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In this paper, belief propagation (BP) detection in real domain for large-scale multiple-in multiple-out (MIMO) systems is proposed. The mathematical analysis of message updating rules for independent identically distributed (i.i.d.) and correlated fading MIMO channels are given in detail. The damped BP with damping on the a priori probability vector is employed to improve the performance for the uplink large-scale MIMO systems with correlation among transmitting antennas or loading factor ρ = 1. Based on the convergence analysis, the method of selecting message damping factor δ is presented also. In addition, the adaptive message updating for BP detection is first proposed to provide a good trade-off between performance and complexity. Simulation results have shown that, for 16 × 16 MIMO with quadrature phase shift keying (QPSK) modulation, this approach can show 1 dB performance improvement at BER of 10−2, compared to complex domain single-edge based BP (SE-BP). For 8 × 32 MIMO with correlation among transmitting and receiving antennas, where 16-Quadrature Amplitude Modulation (16-QAM) is employed, simulation results have shown that the proposed adaptive BP detection achieves a complexity reduction of 50% compared to the general BP detection with negligible performance loss. For i.i.d. and correlated fading channels with various antennas configurations, advantages of the proposed approach over existing BP detections as well as MMSE approach have been demonstrated by thorough simulations. Hence, the proposed BP detection is suitable for large-scale MIMO systems, especially for those with high-order modulations. Furthermore, the adaptive BP detection together with message damping is expected to be a good choice for low complexity detection.

With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: spectrum efficiency (SE) versus energy efficiency (EE), deployment efficiency (DE) versus energy efficiency (EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing (OFDM) and non-orthogonal aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous networks (HetNets). We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks., Comment: revised from IEEE Communications Surveys & Tutorials

Nowadays real-time traffic occupies lots of network resources, thus identification and analysis for this network traffic becomes urgent for operator and commercial company. With the identified traffic types, quality of experience (QoE) monitoring and optimization, user behavior analysis and network resource allocation are more beneficial for open 5G/Beyond 5G (5G/B5G) networks. Exiting studies usually adopt transport or application layer information to identify traffic, while we jointly consider them simultaneously to achieve general purpose identifier. Besides, we also analyze the flow-based features to reduce the corresponding complexity for low-complexity implementation. Based on anatomy of network traffic identification, we propose a traffic type identification framework for real-time traffic. In mainstream voice over Internet protocol (VoIP) call and video streaming services, the proposed method can achieve as much as 30% identification accuracy improvement and have more than 20% reduction in terms of the identification delay if compared with other conventional schemes.

Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload in heterogeneous networks. Prior works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a composite framework consists of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and users respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviors of users. A new algorithm based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q- network (DQN) algorithm are respectively applied to users and SBS due to different complexity of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables users' devices and SBS to prefetch the optimal files while learning, and hence reducing the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively., 5 pages, 7 figures, conference

Environmental Sound Classification (ESC) is an important and challenging problem, and feature representation is a critical and even decisive factor in ESC. Feature representation ability directly affects the accuracy of sound classification. Therefore, the ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. In this paper, we propose a subspectrogram segmentation based ESC classification framework. In addition, we adopt the proposed Convolutional Recurrent Neural Network (CRNN) and score level fusion to jointly improve the classification accuracy. Extensive truncation schemes are evaluated to find the optimal number and the corresponding band ranges of sub-spectrograms. Based on the numerical experiments, the proposed framework can achieve 81.9% ESC classification accuracy on the public dataset ESC-50, which provides 9.1% accuracy improvement over traditional baseline schemes., accepted in the 2019 IEEE International Workshop on Signal Processing Systems (SiPS2019)

The key objective of the fifth generation (5G) wireless technology is to support services with vastly variable requirements, which necessitates the flexible numerology and frame structure for radio resource allocation. In this paper, flexible 2-dimensional resource allocation is investigated to maximize the energy efficiency (EE) for service transmissions with heterogeneous latency requirements. Exploiting the frequency and time diversities of the resource grid, frequency-selective resource allocation, together with an agile "on-off" operation of the power amplifier (PA) are performed by the proposed sliding window-based (SW) algorithm with scalability. Simulation results further demonstrate that the SW algorithm can achieve similar EE performance as the exhaustive method with a substantially lower complexity.

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.

