Iterative Joint Channel Estimation Research Articles

Iterative Joint Frequency Synchronization and Channel Estimation for Uplink Massive MIMO

Iterative Joint Frequency Synchronization and Channel Estimation for Uplink Massive MIMO

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of millimeter-wave V2X communications is, however, not exempt from challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an ultrasonic-aided tensor channel estimation for V2X millimeter-wave massive MIMO systems to improve the safety and traffic efficiency of cooperative automated driving. At the receiver, the dimension of multidimensional complex V2X information is reduced by the subspace tensor decomposition model. In order to quickly track beam angle changes caused by vehicle position changes, the ultrasonic-aided direction of arrival (DOA) tracking method is adopted to provide information about the surrounding environment. Based on the ultra-high resolution quantization grid and adaptive iterative update of the dictionary matrix, the DOA of the ultrasonic signal can be tracked. The angle update information of the millimeter-wave signal can be obtained by converting the estimated angle information. Using the cost function with global characteristics and ultrasonic-aided DOA tracking, a fast-layered alternating iterative tensor algorithm is proposed for joint iterative channel estimation. Simulation results show that the proposed solution outperforms some advanced alternative methods.

Secrecy Coding Analysis of Short-Packet Full-Duplex Transmissions with Joint Iterative Channel Estimation and Decoding Processes.

This paper studies the secrecy coding analysis achieved by the self-jamming technique in the presence of an eavesdropper by considering a short-packet Full-Duplex (FD) transmission developed based on iterative blind or semi-blind channel estimation and advanced decoding algorithms. Indeed, the legitimate receiver and eavesdropper can simultaneously receive the intended signal from the transmitter and broadcast a self-jamming or jamming signal to the others. Unlike other conventional techniques without feedback, the blind or semi-blind algorithm applied at the legitimate receiver can simultaneously estimate, firstly, the Self-Interference (SI) channel to cancel the SI component and, secondly, estimate the propagation channel, then decode the intended messages by using 5G Quasi-Cyclic Low-Density Parity Check (QC-LDPC) codes. Taking into account the passive eavesdropper case, the blind channel estimation with a feedback scheme is applied, where the temporary estimation of the intended channel and the decoded message are fed back to improve both the channel estimation and the decoding processes. Only the blind algorithm needs to be implemented in the case of a passive eavesdropper because it achieves sufficient performances and does not require adding pilot symbols as the semi-blind algorithm. In the case of an active eavesdropper, based on its robustness in the low region of the Signal-to-Noise Ratio (SNR), the semi-blind algorithm is considered by trading four pilot symbols and only requiring the feedback for channel estimation processes in order to overcome the increase in noise in the legitimate receiver. The results show that the blind or semi-blind algorithms outperform the conventional algorithm in terms of Mean Square Error (MSE), Bit Error Rate (BER) and security gap (). In addition, it has been shown that the blind or semi-blind algorithms are less sensitive to high SI and self-jamming interference power levels imposed by secured FD transmission than the conventional algorithms without feedback.

Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

Joint Iterative Channel Estimation and Frequency-Domain Turbo Equalization for Single-Carrier Spatial Modulation

Single-carrier frequency-domain turbo equalization (SC-FDTE) has gained widespread adoption in the emerging broadband spatial modulation (SM) systems operating in frequency-selective channels, where the channel model considered is a quasi-static Rayleigh fading channel. In this paper, a new class of robust FDTE designs based on the minimum mean-square error (MMSE) criterion is conceived for broadband single-carrier SM (SC-SM) systems relying on realistic imperfect channel knowledge. First, a robust time-domain soft-decision feedback (TDSDF)-aided FDTE is proposed to cope with channel estimation errors at the receiver. Furthermore, its robust frequency-domain soft-decision feedback (FDSDF)-aided counterpart is derived to offer a low-complexity approximate solution. Finally, by exploiting the carefully selected reliable soft-decision output of the channel decoder as pilots, we refine the resultant decision-directed channel estimation. As a benefit, the performance of the two robust FDTEs can be further improved. Both our simulation results and our extrinsic information transfer (EXIT) chart analysis demonstrate that the proposed robust FDTEs achieve significant performance improvements over the conventional FDTEs.

Iterative Joint Channel Estimation and Signal Detection for OFDM System in Double Selective Channels

Intercarrier interference caused by fast time-varying multipath fading channels degrades the system performance of high-mobility orthogonal frequency division multiplexing systems. This study consi...

