Search

We characterize the capacity region of the two-user broadcast packet erasure channel (PEC) with single-user delayed channel state information (CSI). More precisely, we assume one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. This is a hybrid CSI at the transmitter (CSIT) setting where the transmitter has the delayed CSI of one user but not the other. Previous results developed opportunistic network coding schemes for this setting, which strictly enlarge the achievable rate region compared to the no-CSIT baseline. Characterization of the capacity region with single-user delayed CSI, however, remained open. In this work, we develop an improved achievability strategy and show that the capacity region, surprisingly, matches that of the broadcast PEC with global delayed CSI of both users. The key to such improvement over previous results is a new precoding strategy for the retransmission phase of the opportunistic network coding scheme. It harnesses the single-user delayed CSI in the retransmission phase, so that interference from the feedback receiver can be aligned at the other receiver. Besides the broadcast PEC with two private messages, an extension to a model with an additional common message is also provided and the corresponding capacity region with single-user CSI also matches that with global delayed CSI. Finally, further extensions to three-user cases are also provided.

ObjectiveTo determine the impact of palliative care (PC) on end-of-life (EoL) care and the place of death (PoD) in children, adolescents, and young adults with life-limiting conditions.MethodEight online databases (PubMed, Medline, EMBASE, Cochrane Library, CINAHL, Airiti, GARUDA Garba Rujukan Digital, and OpenGrey) from 2010 to February 5, 2020 were searched for studies investigating EoL care and the PoD for pediatric patients receiving and not receiving PC.ResultsOf the 6,468 citations identified, 14 cohort studies and one case series were included. An evidence base of mainly adequate- and strong-quality studies shows that inpatient hospital PC, either with or without the provision of home and community PC, was found to be associated with a decrease in intensive care use and high-intensity EoL care. Conflicting evidence was found for the association between PC and hospital admissions, length of stay in hospital, resuscitation at the time of death, and the proportion of hospital and home deaths.Significance of resultsCurrent evidence suggests that specialist, multidisciplinary involvement, and continuity of PC are required to reduce the intensity of EoL care. Careful attention should be paid to the need for a longer length of stay in a medical setting late in life, and earlier EoL care discussion should take place with patients/caregivers, especially in regard to attempting resuscitation in toddlers, adolescents, and the young adult population. A lack of robust evidence has identified a gap in rigorous multisite prospective studies utilizing data collection.

To provide coordinate information for the use of intelligent transportation systems (ITSs) and autonomous vehicles (AVs), the global positioning system (GPS) is commonly used in vehicle localization as a cheap and easily accessible solution for global positioning. However, several factors contribute to GPS errors, decreasing the safety and precision of AV and ITS applications, respectively. Extensive research has been conducted to address this problem. More specifically, several optimization-based cooperative vehicle localization algorithms have been developed to improve the localization results by exchanging information with neighboring vehicles to acquire additional information. Nevertheless, existing optimization-based algorithms still suffer from an unacceptable performance and poor scalability. In this study, we investigated the development of deep learning (DL) based cooperative vehicle localization algorithms to provide GPS refinement solutions with low complexity, high performance, and flexibility. Specifically, we propose three DL models to address the problem of interest by emphasizing the temporal and spatial correlations of the extra given information. The simulation results confirm that the developed algorithms outperform existing optimization-based algorithms in terms of refined error statistics. Moreover, a comparison of the three proposed algorithms also demonstrates that the proposed graph convolution network-based cooperative vehicle localization algorithm can effectively utilize temporal and spatial correlations in the extra information, leading to a better performance and lower training overhead.

This paper investigates the transmission energy minimization problem for the two-user downlink with strictly heterogeneous latency constraints. To cope with the latency constraints and to explicitly specify the trade-off between blocklength (latency) and reliability the normal approximation of the capacity of finite blocklength codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. We first consider the non-orthogonal multiple access (NOMA) based transmission scheme. However, due to heterogeneous latency constraints and channel conditions at receivers, the conventional successive interference cancellation may be infeasible. We thus study the problem by considering heterogeneous receiver conditions under different interference mitigation schemes and solve the corresponding NOMA design problems. It is shown that, though the energy function is not convex and does not have closed form expression, the studied NOMA problems can be globally solved semi-analytically and with low complexity. Moreover, we propose a hybrid transmission scheme that combines the time division multiple access (TDMA) and NOMA. Specifically, the hybrid scheme can judiciously perform bit and time allocation and take TDMA and NOMA as two special instances. To handle the more challenging hybrid design problem, we propose a concave approximation of the FBC rate/capacity formula, by which we obtain computationally efficient and high-quality solutions. Simulation results show that the hybrid scheme can achieve considerable transmission energy saving compared with both pure NOMA and TDMA schemes., Comment: 15 pages, 10 figures, IEEE Transactions on Wireless Communications

