Training Symbols Research Articles

Generalized Synchronization Algorithms for FBMC-OQAM Systems

Synchronization is one of the critical issues for wireless communications. In this paper, we present low-complexity joint timing and carrier frequency offsets synchronization algorithms for filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) systems by exploiting two uncorrelated consecutive real training symbols based on the repeated conjugate-symmetry property. We also develop a new peak-detection algorithm and a ratio-based carrier-frequency-offset estimation without using a threshold or matched filter, which reduces the computational cost. The proposed detection algorithm shows better performance than the threshold-based detection. The proposed algorithm provides similar system performance over the burst transmission with existing schemes while reducing the computational complexity. Moreover, the proposed scheme does not require the identical sequences that are usually adopted at the existing synchronization schemes for FBMC-OQAM systems. We show that the proposed method can be adopted for the space–time block coding for multi-input multi-output (MIMO) FBMC systems while existing schemes are not applicable. Finally, we show that the proposed algorithms are generally applicable for not only in burst and continuous transmission mode but also MIMO systems through the simulation results.

Massive MIMO Channel Estimation for Millimeter Wave Systems via Matrix Completion

Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for satisfactory performance. In this paper, we present a novel channel estimation algorithm that jointly exploits the latter two properties of mmWave channels to provide more accurate recovery, especially for shorter training intervals. The proposed iterative algorithm is based on the Alternating Direction Method of Multipliers (ADMM) and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties.

On channel estimation schemes for APD-based DDM-OFDM-PONs under sub-Nyquist sampling.

Implementing preprocessing in a delay-division multiplexing (DDM) orthogonal frequency-division multiplexing (OFDM) passive optical network (PON) requires a priori knowledge of channel responses, which need to be estimated under the constraint of sub-Nyquist analog-to-digital sampling. The localized approach allocates subcarriers in different frequency zones to training symbols in different time slots for channel estimation without spectral overlap. Unfortunately, the localized scheme is susceptible to inaccurate estimation when using an avalanche photodiode (APD), due to variations in APD saturation associated with different training symbols. Instead of localizing all subcarriers of a training symbol in a single frequency zone, we propose distributing training subcarriers through various frequency zones. This distributed scheme would prevent spectral overlap and also reduce the degree of variation in APD saturation, thereby improving the accuracy of channel estimation. Alternatively, we propose an orthogonal scheme in which each training symbol uses all of the subcarriers simultaneously. The orthogonality specified among consecutive training symbols should make it possible to estimate the channel response with low computational complexity. We conducted experiments to compare various schemes used for channel estimation in a 25-Gbps APD-based OFDM-PON. Our results revealed that the orthogonal scheme achieved the best results, and the localized scheme provided the worst channel estimates. We demonstrate the application of the orthogonal scheme in a penalty-free DDM system at 1/32 of the Nyquist rate, which provided a loss budget of 28 dB after fiber transmission over a distance of 25 km.

Directional Training for FDD Massive MIMO

A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.

One symbol training receiver for the SPAD-based UVLC system.

For the long distance single photon avalanche diode (SPAD)-based underwater visible light communication (UVLC) system, the multiple-symbol union detection scheme is presented. However, an error floor curve of the bit-to-error ratio (BER) occurs and cannot vanish even though the transmitted power approaches infinity. In this paper, to solve the problems of error floor and channel estimation in the long distance SPAD-based UVLC system, we propose the one training symbol maximum likelihood (ML) detection receiver. First, we add one training symbol in the head of each frame to eliminate the error floor and ensure the reliable blind estimation of the long distance UVLC channel. Meanwhile, the training ML detection (TMLD) receiver is developed. And then, to improve the system performance, a training modified (TM) quasi-ML receiver with Anscombe root transformation is proposed. Compared to the traditional mean detection scheme of the SPAD receiver, the simulation results show that the proposed TM quasi-ML and TMLD schemes significantly improve the error-rate performance.

On Adaptive Length of Temporal Filter for Space-Time Equalization With Cochannel Interference

In this letter, we take the initiative in adaptively determining the optimal temporal filter (TF) length for space-time equalization (STE) schemes, where overfitting may occur under too large TF length. We specifically consider the system without employing cyclic-prefix such that the spectral efficiency could be maintained. Based on the delicately derived connection between the mean square error (MSE) of equalized training symbols and unknown data symbols, the MSE of the latter can be predicated and serves as the decision metric for determining the optimal TF length. Numerical results demonstrate that the proposed STE scheme with TF of adaptively optimized length can work properly, even in the presence of cochannel interference.

