Showing papers by "Huawei published in 2015"

Audio augmentation for speech recognition.

Abstract: Data augmentation is a common strategy adopted to increase the quantity of training data, avoid overfitting and improve robustness of the models. In this paper, we investigate audio-level speech augmentation methods which directly process the raw signal. The method we particularly recommend is to change the speed of the audio signal, producing 3 versions of the original signal with speed factors of 0.9, 1.0 and 1.1. The proposed technique has a low implementation cost, making it easy to adopt. We present results on 4 different LVCSR tasks with training data ranging from 100 hours to 1000 hours, to examine the effectiveness of audio augmentation in a variety of data scenarios. An average relative improvement of 4.3% was observed across the 4 tasks.

Neural Responding Machine for Short-Text Conversation

Abstract: We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoderdecoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming stateof-the-arts in the same setting, including retrieval-based and SMT-based models.

Convolutional Neural Network Architectures for Matching Natural Language Sentences

Abstract: Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

On Global Electricity Usage of Communication Technology: Trends to 2030

Abstract: This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

Matching theory for future wireless networks: fundamentals and applications

Abstract: The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.

Massive individual orbital angular momentum channels for multiplexing enabled by Dammann gratings

Abstract: Data transmission rates in optical communication systems are approaching the limits of conventional multiplexing methods. Orbital angular momentum (OAM) in optical vortex beams offers a new degree of freedom and the potential to increase the capacity of free-space optical communication systems, with OAM beams acting as information carriers for OAM division multiplexing (OAM-DM). We demonstrate independent collinear OAM channel generation, transmission and simultaneous detection using Dammann optical vortex gratings (DOVGs). We achieve 80/160 Tbit s−1 capacity with uniform power distributions along all channels, with 1600 individually modulated quadrature phase-shift keying (QPSK)/16-QAM data channels multiplexed by 10 OAM states, 80 wavelengths and two polarizations. DOVG-enabled OAM multiplexing technology removes the bottleneck of massive OAM state parallel detection and offers an opportunity to raise optical communication systems capacity to Pbit s−1 level. Dammann gratings are used to realize multiplexing based on the generation, transmission and detection of optical angular momentum (OAM). The OAM of optical vortex beams offers a new degree of freedom for multiplexing and hence the promise of higher data communication rates, but massive parallel detection of OAM states has proved challenging. Now, researchers in China, Australia and Singapore have used Dammann optical vortex gratings (DOVGs) to realize multiplexing of massive OAM channels with individual modulation and simultaneous detection capabilities. They achieved a data capacity of 80 Tbit s−1 by multiplexing 1600 channels using ten OAM states, 80 wavelengths and two polarizations. This DOVG-enabled OAM multiplexing technology removes the bottleneck of massive parallel detection of OAM states and has the potential to increase optical communication capacities to the Pbit s−1 level.

Filtered OFDM: A new waveform for future wireless systems

Abstract: A spectrally-localized waveform is proposed based on filtered orthogonal frequency division multiplexing (f-OFDM). By allowing the filter length to exceed the cyclic prefix (CP) length of OFDM and designing the filter appropriately, the proposed f-OFDM waveform can achieve a desirable frequency localization for bandwidths as narrow as a few tens of subcarriers, while keeping the inter-symbol interference/inter-carrier interference (ISI/ICI) within an acceptable limit. Enabled by the proposed f-OFDM, an asynchronous filtered orthogonal frequency division multiple access (f-OFDMA)/filtered discrete-Fourier transform-spread OFDMA (f-DFT-S-OFDMA) scheme is introduced, which uses the spectrum shaping filter at each transmitter for side lobe leakage elimination and a bank of filters at the receiver for inter-user interference rejection. Per-user downsampling and short fast Fourier transform (FFT) are used at the receiver to ensure a reasonable complexity of implementation. The proposed scheme removes the inter-user time-synchronization overhead required in the synchronous OFDMA/DFT-S-OFDMA. The performance of the asynchronous f-OFDMA is evaluated and compared with that of the universal-filtered OFDM (UF-OFDM), proposed in [1], [2].

Neural Responding Machine for Short-Text Conversation

Abstract: We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.

Multimodal Convolutional Neural Networks for Matching Image and Sentence

Abstract: In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content, and one matching CNN learning the joint representation of image and sentence. The matching CNN composes words to different semantic fragments and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results on benchmark databases of bidirectional image and sentence retrieval demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. Specifically, our proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and Microsoft COCO databases achieve the state-of-the-art performances.

Multimodal Convolutional Neural Networks for Matching Image and Sentence

Abstract: In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.