Supporting on-line and adaptive traffic engineering in software-defined networks entails the fast, robust control message forwarding from software-defined switches to the controller(s). In-band control using the existing infrastructure is cost-efficient, but imposes a substantial barrier to timely transmissions of control messages. Also, due to the limited computational capability of a single controller, only the use of multiple controllers is practically viable for large-scaled networks. Therefore, in this paper, the optimal software-defined network planning is investigated with multi-controllers. First, the network planning problem is formulated as a nonlinear multi-objective optimization, which aims to simultaneously minimize the number of controllers and the control traffic delay for each switch. This planning problem is then partitioned into two sub-problems, i.e., multi-controller placement and control traffic balancing, which are respectively solved by the proposed fast-convergent algorithms. Furthermore, an adaptive feedback control mechanism is proposed to iteratively work out the two sub-problems and enable the dynamic network replanning, subject to the time-varying traffic volume and network topology. Simulations validate the adaptivity of our control scheme, which significantly reduces delay with maximum throughput for control flows, brings minimal impact to normal data flows, and requires the minimum controllers.

We study the content delivery problem between a transmitter and two receivers through erasure links, when each receiver has access to some random side-information about the files requested by the other user. The random side-information is cached at the receiver via the decentralized content placement. The distributed nature of the receiving terminals may also make it harder for the transmitter to learn the erasure state of the two links and indices of the cached files. We thus investigate the capacity gain due to various levels of availability of channel state and cache index information at the transmitter. More precisely, we cover a wide range of settings from global delayed channel state knowledge and a non-blind transmitter (i.e., one that knows the exact cache index information at each receiver) all the way to no channel state information and a blind transmitter (i.e., one that only statistically knows cache index information at the receivers). We derive new inner and outer bounds for the problem under various settings and provide the conditions under which the two match and the capacity region is characterized. Surprisingly, for some interesting cases, the capacity region remains unchanged even with only single-user channel state or single-user cache index information at the transmitter.

Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultra-broadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware Networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access., This paper was already accepted by the IEEE 2021 INFOCOM Workshop

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.

This paper introduces an energy-efficient, software-defined vehicular edge network for the growing intelligent connected transportation system. A joint user-centric virtual cell formation and resource allocation problem is investigated to bring eco-solutions at the edge. This joint problem aims to combat against the power-hungry edge nodes while maintaining assured reliability and data rate. More specifically, by prioritizing the downlink communication of dynamic eco-routing, highly mobile autonomous vehicles are served with multiple low-powered access points (APs) simultaneously for ubiquitous connectivity and guaranteed reliability of the network. The formulated optimization is exceptionally troublesome to solve within a polynomial time, due to its complicated combinatorial structure. Hence, a distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed for eco-vehicular edges, where multiple agents cooperatively learn to receive the best reward. First, the algorithm segments the centralized action space into multiple smaller groups. Based on the model-free distributed Q learner, each edge agent takes its actions from the respective group. Also, in each learning state, a software-defined controller chooses the global best action from individual bests of the distributed agents. Numerical results validate that our learning solution achieves near-optimal performances within a small number of training episodes as compared with existing baselines.

Objective We aimed to investigate factors associated with nonadherence to antihypertensive medication among middle-aged Taiwanese adults with hypertension. Methods We used data from the 2009 Taiwan National Health Interview Survey (NHIS) to identify adults age 40 to 65 years with hypertension. We used logistic regression analyses to investigate factors associated with nonadherence to antihypertensive medication. Results A total 1,256 respondents with hypertension taking antihypertensive medication were included in this study. Multiple logistic regression analyses revealed that six factors were significantly and independently associated with nonadherence to medication: younger age (odds ratio, [OR] = 1.85), mean monthly personal income Conclusions This secondary data analysis of the population-based NHIS identified six factors associated with nonadherence to antihypertensive medication. These factors should be considered when planning and implementing blood pressure control interventions among middle-aged adults with hypertension.