Although almost blank subframes (ABS) proposed in heterogeneous networks (HetNet) can enhance the performance of user equipments (UEs) in Pico-cell range expansion (CRE) area, it also significantly degrades the Macro-cell throughput. To address this issue, further-enhanced inter-cell interference coordination (FeICIC) scheme is considered in 3GPP Release 11, where low power ABS (LP-ABS) are adopted for the Macro-cell center region users to improve the Macro-cell throughput. However, LP-ABS power, Pico CRE bias and Pico base station (PBS) density will jointly affect on the system performance, which eventually deteriorates the network energy efficiency (EE) without careful configuration. In this paper, we first deduce the closed-form expression of network EE as a function of PBS density, Pico CRE bias and LP-ABS power based on stochastic geometry model. Then we provide Monte Carlo simulations to verify the accuracy of theoretical derivation of the network EE and analyze the impacts of these parameters on the network EE. The simulation results show that the reasonable PBS density, Pico CRE bias and LP-ABS power can improve the network EE obviously.

In this paper, a pre-RTL neural network simulator (SimuNN) is proposed which is initiated as the bridge between the algorithm design and hardware implementation of neural networks. SimuNN is compatible with TensorFlow interface, and supports inference in both floating-point numbers and configurable fixed-point numbers. It can provide inference results at layer-/module-/cycle-level to serve as a golden model for RTL designs. Besides, its embedded model for hardware performance estimation enables SimuNN to provide an accurate reference of processing speed and hardware cost at the ASIC-designed user end for algorithm designers.

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems., 6 pages, 5 figures, conference

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.

The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge. Case studies of typical applications in Autonomous Driving (AD) are demonstrated, i.e., dense platooning and intersection management, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm., Comment: To appear in IEEE Wireless Communications

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime., Comment: 5 pages, 7 figures, VTC2019-Spring

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and achieved the state-of-the-art performance in terms of classification accuracy.

Green radios jointly consider the spectrum efficiency and the energy efficiency in wireless networks in order to provide sustainable development. In the past two decades, various green radio solutions for different types of terminals and radio access networks have been developed, some of which have been used in the designs of wireless products. In this paper, we provide a historical view on some fundamental works and practical issues. In addition to providing a comprehensive overview on theoretical achievements, we also discuss several important power saving solutions with notable engineering impacts, such as Doherty power amplifier and separated baseband unit - remote radio unit architecture, which might be overlooked in previous publications. Moreover, with the huge growth of wireless traffic in the near future, green radio design for future wireless networks shall involve an end-to-end energy efficiency investigation of mobile terminals, radio access and core networks, and different applications. By introducing green radio schemes for advanced terminals and future wireless networks, this article will not only be beneficial for readers with only preliminary background in communications and signal processing but also have reference and historical values for researchers and practical engineers in the area.

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

The sensing and communication layers are both integral parts of the Internet-of-Things. Most of recent studies on sensory status update treat the information sensing and sensory data communication problems separately (i.e., a decoupled approach) and optimize specific latency metrics such as age of information relying on simplified models of communication networks or sensory traffic. In this paper, we propose a deeply integrated sensing and communication scheduling (S2) framework based on status-error-triggered update, focusing specifically on multiaccess wireless networks. We first analyze a motivating example consisting of two-state Markov sensors, showing that when both optimized, S2 outperforms the decoupled approach significantly. For sensors with random-walk state transitions, the closed-form Whittle's index with arbitrary status tracking error functions is presented and the indexability is established. Furthermore, a mean-field approach is applied such that the decentralized status update medium access control design is solved explicitly, for both homogeneous nodes and heterogeneous nodes in terms of status transition behaviors. According to the numerical results, the performance of the proposed S2 scheme is close to the optimum and better than the decoupled approach. In addition, a potential application of dynamic Channel State Information (CSI) update is presented, with CSI generated by a commercial ray-tracing simulator.