Experimental study of PAM-4, CAP-16, and DMT for 100 Gb/s Short Reach Optical Transmission Systems

Abstract: Advanced modulation formats combined with digital signal processing and direct detection is a promising way to realize high capacity, low cost and power efficient short reach optical transmission system. In this paper, we present a detailed investigation on the performance of three advanced modulation formats for 100 Gb/s short reach transmission system. They are PAM-4, CAP-16 and DMT. The detailed digital signal processing required for each modulation format is presented. Comprehensive simulations are carried out to evaluate the performance of each modulation format in terms of received optical power, transmitter bandwidth, relative intensity noise and thermal noise. The performance of each modulation format is also experimentally studied. To the best of our knowledge, we report the first demonstration of a 112 Gb/s transmission over 10km of SSMF employing single band CAP-16 with EML. Finally, a comparison of computational complexity of DSP for the three formats is presented.

Pilot Reuse for Massive MIMO Transmission over Spatially Correlated Rayleigh Fading Channels

Abstract: We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For spatially correlated Rayleigh fading channels, we establish a relationship between channel spatial correlations and channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we show that sum mean square error (MSE) of channel estimation can be minimized provided that channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows feasibility of PR over spatially correlated massive MIMO channels with constrained channel angular spreads. Regarding that channel estimation performance might degrade due to PR, we also develop the closed-form robust multiuser uplink receiver and downlink precoder that minimize sum MSE of signal detection, and reveal a duality between them. Subsequently, we investigate pilot scheduling, which determines the PR pattern, under two minimum MSE related criteria, and propose a low complexity pilot scheduling algorithm which relies on the channel statistics only. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of net spectral efficiency.

Pilot Reuse for Massive MIMO Transmission over Spatially Correlated Rayleigh Fading Channels

Abstract: We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For spatially correlated Rayleigh fading channels, we establish a relationship between channel spatial correlations and channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we show that sum mean square error (MSE) of channel estimation can be minimized provided that channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows feasibility of PR over spatially correlated massive MIMO channels with constrained channel angular spreads. Since channel estimation performance might degrade due to PR, we also develop the closed-form robust multiuser uplink receiver and downlink precoder that minimize sum MSE of signal detection, and reveal a duality between them. Subsequently, we investigate pilot scheduling, which determines the PR pattern, under two minimum MSE related criteria, and propose a low complexity pilot scheduling algorithm, which relies on the channel statistics only. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of net spectral efficiency.

Analysis on Cache-enabled Wireless Heterogeneous Networks

Abstract: Caching the popular multimedia content is a promising way to unleash the ultimate potential of wireless networks. In this paper, we contribute to proposing and analyzing the cache-based content delivery in a three-tier heterogeneous network (HetNet), where base stations (BSs), relays and device-to-device (D2D) pairs are included. We advocate to proactively cache the popular contents in the relays and parts of the users with caching ability when the network is off-peak. The cached contents can be reused for frequent access to offload the cellular network traffic. The node locations are first modeled as mutually independent Poisson Point Processes (PPPs) and the corresponding content access protocol is developed. The average ergodic rate and outage probability in the downlink are then analyzed theoretically. We further derive the throughput and the delay based on the \emph{multiclass processor-sharing queue} model and the continuous-time Markov process. According to the critical condition of the steady state in the HetNet, the maximum traffic load and the global throughput gain are investigated. Moreover, impacts of some key network characteristics, e.g., the heterogeneity of multimedia contents, node densities and the limited caching capacities, on the system performance are elaborated to provide a valuable insight.

Robust sound event classification using deep neural networks

Abstract: The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.

Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis

Abstract: The use of drone small cells (DSCs) which are aerial wireless base stations that can be mounted on flying devices such as unmanned aerial vehicles (UAVs), is emerging as an effective technique for providing wireless services to ground users in a variety of scenarios. The efficient deployment of such DSCs while optimizing the covered area is one of the key design challenges. In this paper, considering the low altitude platform (LAP), the downlink coverage performance of DSCs is investigated. The optimal DSC altitude which leads to a maximum ground coverage and minimum required transmit power for a single DSC is derived. Furthermore, the problem of providing a maximum coverage for a certain geographical area using two DSCs is investigated in two scenarios; interference free and full interference between DSCs. The impact of the distance between DSCs on the coverage area is studied and the optimal distance between DSCs resulting in maximum coverage is derived. Numerical results verify our analytical results on the existence of optimal DSCs altitude/separation distance and provide insights on the optimal deployment of DSCs to supplement wireless network coverage.

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Abstract: Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public 1 . This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.