This paper investigates highly mobile vehicular networks from users' perspectives in highway transportation. Particularly, a centralized software-defined architecture is introduced in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers. Unlike the legacy networks, where a typical user is served from only one access point (AP), in the proposed system model, a vehicle user is served from multiple APs simultaneously. While this increases the reliability and the spectral efficiency of the assisted users, it also necessitates an accurate power allocation in all transmission time slots. As such, a joint user association and power allocation problem is formulated to achieve enhanced reliability and weighted user sum rate. However, the formulated problem is a complex combinatorial problem, remarkably hard to solve. Therefore, fine-grained machine learning algorithms are used to efficiently optimize joint user associations and power allocations of the APs in a highly mobile vehicular network. Furthermore, a distributed single-agent reinforcement learning algorithm, namely SARL-MARL, is proposed which obtains nearly identical genie-aided optimal solutions within a nominal number of training episodes than the baseline solution. Simulation results validate that our solution outperforms existing schemes and can attain genie-aided optimal performances.

In wireless networks, the link connectivity may be intermittent due to shadowing, burstiness of data arrival, or uncoordinated resource allocation. In this paper, we model the intermittence of links as Bernoulli distributed random channel states, termed intermittence channel states , and study the impact of the corresponding channel state information at the transmitter (CSIT) in a two-user Gaussian broadcast channel (BC). Moreover, due to the heterogeneous timeliness of intermittence channel states, the CSIT considered in this paper is hybrid . More specifically, the CSIT of each link can be perfectly (causally or non-causally) available, delayed, or not available. When the links are connected, we adopt a general setting that the received signal-to-noise ratios can be different. Our contribution is the characterization of the capacity regions of intermittent Gaussian BC to within bounded gaps for all combinations of hybrid CSIT, except for scenario DN (delayed CSIT of receiver 1, no CSIT of receiver 2). For scenario DN, we propose an opportunistic physical layer network coding scheme that achieves a strictly larger degree-of-freedom (DoF) region than the no-CSIT DoF region. As a corollary, single-user CSIT is able to increase the sum DoF for intermittent Gaussian BC (also the capacity region for the erasure BC, as a by-product). This result is in sharp contrast to the recent negative result by Davoodi and Jafar, where it is shown that for fast-fading multiple-input single-output BC with continuous channel states, single-user CSIT does not help at all in terms of sum DoF.

In this paper, we consider a multiuser wireless system with one full duplex (FD) base station (BS) serving a set of half duplex (HD) mobile users. To cope with the in-band self-interference (SI) and co-channel interference, we formulate a quality-of-service (QoS) based linear transceiver design problem. The problem jointly optimizes the downlink (DL) and uplink (UL) beamforming vectors of the BS and the transmission powers of UL users so as to provide both the DL and UL users with guaranteed signal-to-interference-plus-noise ratio performance, using a minimum UL and DL transmission sum power. The considered system model not only takes into account noise caused by nonideal RF circuits, analog/digital SI cancellation but also constrains the average signal power at the input of the analog-to-digital converter (ADC) for avoiding signal distortion due to finite ADC precision. The formulated design problem is not convex and challenging to solve in general. We first show that for a special case with a worst case SI channel estimation error, the QoS-based linear transceiver design problem is globally solvable by a polynomial time bisection algorithm. For the general case, we propose a suboptimal algorithm based on alternating optimization (AO). The AO algorithm is guaranteed to converge to a Karush–Kuhn–Tucker solution. To improve the computational efficiency of the AO algorithm, we further develop a fixed-point method by extending the classical uplink–downlink duality in HD systems to the FD system. Simulation results are presented to demonstrate the performance of the proposed algorithms and the comparison with HD systems.

Wireless underground sensor networks enable many applications, such as mine and tunnel disaster prevention, oil upstream monitoring, earthquake prediction and landslide detection, and intelligent farming and irrigation among many others. Most applications are location-dependent, so they require precise sensor positions. However, classical localization solutions based on the propagation properties of electromagnetic waves do not function well in underground environments. This paper proposes a magnetic induction (MI)-based localization that accurately and efficiently locates randomly deployed sensors in underground environments by leveraging the multipath fading free nature of MI signals. Specifically, the MI-based localization framework is first proposed based on underground MI channel modeling with additive white Gaussian noise, the designated error function, and semidefinite programming relaxation. Next, this paper proposes a two-step positioning mechanism for obtaining fast and accurate localization results by: first, developing the fast-initial positioning through an alternating direction augmented Lagrangian method for rough sensor locations within a short processing time, and then proposing fine-grained positioning for performing powerful search for optimal location estimations via the conjugate gradient algorithm. Simulations confirm that our solution yields accurate sensor locations with both low and high noise and reveals the fundamental impact of underground environments on the localization performance.

