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Signal-to-noise ratio

About: Signal-to-noise ratio is a(n) research topic. Over the lifetime, 28022 publication(s) have been published within this topic receiving 356538 citation(s). The topic is also known as: SNR & S/N.

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Papers
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Journal ArticleDOI: 10.1109/PROC.1975.10036
Bernard Widrow1, J.R. Glover, John M. McCool, J. Kaunitz  +5 moreInstitutions (1)
24 Mar 1975-
Abstract: This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a "primary" input containing the corrupted signal and a "reference" input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. Wiener solutions are developed to describe asymptotic adaptive performance and output signal-to-noise ratio for stationary stochastic inputs, including single and multiple reference inputs. These solutions show that when the reference input is free of signal and certain other conditions are met noise in the primary input can be essentiany eliminated without signal distortion. It is further shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution. Experimental results are presented that illustrate the usefulness of the adaptive noise cancelling technique in a variety of practical applications. These applications include the cancelling of various forms of periodic interference in electrocardiography, the cancelling of periodic interference in speech signals, and the cancelling of broad-band interference in the side-lobes of an antenna array. In further experiments it is shown that a sine wave and Gaussian noise can be separated by using a reference input that is a delayed version of the primary input. Suggested applications include the elimination of tape hum or turntable rumble during the playback of recorded broad-band signals and the automatic detection of very-low-level periodic signals masked by broad-band noise.

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Topics: Effective input noise temperature (63%), Noise (63%), Noise measurement (62%) ...read more

4,091 Citations


Journal ArticleDOI: 10.1109/TASSP.1976.1162830
C. Knapp1, G. CarterInstitutions (1)
Abstract: A maximum likelihood (ML) estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise. This ML estimator can be realized as a pair of receiver prefilters followed by a cross correlator. The time argument at which the correlator achieves a maximum is the delay estimate. The ML estimator is compared with several other proposed processors of similar form. Under certain conditions the ML estimator is shown to be identical to one proposed by Hannan and Thomson [10] and MacDonald and Schultheiss [21]. Qualitatively, the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and, simultaneously, to suppress the noise power. The same type of prefiltering is provided by the generalized Eckart filter, which maximizes the S/N ratio of the correlator output. For low S/N ratio, the ML estimator is shown to be equivalent to Eckart prefiltering.

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Topics: Estimation theory (56%), Estimator (55%), Signal-to-noise ratio (53%) ...read more

4,009 Citations


Journal ArticleDOI: 10.1109/36.3001
Abstract: A transformation known as the maximum noise fraction (MNF) transformation, which always produces new components ordered by image quality, is presented. It can be shown that this transformation is equivalent to principal components transformations when the noise variance is the same in all bands and that it reduces to a multiple linear regression when noise is in one band only. Noise can be effectively removed from multispectral data by transforming to the MNF space, smoothing or rejecting the most noisy components, and then retransforming to the original space. In this way, more intense smoothing can be applied to the MNF components with high noise and low signal content than could be applied to each band of the original data. The MNF transformation requires knowledge of both the signal and noise covariance matrices. Except when the noise is in one band only, the noise covariance matrix needs to be estimated. One procedure for doing this is discussed and examples of cleaned images are presented. >

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Topics: Value noise (65%), Gaussian noise (65%), Salt-and-pepper noise (64%) ...read more

2,407 Citations


Open accessJournal ArticleDOI: 10.1109/TIT.2003.809594
Babak Hassibi1, Bertrand M. Hochwald1Institutions (1)
Abstract: Multiple-antenna wireless communication links promise very high data rates with low error probabilities, especially when the wireless channel response is known at the receiver. In practice, knowledge of the channel is often obtained by sending known training symbols to the receiver. We show how training affects the capacity of a fading channel-too little training and the channel is improperly learned, too much training and there is no time left for data transmission before the channel changes. We compute a lower bound on the capacity of a channel that is learned by training, and maximize the bound as a function of the received signal-to-noise ratio (SNR), fading coherence time, and number of transmitter antennas. When the training and data powers are allowed to vary, we show that the optimal number of training symbols is equal to the number of transmit antennas-this number is also the smallest training interval length that guarantees meaningful estimates of the channel matrix. When the training and data powers are instead required to be equal, the optimal number of symbols may be larger than the number of antennas. We show that training-based schemes can be optimal at high SNR, but suboptimal at low SNR.

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  • Fig. 2. Same as Fig. 1, except with = 18 dB.
    Fig. 2. Same as Fig. 1, except with = 18 dB.
  • Fig. 1. The training-based lower bound on capacity as a function ofT when SNR = 6 dB andM = N = 10, for optimized and (upper solid curve, (32)) and for = (lower solid curve, (40) optimized forT ). The dashed line is the capacity when the receiver knows the channel.
    Fig. 1. The training-based lower bound on capacity as a function ofT when SNR = 6 dB andM = N = 10, for optimized and (upper solid curve, (32)) and for = (lower solid curve, (40) optimized forT ). The dashed line is the capacity when the receiver knows the channel.
  • Fig. 5. Capacity as a function of number of transmit antennasM with = 18 dB andN = 12 receive antennas. The solid line is optimized overT for = = (see (40)), and the dashed line is optimized over the power allocation withT = M (Theorem 3). The dash-dotted line is the capacity when the receiver knows the channel perfectly. The maximum throughput is attained atM 15.
    Fig. 5. Capacity as a function of number of transmit antennasM with = 18 dB andN = 12 receive antennas. The solid line is optimized overT for = = (see (40)), and the dashed line is optimized over the power allocation withT = M (Theorem 3). The dash-dotted line is the capacity when the receiver knows the channel perfectly. The maximum throughput is attained atM 15.
  • Fig. 6. Same as Fig. 5, except withT = 20. The maximum throughput is attained atM 7. Observe that the difference between optimizing over and versus setting = = is negligible.
    Fig. 6. Same as Fig. 5, except withT = 20. The maximum throughput is attained atM 7. Observe that the difference between optimizing over and versus setting = = is negligible.
  • Fig. 4. The optimal power allocation (training) and (data transmission) as a function of block lengthT for = 18 dB (shown in the dashed line) with M = N = 10. These curves are drawn from Theorem 2 and (27) forT = M .
    Fig. 4. The optimal power allocation (training) and (data transmission) as a function of block lengthT for = 18 dB (shown in the dashed line) with M = N = 10. These curves are drawn from Theorem 2 and (27) forT = M .
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Topics: Fading (58%), Channel capacity (58%), Communication channel (53%) ...read more

2,325 Citations


Open accessJournal ArticleDOI: 10.1109/TIT.2006.883550
Nihar Jindal1Institutions (1)
Abstract: Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR

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Topics: Precoding (66%), MIMO (63%), Channel state information (59%) ...read more

1,631 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202212
2021879
20201,154
20191,205
20181,476
20171,538

Top Attributes

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Topic's top 5 most impactful authors

Mohamed-Slim Alouini

44 papers, 560 citations

Norman C. Beaulieu

28 papers, 468 citations

Jacob Benesty

19 papers, 270 citations

Octavia A. Dobre

18 papers, 721 citations

Ranjan K. Mallik

18 papers, 985 citations

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