Horizon 2020 and Beyond: On the 5G Operating System for a True Digital Society

Abstract: The advanced fifth-generation (5G) infrastructure will not only be a sheer evolution of the current network generations but, more significantly, a revolution in the information and communication technology (ICT) field. The 5G technology will efficiently enable new secure, dependable, ultrareliable, and delay-critical services to everyone and everything, such as cognitive objects and cyberphysical systems (CPSs). A fully immersive experience and anything as a service are the primary drivers for a global adoption and market uptake of new technology components, beyond today's client-server model, where the network has been reduced to a mere pipe of bits. The network will become the nervous system of the true digital society and economy. This article gives fundamental insight into how the 5G mobile communications system is being designed to be powerful and, especially, flexible enough, thus meeting the foreseen and unknown traffic scenarios and services requirements. We also present how a massive adoption and exploitation of mobile-edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and services virtualization will make the 5G operating system (OS) feasible and business viable.

EASE: EPC as a service to ease mobile core network deployment over cloud

Abstract: The objective of this article is to demonstrate the feasibility of on-demand creation of cloud-based elastic mobile core networks, along with their lifecycle management. For this purpose the article describes the key elements to realize the architectural vision of EPC as a Service, an implementation option of the Evolved Packet Core, as specified by 3GPP, which can be deployed in cloud environments. To meet several challenging requirements associated with the implementation of EPC over a cloud infrastructure and providing it "as a Service," this article presents a number of different options, each with different characteristics, advantages, and disadvantages. A thorough analysis comparing the different implementation options is also presented.

Rapier: Integrating routing and scheduling for coflow-aware data center networks

Abstract: In the data flow models of today's data center applications such as MapReduce, Spark and Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in a coflow is meaningful to an application. To optimize application performance, routing and scheduling must be jointly considered at the level of a coflow rather than individual flows. However, prior solutions have significant limitation: they only consider scheduling, which is insufficient. To this end, we present Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance. Using a small-scale testbed implementation and large-scale simulations, we demonstrate that Rapier significantly reduces the average coflow completion time (CCT) by up to 79.30% compared to the state-of-the-art scheduling-only solution, and it is readily implementable with existing commodity switches.

Beam-searching and transmission scheduling in millimeter wave communications

Abstract: Millimeter wave (mmWave) wireless networks rely on narrow beams to support multi-gigabit data rates. Nevertheless, the alignment of transmitter and receiver beams is a timeconsuming operation, which introduces an alignment-throughput tradeoff. A wider beamwidth reduces the alignment overhead, but leads also to reduced directivity gains. Moreover, existing mmWave standards schedule a single transmission in each time slot, although directional communications facilitate multiple concurrent transmissions. In this paper, a joint consideration of the problems of beamwidth selection and scheduling is proposed to maximize effective network throughput. The resulting optimization problem requires exact knowledge of network topology, which may not be available in practice. Therefore, two standardcompliant approximation algorithms are developed, which rely on underestimation and overestimation of interference. The first one aims to maximize the reuse of available spectrum, whereas the second one is a more conservative approach that schedules together only links that cause no interference. Extensive performance analysis provides useful insights on the directionality level and the number of concurrent transmissions that should be pursued. Interestingly, extremely narrow beams are in general not optimal.

Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing

Abstract: Creating context-aware ad hoc collaborative systems remains to be one of the primary hurdles hampering the ubiquitous deployment of IT and communication services Especially under mission-critical scenarios, these services must often adhere to strict timing deadlines We believe empowering such realtime collaboration systems requires context-aware application platforms working in conjunction with ultra-low latency data transmissions In this paper, we make a strong case that this could be accomplished by combining the novel communication architectures being proposed for 5G with the principles of Mobile Edge Computing (MEC) We show that combining 5G with MEC would enable inter- and intra-domain use cases that are otherwise not feasible

Self-adaptive hierarchical sentence model

Abstract: The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). AdaSent effectively forms a hierarchy of representations from words to phrases and then to sentences through recursive gated local composition of adjacent segments. We design a competitive mechanism (through gating networks) to allow the representations of the same sentence to be engaged in a particular learning task (e.g., classification), therefore effectively mitigating the gradient vanishing problem persistent in other recursive models. Both qualitative and quantitative analysis shows that AdaSent can automatically form and select the representations suitable for the task at hand during training, yielding superior classification performance over competitor models on 5 benchmark data sets.

Iterative multiuser receiver in sparse code multiple access systems

Abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%.

