Joint Iterative Multiuser Detection and Channel Estimation for Differentially Coded Asynchronous CDMA Systems

We propose a layered space time (LST) transceiver in a multiuser code division multiple access (CDMA) mobile communication system. In particular, we examine a multiuser iterative receiver with joint detection, channel estimation and decoding in a frequency selective fading channel. The iterative parallel interference canceller (PIC) with statistics combining is used as the multiuser detection scheme and a least mean square (LMS) adaptive algorithm is applied to estimate the multiple input and multiple output (MIMO) frequency selective fading channel. Simulation results are presented to demonstrate the performance of this receiver. It is shown that the performance of the receiver with iterative channel estimation is close to that of the receiver with perfect channel state information (CSI).

Iterative receiver algorithms for multi-user detection and decoding are a solution for mitigating multiple-access interference (MAI) in multiple access communication systems. After several iterations they outperform noniterative schemes, while maintaining modest complexity. In this paper, we present an iterative receiver for multiple-antenna systems in frequency selective fading. Channel knowledge is not assumed to be available at the receiver. We propose a comprehensive algorithm that performs channel estimation, multi-user detection and decoding in an iterative manner. The multipath channel is estimated using several pilot symbols and soft estimates of data symbols. The performance of the receiver is evaluated by simulations.

In this chapter we investigate the tradeoff between channel estimation and channel diversity for channels with memory. Block memory channels are analytically tractable yet provide practical models for many communication systems. We show that for finite block length transmission techniques there is a fundamental tradeoff between the channel estimator and the channel diversity. When the channel is operating at rates much below capacity, smaller channel memory is better since channel estimation is not crucial. This is because the code contains more than adequate redundancy to compensate for mismatch due to the potentially poor channel estimate as well as channel errors. However, at rates close to capacity, channel state information is crucial for successful decoding and therefore longer channel memory, which allows better estimates, is better. We draw these conclusions based on analysis of some simple discrete-input, discrete-output channels using the channel reliability function and on simulation results for more realistic channels (soft output) using low-density parity check codes with a joint iterative decoder and channel estimator.

In this paper, we propose an efficient data exchange mechanism with a dedicated detection approach for Device-to-Device (D2D) communications. By involving in a third device as Relay Node (RN), both devices of interest can communicate with each other. Within an inband relay mode, Interleave Division Multiple Access (IDMA) is exploited to enable the third device to detect and decode the data of both devices. Afterwards, the third device broadcasts the superposition of both signals. In order to pick out the useful signal component out of the superimposed signals, each device deploys trellis-based iterative joint data detection and channel estimation. Especially, the iterative processing enables the successful D2D communications under very low Signal-to-Noise Ratio (SNR), which can significantly mitigate the interference for the Long Term Evolution (LTE) and LTE-Advanced network.

This paper studies the performance of iterative multiuser decoding, interference cancellation, and channel estimation techniques applicable to third generation WCDMA systems. The concept uses a posteriori probabilities of code symbols to enhance detection, decoding and channel estimation in an iterative fashion. The performance is analyzed in a multipath channel with simulations. It is seen that the proposed concept combats multiuser interference effectively and performs almost as well as single user systems.

Two major factors that limit the performance of DS-CDMA systems are multiple-access interference (MAI) and multipath channel distortion. In this paper, we propose a joint iterative channel estimation scheme for mitigating their effect on the space-time multiuser detector. The joint iterative channel estimation requires a careful design of training sequences, since the channel estimator are based on the cross-correlation matrix. Moreover, the detected decisions are then interleaved back to the channel estimator, which begins the next iteration. The channel estimator for space-time multiuser detection can use both data and training bits and re-estimate channel impulse response. The presented scheme can avoid heavy computations and improve the performance of systems effectively.

