Efficiently using extrinsic gain for candidate vectors selection in QR-LRL based IDD MIMO receiver

Narrowband Internet of Things (NB-IoT) was proposed by 3GPP in Release 13 to provide low power wide area connections to a high-volume of devices. With the millions of devices expected to connect to 5G through its massive machine-type communication use case, the strict orthogonality and synchronization requirement of cyclic-prefix orthogonal division multiplexing (CP-OFDM) will demand a very large amount of control information between devices and the core network. To overcome this challenge, we study a new air interface for NB-IoT, which utilizes a non-orthogonal multicarrier transmission scheme. The waveform considered in this work is filter-bank muilticarrier with quadrature amplitude modulation (FBMC-QAM). The loss of orthogonality in FBMC-QAM results in severe levels of intrinsic interference. Iterative detection and decoding (IDD) using low density parity check (LDPC) encoding and decoding is implemented together with iterative interference cancellation (IIC) to remove the inherent interference and improve the BER rate performance. Simulation results indicate that the proposed IDD receiver can effectively improve BER performance under time-varying channels.

An iterative detection and decoding (IDD) receiver which combines a soft-input soft-output (SISO) detector and a SISO double binary (DB) turbo decoder improves the bit-error rate performance. However, the amount of the extrinsic information sent from the DB turbo decoder to the detector is much different from the amount of the extrinsic information sent from the detector to the decoder. Herein, a parity-enhanced DB turbo decoder is designed to provide the bit-level extrinsic information of systematic and parity bits for the WiMAX-based turbo-coded IDD receiver. Compared to the conventional turbo decoder, the proposed decoder achieves ~0.3 dB coding gain with a 6.06% area overhead.

We consider the uplink of massive MIMO systems with one-bit quantization and propose an iterative detection and decoding (IDD) receiver based on a factor graph representation and the sum-product algorithm. We derive the relevant sum-product messages, introduce Gaussian approximations to control computational complexity, and describe the message schedule. Numerical experiments demonstrate that the BLER performance of our receiver is comparable to the state of the art, even though our method has much lower complexity – quadratic rather than exponential in the number of users – and hence scales well to large system configurations. We show that a proper scheduling of the decoding iterations substantially improves the error rate performance without increasing the computational complexity. In particular, we find that it is not beneficial to schedule the majority of decoding iterations in the initial detection iterations.

This paper proposes an iterative detection and decoding (IDD) scheme with a blind channel estimator in the physical layer of long range widearea network (LoRaWAN). The proposed scheme exchanges log-likelihood ratios (LLRs) as soft decision values between the LoRa demodulator and the Hamming decoder to improve the signal detection capability while obtaining the iterative gain. In the typical LoRa demodulation using hard decision without IDD, the received signal is detected by the binary values, i.e. "0" or "1"; and thus, the demodulation and decoding processes are simplified. However, the resulting rounding error occurs the mutual information loss, leading to non-negligible performance degradation. In order to cope with this issue, it is vital to capture the stochastic behavior of the input and output values of the demodulator and decoder. The LLR based on the stochastic signal model enables to improve the error correction capability of the decoder and enhance the iterative gain. Finally, computer simulation results explicitly demonstrate the efficacy of the proposed method in terms of bit error rate (BER) performance.

An Iterative Detection and Decoding (IDD) scheme in a high-order Multiple-Input Multiple-Output (MIMO) wireless communication system requires a heavy computational complexity for an outstanding Bit Error Rate (BER) performance in a given Signal-to-Noise Ratio (SNR) environment. A new computational complexity reducing IDD scheme designed with optimum parameters is proposed in this paper to guarantee Quality of Service (QoS) without the loss of BER performance. There are three parameters to control the trade-off between computational complexity and BER performance of an IDD scheme: a candidate list size of Dynamic List Sphere Detector (D-LSD), a number of intra-iteration of Turbo decoder, and a number of IDD iteration. A complexity reduction of about 70 % is demonstrated in this study, when the target BER is around 1.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(-4)</sup> at E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> =7dB in the case of a 4 x 4 MIMO system with 16-ary Quadrature Amplitude Modulation (QAM).

In this paper, we present a low-complexity list sphere search algorithm for achieving near-optimal a posteriori probability (APP) detection in iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of lattice points is inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and derive the optimal sphere radius satisfying this requirement. Further, in order to exploit the sphere constraint as it is instead of using necessary conditioned versions, we incorporate a probabilistic tree pruning strategy into the list sphere search. Through simulations on realistic IDD systems, we show that the proposed method provides considerable complexity savings while maintaining near-optimal performance.

Channel coding and multiple-input multiple-output (MIMO) antennas are two key technologies in the modern communication system. For MIMO detection, the expectation propagation (EP) detector has been proved to outperform classical and advanced solutions. Merging with the parity constraints of channel coding, a novel EP detection with linear programming (LP), named EPLP, is proposed to change the common initialization that the symbols are equiprobable by formulating an LP optimization problem. Furthermore, we apply the EPLP detector to a polar-coded MIMO system with an iterative detection and decoding (IDD) scheme. For a (128, 64) polar-coded 16 × 16 QPSK configuration, the EPLP outperforms the state-of-the-art detectors by 1 dB at the bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and the IDD scheme with the EPLP achieves 0.5 dB gain at the frame error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> with a reduced number of EP iterations.