On the "Naturalness" of Buggy Code

Abstract: Real software, the kind working programmers produce by the kLOC to solve real-world problems, tends to be "natural", like speech or natural language; it tends to be highly repetitive and predictable. Researchers have captured this naturalness of software through statistical models and used them to good effect in suggestion engines, porting tools, coding standards checkers, and idiom miners. This suggests that code that appears improbable, or surprising, to a good statistical language model is "unnatural" in some sense, and thus possibly suspicious. In this paper, we investigate this hypothesis. We consider a large corpus of bug fix commits (ca.~8,296), from 10 different Java projects, and we focus on its language statistics, evaluating the naturalness of buggy code and the corresponding fixes. We find that code with bugs tends to be more entropic (i.e., unnatural), becoming less so as bugs are fixed. Focusing on highly entropic lines is similar in cost-effectiveness to some well-known static bug finders (PMD, FindBugs) and ordering warnings from these bug finders using an entropy measure improves the cost-effectiveness of inspecting code implicated in warnings. This suggests that entropy may be a valid language-independent and simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.

Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs

Abstract: In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide on the fly wireless communications to a given geographical area is analyzed. In particular, the co-existence between the UAV, that is transmitting data in the downlink, and an underlaid device-todevice (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, optimal values for the UAV altitude exist for which the system sum-rate and the coverage probability are maximized. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, is minimized. Finally, in order to provide a full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.

Controller, flash memory apparatus, and method for writing data into flash memory apparatus

Abstract: A storage controller for determining an amount of data to be sent to a flash memory apparatus for storage comprises a communications interface for communicating with the flash memory apparatus and a processor. The flash memory apparatus comprises a block including a plurality of pages. And at least one of the pages is unavailable for storage. The processor is configured to receive information of the block sent by the flash memory apparatus, wherein the information includes capacity of one or more unavailable pages in the block. And then, the processor determines an available capacity of the block, based on the information and a total capacity of the block. Further, the processor obtains data to be sent to the flash memory apparatus, wherein an amount of the data is equal to the available capacity of the block. At last, the processor sends the data to the flash memory apparatus.

Age of information of multiple sources with queue management

Abstract: We consider a system of multiple sources generating status update packets, which need to be sent by a single transmitter to a destination over a network. In the model we study, the packet generation time may vary at each source, and the packets go through the network with a random delay. Each update carries a time stamp of its generation, allowing the destination to calculate for each source the so called Age of Information, which measures the timeliness of each status update arriving. Considering that queuing delay can unnecessarily increase the age of a critical status update, we propose here a queue management technique, in which we maintain a queue with only the latest status packet of each source, overwriting any previously queued update from that source. This simple technique drastically limits the need for buffering and can be applied in systems where the history of source status is not relevant. We show that this scheme results in significantly less transmissions compared to the standard M/M/1 queue model. Furthermore, the proposed technique reduces the per source age of information, especially in settings not using queue management with high status update generation rates.

Robust Cooperative Beamforming and Artificial Noise Design for Physical-Layer Secrecy in AF Multi-Antenna Multi-Relay Networks

Abstract: This paper is concerned with an optimization problem in a two-hop relay wiretap channel, wherein multiple multi-antenna relays collaboratively amplify and forward (AF) information from a single-antenna source to a single-antenna destination, and at the same time emit artificial noise (AN) to improve physical-layer information security in the presence of multiple multi-antenna eavesdroppers (or Eves). More specifically, the problem is to simultaneously optimize the AF matrices and AN covariances for secrecy rate maximization, with robustness against imperfect channel state information of Eves via a worst-case robust formulation. Such a problem is nonconvex, and we propose a polynomial-time optimization solution based on a two-level optimization approach and semidefinite relaxation (SDR). In particular, while SDR is generally an approximation technique, we prove that SDR is optimal in the specific context here. This desirable result is obtained by careful reformulation and Karush-Kuhn-Tucker optimality analysis, where, rather interestingly, AN is found to be instrumental in providing guarantee of SDR optimality. Simulation results are provided, and the results show that the proposed joint AF-AN solution can attain considerably higher achievable secrecy rates than some existing suboptimal designs.

Software-defined internet of things for smart urban sensing

Abstract: With more people living in cities, urban sensing is urgently required to create a comfortable and convenient living environment. As Internet of Things (IoT) is the fundamental infrastructure to realize urban sensing, it should be flexible to support various application requirements and convenient management of infrastructure. Inspired by software-defined networking, which aims to make networks more flexible, the authors propose a software-defined IoT architecture for smart urban sensing. This architecture decouples urban sensing applications from the physical infrastructure. Centralized controllers are designed to manage physical devices and provide APIs of data acquisition, transmission, and processing services to develop urban sensing applications. With these properties, various applications can coexist on the shared infrastructure, and each application can request controllers to customize its data acquisition, transmission, and processing on-demand by generating specific configurations of physical devices. This article discusses the background, benefits, and design details of the proposed architecture as well as open problems and potential solutions to realize it, which opens a new research direction for IoT and urban sensing.