With the raising demand for autonomous driving, vehicle-to-vehicle communications becomes a key technology enabler for the future intelligent transportation system. Based on our current knowledge field, there is limited network simulator that can support end-to-end performance evaluation for LTE-V based vehicle-to-vehicle platooning systems. To address this problem, we start with an integrated platform that combines traffic generator and network simulator together, and build the V2V transmission capability according to LTE-V specification. On top of that, we simulate the end-to-end throughput and delay profiles in different layers to compare different configurations of platooning systems. Through numerical experiments, we show that the LTE-V system is unable to support the highest degree of automation under shadowing effects in the vehicle platooning scenarios, which requires ultra-reliable low-latency communication enhancement in 5G networks. Meanwhile, the throughput and delay performance for vehicle platooning changes dramatically in PDCP layers, where we believe further improvements are necessary., 6 pages, APCC 2018

With low-density parity-check (LDPC) codes selected as the standard codes for the fifth generation (5G) enhanced Mobile Broad Band scenario (eMBB) and the raising requirement for 5G network deployment, a LDPC decoder that supports all LDPC coding structures according to Release 15 (R15) 5G standard becomes necessary. In this paper, we propose a LDPC decoding unit with unified reconfigurable datapath to adapt LDPC code structures of 5G data channel. The proposed decoding datapath can be configured into several separate ones for unified check node update and the corresponding variable node updates. The LDPC decoding unit is implemented on Altera Stratix IV FPGA with 333.33 MHz frequency and around 26 Mbps throughput per unit.

In the 5G communication systems, a hybrid approach to support polar codes for control plane and LDPC codes for data plane has been identified as the channel coding solution for enhanced mobile broadband (eMBB) scenario. One of the major challenges to implement this approach is to design powerful decoders at the terminal side. Inspired from a useful machine learning based polar decoder, we proposed a deep learning based unified polar-LDPC by concatenating an indicator section. Through numerical experiments, we show that the proposed deep neural network (DNN) based decoding architecture can achieve the similar decoding performance compared with the traditional BP-based decoding algorithm. Meanwhile, the proposed unified approach shares the same network architecture and parameters with isolated approaches, which saves significant implementation resources consequently.

Simple base-pairing rules of complementarity, perfected by evolution for encoding genetic information, provide unprecedented control over the process of DNA self-assembly. These rules allow us to build exquisite nanostructures and rationally design their morphology, fine-tune their chemical properties, and program their response to environmental stimuli. DNA nanostructures have emerged as promising candidates for transporting drugs across various physiological barriers of the body. In this review, we discuss the strategies used to transform DNA nanostructures into drug delivery vehicles. We provide an overview of recent attempts at using them to deliver small molecule drugs and macromolecular cargoes and present the challenges that lay ahead for these synthetic vectors as they set new paradigms in the field of nanotechnology and medicine.

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

In massive multiple-input and multiple-output (M-MIMO) systems, one of the key challenges in the implementation is the large-scale matrix inversion operation, as widely used in channel estimation, equalization, detection, and decoding procedures. Traditionally, to handle this complexity issue, several low-complexity matrix inversion approximation methods have been proposed, including the classic Cholesky decomposition and the Neumann series expansion (NSE). However, the conventional approaches failed to exploit neither the special structure of channel matrices nor the critical issues in the hardware implementation, which results in poorer throughput performance and longer processing delay. In this paper, by targeting at the correlated M-MIMO systems, we propose a modified NSE based on tridiagonal matrix inversion approximation (TMA) to accommodate the complexity as well as the performance issue in the conventional hardware implementation, and analyze the corresponding approximation errors. Meanwhile, we investigate the very-large-scale integration implementation for the proposed detection algorithm based on a Xilinx Virtex-7 XC7VX690T FPGA platform. It is shown that for correlated massive MIMO systems, it can achieve near minimum mean square error performance and 630 Mb/s throughput. Compared with other benchmark systems, the proposed pipelined TMA detector can get high throughput-to-hardware ratio. Finally, we also propose a fast iteration structure for further research.

Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noise-like nature and may be produced by a wide variety of sources. In this paper, we propose to use a novel deep convolutional neural network for ESC tasks. Our network architecture uses stacked convolutional and pooling layers to extract high-level feature representations from spectrogram-like features. Furthermore, we apply mixup to ESC tasks and explore its impacts on classification performance and feature distribution. Experiments were conducted on UrbanSound8K, ESC-50 and ESC-10 datasets. Our experimental results demonstrated that our ESC system has achieved the state-of-the-art performance (83.7\(\%\)) on UrbanSound8K and competitive performance on ESC-50 and ESC-10.

