the number of users as each user could generate and distribute video streams, while the user upload bandwidth only increases linearly. Recently, hybrid solutions have been proposed that employ helpers to address the bandwidth deficiency in P2P video-conferencing swarms. It is also noticed that a system hosting multiple parallel conferencing swarms can benefit from cross-swarm bandwidth sharing. However, how to optimally share bandwidth in such systems has not been explored so far. In this paper, we study the optimal bandwidth sharing in multiswarm multiparty P2P video-conferencing systems with helpers and investigate two cross-swarm bandwidth-sharing scenarios: (1) swarms are independent and peers from different swarms share a common pool of helpers; (2) swarms are cooperative and peers in a bandwidth-rich swarm can further share their bandwidth with peers in a bandwidth-poor swarm. For each scenario, we develop distributed algorithms for intraswarm and interswarm bandwidth allocation under a utility-maximization framework. Through analysis and simulation, we show that the proposed algorithms are robust to peer dynamics and can adaptively allocate peer and helper bandwidth across swarms so as to achieve the system-wide optimum.

Recent advances have shown a great potential of anchor based mobile data gathering in wireless sensor networks. In such a scheme, during each periodic data gathering tour, the mobile collector stays at each anchor point for a period of sojourn time and collects data from nearby sensors via multi-hop communications. We provide an optimization based distributed algorithm for such data gathering in this paper. We adopt network utility, which is a properly defined function, to characterize the data gathering performance, and formalize the problem as a network utility maximization problem under the constraint of guaranteed network lifetime. To efficiently solve the problem, we decompose it into two sets of subproblems and solve them in a distributed manner, which facilitates the scalable implementations. Finally, we provide numerical results to demonstrate the convergence of the proposed distributed algorithm.

Recently, several multi-channel MAC protocols have been proposed for wireless sensor networks (WSNs) to improve network capacity and boost energy efficiency. In addition, cooperative multiple-input multiple-output (MIMO) technique has been shown to be able to significantly enhance the energy efficiency of WSNs if properly configured. However, these two promising techniques have not been jointly utilized in WSNs. In this paper, we explore such a joint design by proposing a novel MAC protocol for WSNs that takes advantage of both multiple channels and cooperative MIMO. Specifically, sensor nodes in a WSN are organized into clusters and each cluster head selects some cooperative nodes to help forward traffic to or receive from other clusters by utilizing cooperative MIMO technique. For intra-cluster communications, different channels are assigned to adjacent clusters to reduce collisions, while for inter-cluster communications, cooperative MIMO links are scheduled to improve energy efficiency and concurrent transmissions are enabled by assigning different channels to them. We carry out extensive simulations and the results demonstrate that the proposed protocol can significantly increase throughput and improve energy efficiency compared to other schemes. For example, for a WSN with 150 nodes in a 250m × 250m field, with five or more available channels, the protocol can achieve over three times saturated throughput while saving up to 17% energy per bit for inter-cluster communications.

Recent studies have shown that significant benefit can be achieved in wireless sensor networks (WSNs) by employing mobile collectors for data gathering via short-range communications. A typical scenario for such a scheme is that a mobile collector roams over the sensing field and pauses at some anchor points on its moving tour such that it can traverse the transmission range of all the sensors in the field and directly collect data from each sensor. In this paper, we study the performance optimization of such mobile data gathering by formulating it into a cost minimization problem constrained by the channel capacity, the minimum amount of data gathered from each sensor and the bound of total sojourn time at all anchor points. We assume that the cost of a sensor for a particular anchor point is a function of the data amount a sensor uploads to the mobile collector during its sojourn time at this anchor point. In order to provide an efficient and distributed algorithm, we decompose this global optimization problem into two subproblems to be solved by each sensor and the mobile collector, respectively. We show that such decomposition can be characterized as a pricing mechanism, in which each sensor independently adjusts its payment for the data uploading opportunity to the mobile collector based on the shadow prices of different anchor points. Correspondingly, we give an efficient algorithm to jointly solve the two subproblems. Our theoretical analysis demonstrates that the proposed algorithm can achieve the optimal data control for each sensor and the optimal sojourn time allocation for the mobile collector, which minimizes the overall network cost. Finally, extensive simulation results further validate that our algorithm achieves lower cost than the compared data gathering strategy.