Iterative Frequency-Domain Joint Channel Estimation and Data Detection of Faster-Than-Nyquist Signaling

In this paper, we propose semi-blind iterative frequency domain joint channel estimation (CE) and data detection (DD) of faster-than-Nyquist signaling (FTNS). The proposed scheme achieves low-complexity operation, while maintaining a performance close to that of the perfect channel state information scenario. More specifically, we derive low-complexity frequency-domain CE for the faster-than-Nyquist pilot (FTNP) transmission scenario, where the symbol duration of a pilot block is lower than the Nyquist-criterion-based pilot transmission. Moreover, we propose a serially concatenated channel-encoded FTNS transceiver that takes into account FTNS-specific colored noise effects. The proposed low-complexity receiver carries out soft-decision frequency-domain equalization with the aid of the minimum-mean square error criterion while whitening the colored noise. As explicit benefits of the proposed frequency-domain CE for the FTNP, the demodulated FTNS data block can also be used as a long pilot block, so the iterative joint CE and DD becomes realistic. Simulation results demonstrate that the proposed two-stage-concatenated FTNS system relying on binary phase-shift keying-based FTNP and FTNS-data symbols achieves a better error-ratio performance than previous systems that do not consider colored noise effects in the high-symbol-packing FTNS regime.

Improved iterative FD‐CE and turbo equalisation for doubly‐selective high‐frequency channel

In this study, the authors investigate joint iterative frequency-domain channel estimation (FD-CE) and turbo equalisation for doubly selective high-frequency channel. The performance of time and FD turbo equalisers (TD/FD-TE) by applying them to Appendix C of military standard (MIL-STD)-188-110C is compared. Compared to TD-TE, FD-TE has reduced computational complexity; however, its performance degrades for time-varying channels. To improve the performance of FD-TE, we propose a new iterative CE scheme which can be used jointly with FD-TE. We employ low-complexity FD least mean-square algorithm in iterative process. By the use of soft information fed back from the decoder from the previous iteration, we improve the quality of the channel estimate over iterations. Simulation results show that at bit error rate (BER) of 1×10 -5 the proposed algorithm improves the performance of FD-TE ~2.5 and 3.5 dB for 8 phase shift keying (PSK) and 16 quadrature-amplitude modulation (QAM), respectively. Also, we study the impact of CE error on the performance of FD-TE. For 16QAM when SNR values are more than 20 dB, there is a gain between 1 and 3 dB when we consider the variance of CE error.

Iterative Joint Detection and Channel Estimation Algorithm for Large-scale MIMO System

Large-scale Multiple-Input Multiple-Output (MIMO) can promise high spectrum efficiency and low multi-user interference. But the pilot contamination of the uplink channel estimation is the obstacle to acquire the great performance gains. A novel iterative joint channel estimation and detection algorithm is proposed. First, the initial channel information is estimated with the uplink pilots. Then the receiver detects the receiving signals with Match Filter (MF) precoding and Minimum Mean Square Error (MMSE) criterion based on the initial channel estimation. The receiving signal acquires the great large-scale MIMO detection gains and is more lightly interfered than the multiplexed uplink pilots because of the non-coherence of the transmitted data. Thus, it is in turn applied to suppress the pilot contamination in the channel estimation. In the iteration, the updated channel estimation is used for the data detection in the next loop. The theoretical analysis shows that the interference items in the detected signals and the channel estimation are continually decreased with the number of the iteration increasing. At last, the numerical results prove that, the proposed algorithm has significant performance gains comparing with the conventional algorithms. After only one iteration in light pilot contamination cases and three iteration in severe pilot contamination cases, it can obtain the detected data and the channel estimation information with the required performance. The proposed algorithm effectively improves the detection performance and suppress the pilot contamination of the uplink channel estimation, and can be worthy for the large-scale MIMO system.

Decision directed iterative channel estimation and Reed–Solomon decoding over flat fading channels

