Diversity Combination of Fading Signals with Unequal Mean Strengths

Barrow, Bruce B.

doi:10.1109/tcom.1963.1088732

Cited by 33 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantitative perfusion map ( Q Map) was calculated as

Q=\frac{\mathrm{F}{\mathrm{D}}_Q}{{\mathrm{F}\mathrm{D}}_Q\left(\mathrm{blood}\right)}\frac{f_Q}{2}\kern2em \left[\mathrm{ml}/\min /\mathrm{ml}\right]

where

{\mathrm{FD}}_Q

is the whole perfusion‐weighted image obtained via the FD analysis;

{\mathrm{FD}}_Q\left(\mathrm{blood}\right)

is the perfusion amplitude measured in a voxel completely filled with blood; and

{f}_Q

is the number of heart beats per minute 1,6 . The combined functional maps were obtained from quantified functional maps of individual SGs by segmenting lung parenchyma with exclusion of large vessels, and combining maps via a linear weighted combination method, in which the weights were proportional to estimated SNR (i.e., maximal ratio combining) 44 in the lung parenchyma. The lung segmentation was performed semi‐automatically using a region‐growing algorithm, 45 and if needed, the region of interests were corrected manually 31 .…”

Section: Methodsmentioning

confidence: 99%

“…where FD Q is the whole perfusion-weighted image obtained via the FD analysis; FD Q (blood) is the perfusion amplitude measured in a voxel completely filled with blood; and f Q is the number of heart beats per minute. 1,6 The combined functional maps were obtained from quantified functional maps of individual SGs by segmenting lung parenchyma with exclusion of large vessels, and combining maps via a linear weighted combination method, in which the weights were proportional to estimated SNR (i.e., maximal ratio combining) 44 in the lung parenchyma.…”

Section: Image Postprocessing and Analysesmentioning

confidence: 99%

See 1 more Smart Citation

Phase‐cycled balanced SSFP imaging for non‐contrast‐enhanced functional lung imaging

Ilicak

Ozdemir

Schad

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose To introduce phase‐cycled balanced SSFP (bSSFP) acquisition as an alternative in Fourier decomposition MRI for improved robustness against field inhomogeneities. Methods Series 2D dynamic lung images were acquired in 5 healthy volunteers at 1.5 T and 3 T using bSSFP sequence with multiple RF phase increments and compared with conventional single RF phase increment acquisitions. The approach was evaluated based on functional map homogeneity analysis, while ensuring image and functional map quality by means of SNR and contrast‐to‐noise ratio analyses. Results At both field strengths, functional maps obtained with phase‐cycled acquisitions displayed improved robustness against local signal losses compared with single‐phase acquisitions. The coefficient of variation (mean ± SD, across volunteers) measured in the ventilation maps resulted in 29.7 ± 2.6 at 1.5 T and 37.5 ± 3.1 at 3 T for phase‐cycled acquisitions, compared with 39.9 ± 5.2 at 1.5 T and 49.5 ± 3.7 at 3 T for single‐phase acquisitions, indicating a significant improvement (p<0.05$$ p<0.05 $$) in ventilation map homogeneity. Conclusions Phase‐cycled bSSFP acquisitions improve robustness against field inhomogeneity artifacts and significantly improve ventilation map homogeneity at both field strengths. As such, phase‐cycled bSSFP may serve as a robust alternative in lung function assessments.

show abstract

“…The quantitative perfusion map ( Q Map) was calculated as

Q=\frac{\mathrm{F}{\mathrm{D}}_Q}{{\mathrm{F}\mathrm{D}}_Q\left(\mathrm{blood}\right)}\frac{f_Q}{2}\kern2em \left[\mathrm{ml}/\min /\mathrm{ml}\right]

where

{\mathrm{FD}}_Q

is the whole perfusion‐weighted image obtained via the FD analysis;

{\mathrm{FD}}_Q\left(\mathrm{blood}\right)

is the perfusion amplitude measured in a voxel completely filled with blood; and

{f}_Q

Section: Methodsmentioning

confidence: 99%

Section: Image Postprocessing and Analysesmentioning

confidence: 99%

Phase‐cycled balanced SSFP imaging for non‐contrast‐enhanced functional lung imaging

Ilicak

Ozdemir

Schad

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…Since the sets of the random variables and are mutually statistically independent, one can show using (53) in Appendix A that if the phase estimates in all diversity branches are identical, then the decrease in the combiner's mean SNR is given by (12) where denotes the mathematical expectation. Using (56), the first two moments of the correlation loss functions can be expressed as (13) Equation 12gives the degradation in mean SNR of the combiner due to the noisy phase references. Most practical systems are usually designed to satisfy a certain SNR reliability level (i.e., the probability or percentage of time that the SNR exceeds a specified threshold [11]).…”

Section: Snr Analysismentioning

confidence: 99%

Analysis of equal-gain diversity with partially coherent fading signals

Najib¹,

Prabhu

2000

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

An analytical technique based on Gram-Charlier series expansion is presented for the computation of the error probability of equal-gain combiner (EGC) with partially coherent fading signals. Imperfect carrier recovery is attributed to the random noise present in the carrier recovery loops. The resulting noisy phase references are assumed to satisfy Tikhonov distribution. The fades on the diversity branches are assumed to be slowly varying and statistically independent with Rayleigh-distributed envelopes. The error-rate performance of coherent and differentially coherent phase-shift keying (PSK) systems are compared and the phase precision requirement for a reliable coherent detection is computed. Detection loss caused by carrier phase errors is computed for several signal-to-noise ratio (SNR) reliability and bit error probability levels. It is demonstrated that the effect of carrier phase errors on the mean SNR is negligible compared to their effect on deep fades or small bit error probabilities. It is also shown that the carrier phase precision requirement can be reduced through signal combination.

show abstract

“…The effect of multipath delay, spread and fading of signals in the wireless environment is usually unavoidable [2,3]. Extreme fading of the signal amplitude and Inter Symbol Interference (ISI) introduced during the transmission through the conventional channel and the frequency selectivity of the channel appearing at the receiver side [4,5] are responsible for a high probability of errors and reduced overall performance of the system. Several methods like adaptive equalization and channel coding have been developed to reduce the above effect [6,7].…”

Section: Introductionmentioning

confidence: 99%

BER Based Performance Evaluation by Pulse Shaping in OFDM

Sharma¹,

Mishra²,

Saxena³

2015

IJSIP

View full text Add to dashboard Cite

The next generation wireless communication system requires a higher standard in order to provide the customers with high quality of services they demanded. Orthogonal Frequency Division Multiplexing (OFDM) is an attractive modulation method and is a strong candidate for the modulation technique of future wireless systems which can be implemented easily. In this paper an effort is made to analyze how well an OFDM system will perform when transmitted over an Additive White Gaussian Noise (AWGN) channel in terms of Bit Error Rate (BER), it has also been observed that pulse shaping the channels present in an OFDM system promises to improve the overall performance of the system in terms of the BER of the received signals, different window function like Rectangular, Blackman, Gaussian, Hamming, Hanning and Kaiser and some of the self modified pulse shapes have been used to study the BER performance due to any one of the pulse shapes.

show abstract

Diversity Combination of Fading Signals with Unequal Mean Strengths

Cited by 33 publications

References 7 publications

Phase‐cycled balanced SSFP imaging for non‐contrast‐enhanced functional lung imaging

Phase‐cycled balanced SSFP imaging for non‐contrast‐enhanced functional lung imaging

Analysis of equal-gain diversity with partially coherent fading signals

BER Based Performance Evaluation by Pulse Shaping in OFDM

Contact Info

Product

Resources

About