Scheduling Real-Time Information-Update Flows for the Optimal Confidence in Estimation

Design and analysis of linear precoders under a mean square error criterion, Part I: foundations and worst case designs

BART is a state-of-the-art active end-to-end bandwidth measurement method that estimates not only the available bandwidth but also the link capacity of the bottleneck link. It uses a Kalman filter to give estimates in real time during a measurement session. In this paper, we have studied the impact of 802.11 networks on the bandwidth estimates produced by BART. The Kalman filter used by BART is tunable, and one of the contributions of this paper is to show how the Kalman filter should be adjusted to improve real-time tracking and estimation accuracy when the bottleneck is an 802.11 link. Further, the paper contributes by discussing how to interpret the estimates produced by BART and similar bandwidth estimation tools relying on self-induced congestion when used in wireless scenarios. An analysis show that the BART estimates produced are correct - but corresponds to a fair share of the wireless link rather than to the unused capacity. However, the estimates do indicate how much bandwidth an application or device in the wireless network can expect when sending and/or receiving network traffic.

A set of second-order statistics collectively called normalized mean square covariance (NMSV) are defined to characterize the frequency and/or the time selectivity of wide-sense stationary uncorrelated scattering (WSSUS) channels. Normalized frequency mean square covariance (NFMSV) quantifies the frequency selectivity, while normalized time mean square covariance (NTMSV) characterizes the time selectivity. Normalized frequency-time mean square covariance is defined to characterize the combined effect of frequency and time selectivities. The NMSVs of a WSSUS channel can be computed easily from the scattering function. We show that there is a very close relationship between the NMSV of a WSSUS channel and the performance of various diversity combing schemes. Also we discuss, with practical system design problems, how useful the parameters are for efficient system design.

Link quality prediction in mesh networks

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.

In this thesis, we cope with the fundamental mathematical and physical problems of the space-time-Doppler UWB system design for fading channels: multi-input multi-output (MIMO) wireless communication with adaptive filtering. The estimation algorithm from equalizer providing an accurate parameters estimate is investigated. UWB radio is a fast emerging technology with uniquely attractively features inviting major advances in wireless communications, networking, radar, imaging, and positioning systems. By its rule-making proposal in 2002, the Federal Communications Commission (FCC) in the United States essentially unleashed huge new bandwidth (3.6-10.6 GHz) at the noise floor, where UWB radios overlaying coexistence RF systems cerate using low-power ultra-short information bearing pulses. Right now, there are three basic types of UWB: multiband OFDM UWB (MB-OFDM UWB), impulse-UWB (I-UWB), and direct sequence UWB (DS-UWB). We focus on I-UWB and MB-OFDM-UWB, and compare their performance. A simple means for spreading the spectrum of low duty cycle pulse trains is time hopping (TH), with pulse position modulation (PPM) for data modulation at the rate of many pulses per data bit. The main contribution of this thesis for I-UWB is the design of leading edge detectors, coherent correlation detector (CCD), and pilot performance enhancement compared with different Rayleigh and Ricean fading conditions. For MB-OFDM UWB, we have considered the performance analysis and design criteria of MIMO OFDM UWB systems for high data-rate wireless transmission. The signal processing of frame detection, time synchronization, frequency synchronization, channel estimation, synchronization tracking using pilot subcarriers and 2-D MIMO detection algorithms is discussed. The main contributions of this thesis are 1) Compared with receiver employing the Maximum Likelihood (ML) detector, the receiver employing a low-complexity linear Minimum Mean Square Error (MMSE) demodulator can perform a limited performance loss in spatially uncorrelated channels. 2) Due to the operations of spreading and dispreading combined with MMSE-based frequency domain equalization, the adverse effects of the low-SNR subcarriers on the average BER performance are potentially improved. This is a direct consequence of spreading, because even if the signal corresponding to a specific chip is obliterated by a deep frequency domain channel fade, after dispreading these effects are spread over the Walsh-Hadamard Transform (WHT) length. Hence there is a high chance of still recovering all the partially affected subcarrier symbols without errors. 3) As a test case, the OFDM-based 200 Mb/s MB-UWB network (IEEE 802.15.3a) are considered, but the simulation results are shown above section. 4) The simulation channel can be easily applied to NOS and NLOS situation with WHT transformation from Simulink model. The fading channels of UWB with low/high signal to noise ratio (SNR), multipath effects, and multiuser interference may introduce large errors in location. The location engine performs the following tasks: █ Data fusion techniques: Different data fusion techniques are implemented using Time Of Arrival (TOA), Angle Of Arrival (AOA), or a combination of both. █ Channel modeling: A multipath, multiuser channel environment is created that models path loss, shadowing, Rayleigh fading, and Doppler frequency effects. █ Parameter estimation: TOA and AOA estimation algorithms are implemented as part of the location finding engine. Different variations of algorithms are implemented for performance and comparison purposes. █ Configuring the physical layer for different wireless networks. Among the programmable parameters are spreading factor, packet size, training length, constellation type, modulation technique, carrier frequency, level of transmitted signal power, and the number of antennas at the transmitter for both sides. █ Configuring the mobile user conditions. The wireless channel models depend on the Doppler frequencies present in the environment. The Doppler frequency depends on the mobile speed and carrier frequencies as input and generates a Rayleigh fading channel with the U-shape Doppler spectrum. █ Configuring the environmental parameters and network geographical structure. One of the factors affecting the performance of the wireless location system is the environment type (e.g., bad urban, urban, suburban, or rural area). For example, in a bad urban area with many blocking objects and buildings, non-line of sight (NLOS) effects plays an important role in the estimation accuracy.

