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To support millimeter wave (mmWave) frequency bands in cellular communications, both the base station and the mobile platform utilize large antenna arrays to steer narrow beams towards each other to compensate the path loss and improve communication performance. The time-frequency resource allocated for initial access, however, is limited, which gives rise to need for efficient approaches for beam detection. For hybrid analog-digital beamforming (HB) architectures, which are used to reduce power consumption, we propose a compressed sensing (CS) based approach for 5G initial access beam detection that is for a HB architecture and that is compliant with the 3GPP standard. The CS-based approach is compared with the exhaustive search in terms of beam detection accuracy and by simulation is shown to outperform. Up to 256 antennas are considered, and the importance of a careful codebook design is reaffirmed., 5 pages, 6 figures, SPAWC2020

Measuring visual quality, as perceived by human observers, is becoming increasingly important in a large number of applications where humans are the ultimate consumers of visual information. Many natural image databases have been developed that contain human subjective ratings of the images. Subjective quality evaluation data is less available for synthetic images, such as those commonly encountered in graphics novels, online games or internet ads. A wide variety of powerful full-reference, reduced-reference and no-reference Image Quality Assessment (IQA) algorithms have been proposed for natural images, but their performance has not been evaluated on synthetic images. In this paper we (1) conduct a series of subjective tests on a new publicly available Embedded Signal Processing Laboratory (ESPL) Synthetic Image Database, which contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 resolution, and (2) evaluate the performance of more than 50 publicly available IQA algorithms on the new database. The synthetic images in the database were processed by post acquisition distortions, including those arising from compression and transmission. We collected 26,000 individual ratings from 64 human subjects which can be used to evaluate full-reference, reduced-reference, and no-reference IQA algorithm performance. We find that IQA models based on scene statistics models can successfully predict the perceptual quality of synthetic scenes. The database is available at: http://signal.ece.utexas.edu/%7Ebevans/synthetic/ .

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip .

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum-rate capacity, but with less than 4% of the required run time., (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty., Comment: 31 pages, 15 figures, revised and resubmitted to IEEE Transactions on Wireless Communications on October 2, 2019, March 9, 2020, July 2, 2020, and September 1, 2020

We propose a method to improve the performance of the downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The standards-compliant method is based on the construction of a surrogate CoMP trigger function using deep learning. The cooperating set is a single-tier of sub-6 GHz heterogeneous base stations operating in the frequency division duplex mode (i.e., no channel reciprocity). This surrogate function enhances the downlink user throughput distribution through online learning of non-linear interactions of features and lower bias learning models. In simulation, the proposed method outperforms industry standards in a realistic and scalable heterogeneous cellular environment., (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: (1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and (2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal to interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments., (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We investigate uplink user scheduling for millimeter wave (mmWave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mmWave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have (1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and (2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mmWave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. Simulation results validate the sum rate performance of the proposed algorithms and derived ergodic rates expressions., Submitted to Transactions on Wireless Communications

Non-Gaussian noise/interference severely limits communication performance of narrowband powerline communication (PLC) systems. Such noise/interference is dominated by periodic impulsive noise whose statistics varies with the AC cycle. The periodic impulsive noise statistics deviates significantly from that of additive white Gaussian noise, thereby causing dramatic performance degradation in conventional narrowband PLC systems. In this paper, we propose a robust transmission scheme and corresponding receiver methods to combat periodic impulsive noise in OFDM-based narrowband PLC. Towards that end, we propose (1) a time-frequency modulation diversity scheme at the transmitter and a diversity demodulator at the receiver to improve communication reliability without decreasing data rates; and (2) a semi-online algorithm that exploits the sparsity of the noise in the frequency domain to estimate the noise power spectrum for reliable decoding at the diversity demodulator. In the simulations, compared with a narrowband PLC system using Reed-Solomon and convolutional coding, whole-packet interleaving and DBPSK/BPSK modulation, our proposed transceiver methods achieve up to 8 dB gains in $E_{b}/N_{0} $ with convolutional coding and a smaller-sized interleaver/deinterleaver.

