Non-data-aided Research Articles

Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel.

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT's location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.

Joint Method of Moments (JMoM) and Successive Moment Cancellation (SMC) Multiuser Time Synchronization for ZP-OFDM-Based Waveforms Applicable to Joint Communication and Sensing

It has been recently shown that zero padding (ZP)-orthogonal frequency-division multiplexing (OFDM) is a promising candidate for 6G wireless systems requiring joint communication and sensing. In this paper, we consider a multiuser uplink scenario where users are separated in power domain, i.e., non-orthogonal multiple access (NOMA), and use ZP-OFDM signals. The uplink transmission is grant-free and users are allowed to transmit asynchronously. In this setup, we address the problem of time synchronization by estimating the timing offset (TO) of all the users. We propose two non-data-aided (NDA) estimators, i.e., the joint method of moment (JMoM) and the successive moment cancellation (SMC), that employ the periodicity of the second order moment (SoM) of the received samples for TO estimation. Moreover, the coding assisted (CA) version of the proposed estimators, i.e., CA-JMoM and CA-SMC, are developed for the case of short observation samples. We also extend the proposed estimators to multiuser multiple-input multiple-output (MIMO) systems. The effectiveness of the proposed estimators is evaluated in terms of lock-in probability under various practical scenarios. Simulation results show that the JMoM estimator can reach the lock-in probability of one for the moderate range of Eb/N0 values. While existing NDA TO estimators in the literature either offer low lock-in probability, high computational complexity that prevents them from being employed in MIMO systems, or are designed for single-user scenarios, the proposed estimators in this paper address all of these issues.

Robust Estimators for Faster-Than-Nyquist Signaling

Novel, robust, and computationally attractive non-data-aided (NDA) and data-aided (DA) single and joint parameter estimators for faster-than-Nyquist (FTN) signaling are presented. By using the sixth moment alongside the second and fourth, it is possible to formulate an estimator allowing the joint blind estimation of the FTN symbol-packing ratio (SPR) (speed-up parameter) as well as the signal-to-noise ratio (SNR). The proposed estimators are robust in that they are based on uniformly spaced samples of the FTN signal taken at a fairly arbitrary FTN rate, insensitive to carrier and timing phase errors. While avoided in Nyquist signaling, the inter-symbol interference (ISI) term deliberately introduced in FTN signals is advantageously utilized for the estimation of SNR and SPR. The derivations are performed on a general complex base-signaling pulse and are illustrated for the commonly used root-raised cosine (RRC). Novel FTN Cramer-Rao lower bounds (CRLB) are derived to benchmark the efficiency of the proposed estimators. Extensive simulations are provided to illustrate the performance of the proposed estimators with respect to variations in all the relevant parameters and indicate that the estimators work best for the practical range of lower-intermediate values of SPR and SNR. The computational features of the proposed estimators, in addition to their robustness against carrier and sampling errors, make them valuable in many practical applications that incorporate cognitive radio (CR) alongside FTN.

Maximum Likelihood Time Synchronization for Zero-Padded OFDM

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a very challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing algorithms in terms of lock-in probability.

Iterative NDA Positioning Using Angle-of-Arrival Measurements for IoT Sensor Networks

Various positioning techniques have been developed to localize Internet-of-Things (IoT) devices accurately. Because IoT communications are often narrowband, efficient localization can be achieved by deducing the device position from the estimated signal Angle-of-Arrival (AOA) at multiple arrays of antennas. It has recently been shown that significant accuracy gains can further be obtained by iterating between the AOA estimation and multi-lateration steps. However, the existing method relies on the knowledge of the transmitted signal (Data-Aided (DA) estimation) which makes it impractical for narrowband communications where the preamble is short. Non-Data-Aided (NDA) estimation is recommended to improve the positioning accuracy for low capacity IoT sensors. This paper proposes an NDA iterative (NDA-It) algorithm using AOA measurements to determine the position of an IoT sensor. Simulation results show that the proposed algorithm significantly outperforms the DA-It in a Bluetooth Low Energy (BLE) context because it can use a much higher number of samples (snapshots); however, it needs more iterations to converge. The computational complexity analysis proves the competitiveness of the proposed NDA-It. The performance of the algorithms is further investigated in multipath and Non-Line-of-Sight (NLOS) propagation environments. Finally, an experimental setup is built to validate the performance of the proposed algorithms.