The aim of this study was to investigate how lower extremity muscles are influenced by body weight support during running at different speeds. Nine participants (age 24 ± 2 years, height 1.75 ± 0.12 m, mass 73.5 ± 15.7 kg) ran at 100%, 115%, and 125% of preferred speed at 100%, 90%, 80%, 70%, and 60% of body weight on a treadmill that provided body weight support. Preferred speed was self-selected by each participant and represented a speed that he or she could sustain if going for a 30 min run. Electromyography (EMG) data were recorded (1000 Hz, 1 min) from the bicep femoris, rectus femoris, tibialis anterior, and gastrocnemius for each condition together with knee angle (electrogoniometer). Average and root mean square EMG were calculated across 30 s. Muscle patterns were determined by smoothing (low-pass filter, 4 Hz) and extracting patterns for 49 cycles defined by consecutive maximum knee flexion angles. Repeated-measures analyses of variance were used to compare average and root mean square across body weight and speeds. Correlations were computed between the 100% speed/100% body weight condition and all other conditions per muscle. There was no interaction between body weight and speed (P > 0.05). Average and root mean square decreased as body weight decreased for all muscles (P < 0.05) and increased across speeds for all muscles (P < 0.05). Correlations for all muscles between conditions were high (range: 0.921–0.999). Although a percent reduction in body weight did not lead to the same reduction in muscle activity, it was clear that reducing body weight leads to a reduction in muscle activity with no changes in muscle activity patterns.

To guarantee the real-time transmission for time-sensitive traffic, we need to take delay-bound quality-of-service (QoS) into account when designing the wireless cellular networks. The traditional powerful QoS guarantee technique, called the homogeneous statistical QoS provisioning, assumes that the QoS provisioning of each link can be individually processed. In order to further significantly increase the global system throughput, the available resources of all links need to be jointly controlled, which is typically suitable for downlink transmissions over wireless cellular networks. Under this setup, we need to consider the diverse delay-bound QoS provisionings for different links at the same time, which represents the new heterogeneous statistical QoS provisioning framework and imposes many new challenges not encountered before in wireless networks. To overcome these problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for high-speed downlink transmissions in wireless cellular networks. In particular, we formulate the optimization problem to maximize the downlink throughput subject to heterogeneous statistical delay-bound QoS requirements. For solving this optimization problem, we develop the heterogeneous-QoS-driven power allocation scheme to derive the closed-form solutions which can maximize the global system throughput while guaranteeing the heterogeneous delay-bound QoS for the entire wireless cellular networks. We show that the heterogeneous-QoS-driven power allocation provides more generic framework for downlink transmissions. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can achieve the global optimization of wireless resource efficiency, thus significantly increasing the global system throughput as compared with the homogeneous statistical delay-bound QoS provisioning schemes.

The rapid deployment of wireless mesh networks across universities and enterprises, and the pervasiveness of mobile devices equipped with Wi-Fi connectivity, has resulted in a scenario wherein end users have the option to choose from a multitude of access points at any given location. Moreover, with the increasing availability of rich online content, there has been a steady increase in mobile Internet traffic. Since the choice of access point that a user associates with will directly impact his performance, it is imperative that there exist an efficient association control mechanism, in order to enhance the end user's experience. As part of this work, we propose Seeker, a novel framework for association control in wireless networks that utilizes "available bandwidth" as the design metric. The goal of Seeker is to assist the mesh network in making an intelligent decision regarding which access point a client should associate with. As part of our scheme, we implement and evaluate a passive tool to estimate available bandwidth in wireless networks. We then describe how we use this tool to implement our association control scheme, and evaluate it via extensive experiments on an outdoor testbed. Seeker takes into consideration the performance of the mesh backhaul, in addition to the client-to-AP link quality, thereby achieving significant advantages over traditional association control schemes for wireless-LANs.

In this paper, we consider a new design problem of optimizing a linear transceiver for correlated multiple-input multiple-output (MIMO) interference channels in the presence of channel state information (CSI) errors, which is a more realistic and practical scenario than those considered in the previous studies on uncorrelated MIMO interference channels. By taking CSI errors into account, the optimization problem is initially formulated to minimize the average mean square error (MSE) under the general power constraints. Since the objective function is not jointly convex in precoders and receive filters, we split the original problem into two convex subproblems, and then linear precoders and receive filters are obtained by solving two subproblems iteratively. It is shown that the proposed algorithm is guaranteed to converge to a local minimum. The numerical results show that the proposed algorithm can significantly reduce the sensitivity to CSI errors compared with the existing robust schemes in the correlated MIMO interference channel.