The gyroscope is playing a key role in helping estimate 3D camera rotation for various vision applications on cell phones, including video stabilization and feature tracking. Successful fusion of gyroscope and camera data requires that the camera, gyroscope, and their relative pose to be calibrated. In addition, the timestamps of gyroscope readings and video frames are usually not well synchronized. Previous paper performed camera-gyroscope calibration and synchronization offline after the entire video sequence has been captured with restrictions on the camera motion, which is unnecessarily restrictive for everyday users to run apps that directly use the gyroscope. In this paper, we propose an online method that estimates all the necessary parameters, whereas a user is capturing video. Our contributions are: 1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter and 2) generalization of the multiple-view coplanarity constraint on camera rotation in a rolling shutter camera model for cell phones. The proposed method is able to estimate the needed calibration and synchronization parameters online with all kinds of camera motion and can be embedded in gyro-aided applications, such as video stabilization and feature tracking. Both Monte Carlo simulation and cell phone experiments show that the proposed online calibration and synchronization method converge fast to the ground truth values.

In this paper, we propose a hybrid analog-digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain's signal-to-noise ratio raised to the 1/3 power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include 1) ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver, 2) approximation of the capacity with the BA algorithm as a function of channels, and 3) a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound. Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system., Accepted to IEEE Transactions on Signal Processing

Video stabilization smooths camera motion estimates in a way that should adapt to different types of intentional motion. Corrective motion (the difference between smoothed and original motions) should be constrained so that black borders do not intrude into the (cropped) stabilized frames. Although offline smoothing can use all of the frames, online (real-time) smoothing can only use a small number of previous frames. In this paper, we propose an online motion smoothing method based on linear estimation applied to a constant-velocity model. We use estimate projection to ensure that the smoothed motion satisfies black-border constraints, which are modeled exactly by linear inequalities for general 2D motion models. We then combine the estimate projection with multiple-model estimation, which can adaptively smooth the camera motion in a probabilistic way. Experimental results show how the proposed algorithm can better smooth the camera motion and stabilize videos in real time.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip .

We propose a simple low-cost technique that enables civil global positioning system receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the power-distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlation function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against more than 25 high-quality empirical datasets yielding more than 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attacks while maintaining a $ 0.6% single-channel false alarm rate.

We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voice bearers and 2) show that our derived lower bound loss in effective signal to interference plus noise ratio due to neighboring cell failure is sufficient for VoLTE power control purposes in practical cellular networks. In our simulation, the proposed RL-based power control algorithm significantly improves both voice retainability and mean opinion score compared to current industry standards. The improvement is due to maintaining an effective downlink signal to interference plus noise ratio against adverse network operational issues and faults., Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Many wireless data communication systems such as LTE, and Wi-Fi, are increasingly facing interference that is much stronger than thermal noise. Interference may arise due to dense spatial reuse of spectrum intended to increase user data rates, or from other devices emitting radiation in the same spectrum, or from electronic circuitry within the communication platform. For a multi-antenna receiver operating in an interference-limited channel, we evaluate four diversity combining algorithms in terms of outage probability in the low-outage regime. The contributions of this paper are (1) derivation of closed-form expressions for the output signal-to-interference ratio (SIR) statistics of fixed weight, maximal ratio, selection and post-detection combining; (2) comparison of the relative outage performance of these algorithms; and (3) proposed diversity combining algorithms to reduce outage probability. Our results can be applied in analyzing the outage performance and throughput capacity of both centralized and decentralized interference-limited wireless networks.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images which are usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion (MEF) techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly-conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the Gradient Image Quality Assessment algorithm (G-IQA). NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Towards ameliorating this we make here the following contributions: (1) We design a HDR picture NR IQA model and algorithm using both standard space-domain NSS features as well as novel HDR-specific gradient based features that significantly elevate prediction performance, (2) We validate the proposed models on a large-scale crowdsourced HDR image database, and (3) We demonstrate that the proposed model also perform well on legacy natural SDR images. The software is available at: http://signal.ece.utexas.edu/%7Edebarati/higradeRelease.zip.