In this paper, we investigate the convergence behavior of code-aided channel estimation, also denoted as joint iterative channel estimation and decoding. A mutual information transfer chart is introduced in order to trace the information exchange between channel estimator and decoder throughout the iterative process. Furthermore, a heuristic concept to improve code-aided channel estimation is introduced that outperforms the classical concepts and has the major advantage that it allows for accurate convergence analysis.

The paper presents a method for joint channel and data estimation for transmission over frequency-selective and time-variant channels. Hereby, fast time-variant channels can be tracked, which is important for mobile communications at very high velocities. We approach the problem by estimating the scattering function, which is assumed to be limited in delay and Doppler frequency. At the transmitter, a short training sequence is inserted at the beginning of the data block. This sequence is used for initializing a Kalman filter, which carries out the joint channel and data estimation adaptively. After the training sequence, hypothetical data symbols are considered to extend the persecuted paths in the data tree.

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.

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...

This paper derives a MIMO system model that considers the spatial correlation and sparse nature of MIMO UWA channels. Accordingly, we proposes a improved sparse Bayesian learning (I-SBL) algorithm for joint estimating the channel coefficients, residual noise and channel covariance power via the robust expectation maximum (EM) update algorithm. By employing the a priori noise power and the second-order channel covariance matrix acquired from the training sequence, the proposed I-SBL channel estimator achieves faster convergence and easier hyperparameter control than that of the conventional Bayesian algorithm while enjoying better estimation accuracy than the classic channel estimation algorithms. Experimental results have verified that the proposed scheme outperforms the minimum mean square error (MMSE), conventional Bayesian, orthogonal matching pursuit (OMP) and adaptive improved least mean squares (IPNLMS) channel estimators in terms of the channel sparseness control and BER performance.

Joint channel estimation and data detection under fading on reconfigurable fabric

Joint iterative channel estimation and decoding for 16-QAM bit-interleaved coded modulation (BICM) is considered over frequency-flat fading channels. Estimates of the complex channel gain are derived first from known pilot symbols by optimum Wiener filtering and passed to the BICM iterative decoder as initial channel state information. After each decoding iteration, the channel estimates are refined using the extrinsic information fed back from the decoder. An advanced scheme called restart is developed to effectively reduce the bit error rate floor, by restarting the decoder after several iterations with the refined channel gain estimates. Motivated by the observation of the burst errors in the transmitted sequence, the restart algorithm is extended by concatenating Reed-Solomon codes with BICM. When RS (255, 223) code is used for BICM with 16-QAM modified set partitioning labeling and a 16-state code, the BER of 10/sup -6/ can be achieved at E/sub b//N/sub 0/ of 10.5 dB for normalized fade rate f/sub d/T/sub s/ = 0.02.

This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the AMP state evolution function, lacking the analysis of AMP phase transition and Bayes-optimality. In this paper, we propose a novel theoretical framework to analyze the performance of the joint AUD and CE problem by adopting the replica method in the Bayes-optimal condition. Specifically, our analysis is based on a general channel model, which reduces to particular channel models in multiple typical MIMO communication scenarios. Our theoretical framework allows ones to measure the optimality and phase transition of AMP-based joint AUD and CE as well as to predict the corresponding performance metrics under our model. To reify our proposed theoretical framework, we analyze two typical scenarios from the massive random access literature, i.e., the isotropic channel scenario and the spatially correlated channel scenario. Accordingly, our performance analysis produces some novel results for both the isotropic Raleigh channel and spatially correlated channel case.

A novel Multiuser Detection (MUD) scheme is proposed for DS-CDMA systems employing the so-called Harmony Search (HS) algorithm, which is a novel meta-heuristic optimisation method. We specifically design the HS aided MUD for the communications problem considered and apply it in an iterative joint Channel Estimation (CE), MUD and channel decoding framework. The simulation results demonstrate that a near-single-user performance can be achieved by the proposed algorithm while avoiding the excessive-complexity full-search-based optimum detection even in overloaded DS-CDMA systems. Moreover, the HS algorithm can be efficiently applied in the Expectation Maximisation (EM) based CE framework.