Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA’s random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP’s passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43% than the competing protocols.

As the number of wireless devices supporting multiple communication interfaces increases, the connection redundancy is being considered for efficient bandwidth utilization and QoS improvement. Accordingly, network technologies must adapt to emerging multi-interface devices to improve network performance. Multipath TCP (MPTCP) is default multipath transport protocol desired for networks with multi-interface devices and has achieved success in computer networks. However, it has not been well studied for wireless networks, especially for carrier sense multiple access (CSMA) based wireless networks, which present great challenges to round trip time (RTT) computation and multipath scheduling. This paper introduces MPTCP techniques for heterogeneous WiFi and 5G networks. We first model a proposed 5-state congestion control algorithm and WiFi CSMA function. We then present an innovative RTT computation method and a novel loss-ware multipath scheduling mechanism. We evaluated the proposed MPTCP techniques under varying network configurations. Our MPTCP can significantly outperform conventional MPTCP.

This paper explores the transformative potential of the IoT paradigm in promoting smart agriculture. Key challenges lie in how to connect agriculture sensors to remote cloud servers in the absence of feasible communication infrastructure and the unreliable wireless links in rural areas. To address these issues, we propose an innovative two-tier smart agriculture architecture: an Unmanned Aerial Vehicle (UAV) aided agriculture network model, which leverages UAVs as intermediaries to collect and route data from agriculture sensors to cloud servers. This novel architecture leads to two particular problems, i.e., data packet scheduling in the first-tier networks and multi-hop routing in the second-tier UAV mesh network. To that end, we present formal Markov decision process (MDP) based problem formulations for both tiers, with a primary focus on the more challenging multi-hop routing problem in the second-tier network. This problem is approached as a multi-agent reinforcement learning (MARL) framework, for which we introduce a novel distributed algorithm - Focus Coordination: attention-guided Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG). This algorithm reduces communication overhead and mitigates the risks associated with single-node failures. We evaluated the performance of the proposed FC-MADDPG algorithm, demonstrating its efficacy in enhancing data transmission reliability and efficiency.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. Next generation IoT devices can support multiple communication interfaces and perform more functions. Accordingly, IoT network technologies must adapt to the emerging multi-link devices to improve network performance. Multipath TCP (MPTCP) is desired for networks with multi-link devices and has achieved success in computer networks. However, MPTCP has not been well studied for wireless networks. To that end, this paper presents MPTCP techniques for heterogeneous wireless IoT networks consisting of IEEE 802.15.4 nodes and 5G nodes. We propose a path builder, an adaptive congestion controller and an innovative path scheduler. We evaluated our MPTCP techniques under varying network configurations. Compared with conventional MPTCP, the proposed MPTCP can significantly reduce the number of packet transmissions, shorten packet delivery time, improve network throughput and packet delivery rate.

Sub-1 GHz Wireless Communications of LPWAN (Low Power Wide Area Network) are attracting attention in IoT applications. In addition to battery-powered devices, the number of grid-powered and solar-powered sensor devices using LPWAN are also rapidly increasing for various IoT applications. We aim to improve reliability and efficiency of IEEE 802.15.4 CSMA/CA mechanism in the network consisting of devices without power constraint. We propose Suspendable Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms for IEEE 802.15.4 to mitigate packet loss by channel access failure while maintaining compatibility with conventional IEEE 802.15.4 CSMA/CA. We have performed extensive simulations to valid the Suspendable CSMA/CA mechanism. Simulation results show that the proposed Suspendable CSMA/CA improves Packet Delivery Rate (PDR) by 9.7 points (89.9 % to 99.6 %) compared to the conventional IEEE 802.15.4g CSMA/CA and therefore, can lead higher spectrum efficiency for IoT applications operate in the limited Sub-l G Hz wireless bandwidth. The proposed Suspendable CSMA/CA mechanism has been also approved and adopted for the next IEEE 802.15.4 amendment by IEEE 802.15 Working Group.

Along with the fourth industrial revolution, industrial automation systems are evolving into a multi-tier end-edge computing architecture. Edge controllers, which are equipped with a larger computing capacity compared to local controllers, can communicate with local plants over mainstream wireless networks such as WirelessHART, Wi-Fi, and cellular networks. Well-known challenges induced by networks, such as uncertain time delays and packet drops, have been intensively investigated from various perspectives: control synthesis, network design, or control and network co-design. The status quo is that the industry remains hesitant to close the loop between the edge controller and the actuation side due to safety concerns. This work offers an alternative perspective to address the safety concern, by exploiting the design freedom of an end-edge computing architecture. Specifically, we present a smart actuation framework, which deploys (1) an edge controller, which communicates with physical plant via wireless network, accounting for optimality, adaptation, and constraints by conducting computationally expensive operations; (2) a smart actuator, which is co-located with the physical plant on the end tier and executes a local control policy, accounting for system safety in the view of network imperfections, (3) the end-edge control co-design strategies and cooperation logic for both performance and stability. For certain classes of plants, semi-globally asymptotic stability of the resulting end-edge control systems is established when the edge controller is the model predictive control (MPC), or policy iteration-based learning control. We also provide an adaptation strategy for the end-edge control systems facing model parameter mismatches when the edge controller employs reinforcement learning. Extensive simulations demonstrate the advantages of the proposed end-edge co-design and cooperation procedures. Note to Practitioners—Edge computing is gaining momentum in areas that require low latency and high efficiency, i.e., mobile computing, video analytics, and autonomous driving. Industrial automation systems are also evolving into a multi-tier end-edge computing architecture. It pays obvious dividends to leverage the cooperation between end and edge, benefiting from fast and reliable communication on the end side, and powerful computation capacity on the edge side. The current end-edge cooperation focuses on how to partition tasks and offload computation resources in order to minimize delay and energy consumption, as well as how to balance the tradeoff between them. However, the impacts of end-edge cooperation on the safety, optimality, and cost of industrial automation have not been systematically studied. This paper aims to tailor end-edge cooperation in a smart actuation framework, for industrial automation to reconcile the above aspects by leveraging co-design of end and edge controllers and their switching logic. Extensive pure and semi-physical simulations demonstrate the advantages in performance and system stability of the proposed end-edge co-design and cooperation procedures.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. The next generation IoT devices are capable of supporting multiple communication modes and performing more functions. During the migration phase, it is impractical to completely remove the deployed current generation devices. Accordingly, the next generation IoT networks will consist of the mixed current and next generation devices. To that end, how to efficiently route diverse data in next generation IoT networks needs to be addressed. This paper presents a two-topology routing architecture for next generation IoT networks, one topology for regular data delivery and another topology for priority data delivery. The priority routes are discovered to minimize route overlap. We evaluated our route discovery algorithms under varying network configurations. Compared with standard RPL baseline, the proposed routing algorithms can simultaneously reduce route overlap, route transmission time and route length.

Pulse-width-modulation (PWM) inverters generate electromagnetic interference (EMI) due to large dv/dt and di/dt of switching operations. When a periodic carrier is used to generate the PWM control signal, the corresponding EMI spectrum contains strong harmonic components of the carrier frequency. Research has shown that the carrier harmonic spectrum can be mitigated by modulating the carrier frequency. In this paper, we proposed a learning-based method to design the carrier frequency modulation of the inverter by solving an optimization problem, given EMI spectra of the PWM inverter under different carriers as learning data. As a result, a desired EMI spectrum envelop can be achieved according to the EMC regulation. Synthesized EMI results show that our learning-based method generalize the frequency modulation carrier design for EMI mitigation. Experiments provide promising results on EMI reduction.

The software distribution in advanced IoT networks is inevitable. However, distributing software in multi-hop wireless networks consumes enormous communication bandwidth and can also suffer from reliability challenge. This paper proposes an innovative dynamic relay point (DRP) protocol to reduce the number of software packet transmissions. It also introduces a network condition based rateless coding scheme to improve the packet transmission reliability. The NS3 simulator is employed for performance evaluation. The proposed DRP protocol outperforms multi-point relay (MPR) baseline by reducing the software packet transmissions and improving the effective throughput.

This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.

Optical coherence tomography (OCT) using Fourier domain processing can resolve micrometer-scale depth information. However, the conventional volumetric reconstruction approach is unnecessary for opaque samples with only one reflector per lateral position, and the required sample interpolation degrades performance. In this paper, we show that surface depth profilometery with a Fourier-domain OCT system simplifies to a sinusoidal parameter estimation problem. We derive approximate maximum likelihood estimators for the sample depth and reflectivity, which can easily be computed by backprojecting the data without interpolating. Iterative refinement further improves results at high signal-to-noise ratio (SNR). We demonstrate the performance of the technique compared to the conventional Fourier transform approach on both simulated and experimental data collected with a spectral-domain OCT system. Our results show that maximum likelihood profilometry is fast and more robust to noise than the Fourier approaches at moderate SNR.

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.

While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.

With the explosive proliferation of wireless devices, our lives are improved by various applications supported by heterogeneous wireless technologies, such as WiFi and ZigBee. However, the coexistence of WiFi and ZigBee also results in the degradation of the network performance, which cannot be avoided if the WiFi devices are even unaware of the ambient ZigBee devices. To better accommodate the heterogeneous wireless devices, this paper presents X-Disco, the first cross-technology neighbor discovery mechanism, for a WiFi device to detect ZigBee neighbors, without modification to hardware or firmware. With the help of the recently proposed cross-technology communication, X-Disco enables a commodity WiFi device to trigger responses, containing ZigBee neighbor information, from the ambient ZigBee coordinators (including routers). Through exploring the WiFi PHY-layer information accessible by WiFi driver, X-Disco decodes the responded ZigBee messages and obtains the ZigBee neighbor information. To improve X-Disco's reliability, we also propose ZigBee neighbor validation and interruption mitigation to exclude hidden node terminals and mitigate the interference caused by the ambient WiFi traffic respectively. The evaluation of X-Disco is performed on the commodity devices (TP-Link WDR 4300 WiFi router, TelosB motes) and USRP B210. The results demonstrate X-Disco successfully detects nine ZigBee neighbors within 70ms in the office.

Complementary to the fine-grained channel state information (CSI) and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes [i.e., beam SNR (bSNR)] during the millimeter-wave (mmWave) beam training phase were recently repurposed for Wi-Fi sensing applications, such as human activity recognition and indoor localization. This article proposes a multiband Wi-Fi sensing framework to fuse features from both CSI from 5-GHz bands and the mid-grained bSNR at 60 GHz with feature granularity matching (GM) that pairs feature maps from the CSI and bSNR at different granularity levels with learnable weights. To address the issue of limited labeled training data, we propose to pretrain an autoencoder-based multiband Wi-Fi fusion network in an unsupervised fashion. For specific sensing tasks, separate sensing heads can be attached to the pretrained fusion network with fine-tuning. The proposed framework is thoroughly validated for three sensing applications using in-house experimental data sets: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to a list of baseline methods demonstrates the effectiveness of GM. An ablation study is performed as a function of the amount of labeled data, the latent space dimension, and learning rates.

Point cloud is an emerging data format useful for various applications such has holographic display, autonomous vehicle, and augmented reality. Conventionally, communications of point cloud data have relied on digital compression and digital modulation for three-dimensional (3D) data streaming. However, such digital-based delivery schemes have fundamental issues called cliff and leveling effects, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast+, to overcome cliff and leveling effects. Specifically, our method utilizes hybrid digital-analog coding, integrating digital compression and analog coding based on graph Fourier transform (GFT), to gracefully improve 3D reconstruction quality with the improvement of channel quality. We demonstrate that HoloCast+ offers better 3D reconstruction quality in terms of the symmetric mean square error (sMSE) by up to 18.3 dB and 10.5 dB, respectively, compared to conventional digital-based and analog-based delivery methods in wireless fading environments.

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.

This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks, in which the use of multiple clusters or small cells is preferable to increase transmission speeds, reduce latency, and bring transmissions closer to the users. To increase the spectral efficiency and coverage, and to achieve flexible spatial degrees of freedom, a distributed remote radio unit system (dRRUS) is installed in each of the clusters. When the dRRUS is disposed in the private environments, it will be associated with multipath-rich and asynchronous delay propagation. Taking into account of this unique environment of private networks, asynchronous multiple signal reception is considered in the development of operation at the remote radio units to make an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve a full transmit diversity gain without full channel state information. A spectral efficiency of the proposed dCDD-based JT is analyzed by deriving the closed- form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire private network.

