Frequency-selective Channels Research Articles

Low-Complexity ZF/MMSE MIMO-OTFS Receivers for High-Speed Vehicular Communication

Orthogonal time-frequency space (OTFS) scheme, which transforms a time and frequency selective channel into an almost non-selective channel in the delay-Doppler domain, establishes reliable wireless communication for high-speed moving devices. This work designs and analyzes low-complexity zero-forcing (LZ) and minimum mean square error (LM) receivers for multiple-input multiple-output (MIMO)-OTFS systems with perfect and imperfect receive channel state information (CSI). The proposed receivers provide exactly the same solution as that of their conventional counterparts, and reduce the complexity by exploiting the doubly-circulant nature of the MIMO-OTFS channel matrix, the block-wise inverse, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Schur</i> complement. We also derive, by exploiting the Taylor expansion and results from random matrix theory, a tight approximation of the post-processing signal-to-noise-plus-interference-ratio (SINR) expressions in closed-form for both LZ and LM receivers. We show that the derived SINR expressions, when averaged over multiple channel realizations, accurately characterize their respective bit error rate (BER) with both perfect and imperfect receive CSI. We numerically show the lower BER and lower complexity of the proposed designs over state-of-the-art exiting solutions.

Pilot Symbol Aided Channel Estimation for OCDM Transmissions

Pilot-aided channel estimation allows the receiver to acquire channel state information (CSI) for each multicarrier block by multiplexing data and pilot symbols in the same block, as long as they can be decoupled. This work proposes several frequency-domain pilot multiplexing techniques to enable independent channel estimation and detection at the receiver for orthogonal chirp division multiplexing (OCDM) transmissions in frequency-selective channels. Analysis shows that each of the proposed schemes is able to achieve the mean squared error (MSE) lower bound for channel estimation and has greater spectral efficiency than the existing schemes for OCDM and chirp spread orthogonal frequency division multiplexing (OFDM).

Single- Versus Multicarrier Terahertz-Band Communications: A Comparative Study

The prospects of utilizing single-carrier (SC) and multi-carrier (MC) waveforms in future terahertz (THz)-band communication systems remain unresolved. On the one hand, the limited multi-path components at high frequencies result in frequency-flat channels that favor low-complexity wideband SC systems. On the other hand, frequency-dependent molecular absorption and transceiver characteristics and the existence of multi-path components in indoor sub-THz systems can still result in frequency-selective channels, favoring off-the-shelf MC schemes such as orthogonal frequency-division multiplexing (OFDM). Variations of SC/MC designs result in different THz spectrum utilization, but spectral efficiency is not the primary concern with substantial available bandwidths; baseband complexity, power efficiency, and hardware impairment constraints are predominant. This paper presents a comprehensive study of SC/MC waveforms for THz communications, utilizing an accurate wideband THz channel model and highlighting the various performance and complexity trade-offs of the candidate schemes. Simulations demonstrate that discrete-Fourier-transform spread orthogonal time-frequency space (DFT-s-OTFS) achieves a lower peak-to-average power ratio (PAPR) than OFDM and OTFS and enhances immunity to THz impairments and Doppler spreads, but at an increased complexity cost. Moreover, DFT-s-OFDM is a promising candidate that increases robustness to THz impairments and phase noise (PHN) at a low PAPR and overall complexity.

Deep Learning-Aided Optical IM/DD OFDM Approaches the Throughput of RF-OFDM

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as O-OFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

Methods for improving the energy characteristics of OFDM modems in frequency selective fading communication channels

Methods for improving the energy characteristics of OFDM modems in frequency selective fading communication channels

2Pb6-4 The performance of channel adaptive full duplex OFDM using PN pilot signal in underwater frequency selective channel

2Pb6-4 The performance of channel adaptive full duplex OFDM using PN pilot signal in underwater frequency selective channel

Design and Testbed Implementation of Multiuser CFOs Estimation for MIMO SC-FDMA System

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.

Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method

Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channels.

Blind Adaptive Equalization for Aerospace Communication Channels

In some communication systems, it is desirable for the receiver to synchronize to the received signal and to adjust the equalizer without having knowledge of a training sequence. Blind equalization uses the initial adjustment of the coefficients without making use of a training sequence. Different adaptive blind equalization algorithms have been developed over the past four decades. In this paper, we investigate the effect of blind equalization on space communication channels. The space channel under investigation is considered to be a multipath frequency selective channel having four paths. The channel is subjected to the phenomenon of InterSymbol Interference (ISI) which severely degrades the performance of the space communication system. Two blind algorithms are used in equalizer adjustment. The impulse responses of the space channel, the blind equalizer and the combination of channel and equalizer for QPSK and 16-QAM transmission are shown. The scatter diagrams for the transmitted sequence, received sequence, and the output of the equalizer using two of the blind algorithms are shown.

