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Comparison of BPSK, QPSK and 16-QAM modulation schemes for proposed and maximum likelihood (ML) methods for flat fading channel. the signal parameters have been presented. The uncertainty in the estimation performance of the parameters has been observed with respect to simulation and measurement. For example at 15 dB SNR for 16-QAM, the carrier frequency is f c = 5 MHz, the estimated carrier frequency through simulation isˆfisˆ isˆf c,s = 4.8 MHz and the estimated carrier frequency through measurement isˆfisˆ isˆf c,m = 4.6 MHz. The error percentage in simulation is e s = (f c − ˆ f c,s )/f c × 100 %, i.e., e s = 4%. Similarly the error percentage in measurement is e m = (f c − ˆ f c,m )/f c × 100 %, i.e, e m = 8%. Hence, the uncertainty in the estimation of carrier frequency parameter with respect to simulation and measurement is 4%. At 15 dB SNR for 16-QAM, the symbol rate is f s = 4 MHz, the estimated symbol rate through simulation isˆfisˆ isˆf s,s = 3.7 MHz and the estimated symbol rate through measurement isˆfisˆ isˆf s,m = 3.85 MHz. The error percentage in simulation is e s = (f s − ˆ f s,s )/f s × 100 %, i.e., e s = 7.5%. Similarly the error percentage in measurement is e m = (f s − ˆ f s,m )/f s ×100 %, i.e, e m = 3.75%. Hence, the uncertainty in the estimation of carrier frequency parameter with respect to simulation and measurement is 3.75%. Fig. 11 illustrates the constellation points at the receiver in the presence of STO estimation error of 2 samples through the simulation studies. Fig. 12 shows the constellation points from the BWR testbed where the presence of STO error is unknown. The slight phase offset for the constellation remains due to the error in IF estimation, i.e., around 15 Hz and one or two samples error in STO estimation. The observation
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Theoretical studies on nondata aided (blind) parameter estimation algorithms for signals have been carried out over the past decades. However, most of them have not been implemented and measured in order to validate their feasibility. In this paper, at first, the blind parameter estimation algorithms have been presented, which facilitate to the des...
Citations
... The authors investigated estimation of the SRE of BWR for quaternary phase-shift keying (QPSK) under different conditions. In [83], the efficiency of the sequential blind estimation method is demonstrated by measurement setup and experimental validation of the proposed BWR. CFO estimation, SRE, STO estimation, and MC are all included in the proposed BWR. ...
In the very initial stage of fifth-generation (5G) commercialization, researchers, scientists, and engineers are realising the need for further enhancement in 5G parameters and more intelligent techniques to fulfill the expected future requirements. Due to advancement in infrastructure and rapid increment in wireless communication users, after few years, 5G parameters will not be sufficient to fulfill the requirements. The enhanced data rate, data security, ultra-low latency, low computation complexity, ultra-high spectrum efficiency, and power-efficient techniques are the main requirements. Thus sixth-generation (6G) wireless communication is becoming a research hot-spot. The key enablers techniques for 6G communication are auto-pilot unmanned aerial vehicle communication, high mobility communication, satellite communication, advanced device-to-device communication, accurate positioning system, GPS de-nied positioning, free-space optics, energy harvesting, wireless power transfer, internet of everything, massive internet of thing, intelligent reflecting surfaces, and artificial intelligence-enabled transceivers. In this review article, the role of these techniques for future wireless communication enhancement is discussed. We have also discussed enhancement in existing wireless technology such as pilot-based estimation to blind estimation, advanced error controlling techniques, advanced channel coding, millimeter waves, hybrid non-orthogonal multiple access techniques, orthogonal time-frequency, and space modulation.
... Recently, MC has played a significant role in both military and civilian communications, such as cognitive radio, signal intelligence, link adaptation, signal control, and SDR [3][4][5][6][7]. With an intelligent receiver, blind parameter estimation and classification algorithms may be used, resulting in a substantial increase in spectral efficiency since no predefined training or pilot sequence is needed [8][9][10]. ...
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.
... The experimental testbed analysis is configured using NI PXIe-1085 chassis. Its detailed explanation can be found in [35], [36]. The baseband and radio frequency (RF) domain signal processing is implemented using Mathscript and Lab-VIEW programming, respectively as presented in Fig 10. ...
In this paper, we propose and implement blind multiuser carrier frequency offsets (CFOs) estimation for multi-input multi-output (MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system using orthogonal propagator method (OPM) over a noisy frequency-selective channel. The performance of typical OPM is limited at low signal-to-noise ratio (SNR) due to its noise-free signal model. The additive white Gaussian noise mainly impacts the diagonal elements of the data covariance matrix (DCM) of the received signal. First, the proposed fractional CFOs estimator minimizes the noise effect by reconstructing the denoised diagonal elements of the DCM for the received signal through cubic Hermite interpolation. Therefore, we use modified OPM to estimate multiple CFOs for MIMO SC-FDMA systems. Second, we have used an iterative method based on the first-order Taylor series approximation of the CFO vector to avoid peak ambiguity and reduce the number of iterations required in the grid search. The mean square error of the proposed CFO estimator considerably improves compared with the existing methods at a low SNR regime. The bit error rate performance of the proposed method for MIMO SC-FDMA is also compared with the existing methods. Finally, the proposed estimator is validated by implementing it on radio frequency hardware testbed over an indoor propagation environment.