Two-way communication is a key feature in a smart grid. It is enabled by either powerline communication or wireless communication technologies. Utilizing both technologies can potentially enhance communication reliability, and many diversity combining schemes have been proposed. In this paper, we propose a flexible real-time testbed to evaluate diversity combining schemes over physical channels. The testbed provides essential parts of physical layers on which both powerline and wireless communications operate. The contributions of this paper are 1) design and implementation of a real-time testbed for diversity of simultaneous powerline and wireless communications, 2) release of the setup information and complete source code for the testbed, and 3) performance evaluation of maximal ratio combining (MRC) on the testbed. As initial results, we show that performance of MRC from measurements obtained on the testbed over physical channels is very close to that in simulations in various test cases under a controlled laboratory environment., 6 pages, 5 figures, submitted to ICC 2018

Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. This paper assesses the visual quality in mapping of high dynamic range (HDR) images to standard dynamic range (SDR) images with 8 bits/color/pixel. In previous work, the Tone-Mapped image Quality Index (TMQI) compares the original HDR image with the rendered SDR image. TMQI quantifies distortions locally and pools them by uniform averaging, in addition to measuring naturalness of the SDR image. For SDR images, perceptual pooling strategies have improved correlation of image quality assessment (IQA) algorithms with subjective scores. The primary contributions of this paper are: (1) integrating local information-based pooling strategies in the TMQI IQA algorithm, (2) measuring image naturalness by using mean-subtracted contrast-normalized pixels, and (3) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores.

Communication in decentralized wireless networks is limited by interference. Because transmissions typically last for more than a single contention time slot, interference often exhibits a strong statistical dependence over time that results in temporally correlated communication performance. The temporal dependence in interference increases as user mobility decreases and/or the total transmission time increases. We propose a network model that spans the extremes of temporal independence to long-term temporal dependence. Using the proposed model, closed-form single hop communication performance metrics are derived that are asymptotically exact in the low outage regime. The primary contributions are (i) deriving the joint temporal statistics of network interference} and showing that it follows a multivariate symmetric alpha stable distribution, (ii) utilizing the joint interference statistics to derive closed-form expressions for local delay, throughput outage probability, and average network throughput, and (iii) using the joint interference statistics to redefine and analyze the network} transmission capacity that the throughput-delay-reliability tradeoffs in single hop transmissions. Simulation results verify the closed-form expressions derived in this paper and we demonstrate up to 2x gain in network throughput and reliability by optimizing certain parameters of medium access control layer protocol in view of the temporal correlations. � 2012 IEEE.

Many wireless data communications systems, such as LTE, Wi-Fi, and Wimax, have become or are rapidly becoming interference limited due to radio frequency interference (RFI) generated by both human-made and natural sources. Human-made sources of RFI include uncoordinated devices operating in the same frequency band, devices communicating in adjacent frequency bands, and computational platform subsystems radiating clock frequencies and their harmonics. Additive RFI for these wireless systems has predominantly non-Gaussian statistics, and is well modeled by the Middleton Class A distribution for centralized networks, and the symmetric alpha stable distribution for decentralized networks. Our primary contribution is the derivation of joint spatial statistical models of RFI generated from uncoordinated interfering sources randomly distributed around a multiantenna receiver. The derivation is based on statistical-physical interference generation and propagation mechanisms. Prior results on joint statistics of multiantenna interference model either spatially independent or spatially isotropic interference, and do not provide a statistical-physical derivation for certain network environments. Our proposed joint spatial statistical model captures a continuum between spatially independent and spatially isotropic statistics, and hence includes many previous results as special cases. Practical applications include co-channel interference modeling for various wireless network environments, including wireless ad hoc, cellular, local area, and femtocell networks.

In the last two years, the embedded DSP market has been swept up by the general increase in interest in multicore that has been driven by companies such as Intel and Sun. One reason for this is that there is now a lot of focus on tooling in academia and also a willingness on the part of users to accept new programming paradigms. This industry-wide effort will have an effect on the way multicore DSPs are programmed and perhaps architected. But it is too early to say in what way this will occur. Programming multicore DSPs remains very challenging. The problem of how to take a piece of sequential code and optimally partition it across multiple cores remains unsolved. Hence, there will naturally be a lot of variations in the approaches taken. Equally important is the issue of debugging and visibility. Developing effective and easy-to-use code development and real-time debug tools is tremendously important as the opportunity for bugs goes up significantly when one starts to deal with both time and space. The markets that DSP plays in have unique features in their desire for low power, low cost, and hard real-time processing, with an emphasis on mathematical computation. How well the multicore research being performed presently in academia will address these concerns remains to be seen.

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10???5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable (S ?S) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as S ?S interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.