Doubly Selective Channel Estimation Algorithms for Millimeter Wave Hybrid MIMO Systems

In this paper, we propose three new compressive sensing-based algorithms for channel estimation in millimeter wave hybrid MIMO systems over doubly (time and frequency) selective channels. Utilizing the basis expansion model (BEM) for effectively representing doubly selective channels, we first propose a BEM-based block orthogonal matching pursuit (BBOMP) algorithm, which can work with any training sequence structure. Next, we present the second algorithm to reduce the complexity of the BBOMP method by employing a special training sequence which results in a computationally efficient block sparse sensing matrix. Finally, in order to further decrease the computational complexity, we propose the third algorithm, where the channel estimation is split into two separate steps of tap detection and gain estimation. The proposed algorithms exploit the entire available training sequence to estimate all channel parameters, and can capture channel variations across the entire training frames without requiring a feedback channel. The Cramer-Rao lower bound and the computational complexity of the proposed algorithms are also addressed. Intensive computer simulations are conducted to evaluate the accuracy of the proposed approaches, showing that they can significantly improve the mean squared error performance compared with the state-of-the-art approaches.

PARAFAC-Based Multiuser Channel Parameter Estimation for MmWave Massive MIMO Systems over Frequency Selective Fading Channels

Channel estimation is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially with a few training sequences. To solve the problem of uplink channel estimation in mmWave massive MIMO systems, a PARAFAC-based algorithm is proposed for joint estimation of multiuser channels. The orthogonal frequency divisional multiplexing (OFDM) technique is exploited to combat the frequency selective fading channels. In this paper, the received signal at the base station (BS) is formulated as a third-order parallel factor (PARAFAC) tensor, and then a low-complexity algorithm is designed for fast estimation of the factor matrices related to channel parameters, thus leading to joint estimation of multiuser channel parameters via one-dimensional search. Moreover, the Cramér–Rao Bound (CRB) results for multiuser channel parameters are derived for evaluation. Theorical analysis and numerical results reveal that the algorithm performs well with a few training sequences. Compared with existing algorithms, the proposed algorithm has clear advantages both in estimation accuracy and computational complexity.

Supervised learning and frequency domain averaging‐based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.

Performance Analysis of OTFS Under In-Phase and Quadrature Imbalance at Transmitter

The influence of in-phase and quadrature (IQ) imbalance on the performance of orthogonal time frequency space (OTFS) transmission is investigated in this work. OTFS is a recent two-dimensional transmission scheme with promising robustness against the time and frequency selectivity of the time-varying wireless channel, and IQ impairment arises from inevitable imperfections between in-phase and quadrature branches in the transceiver design. In this paper, the mathematical model is presented to illustrate the physical effect of the IQ mismatched transmitter in OTFS, which results in a new type of interference named mirror-Doppler interference (MDI), occurring only along the Doppler axis. Moreover, a closed-form expression for the average bit error rate (BER) of OTFS with IQ imbalanced transmitter is derived taking into account M-ary symbol with a constant envelope and maximum likelihood (ML) detection principle. Importantly, our findings prove that IQ imbalanced transmitter causes a significant reduction in the diversity gain of OTFS transmission, but does not lead to an error floor in the BER performance as in one-dimensional current communication systems. Monte-Carlo simulations are provided to validate the derived mathematical formulas for OTFS transmission with constant envelope M-ary symbols under different system configurations.

DESAIN PROGRAM SIMULASI UNJUK KERJA KODE LARGE KASAMI PADA SISTEM KOMUNIKASI DIRECT SQUENCE SPREAD SPECTRUM MELALUI KANAL MULTIPATH FADING

Selection of Pseudo Noise (PN) code in CDMA multiple access is needed to overcomeproblems related to interference. In this study, we simulate the large kasami code with the aimof knowing the performance of the large kasami code on flat fading channels and frequencyselective fading channels with variations in the number of multipath components. The receiversystem uses a correlation receiver to correlate the code used by each user. The simulationresults from the performance of the large kasami code through the flat fading channel show thatthe simulation carried out is valid, where the simulation BER value is close to the flat theoreticalBER value. The frequency selective fading channel shows different BER values from variationsin the number of multipath components, where the multipath 60 component produces the worstBER value. Auto correlation code large kasami has a value when the time is not equal to zerowhich can interfere with the desired signal and there is a cross-correlation signal level value thatwill interfere with each other's users.

Multilayer Extreme Learning Machine as Equalizer in OFDM-based Radio-over-fiber Systems

Mobile/wireless networks aim to support diverse services with numerous and sophisticated requirements, such as energy efficiency, spectral efficiency, negligible latency, robustness against time and frequency selective channels, low hardware complexity, among others. From the central station to the base stations, radio-over-fiber orthogonal frequency division multiplexing (RoF-OFDM) schemes with direct-detection are then implemented. Unfortunately, laser phase noise, chromatic fiber dispersion, and carrier frequency offset impair the orthogonality of the subcarriers; hence, deteriorating the performance of the RoF-OFDM system. In order to take all the processing tasks to the cognitive level (the last goal in the telecommunication industry), various extreme learning machines (ELMs), composed by only a single hidden layer, have been recently adopted as equalizers. The reason behind this trend comes from the lower computational complexity, higher detection accuracy, and minimum human intervention of the ELM algorithms. In this article, we introduce a multilayer ELM-based receiver for RoF schemes transmitting phase-correlated OFDM signals affected by phase and frequency errors. Results report that by appropriately setting the hyper-parameters of the multilayer ELMs, the ELM with 3 hidden layers outperforms most of the ELMs reported in the literature (the ELM with 2 hidden layers, original ELM, regularized ELM, and 2 fully-independent ELMs defined in the real domain), as well as the benchmark pilot-assisted equalizer in terms of bit error rate. Nevertheless, this benefit comes with excessive computational cost. Finally, we show that the fully-complex ELM is still the best equalizer taking into account several key metrics.