This paper studies the energy efficiency (EE) maximization for an orthogonal frequency division multiple access (OFDMA) downlink network aided by a relay station (RS) with subcarrier pairing. A highly flexible transmission protocol is considered, where each transmission is executed in two time slots. Every subcarrier in each slot can either be used in direct mode or be paired with a subcarrier in another slot to operate in relay mode. The resource allocation (RA) in such a network is highly complicated, because it has to determine the operation mode of subcarriers, the assignment of subcarriers to users, and the power allocation of the base station and RS. We first propose a mathematical description of the RA strategy. Then, a RA algorithm is derived to find the globally optimum RA to maximize the EE. Finally, we present extensive numerical results to show the impact of minimum required rate of the network, the user number, and the relay position on the maximum EE of the network.

LANGUAGE NOTE | Document text in Chinese; abstract also in English.儒家的養老觀念具有深刻的思想根源。它有形上根據，也有現實原因。從表現形式上看，儒家的養老觀蘊含在儒家的“孝道”思想之中，由於孝道在儒家那裏既有著形上的天道根源，亦發揮著宗教性的功能，這使得蘊含在這一孝道思想中的養老觀念也兼具了這兩種思想特徵，即儒家養老觀既是形上的天道的要求，同時又具有宗教性關懷的內蘊。基於血親的自然情感因素和人對老年生活狀態的憂慮則構成了儒家養老觀念現實的、直接的緣由。這種由孝道維繫的養老觀念決定了儒家對“老人”界定的特殊性和養老方式的特殊性。在家庭中，“老”是相對於盡孝的義務而言的，而社會層面的“老”則需要加以特殊規定，以便於社會層面的養老實踐。而在養老方式上，這種由孝道維繫的養老觀也決定了家庭或子女是主要的、基本的養老場所和主體，但國家或政府在宣導、維護這種養老觀念上，以及為家庭養老創造必要的物質條件和保障方面，則負有絕對的義務和責任。China’s citizens are growing old at an alarming rate. The increase in the country’s elderly population has created profound social and economic complications that can be solved only by appropriate policies and moral decisions on caring for aging people. Who will take care of the elderly? Should elderly people depend on their families or on the government? The author of this paper shows that filial or familial piety—the Confucian ideal of moral responsibility for one’s elderly relations—is intrinsic to the traditional Chinese value system, and should continue to shape Chinese society today. The author contends that filial piety is the key ethical principle of Confucianism based on its metaphysical foundation of the Dao. Instead of talking about the rights of elderly people, we should emphasize the moral duty of their adult children.However, the traditional emphasis on filial piety has been challenged by social changes in the last few decades, especially since China’s economic reform. There are fewer and fewer households with “three generations under one roof,” due to China’s urbanization and one-child policy. More and more people are choosing to invest in institutional care (such as nursing-home care and residential care) as an alternative to traditional familial care. Despite these changes, the author maintains that filial piety, as the core value of the Confucian ethical system, should play an important role in modern Chinese society. Supporting one’s elderly relations (both emotionally and financially) offers not only a practical solution to the problem of elderly care in China, but also an ethical way for family members to fulfill their moral duties and maintain family solidarity. Although Western approaches to community care that combine formal and informal sources of support may in some cases help to meet the welfare needs of vulnerable elderly people in China, a complete rejection of traditional filial piety is far from appropriate. The author concludes that commitment to the traditional ethical value of filial piety enables people to live together in peace and harmony.DOWNLOAD HISTORY | This article has been downloaded 2849 times in Digital Commons before migrating into this platform.

To identify clinical predictors of port infections in adult patients with hematologic malignancies.A retrospective chart review identified 223 adult patients (age ≥ 18 y) with hematologic malignancies, including lymphoma (n = 163), leukemia (n = 49), and others (n = 11), who had a port placed from 2012 to 2015. Early (30 d after port placement) and overall port infections (bloodstream and site infections) were recorded. To elucidate clinical predictors for early and overall port infections, proportional subdistribution hazard regression (PSHREG) analyses were conducted with variables including patients' demographics, medications used, laboratory data, and port characteristics.Total duration of follow-up was 83,722 catheter-days (median per patient, 274 catheter-days). Early and overall port infections were identified in 8 (3.6%) and 26 (11.7%) patients, respectively. Early and overall infection rates were 1.2 and 0.3 infections/1,000 catheter-days, respectively. Backward stepwise multivariate PSHREG analyses identified hypoalbuminemia (3.5 mg/dL) at the time of port placement (hazard ratio = 5.03; 95% confidence interval, 1.14-22.16; P = .03) and steroid use (30 d cumulatively during follow-up period) (hazard ratio = 3.41; 95% confidence interval, 1.55-7.47; P = .002) as independent risk factors for early and overall port infections, respectively.In adult patients with hematologic malignancies, hypoalbuminemia at the time of port placement was a clinical predictor for early port infections, whereas steroid use was a clinical predictor for overall port infections.