The Byzantine distributed quickest change detection (BDQCD) is studied, where a fusion center monitors the occurrence of an abrupt event through a bunch of distributed sensors that may be compromised. We first consider the binary hypothesis case where there is only one post-change hypothesis and prove a novel converse to the first-order asymptotic detection delay in the large mean time to a false alarm regime. This converse is tight in that it coincides with the currently best achievability shown by Fellouris et al.; hence, the optimal asymptotic performance of binary BDQCD is characterized. An important implication of this result is that, even with compromised sensors, a 1-bit link between each sensor and the fusion center suffices to achieve asymptotic optimality. To accommodate multiple post-change hypotheses, we then formulate the multi-hypothesis BDQCD problem and again investigate the optimal first-order performance under different bandwidth constraints. A converse is first obtained by extending our converse from binary to multi-hypothesis BDQCD. Two families of stopping rules, namely the simultaneous $d$-th alarm and the multi-shot $d$-th alarm, are then proposed. Under sufficient link bandwidth, the simultaneous $d$-th alarm, with $d$ being set to the number of honest sensors, can achieve the asymptotic performance that coincides with the derived converse bound; hence, the asymptotically optimal performance of multi-hypothesis BDQCD is again characterized. Moreover, although being shown to be asymptotically optimal only for some special cases, the multi-shot $d$-th alarm is much more bandwidth-efficient and energy-efficient than the simultaneous $d$-th alarm. Built upon the above success in characterizing the asymptotic optimality of the BDQCD, a corresponding leader-follower Stackelberg game is formulated and its solution is found., 47pages, 3 figures. Part of the results have been presented at the IEEE ISIT 2019

[EN] Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely SoftAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of software-defined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and perform protocol conversions between IoT networks and radio access networks. Moreover, a cross-domain optimization framework is proposed in this extended SoftAir architecture concerning both upstream and downstream communication, where the respective sum-rates are maximized and system-level constraints are guaranteed, including (i) quality-of-service requirements of IoT transmissions, (ii) total power limit of mmWave RRHs, and (iii) fronthaul network capacities. Simulation results validate the efficacy of our solutions, where the extended SoftAir solution surpasses existing IoT schemes in spectral efficiency and achieves optimal data rates for next-generation IoT communication. (C) 2019 Elsevier B.V. All rights reserved., This work was supported by the US National Science Foundation (NSF) under Grant No. 1547353. A part of this work was supported by the Harry C. Kelly Memorial Fund, AC21 Special Project Fund (SPF), NC State 2019-2020 Internationalization Seed Grants and 2019 Faculty Research and Professional Development (FRPD) Program. The work of V. Pla was supported by Grant PGC2018-094151-B-I00 (MCIU/AEI/FEDER, UE).

The Internet is an increasingly important source of health information for the general population. Both preventive health behavior and Internet use are known to be different between men and women. However, few studies have compared predictors of Internet use for health information between the sexes.To investigate the prevalence and predictors of Internet use for health information among male and female adult Internet users using data from a population-based survey in Taiwan.Respondents between the ages of 20-65 years were identified from the dataset of the 2009 Taiwan National Health Interview Survey. The outcome variable of the study, the utilization of the Internet for health information, was ascertained by asking whether the respondent had ever used the Internet to search for health information or obtain health services. Univariate and multivariate logistic regression analyses were conducted separately for men and women to evaluate factors associated with the use of Internet for health information.Of the 2741 adults aged 20-65 years who had ever used the Internet, 1766 (64.4%) of them had used it for health information or services. Multivariate logistic regression analyses showed that a higher educational level (adjusted odds ratio [AOR]=3.60, P0.001), living alone (AOR=1.77, P=0.019), had exercised in the past two weeks (AOR=2.41, P0.001), residing in city or urban district (AOR=1.28, P=0.049), with a perceived health status of extremely good, very good, or good (AOR=1.34, P=0.022), and had used Western medicine services in the past month (AOR=1.51, P=0.005) were significantly associated with health information use in male Internet users. On the other hand, age between 20-44.9 years (AOR=1.87, P0.001), a higher educational level (AOR=3.57, P0.001), being married (AOR=1.68, P=0.001), had exercised in the past two weeks (AOR=1.56, P0.001), and had a mean monthly personal income of NT$ 20,000 and above were significant factors in female Internet users.This secondary data analysis of a representative sample of Taiwan population revealed that a similar but not identical set of independent factors was associated with the use of Internet for health information between male and female Internet users.

