Iterative detection and frequency synchronization for OFDMA uplink transmissions

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

In recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> for deriving the covariance matrix of the channel and the variance sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Since the computation of the proposed ZF-based SAGE algorithm requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm

An effective design of multistage parallel interference cancellation (PIC) receiver using blind adaptive (BA) despreader and pre‐respreader interference estimator for uplink CDMA is proposed and analysed. A novel algorithm is designed, which exploits constant modulus (CM) property of the users' transmitted signals and inherent channel condition to perform adaptive despreading based on minimum error variance criteria. This is carried out by BA weighting of each chip signal for accurate tracking of the desired user's signal power and hence for more improved data detection at the output of each stage of PIC. Furthermore, the despreader weights are used within the adaptive pre‐respreader interference estimation and cancellation to obtain online scaling factors during every symbol period, without any knowledge of users' channels or the use of training sequences. It is found that this way of estimation is optimal in minimum mean squared error sense, and hence, significant reduction in interference and noise variance is observed in detection and estimation of the desired users' signals compared with conventional PIC. Bit error probability of the proposed PIC is obtained using Gaussian Approximation method. Extensive simulation results are shown, which demonstrate impressive performance advantage in fading environments, high system loading, and severe near far conditions. Copyright © 2008 John Wiley &amp; Sons, Ltd.

In this paper, an efficient channel estimation algorithm is proposed for amplify-and-forward (AF) cooperative relay based orthogonal frequency division multiplexing (OFDM) system in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. The algorithm is based on the combinations of the matching pursuit (MP) and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the Gaussian mixture noise distribution parameters in an iterative way. Computer simulations show that underwater acoustic channel is estimated very effectively and the proposed algorithm has excellent symbol error rate (SER) and channel estimation performance, as compared to the existing ones.

In this paper, the space alternating generalized expectation-maximization (SAGE) method is applied to derive receivers for coded CDMA systems. In the so-called SAGE receiver, hard decision code symbols are fed back from the single user decoders to the joint channel estimator and interference canceler. Using multivariate Gaussian approximations, the SAGE receiver is then modified such that soft values of code symbols are fed back from the single user decoders. We refer to this modified receiver as SAGE-based receiver. Compared to the SAGE receiver, the SAGE-based receiver considers the statistical properties of the code symbols, thus the channel estimation and the interference cancellation take symbol estimation errors into account. A performance improvement in terms of bit error rate (BER) and system load of the SAGE-based receiver compared to the SAGE receiver and other receivers is shown by means of Monte Carlo simulations.

A multi-stage partial parallel interference cancellation receiver is proposed for the multi-rate DS-CDMA system which adopts the multiple processing gain scheme. In each stage of the proposed receiver, partial cancellation is performed with an adaptive decision threshold which is computed from the matched filter outputs of the previous stage. The BER performance of the proposed receiver is obtained by simulation in a Rayleigh fading channel. It is shown that the proposed receiver achieves smaller BER than the multi-stage parallel interference cancellation receiver and extended group-wise successive interference cancellation receiver in the multi-rate DS-CDMA system.

In this paper, an adaptive parallel interference cancellation (PIC) receiver is introduced for asynchronous multicarrier code division multiple access based (MC-CDMA) communications systems. The multiple access interference (MAI) estimates are obtained using the bit estimates from the previous stage and the adaptive weights for the bit estimates. The adaptive weights are updated by minimizing the mean squared error between the received signal and its estimate based on the adaptation of the normalized least mean square (LMS) algorithm. The results from the simulations show that the performance of conventional PIC (CPIC) and adaptive PIC (APIC) can be different depending on the combining schemes used.

In this paper, the joint estimation of frequency offsets and channel for distributed MIMO system in time-varying channel is discussed. We assume that each pair of transmit and receive antennas has a different frequency offset. We promote the multi-parameter estimation based on expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) for a MIMO system operating under a flat-fading environment to the multi-p arameter estimation using EM algorithm for distributed MIMO systems in time-varying channel. Theoretical analysis and simulation results indicate that the improvement can well compensate for the performance loss caused by time-variability and achieve good performance for time-varying distributed MIMO systems

The massive multiple-input-multiple-output (MIMO) technology relies on many antennas at base stations to offer high throughput to meet 5G network requirements. However, each base station requires accurate channel state information during channel estimation. Pilot contamination is one of the significant challenges that limit higher order MIMO deployment because it causes channel estimation error. In this paper, a Wiener predictor (WP) using the temporal-based prediction technique was first proposed and then extended as the coordinated WP (CoWP). The WP method is based on prediction using information that has been stored and processed at the base station from a modified subframe, whereas the extended CoWP method requires neighboring base station coordination to assign collection and prediction with other base stations. Results showed a reduction in channel estimation error due to pilot contamination after the Wiener-based prediction and coordinated base station techniques were used. The proposed CoWP technique can increase the performance of channel estimation error by up to 25 and 30 dB relative to the performance of other research technique and conventional MMSE channel estimation. This paper indicates that the pilot contamination effect can be minimized by exploiting the temporal dimension, and this approach increases the number of MIMO order for future 5G base stations, which currently limited due to pilot contamination.