Novel transmitter IQ imbalance compensation algorithm using training symbols in PDM CO-OFDM system

We systematically deduce the optical fiber channel transmission mathematical model with in-phase/quadrature (IQ) imbalance and channel distortion for polarization division multiplexed (PDM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. With the analysis of model, we propose two novel algorithms using training symbols, which can well realize channel distortion and IQ imbalance estimation. This study shows the proposed algorithms performed significantly better than the commonly used approaches. We examine the two proposed methods with simulation via analyzing the bit error rate in the case of different optical signal to noise ratio (OSNR), transmission distance and IQ mismatch at 80Gb/s and 100Gb/s.

Blind Channel Estimation for Ambient Backscatter Communication Systems

The availability of perfect channel state information is assumed in current ambient-backscatter studies. However, the channel estimation problem for ambient backscatter is radically different from that for traditional wireless systems, where it is common to transmit training (pilot) symbols for this purpose. In this letter, we thus propose a blind channel estimator based on the expectation maximization algorithm to acquire the modulus values of channel parameters. We also obtain the ranges of the initial values of the suggested estimator and derive the modified Bayesian Cramer–Rao bound of the proposed estimator. Finally, simulation results are provided to corroborate our theoretical studies.

Direct-Detection PDM-OFDM RoF System for 60-GHz 2×2 MIMO Wireless Transmission Without Polarization Tracking

This work experimentally demonstrates a 60-GHz direct-detection polarization division multiplexing and orthogonal frequency division multiplexing (PDM-OFDM) system that does not require polarization tracking. The frequency offset of un-modulated optical carriers and a novel training symbol design enable the estimation of optical polarization mixing and wireless multi-input-multi-output (MIMO) channels for signal demodulation. We also adopted an empty subcarrier to mediate noise enhancement. Simulation and experiment results both demonstrate the feasibility of this approach. With pre-compensation, we achieved a BER of 1 × 10−3 under all polarization states over 10-km fiber and 3-m wireless MIMO transmission. The maximal allowable transmission data rate of up to 52 Gb at the worst case of states of polarization can be achieved.

Robust Unambiguous Estimation of Angle-of-Arrival in Hybrid Array With Localized Analog Subarrays

Hybrid array is able to leverage array gains, transceiver sizes, and costs for massive multiple-input-multiple-output systems in millimeter-wave frequencies. Challenges arise from the estimation of angle-of-arrival (AoA) in localized hybrid arrays, due to the array structure and the resultant estimation ambiguities and susceptibility to noises. This paper eliminates the ambiguities and enhances the tolerance to the noises based on our new discoveries. Particularly, by designing new subarray-specific time-varying phase shifts, we discover that the cross-correlations between the gains of consecutive subarrays have consistent signs except the strongest. This enables the cross-correlations to be deterministically calibrated and constructively combined for the noise-tolerant estimation of the propagation phase offset between adjacent subarrays. Given the phase offset, the AoA can be estimated unambiguously with few training symbols. We also derive a closed-form lower bound for the mean square error of AoA estimation. Corroborated by simulations, our approach is able to dramatically improve estimation accuracy by orders of magnitude while reducing complexity and training symbols, as compared to the state of the art. With the ambiguities eliminated, the estimation errors of our method asymptotically approach the lower bound, as training symbols increase.

Blind Density-Peak-Based Modulation Format Identification for Elastic Optical Networks

Optical modulation format identification is critical in the next generation of heterogeneous and reconfigurable optical networks. Here, we present a blind modulation format identification method by applying fast density-peak-based pattern recognition in the autonomous receiver of elastic optical networks. In this paper, we find that the different modulation format types show different energy level features which can be used as a metric to identify these modulation formats in two-dimensional Stokes plane. The proposed method does not require training symbols, and is insensitive to carrier phase noise, frequency offset as well as polarization mixing. The effectiveness is verified via numerical simulations and experiments with PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16PSK, PDM-8QAM, and PDM-16QAM. The results show that high identification accuracy can be realized using our proposed method over wide optical signal-to-noise ratio ranges. Meanwhile, we also discuss the influence of the residual chromatic dispersion, polarization mode dispersion, and polarization dependent loss impairments to our proposed method. We believe that the simple and flexible identification method would certainly bring a great convenience to the future optical networks.

