Blind SNR Estimation of Gaussian-Distributed Signals in Nakagami Fading Channels

Abstract

Abstract-A blind (non-data-aided) SNR estimator using the statistical moments of the received signal is proposed. The proposed envelope-based non-data-aided estimator works for any time-domain Gaussian-distributed signal (e.g., OFDM signals). A closed-form expression for the estimated SNR as a function of the moments of the received signal and the Nakagami-m parameter, is derived. Interestingly, the obtained expression shows that the proposed estimator operation and performance is independent of the constellation of the received signal. Moreover, the existence of the closed-form expression results in lower implementation complexity. Furthermore, to enable theoretical performance analysis, a general mathematical expression is derived for the even moments of the received signal in terms of SNR and the Nakagami-m parameter. The performance of the proposed estimator is evaluated based on the mean-squared-error, under different conditions of the channel. An extension of our SNR estimation method into multiple antennas configurations is provided. Our results reveal that the proposed estimator works better in low SNR conditions, which is attractive to applications such as cognitive radio spectrum sharing scenarios.

Blind Compensation of I/Q Impairments in Wireless Transceivers

“…The assumption of I and Q signals being independent is considered in many works (e.g., reference [ 8 ] in this work and references [20–25] found in [ 11 ]). Orthogonal frequency division multiplexing (OFDM) signals have been shown to exhibit Gaussian characteristics [ 19 ]. The authors in [ 11 ] also assume similar statistics of I and Q signals.…”

Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices

“…The major issue in the data-aided approach to estimating the SNR is the requirement for pilot carriers, which reduces the channel capacity and throughput. A pilot carrier is not required in a non-dataaided approach because it uses the blocks rather than the message-bearing piece for SNR assessment [14]. These assessors are appropriate when unremitting SNR estimation is required.…”

Signal-to-noise ratio estimation for M-QAM signals in η−μand κ−μfading channels

“…In the third case, in which the fading is characterized by the distribution κ − μ, the parameter of the function f(γ ) is given in (26). When inverted in terms of the sample ratiof, the estimateγ under κ − μ fading can be written as…”

“…The normalized mean square error (NMSE) of a parameter θ, considering an estimateθ, is defined as [26] NMSE =…”

“…The SNR estimators obtained for the signal received under these two fading models encompass all the estimators obtained for the other fading models mentioned. The estimation of SNR by the MOM has been the considered technique, as shown, for example, in references [24,25] and more recently in reference [26] in which the transmitted signal is modeled by a complex Gaussian random variable with zero mean by component. In [27], the MOM is proposed in a process of joint estimation of both the K parameter of the Rice fading distribution and SNR in a SIMO communication system.…”