In recent years, wireless mesh networks (WMNs) have been widely deployed to provide wireless access to the Internet. However, due to inter-link interference, the aggregated capacity of WMNs is limited, even with multiple channels. As a result, many links in WMNs are suppressed since interfering links cannot be active (i.e., transmitting packets) simultaneously. In this paper, we propose a joint design of channel assignment and space-division multiple access (SDMA) technique with the objective of maximizing the number of active links in WMNs. We assign different channels to transmission links based on their interference relationship to alleviate the interference. We also apply the SDMA technique to link scheduling, which enables two interfering links that share the same destination to communicate simultaneously on the same channel. By utilizing SDMA, more concurrent transmission links can be accommodated such that the network capacity can be greatly improved. We formulate this joint design into an optimization problem, prove its NP-hardness and then provide two heuristic algorithms to give practically good solutions to the problem. Our simulation results demonstrate that performance of the two heuristic algorithm is close to the optimal solution, and when 90% SDMA pairs are compatible, the proposed algorithms can increase the percentage of active links in a WMN by up to 40% as compared to non-SDMA schedules.

In this paper, we consider data gathering in wireless sensor networks (WSNs) by utilizing multiple mobile collectors and spatial-division multiple access (SDMA) technique. In particular, multiple mobile collectors, for convenience, called SenCars in this paper, are deployed in a WSN and work independently and simultaneously to collect data. The sensing field is divided into several non-overlapping regions, each having a SenCar. Each SenCar takes the responsibility of gathering data from sensors in the region while traversing their transmission ranges. Sensors directly send data to their associated SenCars without any relay in order to achieve uniform energy consumption. We also consider exploiting SDMA technique by equipping each SenCar with two antennas. With the support of SDMA, two distinct compatible sensors in the same region can successfully make concurrent data uploading to their associated SenCar. Intuitively, if each SenCar can always simultaneously communicate with two compatible sensors, the data uploading time in each region can be cut into half in the ideal case. We focus on the problem of minimizing the maximum data gathering time among different regions, which consists of two parts: the data uploading time of the sensors in this region and the moving time of the associated SenCar on a tour. We refer to this problem as data gathering with multiple mobile collectors and SDMA, or DG-MS for short, and formalize it into an integer linear program. We then propose a region-division and tour-planning algorithm to provide a practically good solution to the problem. Simulation results demonstrate that the proposed scheme significantly outperforms other non-SDMA or single mobile collector schemes by efficiently shortening and balancing the data gathering time among different regions.

In this paper we propose a joint design of MIMO technique and network coding (MIMO-NC) and apply it to improve the performance of wireless networks. We consider a system in which the packet exchange among multiple wireless users is forwarded by a relay node. In order to enjoy the benefit of MIMO-NC, all the nodes in the network are mounted with two antennas and the relay node possess the coding capability. For the cross traffic flows among any four users, the relay node not only can receive packets simultaneously from two compatible users in the uplink (users-to-relay node), but also can mix up distinct packets for four destined users into two coded packets and concurrently send them out in the same downlink (relay node-to-users), so that the information content is significantly increased in each transmission. We formalize the problem of finding a schedule to forward the buffered data of all the users in minimum number of transmissions in such a system as a problem of finding a maximum matching in a graph. We also provide an analytical model on maximum throughput and optimal energy efficiency, which explicitly measures the performance gain of the MIMO-NC enhancement. Our analytical and simulation results demonstrate that system performance can be greatly improved by the efficient utilization of MIMO and network coding opportunities.

Recent study reveals that great benefit can be achieved for data gathering in wireless sensor networks by employing mobile collectors that gather the data via short-range communications. To pursue maximum energy saving at sensor nodes, intuitively, a mobile collector should traverse the transmission range of each sensor in the field such that the transmission of each packet can be constrained to a single hop. However, this approach may lead to significantly increased data collection latency due to the low moving velocity of the mobile collector. On the other hand, data collection latency can be effectively shortened by performing local aggregation via multi-hop transmissions and then uploading the packets from relay sensors to the mobile collector. However, local transmission hops should not be arbitrarily increased since it may incur too much energy consumption on packet relays, which would adversely affect the overall efficiency of mobile data collection. Based on these observations, in this paper, we study the tradeoff between energy saving and data collection latency in mobile data gathering by exploring a balance between the relay hop count of local data aggregation and the moving tour length of the mobile collector. We first propose a polling-based mobile collection approach and formulate it into an optimization problem, named bounded relay hop mobile data collection (BRH-MDC). Specifically, a subset of sensors will be selected as polling points that buffer locally aggregated data and upload the data to the mobile collector when it arrives. In the meanwhile, when sensors are affiliated with these polling points, it is guaranteed that any packet relay is bounded within a given number of hops. We then give two efficient algorithms to select polling points among sensors. The effectiveness of our approach is validated through extensive simulations.