In this paper, a new scheme of Joint Channel Estimation and Partial Parallel Interference Cancellation using Simulating Annealing (SA) or Particle Swarm Optimization (PSO) algorithms is proposed. This scheme is proposed for multiuser Multiple-Input Multiple-Output /Space Division Multiple Access (MIMO-SDMA) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed scheme tries to reduce the cancellation error in Conventional Parallel Interference Cancellation (CPIC) detection schemes used in MIMO SDMA-OFDM system. The SA and PSO algorithms are employed in searching the optimal weights of the PPIC for multiple access interference cancellation. The proposed scheme is shown to provide a performance improvement as compared to MMSE and CPIC detectors especially in an overloaded scenario where number of users is high as compared to the Base Station (BS) antennas. In this scenario, channel estimation becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated and the constraint imposed by the rank of the MIMO channel matrix. The proposed scheme provides a reasonable solution to the multiuser MIMO channel estimation problem and multiuser detection in the above-mentioned overloaded scenario.

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists of the celebrated bilinear generalized approximate message passing (BiG-AMP) and loop belief propagation (LBP) is then proposed, where the strong connection of compound channel can be decoupled in LBP part. By exchanging the information between BiG-AMP part and LBP part, the proposed algorithm improves the performance of channel estimation compared with HyGAMP based method, in which the estimated payload data of proposed algorithm are utilized to aid channel estimation and it leads to relatively few pilot symbols to achieve equivalent channel and data estimation performances. The simulation results confirm that our proposed joint estimation algorithm improves the performance of the existing works in terms of user activity, channel and data estimation.

In this paper, we investigate the feasibility of a 2 Dimensional Optical Code Division Multiple Access system (2DOCDMA), with electronic coding and decoding functions. We present a modified construction method of Multi-Wavelength Optical Orthogonal Codes (MWOOC) which permits high flexibility for the code parameters choice. Our objective is to evaluate the 2D code performance, with a Conventional Correlation Receiver (CCR) and a more complex one named Parallel Interference Cancellation receiver (PIC). For example, for a Bit Error Rate (BER)10-9, 30 simultaneous users and an electrical implementation, we show that contrary to the CCR, the use of a PIC receiver leads to workable 2D code solutions.

Due to the channel with characteristic of sparse multi-path in the 60 GHZ wireless communication system, the channel estimation problem can be attributed to that of sparse signals recovery. And with the consideration of the subspace pursuit (SP) algorithm is superior to the orthogonal matching pursuit (OMP) at reconstruction precision, the channel estimation technique based on the SP algorithm is presented in the 60 GHZ wireless communication system, First, design the OFDM multi-carrier modulation communication system. Then, establish the channel estimation mathematical model with indoor Line-of-sight. Finally, complete the reconstruction of sparse signals using SP algorithm. The experimental results and comparison analysis show that the presented technique based on the SP algorithm provides better channel estimation performances in the same pilot conditions, and it is superior to the technique based on OMP algorithm and the technique based on least square (LS) algorithm.

This paper investigates a channel-coded multiuser system operated with orthogonal frequency-division multiplexing (OFDM) and interleaved division multiple-access (IDMA). To realize the potential advantage of OFDM-IDMA, two challenges must be addressed. The first challenge is the estimation of multiple channel parameters. An issue is how to contain the estimation errors of the channel parameters of the multiple users, considering that the overall estimation errors may increase with the number of users because the estimations of their channel parameters are intertwined with each other. The second challenge is that the transmitters of the multiple users may be driven by different RF oscillators. The associated frequency asynchrony may cause multiple CFOs at the receiver. Compared with a single-user receiver where the single CFO can be compensated away, a particular difficulty for a multiuser receiver is that it is not possible to compensate for all the multiple CFOs simultaneously. To tackle the two challenges, we put forth a framework to solve the problems of multiuser channel-parameter estimation, CFO compensation, and channel decoding jointly and iteratively. The framework employs the space alternating generalized expectation-maximization (SAGE) algortihm to decompose the multisuser problem into multiple single-user problems, and the expectation-conditional maximization (ECM) algorithm to tackle each of the single-user subproblems. Iterative executions of SAGE and ECM in the framework allow the two aforementioned challenges to be tackled in an optimal manner. Simulations and real experiments based on software-defined radio indicate that, compared with other approaches, our approach can achieve significant performance gains.

Grant-free access is an attractive approach to enable spectrum-efficient low-latency access for systems with massive number of users. Pilot design plays a crucial role for grant-free access as it needs to provide a large number of access codes with fast collision detection capability and good channel estimation performance. Recently, pilot designs with fast collision detection capability have been proposed for compressed sensing (CS) based channel estimation. But the existing designs are not optimized for the estimation of highly sparse and block-sparse channels. In this paper, we present several propositions related to the performances of the CS based sparse and block-sparse channel estimation. Utilizing these propositions, we develop a novel non-orthogonal pilot design with fast collision detection capability for grant-free access in block-sparse channels. We also propose two methods to optimize the Peak-to-Average Power Ratio (PAPR) of the proposed non-orthogonal pilot codes. The simulation results illustrate that the proposed design provides similar or better channel estimation and collision detection performances with much better pilot resource efficiency when compared to the existing designs. Finally, we investigate the trade-offs among different design parameters and the channel estimation performances to facilitate better design choices.