... As a result, additional overhead needs to be introduced in the transmitted signal to train the secondary receiver. Thus, the spectrum efficiency, i.e., overall information throughput, gets reduced and provides high latency to the communication [10]. Hence, the blind parameter estimated receiver is desirable in these situations as it can blindly retrieve the information at the receiver without any prior knowledge of transmitted signal parameters, modulation formats, and CSI. ...
... In this work, the estimation of OFDM signal parameters, modulation classification, and synchronization is performed. The blind parameter estimated receiver for SC is provided in one of our previous works [10]. In this article, the blind parameters estimated receiver for OFDM signal is designed, implemented, and measured the performance over RF testbed. ...
... The testbed measurement and implementation consist of hardware and software parts as described in [10]. ...
Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low latency characteristics. However, there is no work available till now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.
... The receiver then estimates the changing symbol rate based on the captured data packet when no pilot data sequence is available [10]. Symbol rate estimation is also an important task when performing passive signal analysis on smart devices such as low-power IoT devices [11]. In addition, the symbol rate in commercial systems such as cable digital video broadcasting, satellite digital video broadcasting and the secondgeneration satellite digital video broadcasting is not fixed in order to send the satellite signal to the cable network without any processing at the cable front [4], [12], [13]. ...
Symbol rate is one of the most important parameters in signal demodulation process. In real-time signal processing, traditional symbol rate estimation algorithms for the Multiple Phase Shift Keying (M-PSK) and the Multiple Quadrature Amplitude Modulation (M-QAM) are based on the Fourier transform of signal’s complex envelope. At the low signal-to-noise ratio (SNR), the accuracy of symbol rate estimation can be improved by increasing the number of symbols as much as possible. However, this improvement is infeasible in many applications such as the energy-limited Internet of Things devices and sporadic noncooperative transmissions. In this paper, we propose a data-driven bandpass filter (BPF) design scheme for accurate estimation of symbol rate under low SNR with only a small number of symbols available. The proposed scheme considerably improves the estimation performance by optimizing the BPF design using the equivalent dynamic linearization model with time-varying pseudo-partial derivatives. Specifically, the proposed scheme iteratively optimizes the upper and lower cut-off frequencies of the BPF based on the measured complex envelope spectrum until achieving the optimal BPF. Therefore, the peaks of the complex envelope spectrum are extracted as the estimate of the symbol rate by applying the optimal BPF. Experimental results indicate the promise of the proposed scheme as an efficient symbol rate estimator for sporadic signal at low SNR and with a small number of symbols.
... The frequency range is determined by taking fast Fourier transform (FFT), and then the local oscillator generates the frequencies in the determined range to estimate an IF carrier. In [9], IF carrier estimation is carried out for digital modulation techniques using the cyclic cumulant (CC) method by bandwidth sweeping of the received signal. ...
... Symbol rate estimation of a digital communication system is an important task when performing passive signal analysis for the smart device [1]. It is crucial in monitoring variable data rate transmission and synchronize high speed communication where no pilot sequences are being used, to improve the spectrum efficiency [2]. Symbol rate must be known to the receiver prior to symbol timing recovery, known as symbol timing offset (STO) estimation [3], frequency recovery, known as carrier frequency offset (CFO) estimation [4], modulation class recovery [5]- [9] to complete the signal decoding process. ...
... It was first motivated by its applications in a military scenarios where electronic support, electronic attack, and electronic protection requires the identification of the modulation formats in order to restore the information of the hostile unit, sending jamming signals, and to get back the intercepted signals. It has numerous civilian and military applications such as spectrum surveillance, signal intelligence, electronic surveillance, emitter interception, electronic warfare, cognitive radio as well as control and network management [13][14][15][16]. Algorithms for blind signal classification and parameter estimation can be used with an intelligent receiver, yielding an increase in the spectral efficiency by reducing the overhead [14]. ...
... The term `blind' is used in the estimation or classification process has several advantages [15]. The BMC facilitates adaptive system and dynamic data rate transmission [13]. ...
In this work, we have proposed an intelligent and secure transceiver design and implemented over RF testbed for future wireless communication. To secure the communication, first of all, physical layer security (PLS) has been proposed for underlay multiple-input multiple-output cognitive radio network (MIMO-CRN) by using a bi-directional zero-forcing beamforming. Secondly, (PLS) has been proposed by using transmit beamforming for non-orthogonal multiple access (NOMA) over multiple cells and in the presence of primary user and secondary users. The proposed model has been extended for MIMO-NOMA over double cell and multiple scenarios. To improve further security an adaptive transceiver has been designed where parameter can be varied as per the system requirement and meantime, an intelligent received is developed in which parameter can be estimated through a blind process. The modulation format is also classified for single carrier, orthogonal frequency division multiplexing (OFDM) and MIMO through a statistical method. Finally, sequence design has been proposed to improve peak average power ratio for OFDM and multicarrier code division multiplexing (MC-CDMA). These sequences can also remove multiuse user interference for CDMA system over asynchronous system by improving the correlation properties within a code.