In this paper, we consider using simultaneous Multiple Packet Transmission (MPT) to improve the downlink performance of wireless networks. With MPT, the sender can send two compatible packets simultaneously to two distinct receivers and can double the throughput in the ideal case. We formalize the problem of finding a schedule to send out buffered packets in minimum time as finding a maximum matching problem in a graph. Since maximum matching algorithms are relatively complex and may not meet the timing requirements of real-time applications, we give a fast approximation algorithm that is capable of finding a matching at least 3/4 of the size of a maximum matching in O(|E|) time, where |E| is the number of edges in the graph. We also give analytical bounds for maximum allowable arrival rate, which measures the speedup of the downlink after enhanced with MPT, and our results show that the maximum arrival rate increases significantly even with a very small compatibility probability. We also use an approximate analytical model and simulations to study the average packet delay, and our results show that packet delay can be greatly reduced even with a very small compatibility probability.

Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this important issue in WSNs by adopting mobility and space-division multiple access (SDMA) technique to optimize system performance. Specifically, a mobile data collector, for convenience, called SenCar in this paper, is deployed in a WSN. It works like a mobile base station and polls each sensor while traversing its transmission range. Each sensor directly sends data to the SenCar without any relay so that the lifetime of sensors can be prolonged. We also consider applying SDMA technique to data gathering by equipping the SenCar with two antennas. With SDMA, two distinct compatible sensors may successfully make concurrent data uploading to the SenCar. Intuitively, if the SenCar can always simultaneously communicate with two compatible sensors, data uploading time can be cut into half in the ideal case. We focus on the problem of minimizing the total time of a data gathering tour which consists of two parts: data uploading time and moving time. To better enjoy the benefit of SDMA, the SenCar may have to visit some specific locations where more sensors are compatible, which may adversely prolong the moving path. Hence, an optimum solution should be a tradeoff between the shortest moving path and full utilization of SDMA. We refer to this optimization problem as mobile data gathering problem with SDMA, or MDG-SDMA for short. We formalize the MDG-SDMA problem into an integer program (IP) and then propose three heuristic algorithms that provide practically good solutions to the problem. Our simulation results demonstrate that the proposed algorithms can greatly reduce the total data gathering time compared to the non-SDMA algorithm with only minimum overhead.

Opportunistic medium access (i.e., multiuser diversity) and MIMO techniques (i.e., multiple-antenna techniques) are two effective ways to achieve a substantial throughput gain in a multiuser wireless system. In this paper, we propose a medium access control (MAC) protocol with opportunistic medium access and multiuser MIMO techniques (MAC-OMA/MM) in Multi-channel Multi-radioWireless Local Area Networks (WLANs) to explore the utility of the joint design of these two techniques for the challenging MAC design. Specially, in addition to utilizing multiple channels simultaneously and multiple radio transceivers dynamically, multiuser spatial multiplexing and multiuser diversity are employed in each frequency channel to improve system performance. The key ideas of MAC-OMA/MM can be summarized as follows. By utilizing ATIM (Ad-hoc Traffic Indication Message) windows as in IEEE 802.11 power saving mechanism (PSM) under the distributed coordinate function (DCF) mode, user selection and channel negotiation are conducted between the AP and users via ATIM messages on a common channel. Multiuser diversity are employed to opportunistically schedule among multiple candidate users to optimize data transmission. During data exchange, on each frequency channel, the AP can transmit data to two distinct users simultaneously in the downlink with the help of multiuser spatial multiplexing, and two users can concurrently send data to the AP by uplinkdownlink duality in the uplink, which creates an extra dimension in spatial domain to further leverage the effect of multiuser diversity and multi-channel gains. Another contribution of this paper is to provide an analytical model to characterize the impact of our protocol on the system throughput and energy efficiency performance. Extensive simulations have been conducted and the results demonstrate that our protocol outperforms existing multi-channel MAC protocols with only minimal additional overhead and minor enhancements to the IEEE 802.11 PSM.

Recent years have witnessed a surge of the interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this essential issue in WSNs by introducing the mobility and MIMO capability to the data sink in order to optimize the system performance. Specifically, the mobile sink moves along a pre-determined path and collects data packets at some polling stations. With a proper planning on the moving path, we can reduce the data relaying to the minimum, which effectively improves the energy efficiency and also circumvents the non-uniformity of energy consumption among sensor nodes. We also apply MIMO technique to the mobile sink by mounting multiple antennas on it, which provides a radical way to improve the system capacity. With the support of MIMO technique, the mobile sink can simultaneously collect data from multiple compatible sensors. Finally, we theoretically analyze the performance of mobile data gathering in a WSN enhanced by MIMO support, and also investigate its efficiency by simulation experiments. The results demonstrate that the proposed mechanism can greatly improve the system throughput and energy efficiency with minimum additional overhead.