Iterative channel estimation and decoding over flat Rayleigh fading channels have been shown in literature to improve the performance of receivers in wireless communication systems. Turbo and low density parity check codes have mostly been used as the forward error correction (FEC) scheme in the iterative receiver structures. This study proposes a decision directed iterative channel estimation and decoding receiver using Reed–Solomon codes. In particular, the Koetter and Vardy, Reed–Solomon (KV–RS) soft decision decoder is adopted in the receiver structure as the FEC scheme. Two methods of deriving a priori information in joint iterative channel estimation and decoding receiver structures are also analysed in this study. The first method derives the a priori information employing the log likelihood ratio (LLR) method. A priori information derived using the LLR method is mostly used in iterative receiver structures in the literature. The second method derives the a priori information employing the distance metric (DM) method. The DM method is proposed in this study as an alternative approach to the LLR method of deriving a priori information in the iterative receivers. Hard and soft feedback information from the KV–RS decoder is considered to verify the performance of these methods. The performance of the two a priori information methods is documented through computer simulation assuming an M-ary quadrature amplitude modulation system. Simulation results verify the improvement in symbol error rate (SER) performance in the KV–RS iterative receiver structure in comparison with the KV–RS receiver without feedback. Also, the proposed DM method exhibits the same SER performance in comparison with the LLR method which is mostly used in the literature. More importantly, the proposed DM method has the advantage of less computational delay and time complexity compared with the LLR method.

A Multi-user Receiver for Inter-Cell Interference Reduction in LTE PUCCH Signaling

In 3rd generation partnership project (3GPP) long term evolution (LTE) systems, when no resources has been assigned in the uplink to a given user equipment (UE), the control information associated with layers 1 and 2 in the protocol stack is conveyed back to the base station through the so-called physical uplink control channel (PUCCH). In PUCCH, the data streams transmitted by multiple UEs are multiplexed in the time-domain and in the frequency-domain with the aid of spreading codes. Although the spreading codes associated with UEs within the same cell can be assumed to be orthogonal, the presence of inter-cell interference (ICI) in multi-cell scenarios severely limits receiver performance. In particular, the Format 1 of PUCCH, which is associated with the transmission of hybrid automatic repeat request acknowledgements (ACK/NACK) and scheduling requests, has a major impact on system performance, since an incorrectly decoded ACK/NACK message may introduce significant delay in data transmission. In this contribution, we propose a new multi-user receiver for ICI reduction in PUCCH LTE that operates both in cooperative and non-cooperative multi-cell architectures. The proposed receiver relies on a constrained tensor modeling of the received signal in PUCCH signaling, and affords an iterative joint channel estimation and symbol detection by simultaneously exploiting the energy of the data symbols and the pilot tones present in PUCCH. The formulation of the proposed algebraic receiver model incorporates symbol-basis hopping and slot-basis hopping signaling schemes, which are interference randomization techniques existing in the 3GPP specifications of LTE system. Computer simulation results show the remarkable performance gains of the proposed receiver compared to the conventional time-frequency decorrelator based receiver.

Doubly-Selective Channel Estimation Using Superimposed Training and Weighted First-Order Statistics

Doubly-selective channel estimation using superimposed training and complex exponential basis expansion model is considered. By taking a weighted averaging operation of the received data, a weighted first-order statistical estimator is proposed, where the time-varying channel estimation is reduced to the simple average-based solution of time-invariant coefficients and the dominant effect of information-induced interference on channel estimation can be suppressed. To further improve the estimation performance with a limited training power, a joint iterative channel estimation and symbol detection scheme is developed where the detected symbol is exploited to enhance estimation performance instead of being viewed as interference. Theoretical analysis and simulation results show that the proposed scheme is superior to data-dependent superimposed training scheme and competitive with the conventional time-multiplexed training in terms of symbol error rate over doubly-selective channels.

SAGE Based Suboptimal Receiver for Downlink MC-CDMA Systems

An iterative joint data detection and channel estimation algorithm for downlink multi-carrier code division multiple access (MC-CDMA) systems is proposed. The resulting algorithm is a space alternating generalized expectation-maximization (SAGE) algorithm which updates the data sequences serially and the channel parameters in parallel, leading to a receiver structure that also incorparates interchannel interference cancellation. Its performance is compared with various Minimum Mean Square Error (MMSE) estimators for a multipath frequency selective fading channel using simulations. It is illustrated that the proposed SAGE algorithm performs better than the MMSE estimators considered in this paper.

Near-Optimal Channel Estimation for OFDM in Fast-Fading Channels

In this paper, a low-complexity iterative receiver combining joint iterative channel estimation (CE) with symbol detection is proposed for coded orthogonal frequency-division multiplexing (OFDM) systems in fast-fading channels with Doppler frequencies up to 15% of the OFDM subcarrier spacing. The receiver exchanges information between the channel estimator and detector in an iterative fashion to obtain accurate estimates of the channel state information (CSI). The channel is modeled as a weighted sum of fixed basis expansion model (BEM) functions. The BEM coefficients are characterized as a multivariate autoregressive (AR) processes and estimated with a Kalman filter. The initial channel estimate is performed from sparse pilot signals. Data are detected and decoded, and the channel is estimated again based on the estimated transmitted data. The cycle of detection, decoding, and CE is repeated until convergence. It is shown that with a pilot-to-data ratio of 7/144 and with pilots consuming only 1/145 of transmission power, bit error rate (BER) performance within 0.1 dB of that achieved with perfect CSI is obtained for BER ≤ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> .