This paper considers the resource allocation for an orthogonal-frequency-division-multiplexing (OFDM)-based cognitive decode-and-forward (DF) relay network. Our objective is to maximize the sum rate (over subcarriers) of the cognitive-radio user with the interference introduced to the primary users (PUs) being managed. The optimization is over subcarrier pairing and power allocation, which leads to a mixed-integer programming (MIP) problem. To resolve this complicated MIP problem at a reasonable cost, we adopt the heterogeneous genetic algorithm (HGA) framework. The main motivation for the HGA framework comes from the idea that it can reduce the impact of extra assumptions made in previous works to simplify the problem. In our HGA, the chromosome is divided into an integer string for subcarrier pairing and a real-number string for power allocation. Two new initialization methods of these chromosomes, which are motivated by the convex optimization theory, are proposed. New crossover and mutation schemes are also devised to accommodate these new chromosomes, as well as to manage the interference to the PUs. Furthermore, we also propose a two-stage low-complexity genetic algorithm, which separately determines the proper subcarrier pairs and power allocations. Our simulations show that our HGAs and the two-stage algorithm provide competing performance, compared with similar state-of-the-art works.

Underwater communication systems have drawn the attention of the research community in the last 15 years. This growing interest can largely be attributed to new civil and military applications enabled by large-scale networks of underwater devices (e.g., underwater static sensors, unmanned autonomous vehicles (AUVs), and autonomous robots), which can retrieve information from the aquatic and marine environment, perform in-network processing on the extracted data, and transmit the collected information to remote locations. Currently underwater communication systems are inherently hardware-based and rely on closed and inflexible architectural design. This imposes significant challenges into adopting new underwater communication and networking technologies, prevent the provision of truly-differentiated services to highly diverse underwater applications, and induce great barriers to integrate heterogeneous underwater devices. Software-defined networking (SDN), recognized as the next-generation networking paradigm, relies on the highly flexible, programmable, and virtualizable network architecture to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. In this paper, a software-defined architecture, namely SoftWater, is first introduced to facilitate the development of the next-generation underwater communication systems. More specifically, by exploiting the network function virtualization (NFV) and network virtualization concepts, SoftWater architecture can easily incorporate new underwater communication solutions, accordingly maximize the network capacity, can achieve the network robustness and energy efficiency, as well as can provide truly differentiated and scalable networking services. Consequently, the SoftWater architecture can simultaneously support a variety of different underwater applications, and can enable the interoperability of underwater devices from different manufacturers that operate on different underwater communication technologies based on acoustic, optical, or radio waves. Moreover, the essential network management tools of SoftWater are discussed, including reconfigurable multi-controller placement, hybrid in-band and out-of-band control traffic balancing, and utility-optimal network virtualization. Furthermore, the major benefits of SoftWater architecture are demonstrated by introducing software-defined underwater networking solutions, including the throughput-optimal underwater routing, SDN-enhanced fault recovery, and software-defined underwater mobility management. The research challenges to realize the SoftWater are also discussed in detail.

This paper considers noisy Wyner–Ziv coding (WZC), in which a remote noisy source is compressed with side information at the decoder only. The decoder side information is not limited to being Gaussian, thus this noisy WZC problem cannot be transformed into the conventional one tailored for noiseless sources. A new coding structure, named the residual-quantization (RQ) based noisy WZC, is proposed to solve the problem. This scheme explicitly constructs the theoretical auxiliary random variable to facilitate optimal reconstruction of the noisy source. In this two-stage encoder, the noisy source is quantized twice and the quantization error (residue) of the first stage is the input of the second stage. By sending only the quantization index of the second stage to the decoder, the corresponding code rate can theoretically approach the noisy WZC bound. Moreover, the RQ-based noisy WZC is implemented using graph-based codes. The main challenge is that it is necessary to design a codebook that is simultaneously good for source and channel coding for the first stage quantization, since this quantization code also acts as a channel code at the decoder. This problem is solved by constructing a low-density parity check (LDPC) code with edge degrees optimized for channel coding, and enhancing its performance for source coding by using a modified reinforced belief-propagation quantization algorithm. Simulation results show that the noisy Wyner-Ziv bounds can be practically approached by our implementation. In addition, the proposed implementation offers more flexibility in the code rates compared with the existing practical designs, making it more suitable for emerging applications such as fronthaul compression.