The combination of multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) is regarded as one of the most promising solutions for improving spectrum efficiency and enhancing data rate for next-generation wireless communication systems. However, the applications of these advanced physical-layer techniques also impose great challenges on upper layer protocol designs. In this paper, a cross-layer approach was proposed to exploring the utility of the joint design of multiuser MIMO-OFDM technique and power-saving MAC protocol. Specifically, a novel MIMO-OFDM transceiver architecture and an improved power-saving MAC protocol (namely, PSM-MIMO/OFDM) are proposed. In the proposed downlink transceiver architecture, by processing the data according to the channel state, the access point (AP) makes the data for one user appear as zero at the other user on each subcarrier such that it can send distinct packets to two users simultaneously. In the dual uplink, two users can concurrently transmit data to the AP without co-channel interference by uplink-downlink duality. The key ideas of PSM-MIMO/OFDM can be summarized as follows. A similar timing structure was employed to IEEE 802.11 power-saving mechanism (PSM). By utilizing ATIM (ad-hoc traffic indication message) window, negotiations are conducted between the AP and multiple candidate users via ATIM messages to achieve multiuser diversity gains. At the end of ATIM window, the AP sends an additional beacon to indicate the estimated time duration for data exchange with the measurement result of the number of active links which have been successfully negotiated during ATIM window. The length of a beacon interval is adjustable to satisfy the requirements of active links to the maximum extent. During the data exchange, the AP always communicates with two compatible users simultaneously both in the downlink and uplink, which effectively creates extra dimensions in the spatial domain. Extensive simulations have been conducted and the results demonstrate that the new protocol significantly outperforms other power-saving MAC protocols with only minimum additional overhead and minor modifications to the IEEE 802.11 PSM.

In a multiuser wireless system, multiuser diversity for opportunistic scheduling has been extensively studied for high-rate data transmission. In this paper, we propose a novel MAC protocol named medium access diversity with uplink-downlink duality and transmit beamforming (MAD-UDD/TB). In addition to aggressively utilizing multiuser gains, it takes advantage of uplink-downlink duality and transmit beamforming, which are the transmitting and receiving strategies used in a multiple antenna environment for simultaneous packet transmissions to multiple distinct users. These techniques effectively leverage the effect of multiuser diversity and greatly improve network throughput by taking into account the opportunistic scheduling among multiple users to prioritize data transmissions. Extensive simulation results show that the proposed protocol achieves much better performance than other medium access diversity and auto rate schemes with minimal additional overhead.

In wireless environments, the inherent time-varying characteristics of the channel pose great challenges on medium access control design. In recent years, multiuser diversity and opportunistic medium access control schemes have been proposed to deal with the channel variation in order to efficiently improve the network throughput. In this paper, we propose a novel MAC protocol called Contention-Based Prioritized Opportunistic (CBPO) Medium Access Control Protocol. This protocol takes advantage of multiuser diversity, rate adaptation, which utilizes the multi-rate capability offered by IEEE 802.11, and black-burst (BB) contention to access the shared medium in a distributed manner. In particular, rather than simply measuring the channel condition for a node pair in communications each time, with the help of multicast RTS, the candidate users with qualified channel condition are selected and prioritized. Then the qualified receivers contend to send back prioritized clear-to-send message (CTS) with BB, which is a pulse of energy, the duration of which is proportional to the CTS priority. The user with the best channel quality is always selected to send back CTS and receive packets from the sender. Extensive simulation results show that our protocol achieves much better performance than IEEE 802.11 and other auto rate schemes with minimal additional overhead.

Traditional traffic descriptor-based and measurement-based admission control schemes are typically combined with a node by node resource reservation scheme, rendering them unscalable. Although some Endpoint Admission Control schemes can resolve this problem, they impose significant signaling overhead. To cope with these two problems, this paper proposes a statistical connection admission control framework which can easily and efficiently estimate the network resource for a pair of ingress-egress nodes and make admission decision based on this estimated result. In this framework, the network is considered as a "black box." For a certain ingress-egress node pair, the egress node measures the QoS constraint violation ratio and feeds this information back to the ingress node periodically. With this information and the measured statistical characteristics of the existing aggregated traffic, the ingress node estimates the achievable capacity between the ingress-egress node pair, and makes the admission decision for a new traffic connection request. The signaling overhead of this framework is very small. Simulation results show the effective throughput is relatively high.

QoS routing, which satisfies diverse application requirements and optimizes network resource utilization, needs accurate link states to compute paths. Suitable link state update (LSU) algorithms which ensure timely propagation of link state information are thus critical. Since traffic fluctuation is one of the key reasons for link state uncertainty and existing approaches cannot effectively describe its statistical characteristics, we propose a novel stability-based (SB) LSU mechanism which consists of a second-moment-based triggering policy and a corresponding stability-based routing algorithm. They incorporate knowledge of link state stability in computing a stability measure for link metrics. With extensive simulations, we investigate the performance of the SB LSU mechanism and evaluate its effectiveness compared with existing approaches. Simulation results show that SB LSU can achieve good performance in terms of traffic rejection ratio, successful transmission ratio, efficient throughput and link state stability while maintaining a moderate volume of update traffic.