We propose a wireless fading channel prediction algorithm for a pilot-symbol aided orthogonal frequency division multiplexing (OFDM) system. Assuming a doubly selective (time and frequency varying) ray-based physical channel model and equispaced pilot subcarriers in time and frequency, this algorithm performs channel model parameter acquisition using a 2-Step 1-D ESPRIT (estimation of signal parameters via rotational invariance techniques) as a first stage, and channel prediction via model extrapolation as a second stage. Since the channel model parameter acquisition has cubic complexity, we also propose a linear complexity channel parameter tracking algorithm based on an improved adaptive ESPRIT algorithm to continuously adapt to the time-varying channel model parameters. We derive the Cramer-Rao lower bound (CRLB) and asymptotic CRLB (ACRLB) for the mean squared error (MSE) in OFDM channel prediction. We show that our proposed OFDM channel prediction algorithm has better MSE performance while maintaining similar computational complexity than previous methods in sparse multipath fading channels characterized by specular scattering, which is most suitable for outdoor mobile macrocell scenarios. Thus, our method can be seen as a complement to the existing schemes that are more suitable for dense multipath channels with diffuse scattering, which is typical of urban pico-cell and indoor wireless scenarios. We provide simulation results based on the IEEE 802.16e mobile broadband wireless access standard to corroborate our claims.

OFDMA resource allocation assigns subcarriers and power, and possibly data rates, to each user. Previous research efforts to optimize OFDMA resource allocation with respect to communication performance have focused on formulations considering only instantaneous per-symbol rate maximization, and on solutions using suboptimal heuristic algorithms. This paper intends to fill gaps in the literature through two key contributions. First, we formulate continuous and discrete ergodic weighted sum rate maximization in OFDMA assuming the availability of perfect channel state information (CSI). Our formulations exploit time, frequency, and multi-user diversity, while enforcing various notions of fairness through weighting factors for each user. Second, we derive algorithms based on a dual optimization framework that solve the OFDMA ergodic rate maximization problem with O(MK) complexity per OFDMA symbol for M users and K subcarriers, while achieving data rates shown to be at least 99.9999% of the optimal rate in simulations based on realistic parameters. Hence, this paper attempts to demonstrate that OFDMA resource allocation problems are not computationally prohibitive to solve optimally, even when considering ergodic rates.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.

In this paper, a two-stage receiver structure for interference cancellation in multiuser spatially multiplexed multiple-antenna systems is presented. A space-time equalizer is used in the first stage for joint coantenna/cochannel-interference suppression and shortening of the effective channel for each transmit stream of the desired user. The channel shortening, combined with independent detection, helps reduce the complexity of the second stage Viterbi equalizer, which is used for separate intersymbol-interference equalization for each of the streams. Three objective functions are proposed for determining the coefficients of the space-time equalizer using a direct training data-based approach, which does not require estimation of the interferer's channel. Simulation results show good symbol error performance as compared to existing algorithms with asynchronous multiple-input multiple-output interferers

Internet traffic primarily consists of packets from elastic flows, i.e., Web transfers, file transfers, and e-mail, whose transmissions are mediated via the Transmission Control Protocol (TCP). In this paper, we develop a methodology to process TCP flow measurements in order to analyze throughput correlations among TCP flow classes that can be used to infer congestion sharing in the Internet. The primary contributions of this paper are: 1) development of a technique for processing flow records suitable for inferring congested resource sharing; 2) evaluation of the use of factor analysis on processed flow records to explore which TCP flow classes might share congested resources; and 3) validation of our inference methodology using bootstrap methods and nonintrusive, flow level measurements collected at a single network site. Our proposal for using flow level measurements to infer congestion sharing differs significantly from previous research that has employed packet level measurements for making inferences. Possible applications of our method include network monitoring and root cause analysis of poor performance.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in many applications where humans are the ultimate consumers of visual information. Significant progress has been made for assessing the subjective quality of natural images, such as those taken by optical cameras. Natural Scene Statistics (NSS) is an important tool for no-reference visual quality assessment of natural images, where the reference image is not needed for comparison. In this paper, we take an important step towards using NSS to automate visual quality assessment of photorealistic synthetic scenes typically found in video games and animated movies. Our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database containing 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format, and (2) evaluating the performance of 17 no-reference image quality assessment (IQA) algorithms using synthetic scene statistics. We find that similar to natural scenes, synthetic scene statistics can be successfully used for IQA and certain statistical features are good for certain image distortions.