Physical layer secrecy has attracted much attention in recent years due to its ability to ensure communication secrecy with the use of channel coding and signal processing techniques (and without the explicit use of secret keys) in the physical layer. It serves as a promising technique for highly dynamic or ad hoc systems such as device-to-device and machine-type communication systems. However, the achievable secrecy performance depends highly on the level of CSI at the transmitter, the receiver, and the eavesdropper. In this article, we discuss how different levels of CSI resulting from conventional and unconventional ways of performing training and channel feedback may affect the confidentiality in terms of the information-theoretic (perfect) secrecy rate. The conventional approach refers to the emission of pilot signals from the transmitter and explicit channel feedback from the receiver. This approach is backward compatible with existing systems and allows the receiver to obtain accurate knowledge of the CSI, but may suffer from CSI leakage toward the eavesdropper. Unconventional approaches capitalize on reverse training to prevent CSI leakage and are shown to achieve significant improvements over conventional schemes in certain cases. For example, in a system with four transmit antennas and a single antenna at both the receiver and the eavesdropper, a secrecy rate gain of approximately 0.8 b/channel use at transmit SNR of 16 dB is observed over the full CSI case by providing CSI only to the transmitter (but not the receiver and the eavesdropper).

Cellular network technologies have evolved to support the ever-increasing wireless data traffic, which results from the rapidly-evolving Internet and widely-adopted cloud applications over wireless networks. However, hardware-based designs, which rely on closed and inflexible architectures of current cellular systems, make a typical 10-year cycle for a new generation of wireless networks to be standardized and deployed. To overcome this limitation, the concept of software-defined networking (SDN) has been proposed to efficiently create centralized network abstraction with the provisioning of programmability over the entire network. Moreover, the complementary concept of network function virtualization (NFV) has been further proposed to effectively separate the abstraction of functionalities from the hardware by decoupling the data forwarding plane from the control plane. These two concepts provide cellular networks with the needed flexibility to evolve and adapt according to the ever-changing network context and introduce wireless software-defined networks (W-SDNs) for 5G cellular systems. Thus, there is an urgent need to study the fundamental architectural principles underlying a new generation of software-defined cellular network as well as the enabling technologies that supports and manages such emerging architecture. In this paper, first, the state-of-the-art W-SDNs solutions along with their associated NFV techniques are surveyed. Then, the key differences among these W-SDN solutions as well as their limitations are highlighted. To counter those limitations, SoftAir, a new SDN architecture for 5G cellular systems, is introduced.

Session F: Service provision; International audience; —Sheer number of devices in Internet of Things (IoTs) fundamentally challenge the ubiquitous information transmissions through the backbone networks, such as cellular systems. The heterogeneity of IoT devices and the hardware-based, inflexible cellular architectures impose even greater challenges to enable efficient communication. To address these challenges, this paper introduces the so-called SoftAir architecture on wireless software-defined networking and proposes software-defined gateways (SD-GWs) that jointly optimize cross-layer communication function-alities between heterogeneous IoT devices and cellular systems. First, the SoftAir architecture is proposed to support a unified software-defined platform for quality-of-service aware IoT systems and software-defined radio access networks (SD-RANs) with millimeter-wave transmissions. Next, the SD-GWs are designed in SoftAir to explore the interactions between two-types of networks (i.e., IoTs and SD-RANs) and enable cross-layer solutions that simultaneously achieve optimal energy savings and throughput gain in IoTs and maximum sum-rates in SD-RANs. Simulation results validate that our SoftAir solutions surpass classical IoT schemes by jointly optimizing communication functionalities for both IoTs and SD-RANs and bring significant system synergies for reliable 5G IoT communication.