A non-data-aided SNR estimator based on maximum likelihood method for communication between orbiters

Signal-to-noise ratio (SNR) is an important metric for performance assessment in numerous scenerios. In order to ensure the reliability and effectiveness of the system performance, plenty of situations require the information of SNR estimate. At the same time, Mars exploration has been a hot topic in recent years, which leads to the research attention of scholars extending to deep space. In this paper, a new SNR estimator related to deep space scene is proposed. On the one hand, the time of essential data transmission is limited in Mars exploration system. On the other hand, the relative position and condition between orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration. Therefore, it is obvious that the information of SNR can promote the system to adjust the signal transmission rate automatically. Subsequently, the estimation of SNR becomes a fundamental research in automatic digital communications. In this paper, an SNR estimation method based on non-data-aided (NDA) with maximum likelihood (ML) estimation is proposed to enhance the accuracy and reliability of Mars exploration process. Additionally, the Cramer-Rao lower bound (CRLB) related to the designed ML algorithm is derived. Finally, the Monte Carlo simulation results demonstrate that the proposed ML estimator algorithm obtains a superior performance when compared to the existing SNR estimators.

A Non-Data-Aided OSNR Estimation Algorithm for Coherent Optical Fiber Communication Systems Employing Multilevel Constellations

The performance of existing moments-based non-data-aided (NDA) optical signal-to-noise ratio (OSNR) estimation approaches degrades greatly for coherent optical systems employing multilevel constellations. We propose a novel NDA OSNR estimation algorithm, which provides enhanced performance for such systems. The proposed algorithm utilizes the empirical cumulative distribution function of the signal's amplitude to extract the information on the noise variance. Analytical and extensive simulation results show the feasibility and advantages of the algorithm. For the studied systems employing multilevel constellations such as 8-quadrature amplitude modulation (QAM), 16-QAM, 32-QAM, and 64-QAM, the proposed algorithm attains the derived Cramer–Rao lower bound. Furthermore, it achieves a lower mean square error with significantly lower complexity when compared to the conventional moments-based NDA estimation approach. Moreover, the impact of fiber nonlinearity is investigated with a five-channel Nyquist wavelength division multiplexing system, and the proposed algorithm outperforms the moments-based counterpart.

Low-complexity carrier phase recovery based on principal component analysis for square-QAM modulation formats.

We propose, numerically analyze and experimentally demonstrate a low-complexity, modulation-order independent, non-data-aided (NDA), feed-forward carrier phase recovery (CPR) algorithm. The proposed algorithm enables synchronous decoding of arbitrary square-quadrature amplitude modulation (QAM) constellations and it is suitable for a realistic hardware implementation based on block-wise parallel processing. The proposed method is based on principal component analysis (PCA) and it outperforms the well-known and widely used blind phase search (BPS) algorithm at low signal-to-noise ratio (SNR) values, showing much lower cycle slip rate (CSR) both numerically and experimentally. For operation at higher SNR values, a hybrid two-stage implementation combining the proposed method and BPS is also proposed and their performance are investigated benchmarking them against the two-stage BPS (2S-BPS). The complexity of the proposed simple and hybrid methods are evaluated against 2S-BPS and computational complexity savings of 92% and 40% are expected for the simple and hybrid methods, respectively.

ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions

This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems.

180° rotationally invariant polar code

Non-data-aided (NDA) carrier phase recovery (CPR) is one of simple CPR algorithms which simply rotates received symbols to the nearest constellation point. Unfortunately, when the NDA CPR is used with M-ary phase shift keying, it suffers inherent M-fold phase ambiguity and thus the phase ambiguity must be resolved. On the other hand, the generator matrix of polar code G can be obtained with the Kronecker product of a 2 × 2 lower triangular matrix. Hence, the Hamming weights of all columns of G are given by non-negative integer powers of 2. Based on this observation, an 180 ° rotationally invariant polar code suitable for digital communication systems employing binary phase shift keying and NDA CPR is proposed. The proposed polar code offers virtually identical performance under both 0 ° and 180 ° carrier phase offsets to that of conventional polar code with perfect CPR with identical encoding and decoding complexities.

