Huawei Technologies (United States)

Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to detect and avoid. When detecting individual functionality-specific vulnerabilities, the existing two categories of approaches are ineffective because they consider only the API bodies and are unable to handle diverse implementations of functionality-equivalent APIs. To effectively detect functionality-specific vulnerabilities, we propose APISS, the first approach to utilize API doc strings and signatures instead of API bodies. APISS first retrieves functionality-equivalent APIs for APIs with existing vulnerabilities and then migrates Proof-of-Concepts (PoCs) of the existing vulnerabilities for newly detected vulnerable APIs. To retrieve functionality-equivalent APIs, we leverage a Large Language Model for API embedding to improve the accuracy and address the effectiveness and scalability issues suffered by the existing approaches. To migrate PoCs of the existing vulnerabilities for newly detected vulnerable APIs, we design a semi-automatic schema to substantially reduce manual costs. We conduct a comprehensive evaluation to empirically compare APISS with four state-of-the-art approaches of detecting vulnerabilities and two state-of-the-art approaches of retrieving functionality-equivalent APIs. The evaluation subjects include 180 widely used Java repositories using 10 existing vulnerabilities, along with their PoCs. The results show that APISS effectively retrieves functionality-equivalent APIs, achieving a Top-1 Accuracy of 0.81 while the best of the baselines under comparison achieves only 0.55. APISS is highly efficient: the manual costs are within 10 minutes per vulnerability and the end-to-end runtime overhead of testing one candidate API is less than 2 hours. APISS detects 179 new vulnerabilities and receives 60 new CVE IDs, bringing high value to security practice.

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This article provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information, that is, whether the processing adapts to the instantaneous or average (second-order) channel state information; while the former provides somewhat better signal- to-noise and interference ratio, the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.

Due to the increased popularity of augmented and virtual reality experiences, the interest in capturing the real world in multiple dimensions and in presenting it to users in an immersible fashion has never been higher. Distributing such representations enables users to freely navigate in multisensory 3D media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today's networks. Efficient compression technologies well adopted in the content chain are in high demand and are key components to democratize augmented and virtual reality applications. Moving Picture Experts Group, as one of the main standardization groups dealing with multimedia, identified the trend and started recently the process of building an open standard for compactly representing 3D point clouds, which are the 3D equivalent of the very well-known 2D pixels. This paper introduces the main developments and technical aspects of this ongoing standardization effort.

Many network applications, e.g., industrial control, demand ultra-low latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support. This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

The next-generation passive optical network stage 2 (NG-PON2) effort was initiated by the full service access network (FSAN) in 2011 to investigate on upcoming technologies enabling a bandwidth increase beyond 10 Gb/s in the optical access network. The FSAN meeting in April 2012 selected the time- and wavelength-division multiplexed passive optical network (TWDM-PON) as a primary solution to NG-PON2. In this paper, we summarize the TWDM-PON research in FSAN by reviewing the basics of TWDM-PON and presenting the world's first full-system 40 Gb/s TWDM-PON prototype. After introducing the TWDM-PON architecture, we explore TWDM-PON wavelength plan options to meet the NG-PON2 requirements. TWDM-PON key technologies and their respective level of development are further discussed to investigate its feasibility and availability. The first full-system 40 Gb/s TWDM-PON prototype is demonstrated to provide 40 Gb/s downstream and 10 Gb/s upstream bandwidth. This full prototype system offers 38 dB power budget and supports 20 km distance with a 1:512 split ratio. It coexists with commercially deployed Gigabit PON (G-PON) and 10 Gigabit PON (XG-PON) systems. The operator-vendor joint test results testify that TWDM-PON is achievable by the reuse and integration of commercial devices and components.

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.

A flexible and programmable forwarding plane is essential to maximize the value of Software-Defined Networks (SDN). In this paper, we propose Protocol-Oblivious Forwarding (POF) as a key enabler for highly flexible and programmable SDN. Our goal is to remove any dependency on protocol-specific configurations on the forwarding elements and enhance the data-path with new stateful instructions to support genuine software defined networking behavior. A generic flow instruction set (FIS) is defined to fulfill this purpose. POF helps to lower network cost by using commodity forwarding elements and to create new value by enabling numerous innovative network services. We built both hardware-based and open source software-based prototypes to demonstrate the feasibility and advantages of POF. We report the preliminary evaluation results and the insights we learnt from the experiments. POF is future-proof and expressive. We believe it represents a promising direction to evolve the OpenFlow protocol and the future SDN forwarding elements.

Advanced interference mitigation techniques relying on multipoint coordination have attracted significant attention from the wireless industry and academia in the past few years. In 3GPP LTE-Advanced, a work item on Coordinated Multiple Point transmission and reception (CoMP) was initiated in September 2011, and it is one of the core features of Release 11. The objective of this work item is to provide the necessary specification support to efficiently realize the benefits of cooperative transmission in the downlink and cooperative reception in the uplink. This article discusses the specification support for CoMP and the motivations behind the specific design choices. The deployment scenarios that were considered for the application of CoMP in Release 11 are also presented.