A novel multi-task federated learning (FL) framework is proposed in this paper to optimize the traffic prediction models without sharing the collected data among traffic stations. In particular, a divisive hierarchical clustering is first introduced to partition the collected traffic data at each station into different clusters. The FL is then implemented to collaboratively train the learning model for each cluster of local data distributed across the stations. Using the multi-task FL framework, the route planning is studied where the road map is modeled as a time-dependent graph and a modified A * algorithm is used to determine the route with the shortest traveling time. Simulation results showcase the prediction accuracy improvement of the proposed multi-task FL framework over two baseline schemes. The simulation results also show that, when using the multi-task FL framework in the route planning, an accurate traveling time can be estimated and an effective route can be selected.

Abstract This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederación Hidrográfica del Ebro (CHE), public institution which manages the hydrographic system of Aragón, Spain. Therefore, fault-tolerance is a mandatory requirement, complex to fulfill since it depends on the meteorology, the state of the batteries and the power demand. To solve it, we propose an online voltage prediction solution where GPR is applied in a real and large dataset of two years to predict the behavior of the installation up to 48 hour. The dataset captures electrical and thermal measures of the lead-acid batteries which sustain the installation. In particular, the crucial aspect to avoid failures is to determine the voltage at the end of the night, so different GPR methods are studied. Firstly, the photovoltaic standalone installation is described, along with the dataset. Then, there is an overview of GPR, emphasizing in the key aspects to deal with real and large datasets. Besides, three online recursive multistep GPR model alternatives are tailored, justifying the selection of the hyperparameters: Regular GPR, Sparse GPR and Multiple Experts (ME) GPR. An exhaustive assessment is performed, validating the results with those obtained by Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Exogenous Model (NARX) networks. A maximum error of 127 mV and 308 mV at the end of the night with Sparse and ME, respectively, corroborates GPR as a promising tool.

Screen-camera communications, using a liquid crystal display (LCD) screen and camera image sensors, have been attractive variants of visible light communications (VLC) since any external light-emitting modules and photo detectors are required for recent mobile devices, which are usually equipped with display and camera. A major issue in screen-camera communications is a performance loss in transmission rate due to nonlinear channel impairments with ambient noise. To improve transmission rates, we investigate the impact of nonlinear channel equalization, nonbinary channel coding, probabilistic shaping, and nonlinear precoding for high-order modulation schemes. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.8-3.3 times higher transmission rates compared to existing schemes for a communication distance of 60-160 cm.

In this paper, a new transmit diversity scheme is proposed for cooperative pseudo-non-orthogonal multiple access (Pseudo-NOMA) without assuming full channel state information at the transmitter (CSIT). To support two users under the near-far user pairing constraint, a distributed cyclic delay diversity (dCDD) scheme is adapted into NOMA by dividing a set of remote radio heads (RRHs) into two groups for multiple cyclic-prefixed single carrier transmissions. To maximize a far user's rate and two users' sum rate over independently but non-identically distributed frequency selective fading channels and under a near-far user pairing constraint, we first derive closed-form expressions for the rates of the two users with full CSIT. Considering that only partial CSIT is available, a new RRH assignment and power allocation scheme is proposed for dCDD-Pseudo-NOMA. For various simulation scenarios, the provided link-level simulations verify that higher rates can be achieved by dCDD-Pseudo-NOMA compared with the traditional orthogonal multiple access with dCDD and dCDD-Conventional-NOMA that uses the superimposed signals. Furthermore, the proposed RRH assignment and power allocation scheme makes dCDD-Pseudo-NOMA achieve almost the same rate as that of ideal dCDD-Pseudo-NOMA which requires full CSIT.

For a multi-cluster-based transmit diversity scheme that supports joint transmissions (JT) in private networks, a distributed remote radio unit system (dRRUS) is deployed in each of the clusters to increase the spectral efficiency and coverage, and to achieve flexible spatial degrees of freedom. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server (PNS) and cluster master (CM), which is the main backhaul communication, and between the CM to remote radio units (RRUs), which is the secondary backhaul communication. Thus, this paper mainly investigates the reliability of main and secondary backhaul connections for cluster-based transmit diversity schemes in private networks. Employing a Bernoulli process to model each backhaul reliability, a composite backhaul connection is modeled by an independent product of Bernoulli processes. By employing the distributed cyclic delay diversity scheme over the dRRUS and precision time protocol for clock synchronization, the multicluster-based JT can be achieved without full channel state information of the private network environment at the PNS and CMs. Having developed necessary distributions for the signal-tonoise ratio realized at the receiver, the closed-form expressions for the outage probability and spectral efficiency are derived. To verify their accuracy, the analytical performances are compared with link-level simulations.

In addition to coarse-grained received signal strength indicator (RSSI) measurements and fine-grained channel state information (CSI), a mid-grained channel measurement - spatial beam signal-to-noise ratios (SNRs) - that are inherently available during the millimeter wave (mmWave) beam training as defined in mmWave fifth-generation (5G) and IEEE 802.11ad/ay standards, were recently utilized for fingerprinting-based indoor localization. In this paper, we extend the beam SNR fingerprinting-based indoor localization to more challenging scenarios in non-line-of-sight (NLOS) propagation. Particularly, multi-channel beam covariance matrix (BCM) images are used as the fingerprinting signature and fed into a beam covariance learning (BCL) network to identify the position and estimate the coordinate. Using our in-house testbed with commercial off-the-shelf (COTS) 60-GHz WiFi routers, real-world mmWave BCMs are fingerprinted in several NLOS locations-of-interest in an enclosed L-shape conference room. Given a fingerprinting grid-size of 30 cm, preliminary performance evaluation shows the position classification accuracy can be above 90% using classical classification methods and a coordinate estimation error around 11 cm with the BCL approach.

This paper considers object detection in the case of imperfect waveform separation, in the context of automotive radars with a slow-time MIMO-FMCW signaling scheme. We develop an explicit signal model that accounts for waveform separation residuals and propose a Kronecker subspace-based object detector in the framework of generalized likelihood ratio test (GLRT). Our exact theoretical analysis under both hypotheses shows that the proposed detector holds the desired property of constant false alarm rate (CFAR). Numerical simulations validate our proposed object detection scheme.

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.

Soft image delivery uses pseudo-analog modulation for wireless image transmissions to prevent cliff and leveling effects subject to channel quality fluctuation and to realize graceful quality improvement according to wireless channel quality. Despite its attractive feature of graceful performance, the conventional soft image delivery suffers from low image quality when the analog-modulated symbols are severely impaired by fading and strong channel noise. In this paper, we propose a novel scheme of soft image delivery to reconstruct high-quality images from its low-quality observations. Specifically, the proposed scheme integrates deep convolutional neural network (DCNN)-based image restoration, i.e., deep image prior, into soft image delivery. The deep image prior learns a mapping function from the noisy image to the clean image based on user's perception-aware loss function using multiple training images in prior to soft delivery. The mapping function can restore a clean image even when the received image is distorted by strong fading and additive noise. From the evaluation results, the proposed scheme can remove fading and noise effects from the received images by using DCNN-based image restoration. For example, the proposed scheme achieves up to 0.44 improvement compared with the conventional soft image delivery in terms of structural similarity (SSIM) index at a deep fading channel.

Conventional point cloud delivery schemes use graph-based compression to stream three-dimensional (3D) points and the corresponding color attributes over wireless channels for 3D scene reconstructions. However, the graph-based compression requires a significant communication overhead for graph signal decoding, i.e., inverse graph Fourier transform (IGFT), and such large overhead causes a low 3D reconstruction quality due to power and rate losses. We propose a novel scheme of point cloud delivery to significantly reduce the amount of overhead while allowing a small degradation in the 3D reconstruction quality. Specifically, the proposed scheme exploits Givens rotation for the graph-based transform basis matrix to compress the basis matrix into quantized angle parameters. Even when the angle parameters are strongly quantized for compression, the receiver can reconstruct a clean point cloud by using the basis matrix obtained from the quantized angle parameters. Evaluation results show the Givens rotation in the proposed scheme achieves overhead reduction with a slight quality degradation. For example, the proposed scheme achieves 89.8% overhead reduction with 1.3 dB quality degradation compared with the conventional point cloud delivery scheme.

In this paper, a new transmit diversity scheme for cooperative non-orthogonal multiple access (NOMA) is proposed without perfect channel state information at the transmitter (CSIT). To support two users under a near-far user pairing constraint, a distributed cyclic delay diversity (dCDD) scheme is adjusted into NOMA by dividing a set of remote radio heads (RRHs) into two groups for multiple cyclic-prefixed single carrier transmissions. Using only a limited channel relevant information needed to make dCDD work, a new RRH assignment and power allocation mechanism is proposed. After then, closed-form expressions for the rates of two users achieved by the proposed RRH assignment and power allocation mechanism are derived. For various scenarios, link-level simulations verify that superior rates can be achieved by NOMA with dCDD over the traditional orthogonal multiple access with dCDD.

In this paper, we develop a robust three dimensional tomographic imaging framework to estimate the ionospheric electron density using ground-based total electron content (TEC) measurements from GPS receivers. In order to increase the sampling rate of the domain, we incorporate into the tomographic measurements the TEC readings observed from low-angle satellites that fall outside of the target ionospheric domain. We discount the proportion of the TEC measurements that originate outside of the target domain using the simulation-based NeQuick2 model as reference. We also employ a diffusion kernel regularization function to robustify the reconstruction against errors in the NeQuick2 model. Finally, we demonstrate through simulations that our framework delivers superior reconstruction of the ionospheric electron density compared to existing schemes. We also demonstrate the applicability of our approach on real TEC measurements.

To improve the traffic flow in the interconnected intersections, the vehicles and infrastructure such as road side units (RSUs) need to collaboratively determine vehicle scheduling while exchanging information via vehicle-to-everything (V2X) communications. However, due to a large number of vehicles and their mobility, scheduling in the interconnected intersection is a challenging problem. Moreover, since low-latency information exchange and real-time decision making process are required, it becomes more challenging to design a holistic framework incorporating traffic control and V2X communications. In this paper, an edge computing framework is proposed to solve a travel time minimization problem at the interconnected intersections. The proposed framework enables each RSU to decide intersection scheduling while the vehicles individually determine travel trajectory by controlling their dynamics. To this end, a V2X communications protocol is designed to exchange information among vehicles and RSUs. Then, the road segments around intersection are partitioned into sequence, control, and crossing zones. In the sequence zone, optimal time is scheduled for vehicles to pass the intersection with a minimum delay. In the control zone, the location and velocity of each vehicle are controlled to arrive the crossing zone at the scheduled time by using a control algorithm designed to effectively increase driving comfort and reduce fuel consumption. Thus, the proposed framework enables the vehicles to safely pass the crossing zone without collision. Simulation results show that the proposed edge computing can successfully reduce the total travel time by up to 14.3% based on optimal scheduling for the interconnected intersections.

Motivated by an increasing interest in wireless networking in mission-critical applications, and a recent amendment of the time slotted channel hopping to IEEE 802.15.4, the multichannel delay sensitive scheduling is investigated in the many-to-one network, which is also known as the convergecast network. In such a network, each node has data to be transmitted to a gateway through multi-hop communications. As a realistic setting, packet release time at each node is not assumed to be uniform. Under this assumption, the goal of this work is to design a scheduling scheme that minimizes the schedule length and maximum end-to-end delay, in which the former is essential for repetitive data acquisition, whereas the later improves the freshness of the acquired data. To achieve the scheduling goal, the problem is formulated as a multi-objective integer programming. To obtain a feasible solution and gain an insight into the problem, a lower bound on the schedule length is derived. Based on that, a new scheduling scheme is designed to minimize the two objectives simultaneously. Link level simulations verify the performance improvement of the proposed scheme over the existing schemes.