Non-Data-Aided <italic>k</italic>-Nearest Neighbors Technique for Optical Fiber Nonlinearity Mitigation

We experimentally demonstrate a low-complexity and zero-redundancy fiber nonlinearity mitigation technique in the 16-QAM and 64-QAM coherent optical communication systems based on the non-data-aided (ND) k -nearest neighbors (KNN) algorithm. We measured the bit error rate (BER) performances of the 16-QAM and 64-QAM signals in the back-to-back case and in the single-mode-fiber transmission case and achieved notable BER improvement by the ND-KNN technique. The ND-KNN technique enables us to compensate any nondeterministic transmission impairments and does not require any extra training data, which is powerful to mitigate the fiber nonlinearity impairments in the 16-QAM and 64-QAM coherent optical communication systems. By utilizing the proposed ND-KNN method, we achieved 0.5-dB BER improvement in the 800-km single-mode fiber (SMF) 16-QAM transmission system and approximately 2-dB BER improvement in the 80-km SMF 64-QAM transmission system. The algorithm first utilizes the density parameter of the testing data to extract rapidly the center noiseless data and label them as the classification references and then applies the KNN method to classify the remaining testing data. Therefore, the algorithm is robust to the system noise and can achieve fast convergence. The proposed ND-KNN equalization technique can provide efficient compensation at a low computation cost and zero data redundancy and is promising for real-world application.

Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver.

We propose a blind and fast modulation format identification (MFI) enabled by the digital frequency-offset (FO) loading technique for hitless coherent transceiver. Since modulation format information is encoded to the FO distribution during digital signal processing (DSP) at the transmitter side (Tx), we can use the fast Fourier transformation based FO estimation (FFT-FOE) method to obtain the FO distribution of individual data block after constant modulus algorithm (CMA) pre-equalization at the receiver side, in order to realize non-data-aided (NDA) and fast MFI. The obtained FO can be also used for subsequent FO compensation (FOC), without additional complexity. We numerically investigate and experimentally verify the proposed MFI with high accuracy and fast format switching among 28 Gbaud dual-polarization (DP)-4/8/16/64QAM, time domain hybrid-4/16QAM, and set partitioning (SP)-128QAM. In particular, the proposed MFI brings no performance degradation, in term of tolerance of amplified spontaneous emission (ASE) noise, laser linewidth, and fiber nonlinearity. Finally, a hitless coherent transceiver enabled by the proposed MFI with switching-block of only 2048 symbols is demonstrated over 1500 km standard single mode fiber (SSMF) transmission.

Doppler Spread Estimation in MIMO Frequency-Selective Fading Channels

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramer–Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough.

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

This paper presents an off-line algorithm for dynamical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA), and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared with simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA, and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only considers the current and previous observations, and its mean square error approaches the BCRB.

Maximum Likelihood Time Delay Estimation From Single- and Multi-Carrier DSSS Multipath MIMO Transmissions for Future 5G Networks

In this paper, we address the problem of time delay estimation (TDE) from single-carrier (SC) or multi-carrier (MC) direct-sequence spread spectrum (DSSS) multipath transmissions in the presence of multiple transmit and/or receive antennas that will characterize future 5G radio interface technologies (RITs), such as coded-domain nonorthogonal multiple access. We derive for the first time a closed-form expression for the Cramer-Rao lower bound (CRLB) and develop two maximum likelihood (ML) multipath TDEs for SC DSSS single-input multiple-output (SIMO) in the non-data-aided (NDA) case. The first TDE, based on iterative expectation maximization (EM), provides accurate estimates whenever a good initial guess of the parameters is available at the receiver. The second TDE implements the ML criterion in a non-iterative way and finds the global maximum of the compressed likelihood function using the importance sampling (IS) technique without requiring any initialization. We also extend both the SC DSSS SIMO CRLB and the two new SC DSSS SIMO ML NDA TDEs to MC DSSS RITs and to multiple-input multiple-output structures with any diversity versus multiplexing pre-coding type before generalizing them all to the data-aided (DA) case. Simulations suggest that the EM TDE is suitable for large observation in space, time, and/or frequency, whereas the IS TDE is preferred in the opposite case of very short data records. Moreover, we show in the NDA case, both analytically and by simulations, that spatial (transmit and receive), temporal, and frequency samples interchangeably have the same impact on estimation accuracy and performance bound regardless of the channel correlation type and amount present in each dimension. Furthermore, we are able to properly cope with such channel correlations that do indeed arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the DA case, but that have been so far overlooked in previous works.