Secure Key Distribution Strategy in OFDM-PON by Utilizing the Redundancy of Training Symbol and Digital Chaos Technique

A secure key distribution scheme for orthogonal frequency division multiplexing (OFDM) passive optical network system is proposed and experimentally demonstrated. The training symbol (TS), which is used for time synchronization and channel estimation, is formed by adopting a noise-like chaotic sequence. The redundancy of the TS is utilized to embed secure keys. An experiment is performed, in which 7.64 Gb/s 16-quadrature-amplitude-modulation OFDM data and 28.4 Mb/s keys embedded in are successfully transmitted over 25 km standard single mode fiber. The results indicate a promising key distribution method for physical layer secure optical communication.

A CNA-based blind equalization scheme for ACO-OFDM in VLC system

In this paper, a blind equalization scheme based on the constant norm algorithm (CNA) is proposed for asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) in visible light communication (VLC) system. At the receiver, the CNA can be used to improve system spectral efficiency due to the free of training symbols. The simulation results indicate that for ACO-OFDM signal, the CNA-based blind equalization scheme achieves better mean square error (MSE) performance than that of constant mode algorithm (CMA). Moreover, the CNA-based blind equalization scheme outperforms the CMA scheme in the term of bit error ratio (BER) under same data rate in VLC system.

Noncoherent IR-UWB Receiver Using Massive Antenna Arrays for Wireless Sensor Networks

Noncoherent impulse radio ultrawideband (UWB) receivers do not require channel state information and are also robust against pulse shape distortions. However, their performance is suboptimal, especially at low signal-to-noise ratios. In this article, we propose simple noncoherent UWB receivers for wireless sensor network applications using massive antenna arrays. Two receivers are proposed: based on instantaneous received UWB signal energy and based on weighted instantaneous received UWB signal energy. The proposed receivers use the law of large numbers resulting from the massive receiver arrays and utilize the large degrees of freedom available in a UWB multipath channel. Furthermore, the proposed receivers do not require training symbols for the estimation of decision threshold. Both these receivers outperform the existing noncoherent receivers as verified using simulation results on the standardized IEEE 802.15.4a UWB multipath channel.

Synchronization in Visible Light Communication for Smart Cities

The rapid deployment of light emitting diodes and the prominence of facilitating simultaneous illumination and communication have driven Visible Light Communication (VLC) to gain enormous prevalence in the recent times. This expedited VLC to become amalgamated with many smart city applications in particular, with the lighting infrastructure to enable seamless connectivity. The underlying challenge of this technology is to assure high data rate communication while fulfilling illumination requirements in smart cities. In this regard, optical orthogonal frequency division multiplexing (OOFDM) compatible with intensity modulated/direct detection (IM/DD) system can be exploited for VLC. Where, IM and DD is one approach of envisioning VLC. DC biased Optical OFDM (DCO-OFDM), the earliest variant of OFDM for VLC is employed in this paper. It is apparent that, the advantages of OOFDM cannot be utilized if its orthogonality is destroyed by any means. The striking difference between RF-based OFDM and OOFDM is that, OOFDM necessitates the transmitted time-domain signal to be both real and unipolar. Hence, the synchronization techniques existing for RF cannot be directly applied for VLC and consequently must be revisited. This paper analyzes the effects of carrier frequency offset (CFO) and symbol time offset (STO) in DCO-OFDM over VLC channel. Here, mathematical analysis is performed showing the effects of CFO and STO in OOFDM. Synchronization algorithms, such as Moose, Classen and Training Symbol assisted are revisited. Detailed analysis of Cramer Rao lower bound is carried out considering the joint parameter estimation of both CFO and channel estimation error.

FDD-RT: A Simple CSI Acquisition Technique via Channel Reciprocity for FDD Massive MIMO Downlink

We propose a channel state information acquisition mechanism to reduce the training overhead in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink (DL). While the massive MIMO has demonstrated the great potentials in many aspects, the carrier multiplexing designs in massive MIMO mostly adopt time-division duplexing (TDD) system, which stems from a stereotype that the uplink/DL channel reciprocity only holds for TDD. On the other hand, FDD massive MIMO is generally considered infeasible due to the unaffordable temporal overhead for sending training symbols at base station. Our proposed FDD with reverse training (FDD-RT) modifies the DL band training procedure in typical FDD system to reduce pilot overhead, where the DL band channel reciprocity is exploited in FDD-RT. FDD-RT contributes to providing a feasible and relatively low complexity method to implement massive MIMO in FDD system. Without FDD-RT, communication operators who currently use FDD system may be enforced to change to TDD system in the future to accommodate massive MIMO, where the cost is prohibitive. The most different property of FDD-RT from other solutions is that its training overhead does not scale with the number of antennas at base station. The detail analysis/comparison of FDD-RT will be given in this paper.