This paper investigates the diversity gain of a distributed cyclic delay diversity (dCDD) scheme for cyclic-prefixed single carrier systems in non-identical fading channels. Non-identical small-scale fading is assumed in the environment, in which non-identical line-of-sight and non-line-of-sight fading coexist. A condition for dCDD resulting in intersymbol interference free reception at the receiver, is extended to this new channel environment. For an overpopulated system setup, a generalized performance analysis, is not available from existing works, is conducted after developing closed-form expressions for the distribution of the signal-to-noise ratio (SNR) realized at the receiver. Since the order statistics are involved in the statistical properties of the SNR, the corresponding spacing statistics are utilized to derive feasible closed-form expressions. The finalized closed-form expressions are shown to provide very reliable outage probability and spectral efficiency of dCDD for underpopulated and overpopulated systems. An asymptotic performance analysis verifies the maximum achievable diversity of the dCDD even in the overpopulated case within the considered channel environment. Link-level simulations are conducted and these verify the maximum achievable diversity gain.

A joint data and interference transmission scheme based on a new distributed asynchronous cyclic delay diversity (dACDD) technique is proposed for cooperative communication systems. Without any perfect channel state information from a legitimate user (LU) and an eavesdropping user (EU), joint remote radio head (RRH) selection for the data and jamming signal transmissions is proposed for dACDD to achieve the maximum diversity gain at the LU, while degrading the receive signal-to-interference-plus-noise ratio at the EU. The proposed dACDD is the extension of distributed cyclic delay diversity, which requires a tight synchronization among the central control unit and RRHs. Thus, processing at each RRH causing no intersymbol interference at the LU is developed. Then, the selection scheme for a data RRH is proposed, which selects a single RRH connected with the channel having the greatest channel magnitude as the data RRH to transmit a desired confidential message and controls the remaining RRHs to transmit an artificial interference sequence to the LU and EU. For the proposed distributed system, the marginal secrecy outage probability and marginal probability of non-zero achievable secrecy rate are analyzed by deriving closed-form expressions, whose correctness is verified via link-level simulations over non-identically distributed frequency selective fading channels.

Simultaneous screen-to-camera and screen-to-eye communications, i.e., watermarking, have been proposed in visible light communications. The main purpose of such communications is to provide many data bits for camera devices and visual information for human eyes by using a common displayed image. To this end, the existing studies leverage the capability discrepancy and distinctive features between the human vision system and camera devices. However, the existing techniques mainly require high refresh rates in both screen and camera devices to achieve better throughput while keeping high visual quality. In this paper, we propose a novel transmission scheme for efficient simultaneous screen-to- camera and screen-to-eye communications without a need of high refresh rates. Specifically, we use deep convolutional neural networks (DCNN)-based watermark encoder and decoder to embed many bits into high-quality images, and then to maximize throughput from the bit-embedded image. With end-to-end adversarial learning, the encoder networks learn a mapping function to embed digital data into an original image based on a perceptual loss function while the decoder networks also learn a mapping function from the bit-embedded image to the data bits based on a cross-entropy loss function. From the evaluations, we show that the proposed watermark encoding and decoding networks yield high throughput from the bit-embedded images compared with a simple DCNN-based watermarking. In addition, the bit-embedded images on the screen achieve high quality for human perception.

Soft delivery, i.e., analog transmission, has been proposed to provide graceful video/image quality even in unstable wireless channels. However, existing analog schemes require a significant amount of metadata for power allocation and decoding operations. It causes large overheads and quality degradation due to rate and power losses. Although the amount of overheads can be reduced by introducing Gaussian Markov random field (GMRF) model, the model mismatch can degrade reconstruction quality. In this paper, we propose a novel analog transmission scheme to simultaneously reduce the overheads and yield better reconstruction quality. The proposed scheme uses a deep neural network (DNN) for metadata compression and decompression. Specifically, the metadata is compressed into few variables using the proposed DNN-based metadata encoder before transmission. The variables are then transmitted and decompressed at the receiver for high-quality video/image reconstruction. Evaluations using test images demonstrate that our proposed scheme reduces overheads by 80.0% with 11.2 dB improvement of reconstruction quality compared to the existing analog transmission schemes.

The central challenge in supporting massive IoT connectivity is the uncoordinated, random access by sporadically active devices. The random access protocol and activity detection have been widely studied, while the auxiliary procedures, such as synchronization, channel estimation and equalization, have received much less attention. However, once the protocol is fixed, the access performance can only be improved by a more effective receiver, through more accurate execution of the auxiliary procedures. This motivates the pursuit of joint synchronization and channel estimation, rather than the traditional approach of handling them separately. The prohibitive complexity of the conventional analytical solutions leads us to employ the tools of deep learning in this paper. Specifically, the proposed method is applied to the random access protocol of Narrowband IoT (NB-IoT), preserving its standard preamble structure. We obtain excellent performance in estimating Time-of-Arrival (ToA), Carrier-Frequency Offset (CFO), channel gain and collision multiplicity from a received mixture of transmissions. The proposed estimator achieves a ToA Root-Mean-Square Error (RMSE) of 0.99 us and a CFO RMSE of 1.61 Hz at 10 dB Signal-to-Noise Ratio (SNR), whereas a conventional estimator using two cascaded stages have RMSEs of 15.85 us and 8.05 Hz, respectively.

In this paper, a distributed cyclic delay diversity (dCDD) is proposed to the cooperative infrastructure-to-vehicle (I2V) system comprising one road side unit (RSU). At a particular time, to connect a RSU and a target vehicle outside each other's transmission range, a multiple number of vehicles located within the transmission ranges of both the RSU and the target vehicle are configured to operate as the dCDD based decode-and-forward (DF) cooperative relays. By using dCDD in the I2V system, the transmission range of the RSU is extended without the need of full channel state information of the vehicles at the RSU, and the transmit diversity gain can also be achieved. For this new preliminary setting of the I2V system, we conduct performance analysis. The simulations are conducted to verify the outage probability. The asymptotic outage diversity gain is also investigated and justified by the link level simulations.

A joint data and jamming transmission scheme based on a new distributed asynchronous cyclic delay diversity (dACDD) scheme is proposed for cooperative communication systems. Without any exact knowledge of channel state information (CSI) at the transmitting side, a joint remote radio head (RRH) selection scheme for the data and jamming signal transmissions is proposed for dACDD to achieve the maximum diversity gain at a legitimate user (LU), while degrading the receive signal-to-interference-plus-noise ratio at an eavesdropping user (EU). A single RRH connected with the channel having the greatest channel gain is selected as a data RRH that transmits a desired confidential signal, whereas the remaining RRHs are controlled by the central control unit to transmit an artificial noise sequence (ANS) to the LU and EU. Without assuming exact knowledge of CSI of the whole system, the secrecy outage probability of the distributed communication system is analyzed by deriving a closed-form expression, and through link-level simulations over non-identically distributed frequency selective fading channels over the entire system.

Millimeter-wave (mmWave) communications is an emerging technology expected to bring unprecedented data rates and throughput. WiFi operating at unlicensed 60 GHz range is envisioned to become an ubiquitous technology and the IEEE 802.11ad standard is an initial attempt in that direction. Al-though spatial and temporal resolution of mmWave signals make them suitable for location estimation, a variety of hardware-related issues and commonly encountered difficulties in extract-ing channel measurements from commercial chipsets, challenge opportunistic use of commercial mmWave WiFi chips for indoor localization. We propose in this paper an indoor localization method that fingerprints transmit beam indices that a pair of WiFi transceivers employ to establish a mmWave link, as well as the resulting received signal strength (RSS). In particular, we develop an algorithm that learns possible probabilistic models from the fingerprint data and leverages them to perform indoor localization in the online stage. The proposed algorithm is experimentally evaluated using commercial 60 GHz WiFi routers in an office space area and localization error of around 30 cm is demonstrated.

In this paper, we propose a power encoding method for converting a high-resolution OFDM baseband signal into a signal that assumes only a finite number of values. This is achieved by the series interconnection of, optimally (co)designed, delta- sigma (/spl Delta/ /Spl Sigma/M) and digital pulse-width modulators (DPWM). Given an input signal class with a known mplitude distribution (e.g., OFDM), parameters of the multilevel DPWM are designed such that the mean squared error of the inherent DPWM quantization noise is inimized. Parameters of the /spl Delta/ /Spl Sigma/M are then chosen with respect to the designed DPWM, so to optimally shape the inherent quantization noise out of the spectral band of interest. The superior performance of the proposed novel power encoding scheme is demonstrated by Matlab simulations on several standardized LTE test signals.

In this paper, we propose a variational Bayesian inference approach for a low-complexity symbol detection for massive MIMO systems with symbol- dependent transmit-side impairments. This study is motivated by observations that realworld communication transceivers are often affected by the hardware impairments, such as non-linearities of power amplifiers, I/Q imbalance, phase drifts due to non-ideal oscillators, and carrier frequency offsets. Particularly, symbol-dependent perturbations are fully accounted into the designed hierarchical signal model as unknown model parameters. The developed variational Bayesian symbol detector is able to learn the unknown perturbations in an iterative fashion. Numerical evaluation confirms the effectiveness of the proposed approach.

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements from the MAC layer. In this paper, we propose to use an intermediate channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available during the beam training phase as defined in the IEEE 802.11ad standard - to construct the feature space for location-and-orientation-dependent fingerprinting database. We build a 60GHz experimental platform consisting of three access points and one client using commercial-off-the-shelf routers and collect realworld beam SNR measurements in an office environment during regular office hours. Both position/orientation classification and coordinate estimation are considered using classic machine learning approaches. Comprehensive performance evaluation using real-world beam SNRs demonstrates that the classification accuracy is 99.8% if the location is only interested, while the accuracy is 98.6% for simultaneous position-and-orientations classification. Direct coordinate estimation gives an average root-mean-square error of 17.52 cm and 95% of all coordinate estimates are less than 26.90 cm away from corresponding true locations. This concept directly applies to other mmWave band (e.g., 5G) devices where beam training is also required.

This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the misspecified Cramér-Rao bound (CRB) when system designers choose existing efficient estimation algorithms designed for an independent (decoupled) mixture model due to hardware limits. Our analysis provides an analytical tool to conveniently evaluate performance loss if the implemented system ignores the coupling effect. The achievability of the misspecified CRB is verified by numerical examples.

In power distribution networks, microgrids utilize Phasor Measurement Units (PMUs), to assess the voltage stability at critical nodes in the network. PMUs rely on precise time-keeping sources, such as GPS, to obtain synchronization. However, GPS signals are vulnerable to external spoofing attacks due to their unencrypted signal structure and low received power. To detect the spoofing-induced timing anomaly, an innovative geographically Distributed Multiple Directional Antennas (DMDA) setup is proposed, which is triggered using a common clock. Utilizing the configuration of the proposed DMDA, a Belief-Propagation (BP)-based Extended Kalman Filter (EKF) algorithm is developed to estimate the timing errors caused by spoofing. The BP-EKF algorithm analyzes the single difference pseudorange residuals across each pair of antennas in a probabilistic graphical framework not only to detect the spoofed antennas in the DMDA setup but also to estimate the timing errors associated with the spoofed antennas. Based on the BP estimate of timing error at each antenna and the known baseline distances across antennas, the pseudoranges are corrected, and then adaptive EKF is employed to estimate the GPS timing. The performance of the BP-EKF algorithm is assessed by subjecting the simulated authentic GPS signals to a simulated meaconing attack, which induces a time delay of 60 μs. Both successful detection of meaconing, and also accurate estimation of GPS timing that complies with the IEEE-C37.118 standards, is validated using the experimental results. At a critical node in the simulated microgrid, as compared to scalar tracking, an increased voltage stability is demonstrated using the BP-EKF by assessing a metric, namely, voltage stability index.

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.

In this paper, a cooperative single carrier system comprising multiple cooperating remote radio heads and a single interferer operating in the presence of co-existing line-of-sight and non-line-of-sight paths is investigated. Distributed cyclic delay diversity is employed as the transmit diversity scheme for cyclic-prefixed single carrier transmissions over independent but non-identically distributed frequency selective fading channels. The main focus of this paper is to investigate the achievable diversity gain in the interference-limited and noise-limited regions. In contrast to the outage probability in the noise-limited region, it is shown that the diversity gain is not achievable in the interference-limited region. To justify this finding, the outage probability is derived first, and then verified by link-level simulations.