Wideband Channel Estimation for IRS-Aided Systems in the Face of Beam Squint

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy efficiency of wideband communication systems by using low-cost passive elements for reflecting the impinging signals with adjustable phase shifts. To realize the full potential of IRS-aided systems, having accurate channel state information (CSI) is indispensable, but it is challenging to acquire, since these passive devices cannot carry out transmit/receive signal processing. The existing channel estimation methods conceived for wideband IRS-aided communication systems only consider the channel's frequency selectivity, but ignore the effect of beam squint, despite its severe performance degradation. Hence we fill this gap and conceive wideband channel estimation for IRS-aided communication systems by explicitly taking the effect of beam squint into consideration. We demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded two-hop channel reflected by the IRS has two peaks, which leads to a pair of estimated angles for a single propagation path, due to the effect of beam squint. One of these two estimated angles is the frequency-independent `actual angle', while the other one is the frequency-dependent `false angle'. To reduce the influence of false angles on channel estimation, we propose a twin-stage orthogonal matching pursuit (TS-OMP) algorithm.

DESAIN SIMULASI UNJUK KERJA KODE SMALL KASAMI PADA KANAL MULTIPATH FADING

Telecommunication capable of handling interference, fading and no less importantissues regarding the confidentiality of user information is very much needed in the currenttechnological era, to overcome this spread spectrum technology is quite developed in theadvancement of telecommunication technology. In this study, the small kasami code was usedas the spreading code. The purpose of this study is to determine the performance of the smallkasami code with changes in the number of multipath components using multipath fadingchannels. The modulation technique uses digital modulation, namely binary phase shift keying(BPSK) with a small kasami code with a code length of 15 in the spreading and spreadingprocess. Analyzed based on BER (Bit Error Rate) versus EB/No (Energy Bit per Noise). In thesimulation of flat fading performance using 1 multipath component, flat fading serves as an idealreference where the simulation BER should resemble the theoretical BER as simulationvalidation so that it can be used on selective fading channels. Variation of multipathcomponents in frequency selective fading channel produces different BER because there isinterference of multipath signal to the desired signal. The number of multipath components asmany as 11 have the most interference signals resulting in the worst performance. The value ofcross correlation between users has poor performance, where the correlation value of user 1with 2, 3, and 4 is more than 0, this value shows that many signal levels experience interferencebetween users.

An Efficient QAM Detector via Nonlinear Post-Distortion Based on FDE Bank Under PA Impairments

In this paper, we propose a receiver structure for single-carrier uplink transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two-stage approach is adopted, in which linear communication channel is equalized at the first stage, and it is followed by post-distortion, where nonlinear distortion is reduced. In the literature, nonlinear processing techniques are proposed, which perform memoryless compensation of nonlinear distortion together with FDE. However, in this study, we show that even if memoryless nonlinearity exists, the received signal is impaired by nonlinear inter-symbol-interference. Therefore, we propose a class of symbol rate post-distortion techniques, which use neighboring received symbols to suppress the nonlinear interference. Three different post-distortion methods, Gaussian process regression (GPR), neural network (NN) and Volterra series (VS) based post-distorters, are considered. Also, a combiner, which intelligently combines the outputs of fractional delayed bank of FDE’s after post-distortion, is proposed to overcome performance degradation of FDE for frequency selective channels under nonlinear distortion. Performances of the proposed techniques are compared with state-of-the-art approaches in terms of bit error rate (BER) and achievable information rate (AIR) metrics. Simulation results demonstrate that post-distortion methods together with bank of FDE outperform state-of-the-art techniques.

Blind Detection Design for AF Two-Way Relaying Over Frequency Selective Channels

Blind Detection Design for AF Two-Way Relaying Over Frequency Selective Channels

A Comparative Performance Analysis of Channel Equalizer in OFDM System Under Jamming Attacks

In this article, the accomplishment of OFDM systems under different jamming attacks is analyzed. Partial band jamming signal suppressing OFDM data packets partially and full band jamming signal suppressing all OFDM data packets are examined in this study. Computer simulation works are carried out to analyze the achievement of the OFDM system over AWGN and frequency selective Rayleigh fading channels under both jamming attacks. In addition, comparative performance analysis of different equalizer structures in frequency selective Rayleigh fading channel environment is performed. From the obtained results, it is seen that frequency domain equalizers combat jamming attack signals in the best way and provide the best performances.