Semi-Blind Sparse Channel Estimation Using Reduced Rank Filtering

This paper develops a novel method for semi-blind estimation of sparse channels in single-input, multiple-output and multiple-input, multiple-output single-carrier transmission systems, using single/multiple transmit and multiple receive antennas. Very little work exists on semi-blind sparse channel estimation. By a novel formulation of the received data matrix (called reduced data matrix) for sparse channels, semi-blind channel/data estimation problem has been cast into a reduced-rank filtering problem. The estimation operates on selected, non-uniformly spaced equalizer taps. Then two classes of semi-blind channel/data estimation are developed, the first being a pseudo-inverse based linear prediction method and the second based on reduced rank multi-stage Wiener filtering. The second class has the advantage of not requiring the rank of the reduced data matrix, but estimating it adaptively via stage-by-stage updates. The computational complexity of the novel reduced channel estimators are compared with the full channel estimators, which use all available equalizer taps. Simulation results show that the reduced semi-blind estimators perform much better than the full estimators and existing sparse channel estimators, with limited number of training symbols, at much reduced computational complexity.

A time and frequency synchronization method for CO-OFDM based on CMA equalizers

In this paper, an efficient time and frequency synchronization method based on a new training symbol structure is proposed for polarization division multiplexing (PDM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The coarse timing synchronization is achieved by exploiting the correlation property of the first training symbol, and the fine timing synchronization is accomplished by using the time-domain symmetric conjugate of the second training symbol. Furthermore, based on these training symbols, a constant modulus algorithm (CMA) is proposed for carrier frequency offset (CFO) estimation. Theoretical analysis and simulation results indicate that the algorithm has the advantages of robustness to poor optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). The frequency offset estimation range can achieve [−Nsc2ΔfN,+Nsc2ΔfN] GHz with the mean normalized estimation error below 12×10−3 even under the condition of OSNR as low as 10 dB.

Fast and Accurate Estimation of Angle-of-Arrival for Satellite-Borne Wideband Communication System

Accurate estimation of angle-of-arrival (AoA) is critical to wideband satellite communications, but is susceptible to receive noises and can be ambiguous due to space/cost-effective hybrid antenna array designs with localized analog phased subarrays. As a matter of fact, there has yet to be an unambiguous estimator even for narrow-band systems. This paper proposes a new design of subarray-specific time-varying phase shifts, which enables unambiguous and noise-tolerant estimation of AoA in localized hybrid arrays. Particularly, the new phase shifts deliver deterministic phase changes in the cross-correlations of receive signals between subarrays, and enable the cross-correlations to be coherently accumulated across subarrays and sub-carriers to eliminate ambiguities and tolerate noises. Another important contribution of the paper is that we optimize the frequency interval for coherent accumulation across sub-carriers, leveraging between estimation errors, and accumulation gains. Evident from simulations, our approach is able to dramatically improve the estimation accuracy by orders of magnitudes with significantly reduced requirements of complexities and training symbols, as compared with the state of the art. The approach is robust against noises, with estimation errors asymptotically achieving a rigorously developed lower bound.

Self-Interference Channel Estimation Algorithm Based on Maximum-Likelihood Estimator in In-Band Full-Duplex Underwater Acoustic Communication System

To efficiently cancel the self-interference (SI) caused by the simultaneous transmission and reception in in-band full-duplex (IBFD) underwater acoustic (UWA) communication system, we propose a novel channel estimation approach to estimate the sparse SI channel, the proposed channel estimation algorithm has a better estimation performance than the traditional channel estimator. In the IBFD radio communication system, the SI and intended channels are estimated through using training symbols in different time slots, and therefore, the effect of intended signal can be avoided in SI channel estimation. Because of the time variable and longtime delay in the UWA channel, we cannot estimate the SI and intended channel in different time slots. The intended signal is treated as additive noise when estimating SI channel in the IBFD-UWA communication system, and thus, the SI channel estimation is affected by non-Gaussian noise and the traditional algorithms designed for Gaussian noise typically perform poor. In view of this situation, we propose a maximum likelihood (ML) with sparse constraint to estimate the sparse SI channel. The ML with sparse constraint is derived via combining a sparse constraint into conventional ML cost function and using stochastic gradient algorithm to get the iterative formula, thereby resulting in a better performance. Extensive simulations and experimental results show that the proposed algorithm has faster convergence and better cancelation performance compared with traditional ML and least square. We also present the convergence and steady-state mean-squared error analysis of the proposed algorithm.