Rechargeable batteries supply numerous devices with electric power and are critical part in a variety of applications. An accurate monitoring and prediction of capacity degradation is directly related to making timely decision as to when a battery should be replaced, so that power disruption of the system it supplies power to is avoided. We propose a methodology for predicting capacity of a battery over future time horizon. The proposed method is based on training data consisting of occasional measurements, taken under the same conditions, of capacity and charge/discharge voltage/current of a certain number of batteries sharing the same chemistry and manufacturer, that otherwise undergo different usage patterns. In the operational/online stage, capacity degradation over future time horizon of a test battery cell of unknown state of health and previous usage pattern is predicted based on its capacity and voltage/current measurements over one charge/discharge cycle and the training dataset. The experimental validation reveals that the proposed method predicts capacity of a test battery cell over prediction time horizon of few hundred days of battery's operation with relative prediction error below 1%.

The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.

A wide-area time authentication algorithm is proposed to compute the Global Positioning System (GPS) timing that is resilient against spoofing attacks. The considered wide-area network consists of multiple GPS receiving systems, each comprising of the innovative distributed multiple directional antennas (DMDA) setup triggered via a common clock.Based on the communication infrastructure of the grid, the single-difference pseudorange residuals across the antennas are processed using the wide-area belief propagation-based extended Kalman filter (BP-EKF) algorithm in a distributed manner. To detect spoofing, a KL-divergence-based threshold is used to estimate the dissimilarity in the antenna-specific timing errors. Thereafter, the pseudoranges are corrected using the BP estimates of timing error and processed via adaptive EKF to compute the GPS timing, which is given to the phasor measurement units (PMUs). We have demonstrated the successfully detection and mitigation of the external timing attack, by subjecting one receiving system to a simulated meaconing attack that induces a 60 micro second time delay. Thereafter, by analyzing the voltage stability index of a critical node in the simulated grid, we have also validated the compliance of the proposed wide-area BP-EKF estimated timing with the IEEE-C37.118 standards.

Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast. FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0-25 dB at a transmission symbol rate of 37.5 Msymbols/s.

In this paper, a cooperative single carrier system comprising multiple cooperating remote radio heads and spatially distributed interferers is investigated. Due to the random location of the interferers within the communication range, a mixture of line-of-sight (LoS) and non-line-of-sight (nLoS) paths is considered in the channel model. Under a frequency selective fading channel with a mixture of the LoS and nLoS paths, the distributed cyclic delay diversity is employed to achieve the maximum transmit diversity gain without the exact knowledge of the channel state information at the transmitter side. It is shown that the operating signal-to-noise regions are divided into two regions, i.e., noise-limited and interference-limited. In this paper, the main focus is on the interference-limited region, in which diversity gain is not achieved due to performance limits determined by the system and channel parameters. The existence of these limits on the performance metrics, such as the outage probability and ergodic capacity, is derived analytically and then verified by link-level simulations.

State-of-the-art base stations can be equipped with a massively large number of antenna elements, often several hundreds of elements, thanks to the rapid advancement of wideband radio-frequency (RF) analog circuits and compact antenna design techniques. With massive antenna systems, a relatively large number of users can be served at the same time by means of analog and digital beamforming and spatial multiplexing. We investigate such a large-scale multi-user multiple-input multiple-output (MU-MIMO) wireless system employing an orthogonal frequency- division multiplexing (OFDM)-based downlink transmission scheme. The use of OFDM causes a high peak-to-average power ratio (PAPR), which usually calls for expensive and power-inefficient RF components at the base station. In this paper, we propose a nullspace vector perturbation (VP) which integrates both nonlinear lattice and linear subspace precoding approaches. By exploiting high degrees of freedom available in massive MU-MIMO OFDM systems, the signal PAPR can be significantly reduced with the proposed method. We also introduce a Gaussian process (GP) regression approach to be robust against the imperfect channel knowledge, which is required for the VP operation, in time-varying fading channels. Our analysis of outage capacity reveals that the proposed VP with GP regression offers a significant improvement in sum-rate spectral efficiency while reducing the PAPR.

A growing number of applications may benefit from embedding receiver with the capability to separate collided packets directly on a physical layer. For example, in IoT's random access channel scenario, a number of IoT devices are placed in the same cell, occasionally transmit messages, and are assigned non-orthogonal channel resources, giving rise to packet collisions. While channel access techniques for sharing a common channel are usually employed in most multi-user communication systems, they cause channel underutilization and increased latency. This paper considers a scenario where multiple users asynchronously transmit packets over a shared channel and are not subject to a random access mechanism, or the mechanism itself fails to prevent packet collisions. Assuming that each packet consists of a common preamble and information bearing payload, and experiences random delay, frequency offset, phase noise variation and block flat fading channel, we propose a packet separation algorithm which recovers collided packets, estimates their parameters and detects corresponding payload symbols. The performance of the proposed method is validated using simulations and benchmarked against bounds.

In this paper, we propose a Bayesian inference approach to extract the binary reflectance pattern of samples from compressed measurements in the terahertz (THz) frequency band. Compared with existing compressed THz imaging methods relying on the sparsity of the reflectance pattern, the proposed Bayesian approach exploits the non-negative binary nature of the reflectance without any assumption on its spatial pattern information and enables a pixel-wise iterative inference approach for fast signal recovery. Numerical evaluation confirms the effectiveness of the proposed approach.

A major challenge of screen-camera visual multiinput multi-output (MIMO) communications is to increase the achievable throughput by reducing nonlinear channel effects including perspective distortion, ambient lights, and color mixing. To mitigate such nonlinear effects, an existing transmission method uses linear or simple nonlinear equalizations in decoding operations. However, the throughput improvement from the equalization techniques is often limited because the effects are composed of a combination of various nonlinear distortions. In addition to the above issue, the existing studies consider specific environments, such as indoor and static communications, although screen-camera communications can be used for a variety of applications including outdoor and mobile scenarios. In this study, we propose 1) deep neural network (DNN)- based decoding for screen-camera communications to increase the achievable throughput and 2) Unity 3D-based evaluation methodology to synthetically learn the DNN for being robust against many different screen-camera environments. The DNN finds the best nonlinear kernels for equalization from numerously captured images, and then decodes original bits from newly captured images based on the trained nonlinear kernels. In the Unity-based evaluation tool, we can easily capture numerous photo-realistic images in different screen-camera scenarios to learn the impact of perspective distortion, screen- to-camera distance, motion blur, and ambient lights on the throughput since Unity-based environment can freely set programmable screens, cameras, and ambient lights on a 3D space. As an initial proof of concept, we demonstrate that the proposed DNN-based decoder scheme improves the achievable throughput by up to 148% compared to existing methods by equalizing nonlinear effects.

In this paper, a cooperative cyclic-prefixed single carrier (CP-SC) system to improve physical layer security is investigated. By considering a distributed cyclic delay diversity (dCDD) scheme, a jamming method is proposed to maximize the signal-to-noise ratio (SNR) over the channels from the transmitters to the legitimate user, while degrading the signal-to-interference-plus-noise ratio (SINR) over the channels from the transmitters to the illegitimate user. A CDD transmitter among the set of CDD transmitters is selected as the sentinel transmitter, and it transmits a jamming signal to the illegitimate user. The sentinel transmitter is the transmitter that provides the best channel gain in order to maximize the SNR at the legitimate user and minimize the SINR at the non-legitimate users. This allow us to enhance the security of the CP-SC system. New closed form expressions for the SNR and SINR for the dCDD protocol are derived for frequency selective fading channels. Monte-Carlo simulations are conducted to verify the analytic derivations of the performance metrics for various simulation scenarios.

This paper investigates the diversity gain of a distributed cyclic delay diversity (CDD) scheme for cyclic-prefixed single carrier systems in non-identical frequency selective fading channels. Two conditions are used to obtain an equivalent channel matrix that is free of intersymbol interference. These conditions allows the system to achieve the maximum diversity order at a full rate in frequency selective fading channels. A given number of CDD transmitters is obtained from the set of cooperative transmitters in the system and is shown to be determined by the symbol block size and the maximum time dispersion of the channel. A new expression for the received signal-to-noise ratio (SNR) is derived by using order statistics. To estimate the achievable maximum diversity gain provided by the distributed CDD scheme, we employ asymptotic analysis in the high SNR regime. From the analytical framework, it is shown that the maximum diversity is achieved even for non-identical frequency selective fading channels. Link-level simulations are conducted to verify the maximum achievable diversity gain.

In a growing number of IoT applications, an access point communicates with IoT users whose number prevents assignment of orthogonal channel resources to them, and renders network management and control impractical. Consequently, symbols transmitted from active users collide, which necessitates their separation on the receiver side. Given that an IoT user transmits with low probability, the sparsity in the user activity domain has been exploited in a number of previous works, where the symbol separation problem is formulated and solved as a sparse recovery problem. However, an excessive computational complexity remains a challenge in such approaches, where the number of filters in the equivalent receiver filter bank is equal to the overall number of users. Consequently, reduced-dimension processors facilitating low complexity symbol separation are sought. We propose here two novel reduced-dimension processors. In addition, a scheme, which aims to remove an impractical requirement that the receiver knows channels from all users is proposed and studied in conjunction with the reduced-dimension processors. Furthermore, pre-whitened reduced- dimension processors and a variety of approaches for the design of dimensionality reduction transformation matrix are considered. Finally, the results are validated with simulations. As such, the tests show that the proposed processors considerably outperform the benchmark, and also that the pre-whitened processors outperform their non-pre-whitened counterparts.

Rechargeable batteries supply numerous devices with electric power and are critical part in a variety of applications. While estimation of battery's state of charge (SoC), state of health (SoH) and state of power (SoP) have been in research focus in the past years, prediction of battery degradation has recently started to gain interest. An accurate prediction of the remaining number of charge and discharge cycles a battery can undergo before it can no longer hold charge and is declared dead, is directly related to making timely decision as to when a battery should be replaced so that power interruption of the system it supplies power to is avoided. A methodology for inferring probability distribution of the remaining number of charge-discharge cycles of a battery, based on training dataset containing measured discharge voltage waveforms of one or more batteries of similar type, is presented in this paper. The method-010gy strongly draws on modeling discharge voltage waveforms using Dirichlet Process Mixture Model framework and performs approximate inference using variational Bayes' approach. The experimental results corroborate that the proposed method is able to provide useful predictions of the remaining useful life of a battery in early stages of its life.

IEEE 802.11ah and IEEE 802.15.4g are two wireless technologies designed for outdoor IoT applications. Both technologies have communication range up to 1000 meters. Therefore, 802.11ah network and 802.15.4g network are likely to coexist. Our simulation results show that using standard defined coexistence mechanisms, 802.11ah network can severely interfere with 802.15.4g network and lead to significant packet loss in 802.15.4g network. As a result, additional coexistence control mechanisms are needed. Due to asymmetrical features such as modulation scheme and frame structure, 802.11ah devices and 802.15.4g devices cannot perform automatic cooperation. Thus, self-coexistence control techniques are preferred. This paper proposes learning based self-coexistence control techniques for 802.11ah devices to mitigate the interference impact of 802.11ah network on 802.15.4g network. We first present a α-Fairness based energy detection clear channel assessment (ED-CCA) method that enables 802.11ah devices to detect more ongoing 802.15.4g packet transmissions. We then introduce a Q-Learning based backoff mechanism for 802.11ah devices to avoid interfering with 802.15.4g packet transmission process. The proposed coexistence techniques can achieve fair spectrum sharing between 802.11ah network and 802.15.4g network.

This paper investigates the secrecy performance of a finite-sized in-band selective relaying system with M transmitters connected via unreliable backhaul links, N decode-and-forward relays, and K collaborative eavesdroppers. To send the source message to the destination, a transmitter-relay pair that achieves the highest end-to-end signal-to-noise ratio is selected for transmissions, while the K eavesdroppers combine all the received signals from the selected transmitter and relay using maximal ratio combining. The proposed model introduces backhaul reliability and eavesdropping probability parameters to investigate practical constraints on the transmitter-relay cooperation and eavesdropper collaboration, respectively. Closed-form expressions are derived for the secrecy outage probability, probability of non-zero achievable secrecy rate, and ergodic secrecy rate for non-identical frequency-selective fading channels with robust cyclic-prefixed single carrier transmissions. These results show that the asymptotic secrecy outage probability and probability of non-zero achievable secrecy rate are exclusively determined by the number of transmitters M and their corresponding set of backhaul reliability levels. Under unreliable backhaul connections, it is found that the secrecy diversity gain is determined by M, N, and the number of multipath components in the frequency selective fading channels. Link-level simulations are conducted to verify the derived impacts of backhaul reliability and collaborative eavesdropping on the secrecy performance.

