Nokia (China)

In this article device-to-device (D2D) communication underlaying a 3GPP LTE-Advanced cellular network is studied as an enabler of local services with limited interference impact on the primary cellular network. The approach of the study is a tight integration of D2D communication into an LTE-Advanced network. In particular, we propose mechanisms for D2D communication session setup and management involving procedures in the LTE System Architecture Evolution. Moreover, we present numerical results based on system simulations in an interference limited local area scenario. Our results show that D2D communication can increase the total throughput observed in the cell area.

IEEE 802.11 medium access control (MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is a distributed coordination function (DCF), which is a carrier sense multiple access with collision avoidance (CSMA/CA) with a binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Compared with other models, this model is shown to be able to predict the behavior of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.

This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.

IEEE 802.16m and 3GPP LTE-Advanced are the two evolving standards targeting 4G wireless systems. In both standards, multiple-input multiple-output antenna technologies play an essential role in meeting the 4G requirements. The application of MIMO technologies is one of the most crucial distinctions between 3G and 4G. It not only enhances the conventional point-to-point link, but also enables new types of links such as downlink multiuser MIMO. A large family of MIMO techniques has been developed for various links and with various amounts of available channel state information in both IEEE 802.16e/m and 3GPP LTE/LTE-Advanced. In this article we provide a survey of the MIMO techniques in the two standards. The MIMO features of the two are compared, and the engineering considerations are depicted.

Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation and network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links.

A hybrid system of cellular mode and device-to-device (D2D) mode is considered in this paper, where the cellular uplink resource is reused by the D2D transmission. In order to maximize the overall system performance, the mutual interference between cellular and D2D sub-systems has to be addressed. Here, two mechanisms are proposed to solve the problem: One is mitigating the interference from cellular transmission to D2D communication by an interference tracing approach. The other one is aiming to reduce the interference from D2D transmission to cellular communication by a tolerable interference broadcasting approach. Both mechanisms can work independently or jointly to synergy the transmission in the hybrid system for the efficient resource utilization. In the end, simulation is conducted to study the performance of the proposed schemes, which shows satisfying results.

Compared with the traditional interaction approaches, such as keyboard, mouse, pen, etc, vision based hand interaction is more natural and efficient. In this paper, we proposed a robust real-time hand gesture recognition method. In our method, firstly, a specific gesture is required to trigger the hand detection followed by tracking; then hand is segmented using motion and color cues; finally, in order to break the limitation of aspect ratio encountered in most of learning based hand gesture methods, the scale-space feature detection is integrated into gesture recognition. Applying the proposed method to navigation of image browsing, experimental results show that our method achieves satisfactory performance.

This paper describes a novel hand gesture recognition system that utilizes both multi-channel surface electromyogram (EMG) sensors and 3D accelerometer (ACC) to realize user-friendly interaction between human and computers. Signal segments of meaningful gestures are determined from the continuous EMG signal inputs. Multi-stream Hidden Markov Models consisting of EMG and ACC streams are utilized as decision fusion method to recognize hand gestures. This paper also presents a virtual Rubik's Cube game that is controlled by the hand gestures and is used for evaluating the performance of our hand gesture recognition system. For a set of 18 kinds of gestures, each trained with 10 repetitions, the average recognition accuracy was about 91.7% in real application. The proposed method facilitates intelligent and natural control based on gesture interaction.

By leveraging direct device-to-device interaction, LTE vehicle-to-vehicle (V2V) communication becomes a promising solution to meet the stringent requirements of vehicular communication. In this paper, we propose jointly optimizing the radio resource, power allocation, and modulation/coding schemes of the V2V communications, in order to guarantee the latency and reliability requirements of vehicular user equipments (VUEs) while maximizing the information rate of cellular user equipment (CUE). To ensure the solvability of this optimization problem, the packet latency constraint is first transformed into a data rate constraint based on random network analysis by adopting the Poisson distribution model for the packet arrival process of each VUE. Then, utilizing the Lagrange dual decomposition and binary search, a resource management algorithm is proposed to find the optimal solution of joint optimization problem with reasonable complexity. Simulation results show that the proposed radio resource management scheme can reduce the interference from V2V communication to CUEs and ensure the latency and reliability requirements of V2V communication.

It is expected that Device-to-Device (D2D) communication is allowed to underlay future cellular networks such as IMT-Advanced for spectrum efficiency. However, by reusing the uplink spectrums with the cellular system, the interference to D2D users has to be addressed to maximize the overall system performance. In this paper, a novel method to deal with the resource allocation and interference avoidance issues by utilizing the network peculiarity of a hybrid network to share the uplink resource is proposed and the implementation details are described in a real cellular system. Simulation results prove that satisfying performance can be achieved by using the proposed mechanism.

A mobile wireless sensor network owes its name to the presence of mobile sink or sensor nodes within the network. The advantages of mobile WSN over static WSN are better energy efficiency, improved coverage, enhanced target tracking and superior channel capacity. In this paper we present and discuss hierarchical multi-tiered architecture for mobile wireless sensor network. This architecture is proposed for the future pervasive computing age. We also elaborate on the impact of mobility on different performance metrics in mobile WSN. A study of some of the possible application scenarios for pervasive computing involving mobile WSN is also presented. These application scenarios will be discussed in their implementation context. While discussing the possible applications, we also study related technologies that appear promising to be integrated with mobile WSN in the ubiquitous computing. With an enormous growth in number of cellular subscribers, we therefore place the mobile phone as the key element in future ubiquitous wireless networks. With the powerful computing, communicating and storage capacities of these mobile devices, the network performance can benefit from the architecture in terms of scalability, energy efficiency and packet delay, etc.