Massive connectivity is regarded as a key requirement for future networks to support new communication paradigms, where the human-type communications coexist with machine-type communications. Owing to the limited coherence time but the huge number of potential devices, it is impossible to allocate mutually orthogonal pilot sequence for all potential devices, which may impose severe interference on the device activity detection and channel estimation. Existing nonorthogonal pilot design methods for conventional cellular network are not suitable for the massive connectivity regime. To overcome this challenge, we first formulate the pilot sequences design as an optimization problem to minimize the average mean square error (MSE) of channel estimation under the individual power constraint. The proposed optimization problem is nonconvex and highly coupled. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop a distributed algorithm based on the matrix fractional programming (FP) and the alternating direction method of multipliers (ADMM) methods. Simulations validates that the proposed scheme not only achieves significant gains in channel estimation over state-of-the-art baseline schemes, but also improves the device activity detection performance.

Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization–Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization–Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

Myelin content is a marker for nervous system pathology and is quantifiable by myelin water imaging using multi-echo CPMG sequence, which is inherently slow. One way to accelerate the scan is to utilize compressed sensing. However, reconstructing the images piecemeal by standard compressed sensing methods is not the optimal solution, because it only exploits intraimage spatial redundancy. It does not recognize that the different T2 weighted images are scans of the same anatomical volume and hence correlated. The purpose of this work is to test the feasibility of compressed sensed CPMG with group-sparsity promoting optimization for myelin water imaging. Group-sparse reconstruction was performed at various simulated and actual undersampling factors for an electronic phantom, ex vivo rat spinal cord, and in vivo rat spinal cord. Normalized mean square error was used as the metric for comparison. For both simulated undersampling and the actual undersampling, the method was found to minimally impact myelin water fraction map quality (normalized mean square error < 0.25) when acceleration factor was below two. Compressed sensed CPMG with group-sparse reconstruction is useful for achieving a shorter scan time than traditionally possible.

High resolution and sensitivity brain SPECT is promising for the accurate diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD). Multi-pinhole (MPH) collimators with a good performance in imaging small field-of-view (FOV) could be better used for brain SPECT. In this study, we aim to evaluate the impact of varying the number of pinholes and the number of projections on the performance of MPH brain SPECT. The system design was based on a commercial clinical dual-head SPECT/CT scanner, with target spatial resolutions of 12 mm and 8 mm for AD and PD SPECT, respectively. In total, 1-25 pinholes were modeled for 64, 32, 16, 8, 4, and 2 projections. The 3D NURBS-based HUman Brain (NHUB) phantom was used in the analytical simulation to model 99mTc-HMPAO and 99mTc-TRODAT distributions. The 2D Derenzo hot-rod phantom and star phantom were used in Monte Carlo simulations to evaluate the spatial resolution and angular sampling performance of MPH. The influence of different detector positions was also evaluated for 2, 4, and 6 angular views. The projections were reconstructed using the 3D MPH ML-EM method. Normalized mean square error, coefficient of variation, and image profiles were evaluated. Along with the decrease in the number of projections, more pinholes are required to achieve the optimum performance. For 32 projections, 9- and 7-pinhole collimators provide the best normalized mean square error (NMSE) to the coefficient of variation (COV) trade-off for 99mTc-HMPAO and 99mTc-TRODAT, respectively. Detector positions substantially affect the image quality for MPH SPECT for 2 and 4 angular views. The smallest rod size for the Derenzo hot-rod phantom, which could be resolved, is 7.9 mm for the MPH general purpose collimator (MPGP) with more than 16 projections and 6.4 mm for MPH high-resolution collimator (MPHR) with more than 8 projections. The number of pinholes affects the performance of the MPH collimator, especially when the projection views become fewer. More pinholes are required for fewer projections to provide better angular sampling in MPH for complex activity distributions. Detector positions affect the image quality of MPH SPECT for 2 and 4 angular views, where L-mode acquisition is slightly superior to H-mode. MPH collimators exhibited improved spatial resolution and angular sampling compared with both LEHR and single pinhole collimators.

As it is known, to transform an analogue image into digital form it is necessary to undergo the processes of sampling and quantification. The first of them consists of downloading at defined intervals data from analogue image the second one approximates analogue levels of brightness due to the closest digital levels. Both processes are the reason of an errors formation. Those errors have significant influence on the fields of the digital images transformation, in which it is necessary to synchronize images. This problem becomes particularly significant when we use the images gained from two different sources (scanner, digital camera). Anyone who uses the terms concern to images’ transformation, knows that bad synchronization can lead to wrong results. In his article authors present the algorithm which eliminates problem of bad images adjustment. The paper features the method of determination of the rotation angle and axis based on computation of the Normalized Mean Square Error (NMSE) coefficient.