This paper investigates an energy-efficient non-orthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TDMA., accepted by IEEE GlobeCom workshop on URLLC, 2017

Wireless underground sensor networks (WUSNs) enable many applications such as underground pipeline monitoring, power grid maintenance, mine disaster prevention, and oil upstream monitoring among many others. While the classical electromagnetic waves do not work well in WUSNs, the magnetic induction (MI) propagation technique provides constant channel conditions via small size of antenna coils in the underground environments. In this paper, instead of adopting currently layered protocols approach, a distributed cross-layer protocol design is proposed for MI-based WUSNs. First, a detailed overview is given for different communication functionalities from physical to network layers as well as the QoS requirements of applications. Utilizing the interactions of different layer functionalities, a distributed environment-aware protocol, called DEAP, is then developed to satisfy statistical QoS guarantees and achieve both optimal energy savings and throughput gain concurrently. Simulations confirm that the proposed cross-layer protocol achieves significant energy savings, high throughput efficiency and dependable MI communication for WUSNs.

We consider the block Rayleigh fading multiple-input multiple-output (MIMO) wiretap channel with no prior channel state information (CSI) available at any of the terminals. The channel gains remain constant within a coherence interval of $T$ symbols, and then change to another independent realization in the next coherence interval. The transmitter, the legitimate receiver, and the eavesdropper have $n_{t}$ , $n_{r}$ , and $n_{e} $ antennas, respectively. We determine the exact secure degrees of freedom (s.d.o.f.) of this system when $T\geq 2\min(n_{t},n_{r} )$ . We show that, in this case, the s.d.o.f. is exactly equal to $(\min(n_{t},n_{r})-n_{e})^{+}(T-\min(n_{t},n_{r} ))/T$ . The first term in this expression can be interpreted as the eavesdropper with $n_{e}$ antennas taking away $n_{e} $ antennas from both the transmitter and the legitimate receiver. The second term can be interpreted as a fraction of the s.d.o.f. being lost due to the lack of CSI at the legitimate receiver. In particular, the fraction loss, $\min(n_{t},n_{r} ) /T$ , can be interpreted as the fraction of channel uses dedicated to training the legitimate receiver for it to learn its own CSI. We prove that this s.d.o.f. can be achieved by employing a constant norm channel input, which can be viewed as a generalization of discrete signalling to multiple dimensions.

Background Previous studies have shown that depressive symptoms in middle-aged women were associated with a number of factors such as climacteric symptoms. Nevertheless, studies based on population-based data with a wide range of potential correlates are still scarce. Therefore, the aim of this study was to investigate the correlates of depressive symptoms in late middle-aged Taiwanese women using data from a nationally-representative, population-based survey. Methods Women aged 50.0–65.0 years were identified from the dataset of the 2009 Taiwan National Health Interview Survey. The outcome measure was depressive symptoms in the past week, evaluated using the Center for Epidemiologic Studies Short Depression Scale (CES-D 10) with a cut-off score of 10 or greater. Univariate and multiple logistic regression analyses were used to evaluate the correlates of depressive symptoms. Results The mean age of the 533 respondents was 56.7 years. Depressive symptoms were present in 53 respondents (9.9%). Multiple logistic regression analysis revealed that an education level of elementary school or below (adjusted odds ratio [AOR] = 3.19, P = 0.003), nulliparity (AOR = 8.10, P = 0.001), living alone (AOR = 5.47, P = 0.003), never having worked (AOR = 4.14, P = 0.008), lack of regular exercise (AOR = 3.01, P = 0.003), a perceived health status of fair or bad (AOR = 4.34, P