On Throughput of MIMO-OFDM Systems with Joint Iterative Channel Estimation and Multiuser Detection under Different Multiple Access Schemes

In this letter we compare the throughput performance of multiuser MIMO-OFDM systems operating in time-variant scenarios. The system employes packet-based transmissions and the receiver is twofold-iterative implementing multiuser detection, channel estimation and soft single-user decoding. Different combinations in terms of multiple-access techniques (time division, frequency division, and space division) and modulation schemes (BPSK and QPSK) are compared. Large-size constellations with interfering users in space-division multiple access (SDMA) are preferable in high-SNR range.

EM-Based Joint Channel Estimation and Data Detection for MIMO-CDMA Systems

In this paper, we present an iterative joint channel-estimation and data-detection technique for multiple-input-multiple-output (MIMO) code-division multiple-access (CDMA) systems over Rayleigh fading channels. The proposed receiver performs channel estimation and data detection using the expectation-maximization (EM) algorithm. We derive a closed-form expression for the optimized weight coefficients of the EM algorithm, which is shown to provide large performance improvement relative to the conventional equal-weight EM-based signal decomposition. Our results show that the receiver can achieve near-optimum performance with modest complexity using very few training symbols. Furthermore, our simulation results confirm that the proposed receiver is near-far resistant and offers fast convergence in severe near-far scenarios.

Serial Iterative Joint Carrier Frequency Synchronization and Channel Estimation for OFDM

A scheme that jointly estimates carrier frequency offset and channel is proposed for the orthogonal frequency division multiplexing (OFDM) system. In the proposed scheme, the carrier frequency offset (CFO) and the channel state information (CSI) are first estimated by an minimum mean square error (MMSE) estimator and an maximum likelihood (ML) estimator, respectively. By exchanging the estimation information between these two estimators, the final estimation of CFO and CSI is then obtained by an iterative method. In the iterative process, the effect of imperfect CSI is considered. It can improve the estimation precision for a shorter preamble and accelerate the iterative convergence rate. To reduce the complexity of the proposed scheme, a procedure is adopted to eliminate the inverse operation of covariance matrix that is recalculated at each iteration. In addition, a sufficient condition for the convergence of the proposed method is deduced. The numerical simulation results show that the BER performance of our scheme is better than that of joint MLE for a shorter preamble and is comparable to that of joint MLE for a longer preamble. Furthermore, the average iterative time of our method is reduced by half as compared to the MLE methods without considering the effect of imperfect CSI.

A low-complexity iterative channel estimation and detection technique for doubly selective channels

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

Iterative joint data detection and channel estimation for uplink MC-CDMA systems in the presence of frequency selective channels

This paper is concerned with joint multiuser detection and multichannel estimation (JDE) for uplink multicarrier code-division multiple-access (MC-CDMA) systems in the presence of frequency selective channels. The detection and estimation, implemented at the receiver, are based on a version of the expectation maximization (EM) algorithm and the space-alternating generalized expectation-maximization (SAGE) which are very suitable for multicarrier signal formats. The EM-JDE receiver updates the data bit sequences in parallel, while the SAGE-JDE receiver reestimates them successively. The channel parameters are updated in parallel in both schemes. Application of the EM-based algorithm to the problem of iterative data detection and channel estimation leads to a receiver structure that also incorporates a partial interference cancelation. Computer simulations show that the proposed algorithms have excellent BER end estimation performance.

Iterative Joint Detection, Decoding, and Channel Estimation in Turbo-Coded MIMO-OFDM

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of a list detector, a turbo decoder, and a channel estimator that is based on the space-alternating generalized expectation-maximization (SAGE) algorithm. This paper proposes a way to improve the convergence of the iterative detection and decoding by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits. A new list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal losses of performance. Furthermore, the organization of spectrally efficient decision-directed (DD) SAGE channel estimation under a constrained number of detector-decoder iterations is optimized by computer simulations, and the SAGE algorithm itself is modified for nonconstant envelope constellations. The list recalculation is shown to improve convergence. It is also shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. Despite the low preamble density and fast-fading channel, the proposed iterative receiver shows robust performance.