Soft video delivery, i.e., analog video transmission, has been proposed to provide high video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations causing large overhead and quality degradation due to rate and power losses. To reduce the overhead while keeping the video quality high, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that the proposed scheme reduces overhead by 99.7% with 1.2-dB improvement of video quality (in terms of peak signal-to-noise ratio) compared to the existing analog video transmission scheme. We also investigate the impact of bandwidth limitation, showing a significant gain up to 2.7 dB for narrow-band systems.

In this paper, a cooperative cyclic-prefixed single carrier (CP-SC) system is studied and a scheme to improve its physical layer security is proposed. In particular, a distributed cyclic delay diversity (dCDD) scheme is employed and a deliberate interfering method is introduced, which degrades the signal-to-interference-plus-noise ratio (SINR) over the channels from a group of remote radio heads (RRHs) to an eavesdropper, while minimizing the signal-to-noise ratio loss over the channels from the RRHs to an intended user. This is obtained by selecting one RRH that acts as an interfering RRH and transmits an interfering artificial noise sequence to the eavesdropper. Through the use of the dCDD scheme, a channel that minimizes the receive SINR at the eavesdropper is selected for the interfering RRH. This choice enhances the secrecy rate of the CP-SC system. The system performance is evaluated by considering the secrecy outage probability and the probability of non-zero achievable secrecy rate, which are formulated in closed-form analytical expressions for the case of identically and non-identically distributed frequency selective fading channels. Based on the proposed analytical framework, the diversity order of the system is studied. Monte Carlo simulations are employed to verify the analytical derivations for numerous system scenarios.

Molecule loss is a critical reliability issue in diffusive molecular communications. This paper proposes a communica- tion framework that allows biologically-enabled machines (bio- nanomachines) to transmit and receive information-carrying molecules (information molecules) in a robust manner against molecule losses. The proposed framework, called molecular fountain, employs deoxyribonucleic-acid (DNA) molecules as information carriers and leverages molecular fragmentation (i.e., packetization) between transmitter (Tx) and receiver (Rx) bio-nanomachines. It performs feedback-aided rateless erasure coding that considers biochemical constraints in DNA synthesis and sequencing to generate molecular packets. The Tx bio- nanomachine repeatedly generates molecular packets with Luby transform codes and transmits them to the Rx bio- nanomachine until it receives an acknowledgment from the Rx bio-nanomachine. The Rx bio-nanomachine can reconstruct lost molecular packets from other packets that have been successfully transmitted. Simulation results show that molecular fountain enhances robustness against molecular packet losses and in turn improves communication performance such as transmission latency, jitter, error rate, and coding overhead.

This paper investigates a method to improve performance of diffusive molecular communications between biologically-enabled nanomachines in in-vivo aqueous environment. The proposed method exploits periodic flow, e.g., induced by repeated heart pumping. We make an analysis of channel impulse response (CIR) for such drift- diffusion fluid systems. In order to take the cyclic CIR into account, the proposed method optimizes the release timing and size of information molecules so that highest equalization gain can be achieved. We reveal that error rate performance can be significantly improved with adaptive molecule loading by taking care of the cyclic CIR.

In millimeter wave (mmWave) communication, analog and hybrid beamforming systems are proposed to reduce the number of RF chains when a large antenna array is utilized to achieve a high beamforming gain. In this paper, a new transmission scheme is first proposed by leveraging the hardware architecture of analog and hybrid beamforming to improve the spectral efficiency. The new scheme is called spatial scattering modulation (SSM), since it exploits the spatial scattering dimension to modulate information bits. And then, an adaptive transmission strategy (ATS) which chooses the best transmission scheme under instantaneous channel state information, is proposed to improve the performance. Link-level simulation results demonstrate the superiority of the proposed ATS in all the simulated signal-to-noise (SNR) values.

Wireless video streaming for multi-view plus depth (MVD) frames enables free viewpoint video on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. To transmit MVD frames over wireless links, existing schemes use digital-based compression to achieve better compression efficiency. However, the digital-based schemes have an issue called cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to allocate quantization levels and transmission power across MVD frames is cumbersome. To facilitate the MVD video streaming, we propose an analog-based transmission scheme. Our scheme directly transmits linear-transformed signals based on five-dimensional discrete cosine transform (5D-DCT), without relying on quantization and entropy coding. The proposed scheme achieves graceful video quality with the improvement of wireless channel quality. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling transmission power assignment. It is demonstrated with test MVD video sequences that the analog-based scheme offers a great advantage over the conventional digital-based scheme. For instance, the video quality of one intermediate virtual viewpoint is approximately 2.1 dB higher than digital-based schemes at a wireless channel quality, i.e., signal-to-noise ratio (SNR), of 15 dB.

We investigate non-coherent multi-antenna signal processing which requires no channel state information (CSI) at either transmitter or receiver ends. With non-coherent constellations over Grassmannian manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. The conventional GLRT relies on the assumption that the wireless channel is time-invariant during a block. We propose an improved GLRT algorithm which employs a novel two-dimensional basis expansion model (2D-BEM) to cope with doubly-selective fading channels. The proposed method uses sequential GLRT for multi-symbol detection to keep high performance yet low complexity. Furthermore, we introduce Fourier-Legendre product basis to be robust against hardware impairments including carrier frequency and timing offsets. We demonstrate that the proposed scheme significantly improves performance in rapid fading channels, and realizes highly spectrum-efficient transmission up to 6 bps/Hz without any pilots.

In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature, we propose a two-dimensional sparse Bayesian learning method without a priori knowledge of two-dimensional angular spread patterns. It essentially couples the channel path power at one angular direction with its two-dimensional AoD-AoA neighboring directions. Compared with existing sparse mmWave channel estimation methods, the proposed method is numerically verified to reduce the training overhead and channel estimation error.

This paper considers speed estimation for contactless electromagnetic (EM) encoder system with a moving read-head and spatially periodic placed reflectors. We first introduce a new signal model to capture reflected signals from these spatially periodic placed reflectors. Then an instantaneous phase-based speed estimator is proposed by using the phase unwrapping technique followed by a nonlinear least square method for motion-related parameters. The proposed speed estimator is verified by 1) Monte-Carlo simulations to confirm its statistical efficiency as the numerical mean squared errors approach to corresponding Cramér-Rao bounds and 2) a semi-analytical dataset by accounting for real system specificaltions such as the antenna beampattern, 3-dB beamwidth, and noise.

We consider in this article battery state of power (SoP) estimation, in particular, we propose two algorithms for predicting voltage corresponding to a future current profile that is known to be demanded by the battery load. The proposed algorithms belong to the class of data-driven methods and are based on the Gaussian Process Regression (GPR) framework. In comparison to conventional model-based approaches, data-driven approaches circumvent the issue of observability of SoP from measurements, especially pronounced in batteries with flat open circuit voltage (OCV) characteristic. In addition, the GPR framework admits accurate modeling of a fairly complicated battery dynamics using training data. Finally, the considered setup enables a relatively easy access to training data whenever the necessity for retraining arises, such as due to battery aging. The proposed algorithms aim to handle diverse battery operating conditions involving smooth and abruptly changing voltage/current measurements with both relatively small and large training datasets. The algorithms are tested on two such datasets, and the measured prediction performance and computation time verify their viability for real-time industrial use. We conclude with a number of possible directions for future research.

This paper considers parameter estimation of a hybrid sinusoidal frequency modulated (FM) and polynomial phase signal (PPS) from a finite number of samples. We first show limitations of an existing method, the high-order ambiguity function (HAF), and then propose a new method by adopting the high-order phase function which was originally designed for the pure PPS. The proposed method estimates parameters of interest from peak locations in the time-frequency rate domain, which are less perturbed by the noise than peak values used by the HAF-based method. Numerical evaluation shows the proposed method can handle the hybrid FM-PPS signal with low sinusoidal frequency and improve estimation accuracy in terms of mean squared error for several orders of magnitude.

This letter introduces a new coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, motivated by real-world applications, for example, contactless linear encoders. Specifically, the coupling is introduced to express the sinusoidal FM frequency as a function of the PPS-related parameters. Given the coupling mixture, it generalizes two existing models: the pure PPS model and the independent mixture model. Performance bounds of parameter estimation for the coupled mixture model are established in terms of the Cramér-Rao bound (CRB). Unlike the pure PPS case, the derived CRB shows its dependence on the PPS-related and sinusoidal FM-related parameters due to the coupling mixture. On the other hand, the derived CRBs for the PPS-related parameters are lower than their counterparts of the independent mixture model, as the sinusoidal FM frequency provides additional information on the PPS parameters.

Energy harvesting is an emerging technology for enabling green, sustainable, and autonomous wireless networks. In this paper, a large-scale wireless network with energy harvesting transmitters is considered, where a group of transmitters forms a cluster to cooperatively serve a desired receiver amid interference and noise. To characterize the link-level performance, closed-form expressions are derived for the transmission success probability at a receiver in terms of key parameters such as node densities, energy harvesting parameters, channel parameters, and cluster size, for a given cluster geometry. The analysis is further extended to characterize a network-level performance metric, capturing the tradeoff between link quality and the fraction of receivers served. Numerical simulations validate the accuracy of the analytical model. Several useful insights are provided. For example, while more cooperation helps improve the link-level performance, the network-level performance might degrade with the cluster size. Numerical results show that a small cluster size (typically 3 or smaller) optimizes the network-level performance. Furthermore, substantial performance can be extracted with a relatively small energy buffer. Moreover, the utility of having a large energy buffer increases with the energy harvesting rate as well as with the cluster size in sufficiently dense networks.

This paper investigates a distributed cyclic delay diversity (CDD) transmission scheme for cyclic-prefixed single carrier systems in non-identically and identically distributed frequency selective fading channels. The distinguishable feature of the proposed scheme lies in providing a transmit diversity gain while reducing the burden of estimating the channel state information, which is a challenging task in distributed and cooperative systems. To effectively use the distributed CDD scheme at the transmitters, two sufficient conditions are derived to eliminate the intersymbol interference at the receiver and leveraged to convert the multi-input single-output channel into a single-input single-output channel. These conditions allow the system to achieve the maximum diversity for frequency selective fading channels at a full rate. To achieve this maximum diversity, a fixed number of CDD transmitters are selected based on the channel conditions, symbol block size, and maximum time dispersion of the channel, and a new two-stage transmission mode is proposed. Based on the distributed CDD and the proposed selection schemes, a new expression for the signal-to-noise ratio at the receiver is obtained with the aid of order statistics, and then closed-form expressions for the outage probability and average symbol error rate (ASER) are derived. As far as the identically distributed frequency selective fading channel model is concerned, the achievable maximum diversity gain is proved, with the aid of asymptotic analysis, to be equal to the product of the total number of transmitters in the system and the number of multipath components. Link-level simulations are also conducted to validate the analytical expressions for outage probability, ASER, and maximum achievable diversity gain.

Screen-camera communication, which uses a liquid crystal display (LCD) screen and camera image sensors, has been an attractive variant of visible light communications (VLC) since display and camera have been equipped with in various mobile devices. To improve transmission rates, we investigate the impact of nonlinear channel equalization and nonbinary channel coding as well as high-order modulation schemes. Equalization techniques can reduce the effect of channel impairments, such as color mixing. Nonbinary coding improves reliability of high-order modulation and thus increases the transmission rate. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.6-2.4 times higher transmission rates compared to existing schemes for a communication distance of 40-100 cm.

In this paper, the secrecy outage probability of cooperative cyclic prefixed single carrier (CP-SC) systems with multiple transmitters and unreliable backhaul connections is derived. The transmitters communicate with the destination in the presence of an eavesdropper over two-hop relay channels with non-identical frequency-selective fading. The existence of asymptotic limits on the secrecy outage probability is verified for various backhaul scenarios. For a fixed eavesdropper signal-to-noise ratio (SNR), the limit is found to be exclusively determined by the backhaul reliability. This shows that the diversity gain promised by cooperative CP-SC systems cannot be achieved in the high SNR region. Simulations are presented to verify the derived impact of backhaul reliability on the secrecy performance.