Low-complexity DOA estimation from short data snapshots for ULA systems using the annihilating filter technique

This paper addresses the problem of DOA estimation using uniform linear array (ULA) antenna configurations. We propose a new low-cost method of multiple DOA estimation from very short data snapshots. The new estimator is based on the annihilating filter (AF) technique. It is non-data-aided (NDA) and does not impinge therefore on the whole throughput of the system. The noise components are assumed temporally and spatially white across the receiving antenna elements. The transmitted signals are also temporally and spatially white across the transmitting sources. The new method is compared in performance to the Cramér-Rao lower bound (CRLB), the root-MUSIC algorithm, the deterministic maximum likelihood estimator and another Bayesian method developed precisely for the single snapshot case. Simulations show that the new estimator performs well over a wide SNR range. Prominently, the main advantage of the new AF-based method is that it succeeds in accurately estimating the DOAs from short data snapshots and even from a single snapshot outperforming by far the state-of-the-art techniques both in DOA estimation accuracy and computational cost.

Non-Data-Aided Measurement of Noise Variance for OFDM System in Frequency-Selective Channels

In this paper, we propose a non-data-aided (NDA) method to estimate the noise variance for orthogonal frequency division multiplexing (OFDM) system in frequency-selective channels. The proposed method is developed based on the signal correlation introduced by the use of the cyclic prefix (CP) and uses the difference between two discrete Fourier transform (DFT) outputs in the CP interval to estimate the noise variance. Analytical result shows that the obtained noise variance is an unbiased estimate in a quasi-static fading channel. Simulation results show that, even in the case in which the maximum delay spread is equal to the CP length and the channel is not quasi-static, the proposed method still works well.

Design of new digital blind feedforward timing recovery algorithm for linear modulations in digital communication receivers

Synchronisers are a significant part of digital communication receivers since they are used to estimate the received signal timing. Among these synchronisers, non-data-aided (NDA) or blind synchronisers are even more important due to bandwidth efficiency. In this study, a different approach is investigated to derive new feedforward NDA timing estimators based on Newton algorithm. Two novel estimators with four and two samples per symbol are presented and compared with traditional timing estimators, respectively. One of the main advantages of the second proposed estimator is the ability to control the loop gain automatically. This ability improves the convergence speed and robustness of the design. Simulation results show that the presented estimators outperform the conventional estimators over a noisy channel, especially at small excess bandwidth and low signal to noise ratio.

Modulation Format Identification in Coherent Receivers Using Deep Machine Learning

We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals’ amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks.

NDA SNR estimation using fourth‐order cross‐moments in time‐varying single‐input multiple‐output channels

In this study, the authors propose a moment-based estimator of the signal-to-noise ratio (SNR) over time-variant Rayleigh fading single-input multiple-output channels. The correlated time-variant channel is modelled with the well-known Jakes’ model. The authors’ approach uses the fourth-order cross-moments of the received signal to estimate the SNR with the presence of an additive white Gaussian noise which is uncorrelated between antenna elements. The SNR is deduced by estimating, respectively, the powers of the useful signals and the noise. The proposed SNR estimator is a non-data-aided (NDA) method since it does not require a training sequence. The performances of this algorithm are investigated in terms of normalised mean square error over a wide range of scenarios. Simulation results show that the proposed algorithm outperforms the NDA maximum-likelihood-based estimators and the moment-based estimators.