Distributed antenna systems (DAS) augment the base station's transmit capability by adding multiple remote radio units, connected to the base station via a high bandwidth and low latency link. With DAS, the base station operates as if it had multiple antennas, but the antennas happen to be in different geographic locations. DAS have been shown to enhance coverage and capacity in cellular systems, in a variety of different configurations. This paper proposes, analyzes, and compares several downlink multiuser multiple input multiple output (MIMO) DAS strategies in terms of per-user throughput and area spectral efficiency. Zero-forcing transmit beamforming is used for transmission, the remote radio units may have one or more antennas, and the subscriber has a single receive antenna. Techniques considered include beamforming across all remote radio units (full transmission), using the same beamforming vector for each remote radio unit (simplified transmission), and selecting a subset of remote radio units. To facilitate rapid simulation and design space exploration, approximations of the ergodic rate are proposed for each technique assuming path-loss, small-scale Rayleigh fading, and out-of-cell interference. Simulations accounting for multiple interfering cells are used to compare the different transmission techniques. Full transmission is found to have the best performance even accounting for out-of-cell interference, though gains diminish for higher numbers of active users. Simplified transmission improves over no DAS but performance degrades with more active remote radio units.

In this paper, we propose a new data-gathering mechanism for large-scale wireless sensor networks by introducing mobility into the network. A mobile data collector, for convenience called an M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and refer to this as the single-hop data-gathering problem (SHDGP). We first formalize the SHDGP into a mixed-integer program and then present a heuristic tour-planning algorithm for the case where a single M-collector is employed. For the applications with strict distance/time constraints, we consider utilizing multiple M-collectors and propose a data-gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time constraints. Our single-hop mobile data-gathering scheme can improve the scalability and balance the energy consumption among sensors. It can be used in both connected and disconnected networks. Simulation results demonstrate that the proposed data-gathering algorithm can greatly shorten the moving distance of the collectors compared with the covering line approximation algorithm and is close to the optimal algorithm for small networks. In addition, the proposed data-gathering scheme can significantly prolong the network lifetime compared with a network with static data sink or a network in which the mobile collector c- n only move along straight lines.

Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.

This paper reviews thermal management challenges encountered in a wide range of electronics cooling applications from large-scale (data center and telecommunication) to small-scale systems (personal, portable/wearable, and automotive). This paper identifies drivers for progress and immediate and future challenges based on discussions at the 3rd Workshop on Thermal Management in Telecommunication Systems and Data Centers held in Redwood City, CA, USA, on November 4-5, 2015. Participants in this workshop represented industry and academia, with backgrounds ranging from data center thermal management and energy efficiency to high-performance computing and liquid cooling, thermal management in wearable and mobile devices, and acoustic noise management. By considering a wide range of electronics cooling applications with different lengths and time scales, this paper identifies both common themes and diverging views in the thermal management community.

Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling sequential data, recent works utilize RNNs to model human-skeleton motions on the observed motion sequence and predict future human motions. However, these methods disregard the existence of the spatial coherence among joints and the temporal evolution among skeletons, which reflects the crucial characteristics of human motions in spatiotemporal space. To this end, we propose a novel Skeleton-Joint Co-Attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space. First, a skeleton-joint feature map is constructed as the representation of the observed motion sequence. Second, we design a new Skeleton-Joint Co-Attention (SCA) mechanism to dynamically learn a skeleton-joint co-attention feature map of this skeleton-joint feature map, which can refine the useful observed motion information to predict one future motion. Third, a variant of GRU embedded with SCA collaboratively models the human-skeleton motion and human-joint motion in spatiotemporal space by regarding the skeleton-joint co-attention feature map as the motion context. Experimental results of human motion prediction demonstrate that the proposed method outperforms the competing methods.

Passive optical networks are the most important class of fiber access systems in the world today. This article first reviews the reasons why the PON as a general architecture is so important. We then outline in some depth the technologies used to implement this architecture, including the G-PON and E-PON systems being deployed today, and the advanced PON systems that provide the evolution path to ever higher bandwidths

In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to reduce complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.

Mobile IPv6 (MIPv6; RFC 3775) provides a mobile node with IP mobility when it performs a handover from one access router to another, and fast handovers for Mobile IPv6 (FMIPv6) are specified to enhance the handover performance in terms of latency and packet loss. While MIPv6 (and FMIPv6 as well) requires the participation of the mobile node in the mobility-related signaling, Proxy Mobile IPv6 (PMIPv6; RFC 5213) provides IP mobility to nodes that either have or do not have MIPv6 functionality without such involvement. Nevertheless, the basic performance of PMIPv6 in terms of handover latency and packet loss is considered no different from that of MIPv6. When the fast handover is considered in such an environment, several modifications are needed to FMIPv6 to adapt to the network-based mobility management. This document specifies the usage of fast handovers for Mobile IPv6 (FMIPv6; RFC 5568) when Proxy Mobile IPv6