We consider the problem of channel estimation for millimeter wave communications (mmWave). We formulate channel estimation as a structured sparse signal recovery problem, in which the signal structure is governed by a priori knowledge of the channel characteristics. We develop a Bayesian group sparse recovery algorithm which takes into account for several features unique to mmWave channels, such as spatial (angular) spreads of received signals and power profile of rays impinging on the receiver array. We validate the developed method via numerical simulations and demonstrate an improved estimation performance relative to the existing methods.

Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations. It causes large overheads and quality degradation because of rate and power losses. To reduce the overheads while keeping high video quality, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that our proposed scheme reduces overheads by 97 % with 3.4 dB improvement of video quality compared to the existing analog video transmission scheme.

This paper presents an application of Gaussian process regression (GPR) to estimate a state of charge (SoC) of Lithium-ion (Li-ion) batteries with different kernel functions. One of the practical advantages of using GPR is that uncertainties in the estimates can be quantified, which enables reliability assessment of the SoC estimate. The inputs of GPR are voltage, current and temperature measurements of the battery and the output is an estimate of SoC. First, training is performed in which optimal hyperparameters of a kernel function are determined to model data properties. Then, the battery SoC is estimated online based on the trained model. The kernel function is the key element in the GPR model since it encodes the prior assumptions about the properties of the function being modeled. Therefore, the impact of kernel function selection on the estimation performance is analyzed using both simulated data and experimental data collected from a LiMn2O4/hard-carbon battery with a nominal capacity of 4.93Ah operating under constant charge and discharge currents.

Wireless machine-to-machine (M2M) communications are widely considered as part of the Internet of Things (IoT) infrastructure. Resource management is crucial for heterogeneous M2M networks. In this paper, we present new distributed sleep management techniques to prolong network lifetime for multi-hop heterogeneous wireless M2M networks, which consist of battery-powered nodes and mains-powered nodes. We also propose two novel battery energy aware routing metrics to efficiently select routes that satisfy performance guarantees. Finally, we present an extensive performance evaluation of our sleep management techniques and routing metrics. Simulation results show that our schemes achieve high packet delivery rate, long network lifetime and low energy consumption even in very low percentage of mains-powered nodes.

This paper presents a performance analysis of a finite-sized co-operative wireless system, where a group of transmitters with unreliable backhauls serve a desired receiver using noncoherent joint transmission. To facilitate analysis, an analytical expression for the distribution of the spatially averaged signal-to-interference-plus-noise ratio (SA-SINR) is derived in terms of key system and channel parameters. Leveraging the derived expression, the joint impact of node co-operation, backhaul reliability, interference, and communication range is investigated in the considered finite-sized co-operative system. Furthermore, based on the SA-SINR, closed form expressions for the average bit error rate (ABER) and average spectral efficiency (ASE) are derived. Further insights are established by analyzing the asymptotic performance in the high transmission power regime. From analytical derivations for the outage probability, ABER, and ASE and link-level simulations, it is verified that these asymptotic performance metrics are exclusively influenced by unreliable backhauls, so that the conventional diversity gains are not achievable.

The quality of wireless video delivery is susceptible to wireless channel instability, including channel fading, interference, noise, and packet loss. To improve the video quality in such wireless channels, analog transmission schemes have been proposed recently. However, the existing analog schemes have two drawbacks in high energy of video signals and loss resilience. To overcome the issues, we propose a new hybrid digital-analog transmission scheme, which jointly uses digital coding and analog coding based on compressive sensing. Digital coding generates the residual signals between the original and the encoded signals to decrease the energy of data for analog coding. Compressive sensing redistributes the energy across whole video packets to increase the resistance to packet loss. We show that our proposed hybrid scheme improves video quality by up to 11.6 dB compared to the existing analog scheme in an erasure wireless channel. In addition, our proposed scheme also improves video quality by 1.5 dB compared to the existing hybrid scheme.

Routing algorithm can significantly impact network performance. Routing in a network containing heterogeneous nodes differs from routing in a network with homogeneous nodes. If the routing algorithm is designed to fit less powerful nodes, the resources of more powerful nodes are wasted and network performance can be degraded. If the routing algorithm is developed to suit more powerful nodes, less powerful nodes may not have sufficient resources to run the algorithm and network may break down. Routing algorithms developed for homogeneous networks do not work well for heterogeneous networks. The IETF designed the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) by taking into account resource heterogeneity and defined four modes of operation. However, RPL only allows one mode of operation for all routers in a network. This paper proposes a resource-aware adaptive mode RPL (RAM-RPL) to achieve adaptive mode of operation in heterogeneous wireless machine-to-machine (M2M) networks. RAM-RPL not only allows routers to have mixed modes of operation in a network but also allows routers to adaptively adjust their modes of operation during network operation. Acting parent and acting root techniques are introduced to realize adaptive mode of operation and route compression. RAM-RPL exploits resource heterogeneity and shifts routing workload from less powerful nodes to more powerful nodes. Simulation results show that RAM-RPL can improve data packet delivery rate by 26% and reduce control message overhead by 53% while maintaining similar packet latency.

Machine-to-machine (M2M) communications have been considered as an important application to connect a massively large number of different devices in networks. In particular for M2M wireless networks, low latency and high reliability are of great importance. To fulfill the requirements, a diversity technique exploiting limited resources is proposed in this paper for short-message transmissions. The proposed method uses multiple stages of fast unitary transforms and diagonal phase rotations to achieve full- diversity gain. A monomial phase rotation is also proposed to facilitate an optimization of the precoding matrix. It is verified that the proposed four-stage precoding provides universal diversity gain irrespective of channel selectivity in time and frequency, without changing monomial parameters. In addition, it is shown that the four-stage precoding based on the discrete Haar transform (DHT) achieves a full diversity while the computational complexity is significantly reduced from a log- linear order to a linear order compared to the other unitary transforms such as the discrete Fourier transform (DFT).

We propose an unsupervised, received signal strength (RSS)- based indoor localization method, which as an infrastructure uses commercial WiFi chipsets and does not require any changes in the existing hardware. The method relies on path loss model for measured RSS levels where path loss coefficient is treated as a discrete random variable which takes values from some finite alphabet. The unknown location and path loss coefficient corresponding to each access point are jointly estimated using the Expectation Maximization (EM) approach. The algorithm is experimentally tested in an office space area of dimensions 32-by-52 m (1600 m2) with only five access points and the achieved average localization error is below 4.5 m.

A large wireless network with energy harvesting transmitters is considered, where a group of K transmitters form a cluster to cooperatively serve a desired user. Using stochastic geometry, simple closed-form expressions are derived to characterize the outage performance as a function of important parameters such as the energy harvesting rate, buffer size and cluster size for a given cluster geometry. The developed framework also allows the K in-cluster transmitters to have different energy harvesting capabilities. A comparison with simulation results reveals that the derived expressions closely model the signal-to-interference-and-noise ratio distribution at the receiver, particularly in the low-outage regime. Lastly, the developed framework is used to investigate the impact of different parameters such as cluster and buffer size on outage performance.

The IETF standardized the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) to meet routing requirements of the emerging applications. RPL is a distributed routing protocol and shows good scalability and fast network setup. However, RPL does not support sleep operation well. To provide efficient energy management and enhance RPL for sleep operation support, this paper presents battery energy management solutions for heterogeneous wireless machine-to-machine networks containing both battery powered nodes and mains powered nodes. We introduce a distributed sleep model for battery powered nodes to manage their own sleep schedules based on their internal parameters and observed network conditions. We propose two broadcast message delivery methods for battery operated networks that use distributed sleep control. Two battery node aware routing metrics are introduced to discover more battery energy efficient routes. We also present a battery energy efficient routing protocol called B-RPL to leverage distributed sleep model and introduced routing metrics. A battery energy efficient data packet transmission and forwarding method is provided to select the most battery energy efficient route among multiple active routes to transmit and forward data packets. Simulation results show that compared with standard RPL, the proposed B-RPL can extend network lifetime by two times and improve data packet delivery rate by 75%.

Probabilistic Data Association (PDA) algorithm has shown promising performance in symbol detection and interference cancellation in different communication schemes. This paper proposes new algorithms that build on PDA and introduce modifications in the way the symbol being detected is treated. While PDA models this symbol as a discrete sample from a constellation, PDA with symbol uncertainty (SU-PDA) views it as a sum of a deterministic symbol and random noise, while the Gaussian PDA (G-PDA) models it as a random variable with either a single Gaussian or Gaussian mixture distribution. The proposed algorithms are tested via computer simulations on both simulated and experimentally measured channels. The performance study reveals that the SU-PDA and G-PDA outperform the conventional PDA with the performance gain ranging from few dBs on measured channel with block fading up to and exceeding 10 dB on the simulated channel with fast fading.

To design a routing protocol for applications over low-power and lossy networks (LLNs), the IETF ROLL Working Group standardized the IPv6 Routing Protocol for LLNs (RPL), which organizes nodes in a LLN into a tree-like topology called Destination Oriented Directed Acyclic Graph (DODAG). RPL shows good scalability and fast network setup. However, it may suffer from severe unreliability due to the selection of suboptimal routes with low quality links. To optimize the reliability of RPL routes, this paper proposes a stability metric based routing protocol named sRPL for reliable routing and data collection in LLNs. We introduce a new routing metric for RPL called stability index (SI), which exploits stability characteristics of RPL nodes to select more stable routes. In addition, we present a passive and lightweight network layer technique to measure the bi-directional expected transmission count (ETX) for wireless links in LLNs. As a use case of SI, we combine SI metric with ETX metric to make routing decisions. Simulation results show that sRPL can improve packet delivery rate of RPL routing protocol by 20%.

We1propose two methods for the detection of RF interference. The first one is for the detection of the interferences from microwave ovens, and the second one is for Wi-Fi and Bluetooth signals. The motivation of this work is to design a system for reliable wireless communication. Specifically, the systems equipped with interference detectors will be able to choose the appropriate time intervals to transmit signals in the presence of other interferences, therefore avoid unnecessary collisions and retransmissions.

This paper presents a novel framework, which enables a design of rateless codes for binary input additive white Gaussian noise (BIAWGN) channels, using the ripple-based approach known from the works for the binary erasure channel (BEC). We reveal that several aspects of the analytical results from the BEC also hold in BIAWGN channels. The presented framework is applied in a code design example, which shows promising results compared to existing work. In particular it shows a great robustness towards variations in the signal-to-noise power ratio (SNR), contrary to existing codes.

Machine-to-Machine (M2M) wireless communication requires the transmission of short blocks of data with high reliability over fading channels. We discuss the use of the probabilistic data association (PDA) detector in conjunction with precoding to design high-performance systems for these links. First, the performance of the traditional PDA algorithm with precoding over ideal Rayleigh fading links is analyzed, which provides insight into its performance, and evidence of an error floor at high SNRs. Then, a novel ordering mechanism is proposed that takes advantage of the precoder characteristics. It is shown by simulation that the proposed modified algorithm can achieve near-ML performance for block sizes as small as 32 symbols.

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossy networks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routing protocol based on the RPL protocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non-intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPL protocol over original RPL through extensive simulations.

Due to the fine delay resolution in ultra-wideband (UWB) wireless propagation channels, a large number of multipath components (MPC) can be resolved; and the first arriving MPC might not be the strongest one. This makes time-of-arrival (ToA) estimation, which essentially depends on determining the arrival time of the first MPC, highly challenging. In this paper, we consider non-coherent ToA estimation given a number of measurement trials, at moderate sampling rate and in the absence of knowledge of pulse shape. The proposed ToA estimation is based on detecting the presence of a signal in a moving time delay window, by using the largest eigenvalue of the sample covariance matrix of the signal in the window as the test statistic. We show that energy detection can be viewed as a special case of the eigenvalue detection. Max-eigenvalue detection (MED) generally has superior performance, due to the following reasons: 1) MED collects less noise, namely only the noise contained in the signal space, and 2) if multiple channel taps fall into the time window, the MED detector can collect energy from all of them. Simulation results confirm that MED outperforms the energy detection in IEEE 802.15.3a and 802.15.4a channels. Finally, the selection of the threshold of the MED is studied both by simulations and by random matrix theory.