Path metric sorting unit of successive cancellation list (SCL) decoders for polar codes is the main concern in this paper. After reviewing existing sorting units in SCL decoders, we propose 2 new sorting schemes namely quick select (QS) based selection algorithm and simplified bitonic sorter (SBT), which exploit the special data dependency of path metrics in log-likelihood ratio based SCL decoding. Theoretical analysis shows that for the list size of L ≤ 8, QS-based selection algorithm has lower delay than existing schemes. FPGA implementation based on Artix7 Family shows that for the list size of L ≥ 16, SBT has the same delay while the hardware reduction is over 40%.

Traditional wireless networks are designed for ubiquitous network access provision with low-rate voice services, which thus preserve the homogeneous architecture and tight coupling for infrastructures such as base stations. With the traffic explosion and the paradigm shift from voice-oriented services to data-oriented services, traditional homogeneous architecture no longer maintains its optimality, and heterogeneous deployment with flexible network control capability becomes a promising evolution direction. To achieve this goal, in this article, we propose a two-layer network functionality separation scheme, targeting at low control signaling overhead and flexible network reconfiguration for future mobile networks. The proposed scheme is shown to support all kinds of user activities defined in current networks. Moreover, we give two examples to illustrate how the proposed scheme can be applied to multicarrier networks and suggest two important design principles for future green networks. Numerical results show that the proposed scheme achieves significant energy reduction over traditional LTE networks, and can be recommended as a candidate solution for future green mobile networks.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

LANGUAGE NOTE | Document text in Chinese; abstract also in English.正義是一個歷史範疇，任何具有實踐品格的正義理論都只能是與特定道德共同體特殊的善的規定相聯繫的正義論，從這一意義上說，儒家當然也有自己的正義論。儒家正義論是以天道性命的邏輯關係為起點，通過仁、禮、義三維一體的倫理結構而表現的正義論。這種正義論將社會正義與個人正義統一起來，總體上體現為一種“厚生”的思想意識和行動。由於“生”具有不同的實踐領域和境界層次，從而使儒家正義論具有多維的展現向度。把這種正義論應用於醫療公正領域，儒家主張醫療保健政策必須以維護國民的生命意義和完整實現為前提，主張政府應提供一種基礎性的全民性的醫療保障和保健制度。但儒家不認同單級的平等主義的分配制度，而是強調資源的差等分配，但資源差等分配的前提不在於每個人實際的貧富狀態，而取決於人的修德程度或者說後天努力與貢獻的程度。個體對自我生命的完整實現負有天定的義務，因此儒家主張在醫療保健領域應當賦予個體和家庭以更多的自主權。In the West, “justice” is a complex ethical principle, with meanings that range from the fair treatment of individuals to the equitable allocation of healthcare resources. Justice in bioethics is perhaps the most contested and controversial principle. This paper argues that the Confucian notion of justice is neither rights-based nor distributive; rather, it is based on the virtues of humanness and benevolence (ren), correct behavior and propriety (li), and uprightness and appropriateness (yi). Those virtues cherished in the Confucian tradition constitute what can be called a Confucian concept of justice, the primary principle of which is to respect human life. This means that in the healthcare system, the Confucian idea of justice is approached from the perspective of equality and fairness. On the one hand, the government should provide basic care for all persons according to the virtue of humanness/benevolence; on the other hand, the government should allow for diversity and differences in medical treatment and healthcare resource allocation according to the virtues of propriety and appropriateness, given that medical resources are limited and China supports a huge population. In other words, the government has the responsibility of providing public health care to those who cannot afford to pay for their own basic healthcare needs. At the same time, the government should allow for alternatives and should permit people to choose between ways of dealing with their medical issues.Clearly, the language of “rights” is absent from the Confucian tradition. However, this essay argues that because the Western notion of justice, particularly in the legal sense, does not take into account what is good, the Confucian virtue-based justice better fits the cultural milieu of medical practice in China. From the standpoint of Confucianism, healthcare and bio-medical ethics should be more concerned about what is good for society, family, and the individual than about absolute equality or the principle of fair equality, which engender both moral and economic hazards.DOWNLOAD HISTORY | This article has been downloaded 259 times in Digital Commons before migrating into this platform.