Narrowband powerline communications (NB-PLC) and unlicensed wireless communications are considered as two leading communications technologies for emerging Smart Grid applications. The diversity provided by the simultaneous transmission of the same information over powerline and wireless links can be exploited to enhance the overall system reliability. In this paper, we propose an efficient technique to combine the received signals of the NB-PLC and wireless links considering the impulsive nature of the noise and interference on both links. We derive an expression for the average bit-error-rate of the proposed technique. In addition, we present simulation results that quantify the performance gains achieved by our proposed combining technique compared to conventional techniques.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. For assessing subjective quality of natural images, such as those taken by optical cameras, significant progress has been made for several decades. To aid in the benchmarking of objective image quality assessment (IQA) algorithms, many natural image databases have been annotated with subjective ratings of the images by human observers. Similar information, however, is not as readily available for synthetic images commonly found in video games and animated movies. In this paper, our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database, and (2) evaluating the performance of more than 20 full reference IQA algorithms for natural images on the synthetic image database. The ESPL Synthetic Image Database contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format. After collecting 26000 individual human ratings, we compute the differential mean opinion score (DMOS) for each image to evaluate IQA algorithm performance.

Non-Gaussian noise/interference severely limits communication performance of narrowband powerline communication (PLC) systems. Such noise/interference is dominated by periodic impulsive noise whose statistics varies with the AC cycle. The periodic impulsive noise statistics deviate significantly from that of additive white Gaussian noise, thereby causing dramatic performance degradation in conventional narrowband PLC systems. In this paper, we propose a robust transmission scheme and corresponding receiver methods to combat periodic impulsive noise in OFDM-based narrowband PLC. Towards that end, we propose (1) a time-frequency modulation diversity scheme at the transmitter and a diversity demodulator at the receiver to improve communication reliability without decreasing data rates; and (2) a semi-online algorithm that exploits the sparsity of the noise in the frequency domain to estimate the noise power spectrum for reliable decoding at the diversity demodulator. In the simulations, compared with a narrowband PLC system using Reed-Solomon and convolutional coding, whole-packet interleaving and DBPSK/BPSK modulation, our proposed transceiver methods achieve up to 8 dB gains in E b /N 0 with convolutional coding and a smaller-sized interleaver/deinterleaver.

Providing reliable smart grid communication from customers to the local utility faces significant challenges due to noise, interference, frequency selectivity, path loss and fading. For transmission over narrowband powerline channels (3–500 kHz) and unlicensed wireless channels (902–928 MHz), this paper reviews previous approaches for (a) channel modeling and estimation and (b) noise/interference modeling and mitigation. Based on these approaches, the paper reviews methods for joint transmission over the powerline and wireless channels to improve reliability further. The key contribution of this paper is to explore design tradeoffs in communication performance vs. implementation complexity for joint transmission.

A perceptual image hash function maps an image to a short binary string based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). For any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. We prove that the decision version of our clustering problem is NP complete. Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness versus fragility tradeoffs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.

We describe the design and implementation of a hybrid intelligent surveillance system consisting of an embedded system and a personal computer (PC)-based system. The embedded system performs some of the image processing tasks and sends the processed data to a PC. The PC tracks persons and recognizes two-person interactions by using a grayscale side-view image sequence captured by a stationary camera. Based on our previous research, we explored the optimum division of tasks between the embedded system and the PC, simulated the embedded system using dataflow models in Ptolemy, and prototyped the embedded systems in real-time hardware and software using a 16-bit CISC microprocessor. This embedded system processes one frame image in 89 ms, which is within three frame-cycle periods for a 30 Hz video system. In addition, the real-time embedded system prototype uses 5.7 Kbytes of program memory, 854 Kbytes of internal data memory and 2 Mbytes external DRAM.

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.

This paper presents an efficient method for updating echo canceller (EC) coefficients in asymmetric digital subscriber line transceivers during transmission that removes the dependency of updates on the far-end signal. The new method improves on the frequency-domain EC architecture by Ho et al. (1996), in that it converges faster, improves the stability to perturbation, and is not prone to bias from pilot tones. The new EC updating system architecture is presented for both the central office and remote terminal systems and is fully compliant with the ITU-T G.DMT standard.