This paper proposes a concept called rateless feedback coding. We redesign the existing LT and Raptor codes, by introducing new degree distributions for the case when a few feedback opportunities are available. We show that incorporating feedback to LT codes can significantly decrease both the coding overhead and the encoding/decoding complexity. Moreover, we show that, at the price of a slight increase in the coding overhead, linear complexity is achieved with Raptor feedback coding.

In this paper, we present an analytical framework for designing LT codes in additive white Gaussian noise (AWGN) channels. We show that some of analytical results from binary erasure channels (BEC) also hold in AWGN channels with slight modifications. This enables us to apply a ripple-based design approach, which until now has only been used in the BEC. LT codes designed by this way show promising performance which is near the Shannon limit even with short codewords.

We study precoder optimization gains and multiuser diversity gains with interference alignment in a two-cell wireless network. In this paper, we propose algorithms to improve the achievable sum rate by optimizing alignment directions. The proposed iterative algorithm can provide a substantial gain in the achievable rate while it requires only a few iterations. In addition, we investigate interference alignment to exploit multiuser diversity gains. A new criterion of user selection scheduling is proposed. This user selection can be done independently in different cells. Therefore, both the searching space and the information exchanged between base stations are significantly reduced compared to joint user scheduling over two cells.

This paper proposes four schedulers using location information and side information in telematics applications for vehicular networks. The scheduler algorithms consider peak traffic and link reliability, achieving savings of channel resources and reducing the number of retransmissions. A key feature of the proposed schedulers is the use side information in the form of a coverage map, which provides a map of link quality for the area covered by the radio access networks. In this paper, the total offered load and average excess delay are considered as two metrics for measuring the proposed schedulers, which are evaluated by simulation results. The performance of schedulers is reported and compared.

In this paper, we develop a general reduced-rate orthogonal frequency division multiplexing (OFDM) transmission scheme for inter-subchannel interference (ICI) self-cancellation over high-mobility fading channels. Via transmit and receive processing, we transform the original OFDM system into an equivalent one with fewer subcarriers. By reducing transmission rate, we are able to design a transmitted signal structure with inherent ICI self-cancellation capability without requiring the instantaneous channel state information. We develop a general structure of transmit and receive processing matrices so that all equivalent subchannels in the transformed OFDM system have the same average signal-to-interference ratio (SIR). For the developed structure, we further optimize the transmit and the receive processing coefficients to maximize the SIR based on channel statistics. Numerical and simulation results demonstrate that the developed reduced-rate OFDM transmission achieves an SIR gain of around 5 dB over the existing ICI self-cancellation schemes and significantly reduces the error floor at the receiver.

We develop a recursive least-squares (RLS) algorithm which employs L1-Lqregularized sparse regressions to estimate a sparse channel matrix in frequency-and-time selective fading for multi-input multi-output (MIMO) wireless communications. We propose an improved sparse RLS by using an order extension technique for rapid fading channels. Simulation results demonstrate that the proposed sparse RLS algorithm offers a significant improvement over the conventional RLS algorithm.

We investigate non-coherent multi-input multi-output (MIMO) signal processing which requires no channel state information (CSI) at either the transmitter or the receiver. With non-coherent codes on Grassmann manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. However, the conventional GLRT suffers from a severe performance degradation when the channel changes fast within a coding block duration.We propose an improved GLRT algorithm referred to as high-order super-block techniques. The super-block scheme makes effective use of correlated channels for adjacent blocks in slow fading, whereas the high-order scheme can overcome the channel fluctuation during a block in fast fading. We demonstrate that the proposed scheme significantly improves performance for MIMO-OFDM with non-coherent Grassmann space-frequency block codes (SFBC).

In this paper, we propose a distributed interference alignment which employs physical-layer network coding and superposition coding for successive-cancelling multiuser detection (MUD) receivers in K-pair bidirectional relaying networks. The proposed scheme enables the transmitter to align only partial interference while strong interference is cancelled by MUD, and the transmitter can have more degrees of freedom in controlling the filter designs. Simulation results demonstrate that our proposed scheme significantly improves sum-rate performance in multiuser bidirectional relaying systems.

In this paper, we develop a general reduced-rate orthogonal frequency division multiplexing (OFDM) transmission scheme for inter-subchannel interference (ICI) mitigation in a high-mobility environment. By transmit and receive preprocessing, we transform the original N-subcarrier OFDM system into an equivalent K-subcarrier one with significantly reduced ICI. In particular, we develop a general structure of transmit and receive preprocessing matrices so that the K subchannels in the equivalent OFDM system share a common average signal-to-interference ratio (SIR). Without requiring the instantaneous channel state information, we optimize the preprocessing coefficients to maximize the SIR based on channel statistics. Numerical and simulation results demonstrate that the developed reduced-rate OFDM transmission achieves significant performance improvements over the existing ICI self-cancellation schemes.

In this work, we propose a super-resolution blind channel modeling algorithm to characterize wide-band channels comprised of disjoint frequency subbands. Since sounding signals are not available over the frequency guard bands separating adjacent subbands, conventional channel modeling methods suffer from poor performance in modeling the channel frequency response over the guard bands. To circumvent this obstacle, a three-step super-resolution blind algorithm is developed. First, the path delays are estimated by exploiting super-resolution algorithms such as MUSIC or ESPRIT. After that, the proposed algorithm performs blind channel estimation over the guard bands and subsequently derive the frequency response over the whole wideband channel. Finally, estimates derived from different subbands are combined via a soft combining technique. It is shown by computer simulations that the proposed superresolution blind algorithm can achieve a significant performance gain over conventional methods.

Block transmission with cyclic prefix is a promising technique to realize high-speed data rates in frequency-selective fading channels. Many popular linear precoding schemes, including orthogonal frequency-division multiplexing (OFDM), single-carrier (SC) block transmission, and time-reversal (TR), can be interpreted as such a block transmission. This paper presents a unified performance analysis that shows how the optimal precoding strategy depends on the optimization criterion such as capacity, mean-square error, and secrecy. We analyze three variants of TR methods (based on maximum-ratio combining, equal-gain combining and selective combining) and two-types of pre-equalization methods (zero-forcing and minimum mean-square error). As one application of our framework, we derive optimal precoding (i.e., OFDM with optimal power and phase control) in the presence of interference limitation for distributed antenna systems; we find that without power/phase control, OFDM does not have any capacity advantage over SC transmissions. When comparing SC and TR, we verify that for single-antenna systems in the high SNR regimes, SC has a capacity advantage; however, TR performs better in the low SNR regime. For distributed multiple-antenna systems, TR always provides higher capacity, and the capacity of TR can approach that of optimal precoders with a large number of distributed antennas. Furthermore, we make an analysis of secrecy capacity which shows how high-rate messages can be transmitted towards an intended user without being decoded by the other users from the viewpoint of information-theoretic security. We demonstrate that TR precoding can be the best candidate among the non-optimal precoders for achieving high secrecy capacity, while the optimal precoder offers a significant gain over those non-optimal precoders.

In this paper, we consider a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system consisting of a source node (SN), a destination node (DN), and a relay node (RN) that simply amplifies and forwards its received signal to the DN without any further processing. In this system, the overall channel from the SN to the DN is a cascade of the backward relay channel over the SN-RN hop, the amplifying matrix at the RN, and the forward relay channel over the RN-DN hop. We investigate the estimation of the two cascaded relay channels at the DN based on the predefined amplifying matrix applied at the RN and the corresponding overall channel obtained through the conventional channel estimation algorithms with the help of pilots transmitted by the SN. In particular, we find necessary and sufficient conditions on the pilot amplifying matrix sequence at the RN to ensure feasible relay channel estimation at the DN. Based on these conditions, we present rules to design diagonal or quasi-diagonal pilot amplifying matrices so that the cascaded relay channels can be estimated with minimum complexity at the RN. In the presence of imperfect overall channel state information at the DN, we further develop the approximate linear least-square estimation of the relay channels based on the designed pilot matrix sequence and demonstrate its performance by simulation results.

Statistics-based inter-subchannel interference (ICI) mitigation schemes are developed for orthogonal frequency division multiplexing (OFDM) transmission over high-mobility channels with line-of-sight components. By utilizing the channel statistics, we develop the Wiener filtering in the downlink and the transmit preprocessing in the uplink for ICI mitigation. Numerical and simulation results demonstrate that the proposed schemes effectively mitigate ICI and lower the error floor, especially in the presence of a strong line-of-sight path.

We investigate non-coherent multi-input multi-output (MIMO) signal processing which requires no channel state information (CSI) at either the transmitter or the receiver. With non-coherent codes on Grassmann manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. However, the conventional GLRT suffers from a severe performance degradation when the channel changes fast within a coding block duration. We propose an improved GLRT algorithm referred to as high-order super-block techniques. The super-block scheme makes effective use of correlated channels for adjacent blocks in slow fading, whereas the high-order scheme can overcome the channel fluctuation during a block in fast fading. We demonstrate that the proposed scheme significantly improves performance for MIMO-OFDM with non-coherent Grassmann space-frequency block codes (SFBC).

Abstract The IEEE 802.16/WiMAX standard has fully embraced multi‐antenna technology and can, thus, deliver robust performance and high transmission rates. Nevertheless, due to its inherent cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. This is because the former is often substantially more expensive than the latter. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited number of RF chains for transceiving, is a highly appealing performance enhancement technique for multi‐antenna WiMAX terminals. In this paper, a novel protocol for antenna selection in space division multiple access (SDMA) transmission is developed for the next‐generation IEEE 802.16 mobile stations. Both locally and globally optimal selection rules are considered together with various linear precoding designs at the base station (BS). The proposed protocol can readily accommodate various channel situations (e.g., reciprocal and non‐reciprocal channels). As demonstrated by analysis and simulation, the proposed protocol delivers considerable performance improvement over conventional IEEE 802.16 terminals that lack antenna selection capability. Moreover, the proposed protocol leverages the existing signaling method defined in IEEE 802.16, thereby incurring a negligible signaling overhead and requiring only minimal modifications of the standard. To the best of our knowledge, this paper represents the first effort to support antenna selection capability for SDMA transmissions in IEEE 802.16 mobile stations. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we are concerned with a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system consisting of a source node (SN), a relay node (RN), and a destination node (DN). Since the simple RN in this system is unaware of the structure of its received signal and incapable of performing complicated signal processing, the interim channels over the SN-RN and the RN-DN hops can not be estimated directly. Therefore, we develop a novel interim channel estimation approach in this paper. Furthermore, we find necessary and sufficient conditions for the pilot amplifying matrix sequence at the RN to ensure successful interim channel estimation at the DN, and present rules to design low-complexity pilot amplifying matrices meeting these conditions.

The IEEE 802.16/WiMAX standards has fully embraced multi-antenna technology and can, thus, deliver robust performance and high transmission rates. Nevertheless, due to its inherent cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited number of RF chains for transceiving, is a highly appealing performance enhancement technique for multiantenna WiMAX terminals. In this paper, a novel protocol for antenna selection in space division multiple access (SDMA) transmission is developed for the next-generation IEEE 802.16 mobile stations. Both locally and globally optimal selection rules are considered at the base station (BS). The proposed protocol can readily accommodate various channel situations (e.g., reciprocal and non-reciprocal channels). As demonstrated by analysis and simulation, the proposed protocol delivers considerable performance improvement over conventional IEEE 802.16 terminals that lack antenna selection capability. Moreover, the proposed protocol leverages the existing signaling method defined in IEEE 802.16, thereby incurring a negligible signaling overhead and requiring only minimal modifications of the standard.

In this paper, we propose a static power allocation algorithm for a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system in which the interim channel state information over the first and the second hops is unavailable. Based on the path losses over the first and the second hops, this algorithm performs static power allocation between the source and relay nodes to maximize the equivalent received SNR of the system. We further investigate the optimal location of the relay node when the conventional fixed and the proposed optimal static power allocation schemes are applied. Our comparison between direct transmission and relay-based two-hop transmission indicates that whether the latter outperforms the former depends on a tradeoff between the received SNR gain and the multiplexing loss in the relay-based two-hop transmission scheme.