Purpose: Colorectal cancer is the third most common cancer and also the fourth most common cause of cancer mortality worldwide. For rectal cancer, neoadjuvant concurrent chemoradiotherapy (CCRT) followed by radical proctectomy is gold standard treatment for patients with stage II/III rectal cancer. By data mining a documented database of rectal cancer transcriptome (GSE35452) from Gene Expression Omnibus, National Center of Biotechnology Information, we recognized that DUOX2 was the most significantly up-regulated transcript among those related to cytokine and chemokine mediated signaling pathway (GO:0019221). Hence, the aim of this study was to assess the DUOX2 expression level and its clinicopathological correlation and prognostic significance in patients of rectal cancer. Materials and Methods: DUOX2 immunostain was performed in 172 rectal adenocarcinomas treated with preoperative CCRT followed by radical proctectomy, which were divided into high- and low-expression subgroups. Furthermore, statistical analyses were examined to correlate the relationship between DUOX2 immunoreactivity and important clinical and pathological characteristics, as well as three survival indices: disease-specific survival (DSS), local recurrence-free survival (LRFS) and metastasis-free survival (MeFS). Results: DUOX2 overexpression was linked to post-CCRT tumor advancement, pre- and post-CCRT nodal metastasis and poor response to CCRT (all P ≤ 0.021). Furthermore, DUOX2 high expression was significantly associated with inferior DSS, LRFS and MeFS in univariate analysis (P ≤ 0.0097) and also served as an independent prognosticator indicating shorter DSS and LRFS interval in multivariate analysis (hazard ratio (HR) = 3.413, 95% confidence interval (CI): 1.349-8.633; HR = 4.533, 95% CI: 1.499-13.708, respectively). Conclusion: DUOX2 may play a pivotal role in carcinogenesis, tumor progression and response to neoadjuvant CCRT in rectal cancers, and serve as a novel prognostic biomarker. Additional researches to clarify the molecular and biochemical pathways are essential for developing promising DUOX2-targeted therapies for patients with rectal cancers.

Wireless communication technology continues to advance at a rapid pace, and researchers have made tremendous progress in extending single-input single-output (SISO) systems to multiple-input multiple-output (MIMO) systems such as IEEE 802.11n, WiMAX, and LTE. However, a MIMO system requires a detector circuit to separate received data. To reduce the number of comparators required, the method of modified merge sort is proposed, and the results are compared with those of five sorting algorithms. This modified merge sort requires approximately 56% fewer comparators than a bitonic merge sort and approximately 46% fewer comparators than an odd-even merge sort. When implemented in a TSMC 0.18-μm process, the proposed chip demonstrates a throughput of up to 1200 Mbps at an operating frequency of 150 MHz.

This paper considers the multiantenna multiple-access relay channel (MARC), in which multiple users transmit messages to a common destination with the assistance of a relay. In a variety of MARC settings, the dynamic decode-and-forward (DDF) protocol is very useful due to its outstanding rate performance. However, the lack of good structured codebooks so far hinders practical applications of DDF for MARC. In this work, two classes of structured MARC codes are proposed: 1) one-to-one relay-mapper-aided multiuser lattice coding (O-MLC); and 2) modulo-sum relay-mapper-aided multiuser lattice coding (MS-MLC). The former enjoys better rate performance, whereas the latter provides more flexibility to tradeoff between the complexity of the relay mapper and the rate performance. It is shown that, in order to approach the rate performance achievable by an unstructured codebook with maximum-likelihood decoding, it is crucial to use a new K-stage coset decoder for structured O-MLC instead of the one-stage decoder proposed in previous works. However, if O-MLC is decoded with the one-stage decoder only, it can still achieve the optimal DDF diversity-multiplexing gain tradeoff in the high signal-to-noise ratio regime. As for MS-MLC, its rate performance can approach that of the O-MLC by increasing the complexity of the modulo-sum relay-mapper. Finally, for practical implementations of both O-MLC and MS-MLC, practical short-length lattice codes with linear mappers are designed, which facilitate efficient lattice decoding. Simulation results show that the proposed coding schemes outperform existing schemes in terms of outage probabilities in a variety of channel settings, especially when the users-to-relay links are better than the other channel links.

Cognitive radio (CR) has emerged as a key technology for enhancing spectrum efficiency by creating opportunistic transmission links. Supporting the routing function on top of opportunistic links is a must for transporting packets in a CR ad hoc network (CRAHN) consisting of cooperative relay multi-radio systems. However, there lacks a thorough understanding of these highly dynamic opportunistic links and a reliable end-to-end transportation mechanism over the network. Aspiring to meet this need, with innovative establishment of the spectrum map from local sensing information, we first provide a mathematical analysis to deal with transmission delay over such opportunistic links. Benefitting from the theoretical derivations, we then propose spectrum-map-empowered opportunistic routing protocols for regular and large-scale CRAHNs with wireless fading channels, employing a cooperative networking scheme to enable multipath transmissions. Simulations confirm that our solutions enjoy significant reduction of end-to-end delay and achieve dependable communications for CRAHNs, without commonly needed feedback information from nodes in a CRAHN to significantly save the communication overhead at the same time.