Image halftoning converts a high-resolution image to a low-resolution image, e.g., a 24-bit color image to a three-bit color image, for printing and display. Vector error diffusion captures correlation among color planes by using an error filter with matrix-valued coefficients. In optimizing vector error filters, Damera-Venkata and Evans (see IEEE Trans. Image Processing, vol.10, p.1552-65, Oct. 2001) transform the error image into an opponent color space where Euclidean distance has perceptual meaning. This letter evaluates color spaces for vector error filter optimization. In order of increasing quality, the color spaces are YIQ, YUV, opponent (by Poirson and Wandell, 1993), and linearized CIELab (by Flohr, Kolpatzik, Balasubramanian, Carrara, Bouman, and Allebach, 1993).

Lossy video compression methods often rely on modeling the abilities and limitations of the intended receiver, the human visual system (HVS), to achieve the highest possible compression with as little effect on perceived quality as possible. Foveation, which is non-uniform resolution perception of the visual stimulus by the HVS due to the non-uniform density of photoreceptor cells in the eye, has been demonstrated to be useful for reducing bit rates beyond the abilities of uniform resolution video coders. In this work, we present real-time foveation techniques for low bit rate video coding. First, we develop an approximate model for foveation. Then, we demonstrate that foveation, as described by this model, can be incorporated into standard motion compensation and discrete cosine transform (DCT)-based video coding techniques for low bit rate video coding, such as the H.263 or MPEG-4 video coding standards, without incurring prohibitive complexity overhead. We demonstrate that foveation in the DCT domain can actually result in computational speed-ups. The techniques presented can be implemented using the baseline modes in the video coding standards and do not require any modification to, or post-processing at, the decoder.

We propose two real-time motion smoothing algorithms for video stabilization using a pure 3D rotation motion model with known camera projection parameters. Both proposed algorithms aim at smoothing 3D rotation matrix sequences in a causal way. The first algorithm smooths the 3D rotation sequences in a way similar to 1st-order IIR filtering. The second algorithm uses sequential probabilistic estimation under a constant angular velocity model. These two algorithms are generalized from classical 2D motion smoothing algorithms. We exploit the manifold structure of the rotation matrices so that the proposed algorithms directly smooth the 3D rotation sequences on the manifold. In addition, we introduce a simple projection step in order to guarantee that no black borders intrude into the stabilized video frames. Experimental results show that the proposed algorithms are able to effectively stabilize video sequences and outperform their 2D counterparts with less jitter and distortion.

Sonar sensor arrays potentially produce huge amounts of data to be recorded or transmitted over a telemetry system. Compression can reduce the required storage or transmission bandwidth, or allow a larger or higher fidelity array. We use a dataset for a sonar array receiving acoustic communication signals from a transmitter in a lake test. We compress the received signals to evaluate the effect of compression on performance. Based on analysis of the dataset, we use non-uniform quantization with a Laplace distribution along with noise-shaped feedback coding. We demonstrate that this sonar data can be compressed from 16-bit to five-bit values with little or no change in performance using our technique.

Natural Scene Statistics (NSS) has been applied to natural images obtained through optical cameras for automated visual quality assessment. Since NSS does not need a reference image for comparison, NSS has been used to assess user quality-of-experience, such as for streaming wireless image and video content acquired by cameras. In this paper, we take an important first step in using NSS to automate visual quality assessment of synthetic images found in video games and animated movies. In particular, we analyze NSS for synthetic images in the spatial domain using mean-subtracted-contrast-normalized (MSCN) pixels and their gradients. The primary contributions of this paper are (1) creation of a publicly available ESPL Synthetic Image database, containing 221 color images, mostly in high definition resolution of 1920 × 1080, and (2) analysis of the statistical distributions of the MSCN coefficients (and their gradients) for synthetic images, obtained from the image intensities. We find that similar to the case for natural images, the distributions of the MSCN pixels for synthetic images can be modeled closely by Generalized Gaussian and Symmetric α-Stable distributions, with slightly different shape and scale parameters.

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.

Grayscale digital image halftoning quantizes each pixel to one bit. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the quantization error among the neighboring grayscale pixels. Error diffusion introduces nonlinear distortion (directional artifacts), linear distortion (sharpening), and additive noise. Threshold modulation, which alters the quantizer input, has been previously used to reduce either directional artifacts or linear distortion. This paper presents an adaptive threshold modulation framework to improve halftone quality by optimizing error diffusion parameters in the least squares sense. The framework models the quantizer implicitly, so a wide variety of quantizers may be used. Based on the framework, we derive adaptive algorithms to optimize 1) edge enhancement halftoning and 2) green noise halftoning. In edge enhancement halftoning, we minimize linear distortion by controlling the sharpening control parameter. We may also break up directional artifacts by replacing the thresholding quantizer with a deterministic bit flipping (DBF) quantizer. For green noise halftoning, we optimize the hysteresis coefficients.