Wireless sensor networks are emerging as an important area for communications. They enable a wealth of new applications including surveillance, building control, factory automation, and in-vehicle sensing. The sensor nodes have to operate under severe constraints on energy consumption and form factor, and provide the ability for precise self-location of the nodes. These requirements can be fulfilled very well by various forms of ultra-wide-band (UWB) transmission technology. We discuss various techniques and tradeoffs in UWB systems and indicate that time-hopping and frequency-hopping impulse radio physical layers combined with simple multiple-access techniques like ALOHA are suitable designs. We also describe the IEEE 802.15.4a standard, an important system that adopts UWB impulse radio to ensure robust data communications and precision ranging. In order to accommodate heterogeneous networks, it uses specific modulation, coding, and ranging waveforms that can be detected well by both coherent and noncoherent receivers.

In this paper, we investigate the low-complexity synchronization design for multi-band orthogonal-frequency-division-multiplexing (MB-OFDM) ultra-wideband (UWB) systems. We propose a unified synchronizer design based on auto-correlation-function. The key component in the proposed synchronizer is a parallel signal detector structure in which multiple auto-correlation units are instantiated and their outputs are shared by other functional units in the synchronizer, including time-frequency pattern detection, symbol timing, carrier frequency offset estimation and correction and frame synchronization. We show that, via analysis and simulations, such a design achieves not only a low computation cost which makes it attractive in implementation, but also equal or better performance compared to the cross-correlation based designs.

Ultrawideband communications often occur in heterogeneous networks where different receivers have different complexity and energy consumption requirements. In this case it is desirable to have a modulation scheme that works well with coherent receivers as well as simpler receivers, namely transmitted-reference (TR) receivers. In particular, we consider a TR scheme that employs slightly frequency-shifted reference (FSR) signals [15] and thus avoids one of the main drawbacks of conventional TR schemes, namely the need to implement a delay line. We propose and analyze a modulation scheme that works well with both FSR receivers (where it has the same performance as conventional TR modulation), and coherent receivers. Coherent receivers receiving conventional TR modulation suffer a 3 dB penalty, because they cannot make use of the energy invested into the reference pulse. Our proposed scheme avoids this drawback by including a data preprocessor that can be viewed as a nonsystematic rate-1/2 convolutional code with a high constraint length. These codes give 1.5 dB gain over our previously proposed constraint-length-two systematic codes at a BER of 1 times 10-4in 802.15.3a CM4 multipath fading channels.

A new form of transmitted reference technique is proposed in this paper, where compactly spaced or even multiple contiguous reference and data pulses are used for transmission. This structure enables a simple, robust and practical autocorrelation detector to be implemented in the receiver. It is also compatible with the signal format proposed within IEEE 802.15.4a working group for coherent and non-coherent systems. The performance of the proposed receiver and a simple noncoherent energy detector are analyzed and compared. Simulation results show that this new transmitted reference system outperforms the non-coherent system by about 1.3-1.9 dB for 802.15.4a channel model 1 and 1.3-2.3 dB for channel model 8, while offering similar implementation complexity compared to that of the non-coherent system.

This paper considers signaling schemes for heterogeneous ultrawideband communications networks that contain both coherent (rake) and transmitted-reference (TR) receivers. While coherent receivers are capable of receiving TR signals, they do so with a 3 dB penalty, because they cannot make use of the energy invested into the reference pulse. We propose a new signaling scheme that avoids this drawback, by encoding redundant information on the reference pulse. The resulting scheme does not affect the operation of a TR receiver, while recovering the 3 dB penalty and furthermore providing an additional 1.7 dB coding gain to a coherent uncoded binary scheme. This can be explained by interpreting the scheme as a trellis-coded modulation. We also provide an alternative implementation that can be viewed as a recursive systematic convolutional encoder. Combining this version further with a simple forward error correction encoder results in a concatenated code that can be decoded iteratively, providing a bit-error rate of 10-3at 2.8 dB signal-to-noise ratio in additive white Gaussian noise. The convergence behavior of this iterative code is analyzed by using extrinsic information transfer charts.

In a pulsed UWB system, either coherent receivers or transmit-reference (TR) receivers can be used to demodulate the signals. For coherent receivers, which are typically based on a Rake stfucture, the large number of resolvable multipaths is a major challenge for channel estimation and receiver complexity. TR receivers can effectively collect energy from all the received multipath components with much smaller complexity, but show reduced performance. Current modulation formats are tuned to either coherent or TR receivers, but cannot interoperate with both of them at the maximum performance possible for each of them. In this paper, we propose an innovative modulation scheme that enables the use of TR and coherent receivers in the same wireless network. The modulation allows demodulation by TR receivers, while at the same time enabling a coherent receiver to fully exploit all available signal energy, and actually to perform slightly better (by 1.8 dB) than a coherent receiver working with conventional BPSK modulation; this additional gain can be interpreted as a coding gain of a trellis-coded modulation scheme. The scheme thus allows the network designers to trade off performance vs. complexity in the receivers, while allowing a uniform signaling (modulation) scheme for all transmitters.

This paper studies efficient and simple data broadcast in IEEE 802.15.4-based ad hoc networks (e.g., ZigBee). Since finding the minimum number of rebroadcast nodes in general ad hoc networks is NP-hard, current broadcast protocols either employ heuristic algorithms or assume extra knowledge such as position or two-hop neighbor table. However, the ZigBee network is characterized as low data rate and low cost. It cannot provide position or two-hop neighbor information, but it still requires an efficient broadcast algorithm that can reduce the number of rebroadcast nodes with limited computation complexity and storage space. To this end, this paper proposes self-pruning and forward node selection algorithms that exploit the hierarchical address space in ZigBee networks. Only one-hop neighbor information is needed; a partial list of two-hop neighbors is derived without exchanging messages between neighboring nodes. The ZigBee forward node selection algorithm finds the minimum rebroadcast nodes set with polynomial computation time and memory space. Using the proposed localized algorithms, it is proven that the entire network is covered. Simulations are conducted to evaluate the performance improvement in terms of the number of rebroadcast nodes, number of duplicated receivings, coverage time, and communication overhead.

Due to scarce resources, such as transmission power, storage space and communication bandwidth, current broadcast approaches for general ad hoc networks can not be applied to IEEE 802.15.4 based ad hoc networks (e.g., ZigBee networks). This paper proposes a forward node selection algorithm that significantly reduces broadcast redundancy. The algorithm exploits the hierarchical address space in ZigBee networks. Only one-hop neighbor information is needed: a partial list of two-hop neighbors is derived at a node without exchanging messages between neighboring nodes. The complexity of the proposed algorithm is polynomial in terms of both computation time and memory space. The localized algorithm provides an optimal and feasible solution of selecting the minimum number of rebroadcast nodes in ZigBee networks, which is an NP-hard problem for general ad hoc networks. The proposed algorithm is extended to deal with packet loss during data transmission. A ZigBee rebroadcast algorithm is also proposed to further reduce the number of rebroadcast nodes and cover the whole network faster by assigning a non-random rebroadcast timer determined by the number of neighbors to be covered, distance and link quality. Simulations are conducted to evaluate the broadcast redundancy, coverage time, and coverage ratio.

We investigate the effect of symbol spreading on the performance of ultrawideband systems based on multiband OFDM. Ultrawideband channels offer the possibility of very high frequency diversity, as the used bandwidth is much larger than the coherence bandwidth of the channel. In multiband-OFDM, frequency diversity stems from (i) forward error correction coding, which distributes the symbols associated with one bit among different tones, and (ii) interleaving, which distributes the symbols among different employed frequency bands. We show that for weak (rate 3/4) error correcting codes, the achieved diversity order is insufficient. However, the use of Walsh-Hadamard spreading gives additional diversity within each frequency band and greatly improves the performance. Furthermore, we propose a novel scheme for grouping OFDM tones before the spreading is performed. This grouping gives additional flexibility in the system design.

It is known that relay assisted wireless transmission in a triplet, which consists of a source node, intermediate relay node and a destination node results in less power consumption than direct transmission between the source and destination pair. However, extra power compensation is needed for error propagation in the relay-assisted scenario to provision the same end-to-end bit error rate (BER) constraint as the direct transmission. This key point is neglected in the literature. In this paper, we first quantify the impact of power compensation on power savings due to relaying for M-ary QAM modulation schemes, and compare power savings performances of simple relaying and regenerative repeating at the intermediate node. Second, we present upper and lower bounds for the power savings under consideration of lognormal shadowing.

UWB systems based on impulse radio have the potential to provide very high data rates over short distances. In this paper, a new pulse shape is presented that satisfies the FCC spectral mask. Using this pulse, the link budget is calculated to quantify the relationship between data rate and distance. It is shown that UWB can be a good candidate for reliably transmitting 100 Mbps over distances at about 10 meters.

In this paper, a trellis based blind channel estimation and equalization technique is presented. First, blind channel estimation is accomplished by incorporating a list parallel Viterbi algorithm with the least mean square (LMS) updating approach. In this operation, multiple trellis mappings are preserved and ranked in terms of path metrics. Equivalently, multiple channel estimates are maintained and updated once a single symbol is received. Second, at a certain point, which is determined by the evolution of path metrics and the linear constraint possessed in the trellis mapping, only the best channel estimate is maintained. Third, this channel estimate is adopted to construct the whole trellis that is used by a conventional adaptive Viterbi algorithm. Signal detection and further channel updating are then conducted alternatively. To alleviate the noise impact, a small delay is introduced before the feedback of detected symbols to further update the channel estimate. Simulation has shown the overall good performance of the proposed scheme in terms of mean square error (MSE) convergence of the channel estimation, stability to the initial channel guess, and computational complexity etc.

Hand-offs in cellular communication systems cause interactions among cells that can be modeled using multi-dimensional birth-death process approaches and the concept of system state. However, exact numerical calculation of traffic performance characteristics is hindered by unmanageably large system state spaces even for systems of modest size. Previous analytical models get around the difficulty by isolating a cell of interest and invoking a Poisson process assumption for hand-off arrivals to the cell. The current paper seeks to explore the interactions in more detail. Two additional approximate analytical models are developed for this purpose. Each of these is more complicated than the simple Poisson process model, but is analytically tractable-at least for small system sizes. One model isolates a cluster of cells (rather than just the cell of interest) from the system and invokes a Poisson process assumption for cells on the cluster periphery. Performance is calculated for the central cell. The second model also isolates a cluster of cells surrounding the cell of interest, but uses an equivalent two-state Markov modulated Poisson process (MMPP) to characterize hand-off arrival processes to the cell of interest from each of the neighboring cells. Poisson hand-off arrivals to cells on the cluster periphery are assumed. This approach has fewer states than the cluster approach. It was found that all are in close agreement with the original "single isolated cell, Poisson hand-off arrival model", which requires the least states.

Channel holding times and user mobility are important topics in the study of wireless cellular communications. We present an approach to modeling user mobility and session time which enables both the calculation of teletraffic performance characteristics and a characterization of holding time which agrees with published reports. The model allows both the dwell time and unencumbered session time to have general distributions. A derivation of the channel holding time distribution is given. We then show how the model's parameters can be chosen to fit empirical data including observations of channel holding time.

In modeling teletraffic performance of mobile cellular networks, previous work has made use of the concept of dwell time-a random variable that describes the amount of time a platform remains in a cell, sector, or microcell. The dwell time was characterized as a negative exponential variate or the sum of negative exponential variates. With a suitable state description, this allows the use of the memoryless property of negative exponential variates with the result that the problem of computing the traffic performance characteristics can be cast in an underlying framework based on multidimensional birth-death processes. Many alternative system configurations and issues can be investigated using this approach. In small cell (microcellular) systems, however, cell sizes tend to be much less regular in shape and size, and differences in paths traversed by mobiles have a large impact on dwell time realizations. Succinctly, some mobile platform classes have mobility characteristics that are highly variable, in that the dwell time standard deviation is greater than the mean. The previous models, in which the dwell time is a negative exponential or sum of exponential variates, may not be adequate in such cases because they can only accommodate dwell time variates for which the standard deviation does not exceed the mean. We extend the analytical framework so that highly variable mobilities can be considered while insights, tools, approaches, and formulations that are facilitated by the framework can be exploited. The approach allows computation of major teletraffic performance characteristics for cellular communications in which mobility issues are important. We consider multiple platform types as well as cutoff priority for handoffs. Additional issues can be considered using this same approach. Computational issues are discussed, and some theoretical performance characteristics are obtained to demonstrate the method and compare with previous work.