In-service monitoring of multipath delay and cochannel interference for indoor mobile communication systems

Brandao, A.L.; Lopes, L.B.; McLemon, D.C.

doi:10.1109/icc.1994.368788

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…Signal to Variation Ratio (SVR) method, as was first explained in [11], is a high order moments based estimation that was originated for channel quality monitoring in multipath fading channels. It can be applied to channel quality measurement for AWGN channel with an M-PSK modulated signal, however, in general, it is not applicable to other modulation scheme.…”

Section: B Svr Original Algorithmmentioning

confidence: 99%

Non-data-aided SNR estimation for QPSK modulation in AWGN channel

Salman

Badawy

Elfouly

et al. 2014

2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signalto-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

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Section: B Svr Original Algorithmmentioning

confidence: 99%

Non-data-aided SNR estimation for QPSK modulation in AWGN channel

Salman

Badawy

Elfouly

et al. 2014

2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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“…In [9], the second-order and fourth-order moments (M2M4) estimator was studied using the second-and fourth-order moments of the signal to avoid carrier phase recovery. In [10], signalto-variation ratio (SVR) estimator was designed for M-ary PSK-modulated signals. An in-service SVR estimator for complex channels was also developed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we provide such a performance evaluation for SNR estimation in AF relaying systems by applying the estimators developed in [8][9][10][11] to each hop and examining the accuracy of the end-to-end SNR estimate in AF relaying. Different forms of the end-to-end SNR are considered: the exact end-to-end SNR, the harmonic mean and the minimum hop SNR.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of end-to-end SNR estimators for AF relaying

Zhou

Chen

2017

Telecommun Syst

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Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional onehop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signal-to-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems.

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“…The SVR estimator, described by Brandão and others in [7], is a moment-based method developed to monitor channel quality in multipath fading channels, which can also be applied as a measure of channel quality in an AWGN channel.…”

Section: Svr Estimatormentioning

confidence: 99%

A Linear Prediction Based Estimation of Signal-to-Noise Ratio in AWGN Channel

Kamel¹,

Jeoti²

2007

ETRI J

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Nidal S. Kamel et al. 607Most signal-to-noise ratio (SNR) estimation techniques in digital communication channels derive the SNR estimates solely from samples of the received signal after the matched filter. They are based on symbol SNR and assume perfect synchronization and intersymbol interference (ISI)-free symbols. In severe channel distortion where ISI is significant, the performance of these estimators badly deteriorates. We propose an SNR estimator which can operate on data samples collected at the front-end of a receiver or at the input to the decision device. This will relax the restrictions over channel distortions and help extend the application of SNR estimators beyond system monitoring. The proposed estimator uses the characteristics of the second order moments of the additive white Gaussian noise digital communication channel and a linear predictor based on the modified-covariance algorithm in estimating the SNR value. The performance of the proposed technique is investigated and compared with other in-service SNR estimators in digital communication channels. The simulated performance is also compared to the Cramér-Rao bound as derived at the input of the decision circuit.Keywords: Signal-to-noise ratio estimation, in-service estimators, phase-shift keying. Many SNR estimators in digital communication channels have been proposed over the last few decades [3]. Most of these techniques derive the symbol SNR estimates solely from the received signal at the output of the matched filter (MF). The estimators assume perfect carrier and symbol synchronization while at the same time implicitly assuming intersymbol interference (ISI)-free output of the MF (the decision variable). However, in practice, multipath wireless communication gives rise to much intersymbol interference, especially in indoor and urban areas. In these ISI dominated scenarios, SNR estimators that do not presume ISI-free reception are highly desirable.In this paper, an in-service SNR estimation technique in digital communication channels is presented. In contrast to other SNR estimators, the proposed technique can operate on data collected at the front-end of the receiver without any restriction on ISI. This will improve the SNR estimates in severe ISI channels and also help in extending the implementation of SNR estimators in systems that require SNR estimates at the input of the receiver. One such application is antenna diversity combining, where at least two antenna signal paths are communicably connected to a receiver. The combiner can use the SNR estimates obtained for each antenna signal to respectively weight each signal and thereby generate a combined output signal.The proposed technique depends on the characteristics of the second-order moments of the additive white Gaussian noise

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In-service monitoring of multipath delay and cochannel interference for indoor mobile communication systems

Cited by 19 publications

References 9 publications

Non-data-aided SNR estimation for QPSK modulation in AWGN channel

Non-data-aided SNR estimation for QPSK modulation in AWGN channel

Performance analysis of end-to-end SNR estimators for AF relaying

A Linear Prediction Based Estimation of Signal-to-Noise Ratio in AWGN Channel

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