This paper presents an effective scheduling scheme called semi-persistent scheduling for VoIP service in LTE system. The main challenges of effectively supporting VoIP service in LTE system are 1) the tight delay requirement combined with the frequent arrival of small packets of VoIP traffic and 2) the scarcity of radio resources along with control channel restriction in LTE system. Simulation results show that semi-persistent scheduling can support high system capacity while at the same time guaranteeing the QoS requirements such as packet delay and packet loss rate of VoIP. Furthermore, semi- persistent scheduling requires less control signaling overhead which is very important for efficient resources utilization in a practical system.

Opportunistic routing has recently attracted much attention as it is considered a promising direction for improving the performance of wireless ad hoc and sensor networks. With opportunistic routing, intermediate nodes collaborate on packet forwarding in a localized and consistent manner. Opportunistic routing greatly increases transmission reliability and network throughput by taking advantage of the broadcast nature of the wireless medium. In this article we first illustrate the basic idea behind opportunistic routing, and then categorize current research work based on different criteria. We illustrate how different protocols work, and discuss their merits and drawbacks. Finally, we point out potential issues and future directions in opportunistic routing for wireless ad hoc and sensor networks.

Narrowband Internet of Things (NB-IoT) is a new narrow-band radio technology introduced in the Third Generation Partnership Project release 13 to the 5th generation evolution for providing low-power wide-area IoT. In NB-IoT systems, repeating transmission data or control signals has been considered as a promising approach for enhancing coverage. Considering the new feature of repetition, link adaptation for NB-IoT systems needs to be performed in 2-D, i.e., the modulation and coding scheme (MCS) and the repetition number. Therefore, existing link adaptation schemes without consideration of the repetition number are no longer applicable. In this paper, a novel uplink link adaptation scheme with the repetition number determination is proposed, which is composed of the inner loop link adaptation and the outer loop link adaptation, to guarantee transmission reliability and improve throughput of NB-IoT systems. In particular, the inner loop link adaptation is designed to cope with block error ratio variation by periodically adjusting the repetition number. The outer loop link adaptation coordinates the MCS level selection and the repetition number determination. Besides, key technologies of uplink scheduling, such as power control and transmission gap, are analyzed, and a simple single-tone scheduling scheme is proposed. Link-level simulations are performed to validate the performance of the proposed uplink link adaptation scheme. The results show that our proposed uplink link adaptation scheme for NB-IoT systems outperforms the repetition-dominated method and the straightforward method, particularly for good channel conditions and larger packet sizes. Specifically, it can save more than 14% of the active time and resource consumption compared with the repetition-dominated method and save more than 46% of the active time and resource consumption compared with the straightforward method.

For heterogeneous network, which has been viewed as one pioneering technology for making cellular networks be evolved into 5G systems, reducing energy consumption by dynamically switching off base stations (BSs) has attracted increasing attention recently. With aiming at optimization on energy saving only or another energy-related performance tradeoffs, several BS switch-off strategies have been proposed from different design perspectives, such as random, distance-aware, load-aware, and auction-based strategies. Furthermore, work has been done to consider joint design for BS switch-off strategy and another strategies, such as user association, resource allocation, and physical-layer interference cancellation strategies. Finally, there have been research results about this topic in emerging cloud radio access networks. In this paper, we take an overview on these technologies and present the state of the art on each aspect. Some challenges that need to be solved in this research filed for future work are also described.

WiFi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing WiFi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, WiFi clients must actively discover new WiFi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFi that utilizes ZigBee radios to identify the existence of WiFi networks through unique interference signatures generated by WiFi beacons. We develop a new digital signal processing algorithm called Common Multiple Folding (CMF) that accurately amplifies periodic beacons in WiFi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of WiFi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect WiFi APs with high accuracy (<5% total FP and FN rate), short delay (~780 ms), and little computation overhead

Being a part of IEEE project 802, the 802.11 medium access control (MAC) is used to support asynchronous and time bounded delivery of radio data packets. It is proposed that a distributed coordination function (DCF), which uses carrier sense multiple access with collision avoidance (CSMA/CA) and binary slotted exponential backoff, be the basis of the IEEE 802.11 WLAN MAC protocols. This paper proposes a throughput enhancement mechanism for DCF by adjusting the contention window (CW) resetting scheme. Moreover, an analytical model based on Markov chain is introduced to compute the enhanced throughput of 802.11 DCF. The accuracy of the model and the enhancement of the proposed scheme are verified by elaborate simulations.

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

Recognizing the transportation modes of people's daily living is an important research issue in the pervasive computing. Prior research in this field mainly uses Global Positioning System (GPS), Global System for Mobile Communications (GSM) or their combination with accelerometer to recognize transportation modes, such as walking, driving, etc. In this paper, we will introduce transportation mode recognition on mobile phones only using embedded accelerometer. In order to deal with uncertainty of position and orientation of mobile phone, acceleration synthesization based method and acceleration decomposition based method are introduced. Performance comparison indicates that acceleration synthesization based method outperforms acceleration decomposition based method. We will discuss the factors affect the recognition accuracy of acceleration decomposition based method and present potential improvements.