In this paper, the successive cancellation (SC) heap polar decoding scheme is firstly proposed to reduce the complexity. Unlike SC list decoder which keeps $L$ same length paths, SC heap decoding stores different length paths in a heap and always decodes the global optimal path in the root. It has been strictly proved that SC heap decoding is superior to SC stack decoding because the time complexity of inserting new paths is only related to the height of the heap. Proposed SC heap decoding is a dynamic decoding scheme with robustness in various scenarios. Numerical results with binary-input additive white \emph{Gaussian} noise channel (BI- AWGNC) show that SC heap decoding reduces $63.53\%$ decoding complexity compared with SC list decoding on the same performance at SNR of $2.5$ dB. A low- complexity hardware architecture for proposed SC heap decoder is also designed.

In order to improve the decoding performance of successive cancellation (SC) decoder for polar code, SC stack (SCS) polar decoder has been proposed by existing literatures. Compared with SCL decoder, SCS decoder successfully reduces the decoding complexity especially at high SNR while keeping the same decoding performance. However in correlated channels at low SNR, SCS decoder suffers a lot from the overwhelming complexity due to frequent backtracking operations. Aim to lower such complexity as well as the required storage resources of SCS decoder, this paper proposes a low complexity SCS polar decoder based on segmented CRC scheme. Employing the segmented CRC as an early termination scheme, the proposed SCS decoder can successfully reduce the complexity especially at low SNR without any performance degradation. Numerical results have shown that for (1024, 512) polar code, the proposed approach achieves more than 34.4% complexity reduction and 29.7% resource occupation reduction compared to the conventional CRC-aided SCS (CA-SCS) decoder at SNR of 1.5 dB.

As improved versions of the successive cancellation (SC) decoder, the adaptive SC list (SCL) decoder and the segmented CRC-aided SCL (SCA-SCL) decoder can achieve notable complexity reduction compared to the traditional SCL one. However, the former only shows good reduction in high SNR region whereas the latter only works good in low SNR region. To this end, two novel efficient adaptive SCL decoding schemes are proposed to achieve good complexity reduction in both low and high SNR regions. The first one is adaptive SCA-SCL (ASCA-SCL) decoder, which is based on SCA-SCL but with varying list size. The second one is adaptive switching SCL (AS-SCL) decoder, whose list reduction metric switches with regarding to specific SNR value. Numerical results have shown that for (1024, 512) polar code and list size L = 2, compared to the state-of-the-art decoder, the proposed ASCA-SCL decoder and AS-SCL decoder can achieve up to 45.40% and 32.39% computational complexity reduction respectively, while keeping the similar decoding performance.

Sparse code multiple access (SCMA) is a new kind of multiple access (MA) technology which ranks one of the most promising candidates for 5G wireless because of its outstanding performance. SCMA enjoys stronger overloading tolerance compared with traditional MA technologies. It also takes the advantage of sparse property to achieve lower complexity when using message passing algorithm (MPA). In this paper, a deterministic MPA (DMAP) is proposed for SCMA. Numerical result shows that DMPA achieves good performance in term of bit error rate (BER) and is capable to transmit more information with the same amount of physical resources. Furthermore, hardware architecture of DMPA is also put forward in this paper. For original DMPA, the data is updated in single but bulky step, which slows down the operation frequency. Apart from this, the DMPA decoder has 4 basic units, in need of large amount of hardware. Therefore, a stage-level folded structure for DMPA is first proposed with consideration of both the speed and efficiency, which is the main contribution. For better demonstration of the advantages, a loop bound analysis of it has also been made in this paper.

Successive cancellation list (SCL) decoding for polar codes is promising in data communication. However, in addition to L times complexity of conventional SC, both path selecting and updating result in extra complexity. In detail, the copy of intermediate values suffers from a long latency, especially when list size L is large. In this paper, a stage-located copy algorithm is proposed to avoid copying the same contents in candidate paths, which significantly reduces the processing latency. Furthermore, the resulting data processing speedup increases with code length. For (2048, 1723) polar codes, experimental results have shown that by employing the proposed stage-located copy, throughput of software-based SCL decoder with L = 32 achieves up to 1.1 Mbps throughput with 45% increase compared to the state-of-the-art software SCL decoders.