... and classification algorithms can be used with an intelligent receiver, resulting in a significant improvement in the spectral efficiency, as no predefined training or pilot sequence is required [5], [6]. Orthogonal frequency division multiplexing (OFDM) is a well-known multicarrier modulation technology for future wireless communication systems. ...
... The received IF signal is fed to the MATLAB script, which runs inside the LabVIEW environment. First, the IF carrier frequency and signal bandwidth are estimated from the power spectral density of the signal discussed in [5] then by another downconversion process, we get the baseband signal. Now, the MC algorithm is performed in two stages as described in Section III to classify five-class of modulation formats. ...
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. However, till date, only a few works have been reported in the literature for blind MC of orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading environment. In this paper, a blind MC algorithm has been proposed and implemented over National Instruments (NI) testbed setup for linearly modulated signals of OFDM system by using discrete Fourier transform (DFT) and normalized fourth-order cumulant. The proposed MC algorithm works in the presence of synchronization errors, i.e., frequency, timing, and phase offsets and without the prior information about the signal parameters and channel statistics. To nullify the effect of timing offset in the feature extraction process, a statistical average has been taken over OFDM symbols after introducing uniformly distributed random timing offsets in each of the OFDM symbols. In this work, we have classified a more extensive pool of modulation formats for OFDM signal, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16 quadrature amplitude modulation (16-QAM). Classification is performed in two stages. At the first stage, a normalized fourth-order cumulant is used on the DFT of the received OFDM signal to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, first we compute the DFT of the square of the received OFDM signal and then a normalized fourth-order cumulant is used to classify BPSK and QPSK modulation formats. The success rate of the proposed MC algorithm is evaluated through analytical and Monte Carlo simulations and compared with existing methods. Finally, the work is validated by providing an experimental setup on NI hardware over an indoor propagation environment.
... Despite shortage of spectrum, huge amount of spectrum is used just to transmit redundant data or training sequences and thus immense portion of allocated spectrum is not utilized by the desired users. The most effective way to overcome this is by performing spectrum detection and blind estimation of the CFO and STO parameters or by designing a blind wireless receiver (BWR) for the non-legitimate user where the unknown signal parameters can be estimated blindly [35][36][37][38][39]. However, to the best of authors' knowledge, the utilization of spectrum and diversity gain of PU by joint detection and estimation for OFDM based cooperative CRN has not been addressed. ...
... Although various schemes for the purpose of spectrum sensing in CRN as well as blind estimation of synchronization parameters have been proposed, they have not been implemented on a hardware testbed to ascertain their feasibility. This work is an extension to [36,37] where a joint log-likelihood (LL) function has been presented for designing a detector and estimator for full spectrum and diversity gain utilization in CRN. The validation of algorithms through an experimental setup has been performed in order to ascertain their practical implementation. ...
... The algorithms proposed and discussed have been implemented on an OFDM NI hardware testbed. The detailed description of experimental setup procedure has been elaborated comprehensively in [35] and [37]. A testbed has been configured using a transmitter of NI PXIe-5673 and a receiver of NI PXIe-5663, both based on software defined radio. ...
In the cooperative cognitive radio networks (CRN), often secondary user (SU) relays the information of primary user (PU) as a rewarding relay to improve diversity gain of PU without being a legitimate user. So the SU needs to detect the signal, blindly estimate the parameters introduced in channel and reconstruct the signal before relaying it to the primary receiver. In this paper, a joint scheme for signal detection and non-data-aided (blind) parameter estimation of orthogonal frequency division multiplexing (OFDM) based CRN has been discussed. Based upon binary hypothesis testing problem, the SU formulates a minimum cost signal detection scheme for the presence of OFDM based PU signal in CRN. The probability of detection, probability of false alarm and receiver operating characteristics have been presented to illustrate the performance of signal detection scheme in the CRN. Further, the effective throughput analysis of the secondary system has been demonstrated in the context when the primary system is detected as idle. Blind synchronous parameters of OFDM signal such as carrier frequency offset and symbol timing offset has been presented over the wireless fading channel in the CRN. Existing theoretical studies on blind parameter estimation algorithms for signals have been carried out but most of them have not been implemented in order to validate their feasibility. Here, a software-defined radio testbed has been implemented using national instruments (NI) hardware in a multipath indoor environment and experimental results have been provided using real measurement system. The preliminary measurement and simulation results demonstrate that the proposed blind estimator is capable of estimating the concerned parameters and constellation symbols over an indoor propagation environment .