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, the log-likelihood ratio (LLR) of each coded bit can be found by an optimal bit-wise maximum a posteriori probability (MAP) detector. However, since this MAP detector requires a prohibitively high computational complexity, low-complexity suboptimal detectors are desirable. In this paper, lattice reduction (LR)-based MIMO detection is investigated to derive a low-complexity detector that can achieve near MAP performance for IDD. In order to approximate LLR values incorporating the extrinsic information provided by a soft-input soft-output (SISO) decoder, bit-wise LR-based minimum mean square error (MMSE) filters are derived. Furthermore, in order to minimize the performance degradation due to quantization (or rounding) errors in the LR-based detection, a low-complexity integer perturbed list generation method is proposed, where no tree search is used by taking advantage of a near orthogonal channel basis obtained by LR. Through a complexity analysis and simulations, it is shown that the proposed approach achieves near optimal performance, while the complexity is comparable with that of the MMSE soft cancellation method, which is known to be computationally efficient. As a bit-wise detector, a parallel implementation of the proposed method would be straightforward, which lowers the detection delay.

This paper proposes a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture with joint list-based detection with soft interference cancellation (soft-IC) and access points (APs) selection. In particular, we derive a new closed-form expression for the minimum mean-square error receive filter while taking the uplink transmit powers and APs selection into account. This is achieved by optimizing the receive combining vector by minimizing the mean square error between the detected symbol estimate and transmitted symbol, after canceling the multi-user interference (MUI). By using low-density parity check (LDPC) codes, an iterative detection and decoding (IDD) scheme based on a message passing is devised. In order to perform joint detection at the central processing unit (CPU), the access points locally estimate the channel and send their received sample data to the CPU via the front haul links. In order to enhance the system's bit error rate performance, the detected symbols are iteratively exchanged between the joint detector and the LDPC decoder in log likelihood ratio form. Furthermore, we draw insights into the derived detector as the number of IDD iterations increase. Finally, the proposed list detector is compared with existing detection techniques.

An iterative detection and decoding (IDD) technique has recently received much attention due to its capacity approaching performance and manageable complexity for (parallel) turbo-coded systems. The existing IDD receivers process indiscriminately the a priori information of the systematic bits and the parity bits generated by the turbo decoder in the front-end detection. In this paper, we first show that the parity bits of the parallel turbo codes are inherently less protected than the systematic bits, which leads to suboptimality for the IDD receivers. Based on this observation, we then propose an unequal-error-correcting-capability-aware technique to mitigate the associated performance loss. As an illustrative example, the minimum-mean-square-error (MMSE) filtering and soft interference cancelation (MMSE-SIC)-aided iterative receiver for parallel-turbo-coded multiple-input-multiple-output (MIMO) systems is employed in this paper. Numerical results manifest that the MMSE-SIC-aided iterative receiver based on the proposed paradigm achieves noticeable performance gains over the conventional unequal-error-correcting-capability-agnostic one even with reduced computational complexity. The extension of the proposed principle to other iterative algorithms for parallel-turbo-coded data transmission is straightforward.

The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios.

A soft iterative detection and decoding (IDD) scheme based on the sphere decoding technique for coded multiple-input multiple-output spatial multiplexing (MIMO-SM) systems is considered in this paper. We propose a new approach to approximate the maximum a-posteriori (MAP) cost function by a quadratic function such that the MAP search can be carried out by list sphere decoding (LSD). Performance of the coded MIMO-SM system with this approach is evaluated by simulation. It is shown that the proposed approach can achieve performance comparable to an existing approach.

Instead of using sphere decoding, list sphere decoding (LSD) has been introduced to increase the reliability of log-likelihood ratio (LLR) in recent soft decoding schemes employing iterative detection and decoding (IDD). Although LSD provides improved performance, it does not obtain complexity gain due to signal-to-noise ratio (SNR) increment as it detects large number of lattice points. Especially, its inefficient scenario arises when it has to search for lattice points which have small affect for obtaining LLR with high reliability. In this paper, we study an efficient algorithm to remove such lattice points, which results in complexity reduction based on radius optimization.

Researches on the application of iterative differential demodulation and decoding in SCCC-coded systems are presented. Knowing that the tradition algorithm of iterative decoding in SCCC-coded systems has a high complexity with a two-level iteration structure, a low complexity approach is proposed. In each iteration of the channel decoding, the demodulator uses the extrinsic information of the outer decoder of the channel decoder as a priori information to make a soft decision to update the codewords. As a result, the ‘turbo processing’ of both the entirety iteration and the inner iteration of channel decoder is simplified to a single-level iterative system. It is shown that iterative demodulation and decoding can effectively improve the performance of the systems. The optimized single-level method almost keeps the similar performance and reduces the complexity significantly.

The estimation of channel parameters, such as the fading amplitude and the power spectral density (PSD) of the interference and noise, is essential to the effective use of soft-decision decoding. Channel estimation may be implemented by the transmission of pilot signals that are processed by the receiver, but pilot signals entail overhead costs, such as the loss of data throughput. Deriving maximum-likelihood channel estimates directly from the received data symbols is often prohibitively difficult. There is an effective alternative when turbo or low-density parity-check codes are used. The expectation-maximization algorithm, which is derived and explained in this chapter, provides an iterative approximate solution to the maximum-likelihood equations and is inherently compatible with iterative demodulation and decoding. Two examples of advanced spread-spectrum systems that apply iterative channel estimation, demodulation, and decoding are described and analyzed. These systems provide good illustrations of the calculations required in the design of advanced systems.