The scaling up of antennae and terminals in massive multiple-input multiple-output (MIMO) systems helps increase spectral efficiency at the penalty of prohibitive computational complexity. In linear minimum mean square error (MMSE) detection, this complexity is mainly resulted from solving large-scale linear equations. Admittedly, iterative approaches such as conjugate gradient (CG) method have theoretically demonstrated their capability in balancing both performance and complexity for massive MIMO detection. Their convergence rate turns out to be really slow for common applications where the base station-to-user antenna ratio decreases. To this end, by introducing a pre-conditioner based on incomplete Cholesky (IC) factorization, this paper proposes a pre-conditioned conjugate gradient (PCG) method, which successfully speeds up the convergence even for small station-to-user antenna ratio scenarios. The analytical as well as numerical results have indicated that the proposed PCG method outperforms the conventional CG method due to the 50% reduced spectral condition number κ. Complexity analysis shows that the proposed PCG method achieves over 75% reduction compared to the conventional Cholesky factorization scheme when N = 40.

For linear minimum mean square error (MMSE) detection massive multiple-input multiple-output (MIMO) system uplink, the convergence rate of some iterative methods such as conjugate gradient (CG) slows down with more complicated antenna configurations. And the conventional Cholesky method relies on a higher computational complexity to achieve a better performance. In this paper, we introduce the steepest descent (SD)-based soft-output method to solve the reduction of convergence rate with a low complexity by introducing an effective preconditioner. Further more, an efficient approximated method with much lower complexity to calculate the log-likelihood ratios (LLRs) is proposed. The analytical as well as the numerical results have indicated the proposed SD-based method outperforms the recent proposed classical Conjugate Gradient (CG) method with around 1 dB performance improvement at the BER of 10-3, and also achieves 75% complexity reduction from conventional Cholesky approach.

In this paper, a hardware-efficient folded SC polar decoder based on k-segment decomposition is first proposed. The proposed k-segment scheme employs (k – 1) N1/k-bit decoders to implement the original N-bit decoder, and reduces the number of mixed-nodes from (N – 1) to (k a 1)( N1/k – 1) with slightly increased latency. In addition, pipelining technique, partial parallel processing, and pre-computation are employed to refine the timing issue resulting from folding scheme. Meanwhile, the hardware utilization ratio (HUR) of processing elements (PEs) is drastically increased. Both SC decoders with (N = 8,k = 3) and (N = 1024,k = 2) are implemented with Altera Stratix V FPGA. The former one requires only 13.46% ALMs and 10.66% register compared to the k = 1 scheme at the cost of slightly increased latency. The latter one consumes only 55.86% ALMs compared to the k = 1 scheme. Thus, the proposed k-segment decoder is suitable for scenarios requiring high-HUR.

Minimum mean square error (MMSE) detection algorithms in massive multiple-input multiple-output (MIMO) uplink suffer from prohibitively high complexity of exact matrix inversion. Conventional detection algorithms based on Neumann series expansion (NSE), which approximates the matrix inversion, can only be efficient with small number of NSE terms. Worse still, conventional NSE-based approaches are facing convergence problems in poor propagation environments. In this paper, we introduce an adjustable iterative detection algorithm based on NSE to solve the complexity and convergence problems. Furthermore, we propose an efficient approach to approximately compute the log-likelihood ratios (LLRs) with low-complexity. Both the analytical and numerical results have shown that the proposed approach has benefits in terms of computational complexity and convergence rate, especially in the case of poor propagation environments.

With the development of flash technology, multilevel per cell (MLC) NAND flash memory has mastered the key market because of its storage capacity superiority. However, excessive density increment leads to decay of reliability of MLC NAND flash memory, thus an appropriate error correction code (ECC) is required to address this issue. This paper presents an efficient multi-strategy ECC scheme called pre-check scheme which consists of hard, quantized-soft, and pure-soft decoders geared to MLC NAND flash memory based on polar code proposed by E. Arikan to overcome the aforementioned drawbacks with better performance and lower complexity. The hard decoder is designed to correct the majority of erroneous codewords in the initial phase of the flash memory, while the soft decoders aim to correct codewords which contain larger amount of errors. In addition, we clarify that the mapping scheme using Gray code can achieve best performance compared to other alternatives.