Modern cellular networks such as Long-Term Evolution (LTE) transport complex-baseband samples between remote radio hardware and processing equipment. Common Public Radio Interface (CPRI) links are widely used in practice and enable flexible radio head deployments, distributed antenna systems, and advanced spatial processing such as coordinated multi-point (CoMP) transmission and reception. Current CPRI links already have insufficient capacity to support 20 MHz bandwidth LTE for a basestation with three sectors and four antennas per sector. By supporting eight antennas per sector and up to 5× system bandwidth, LTE-A will require substantial increases in CPRI capacity. In this work, we develop compression methods that exploit the temporal and spectral structure of LTE signals with the goal of achieving high compression with limited impact on end-to-end communication performance. Our contributions include (i) design of a low-complexity compression method for LTE and (ii) validation of this method using an LTE link-level simulation. Our method achieves up to 5× compression for uplink and downlink signals.

Gyroscope is playing a key role in helping estimate camera rotation during mobile video capture. The fusion of gyroscope and visual measurements needs the knowledge of camera projection parameters, the gyroscope bias and the relative orientation between gyroscope and camera. Moreover, the timestamps of gyroscope and video frames are usually not well synchronized. In this paper, we propose an online method that estimates all the necessary parameters while capturing videos. Our contributions are (1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter, and (2) generalization of coplanarity constraint of camera rotation in a rolling shutter camera model for cellphones. The proposed method is able to accurately estimate the needed parameters online with all kinds of camera motion, and can be embedded in gyro-aided applications such as video stabilization and feature tracking.

This paper introduces a new binary shape coding technique called generalized predictive shape coding (GPSC) to encode the boundary of a visual object compactly by using a vertex-based approach. GPSC consists of a contour pixel matching algorithm and a motion-compliant contour coding algorithm. The contour pixel matching algorithm utilizes the knowledge of previously decoded contours by using a uniform translational model for silhouette motion, and generalizes polygon approximation for lossless and lossy motion estimation by adjusting a tolerance parameter d max . To represent motion-compliant regions with minimum information in the transmitted bitstream, we develop a reference index-based coding scheme to represent the 2D positions of the matched segments using 1D reference contour indices. Finally, we encode the mismatched segments by sending residual polygons until the distortion is less than d max . While GPSC realizes polygon approximation exactly at every encoding stage, we can guarantee quality of service because the peak distortion is no greater than d max , and we improve coding efficiency as long as a silhouette complies with the model. The tolerance parameter d max can be assigned to each contour to smooth the transmitted data rate, which is especially useful for constant bandwidth channels. Compared with non-predictive approaches, simulation using MPEG-4 sequences demonstrates that GPSC not only improves objective gain but also enhances visual quality based on MPEG-4 subjective tests. The significance of GPSC is that it provides a generic framework for seamlessly extending conventional vertex coding schemes into the temporal domain yet it retains the advantages of existing polygon-based algorithms for visual content description while furnishing better geometric compression.

Digital halftoning quantizes a graylevel image to one bit per pixel. Halftoning by error diffusion reduces local quantization error by filtering the quantization error in a feedback loop. In this paper, we linearize error diffusion algorithms by modeling the quantizer as a linear gain plus additive noise. We confirm the accuracy of the linear model in three independent ways. Using the linear model, we quantify the two primary effects of error diffusion: edge sharpening and noise shaping. For each effect, we develop an objective measure of its impact on the subjective quality of the halftone. Edge sharpening is proportional to the linear gain, and we give a formula to estimate the gain from a given error filter. In quantifying the noise, we modify the input image to compensate for the sharpening distortion and apply a perceptually weighted signal-to-noise ratio to the residual of the halftone and modified input image. We compute the correlation between the residual and the original image to show when the residual can be considered signal independent. We also compute a tonality measure similar to total harmonic distortion. We use the proposed measures for edge sharpening, noise shaping, and tonality to evaluate the quality of error diffusion algorithms.