Secure communications become more, more important with the exponential growth of Internet data services. However, to massively deploy such services in current networks, we need to deal with different architectures, time-consuming encryption/decryption algorithms. Recently, numerous research efforts have been focused on parallel computing using high-end discrete GPGPU to achieve peak throughput, while the study on more cost-efficient integrated GPGPU is still open, which is included in almost all of the Intel CPUs currently, e.g. Celeron, Pentium, Core, even Xeon-E3 families. In this paper, we propose a generic heterogeneous computing approach for the decryption of RSA algorithm. Based on OpenCL, we are able to implement our idea on the off-the-shelf mini PC with low-cost integrated GPGPU. According to the experimental results, we show that the proposed scheme can fully utilize the capacity of integrated GPGPU,, a 2 times throughput improvement over the CPUonly solution can be expected.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems., Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 2011

With explosive growth of high-data-rate applications, more and more energy is consumed in wireless networks to guarantee quality of service. Therefore, energy-efficient communications have been paid increasing attention under the background of limited energy resource and environmental- friendly transmission behaviors. In this article, basic concepts of energy-efficient communications are first introduced and then existing fundamental works and advanced techniques for energy efficiency are summarized, including information-theoretic analysis, OFDMA networks, MIMO techniques, relay transmission, and resource allocation for signaling. Some valuable topics in energy-efficient design are also identified for future research.

LANGUAGE NOTE | Document text in Chinese; abstract also in English.本文從儒家倫理的天道基礎和現實基礎兩個角度分析儒家關於克隆人問題的立場與態度。本文認為，論證儒學關於現代生命倫理議題的看法，不應只局限於應用傳統儒學的某種觀點得出某種結論，或樂衷於表面的否定或肯定這樣的判斷。而應該對體現在儒家身上那種對人類生存與命運的強烈憂患意識和人文關懷有一種自覺的體認，這樣才能對儒學關於諸如克隆人這樣的現代生命倫理議題的立場與態度獲得較為全面和深刻的認識。從這樣的觀點出發，本文認為從儒家倫理的天道基礎而言，儒家不會對克隆人的正當性輕率地表示肯定和否定，而是根據具體的情境和目的決定自己的態度，但一般而言，儒家將不會主張克隆人。從儒家倫理的現實基礎來看，儒家對克隆人的態度是鮮明的，那就是認為克隆人缺乏倫理上的正當性，從而反對克隆人。This essay attempts to offer a Confucian view of human cloning. It analyzes the nature of human cloning from the perspective of Confucian ethics. It argues that we should not make simple “Confucian” conclusions about human cloning based on certainisolated Confucian views, but should take a personal view on the fundamental spirit of Confucianism. Evidently, the foundation of Confucian ethics is its view of the Dao of Heaven (tiendao). From the perspective of this essay, the Dao of Heaven offers twoprinciples with which people should comply. The first principle is that the Dao of humans must follow the Dao of Heaven. That is, people’s activities should not violate the will of Heaven. The second principle is the principle of continuous creation or generation (shengsheng buyi). Such continuous creation or generation is seen as the great virtue (dade) of Heaven. Accordingly, the Dao of Heaven is primarily the method of creation, which characterizes Confucian ethics.This Confucian view of the Dao of Heaven and its two basic principles should be brought to bear on the issue of human cloning in the full sense. By mechanically understanding and applying the principle of generation, it is possible to develop a view of human cloning that grants it legitimacy as a method of reproduction. However, the authentic Confucian sense of generation cannot be understood without the normal relationship between a man and a woman, or specifically, the relationship between husband and wife, which Confucians see as the ultimate starting point of any meaningful human generation. Without the union of husband and wife, human reproduction becomes an unnatural, abnormal event that goes beyond the scope of the Dao of Heaven. That is why Confucianism emphasizes the crucial function of marriage in the normal way of human life. Accordingly, Confucians think that both a father and a mother are necessarily (morally) required to create a normal human child. Techniques such as reproductive human cloning destroy the normal means of human generation and undermine the foundation of Confucian ethics. Confucians cannot accept such techniques.DOWNLOAD HISTORY | This article has been downloaded 185 times in Digital Commons before migrating into this platform.