Iterative reduced-state multiuser detection for asynchronous coded CDMA

The conventional maximum a posteriori receiver for coded code-division multiple-access (CDMA) systems has exponential computational complexity in terms of the number of users and the memory of the channel code. In this letter, we propose a low-complexity soft-input soft-output (SISO) multiuser detector based on the reduced-state a posteriori probability algorithm. Per-survivor processing and soft interference cancellation are used to remove the residual past and future interference in the branch metric computation. The complexity of the proposed receiver is related to the reduced memory of the CDMA channel and can be adjusted according to the complexity/performance tradeoff. Simulation results show that for asynchronous convolutionally coded systems, the proposed receiver can achieve the near-single-user performance for moderate to high signal-to-noise ratios.     

双向中继信道中物理层网络编码的检测

对双向中继信道中物理层网络编码的检测进行了研究,最大似然检测性能好但是实现复杂度高。因此,在信源节点未知信道状态信息情况下,提出了2种检测方案:基于似然比函数的似然比检测和基于最大后验概率准则的最大后验概率检测。同时,针对信源节点已知信道状态信息的特殊情形,进行了同样的推导。分析和仿真结果表明,相比于分别检测出2个信源信息的最大似然检测,似然比检测的BER性能更优,但似然比检测需要知道额外的噪声方差信息,最大后验概率检测与最大似然检测等价,而且最大后验概率检测在实现复杂度上相对较低。     

VLSI architectures for the MAP algorithm

This paper presents several techniques for the very large-scale integration (VLSI) implementation of the maximum a posteriori (MAP) algorithm. In general, knowledge about the implementation of the Viterbi (1967) algorithm can be applied to the MAP algorithm. Bounds are derived for the dynamic range of the state metrics which enable the designer to optimize the word length. The computational kernel of the algorithm is the add-MAX* operation, which is the add-compare-select operation of the Viterbi algorithm with an added offset. We show that the critical path of the algorithm can be reduced if the add-MAX* operation is reordered into an offset-add-compare-select operation by adjusting the location of registers. A general scheduling for the MAP algorithm is presented which gives the tradeoffs between computational complexity, latency, and memory size. Some of these architectures eliminate the need for RAM blocks with unusual form factors or can replace the RAM with registers. These architectures are suited to VLSI implementation of turbo decoders.     

Area-efficient high-throughput MAP decoder architectures

Iterative decoders such as turbo decoders have become integral components of modern broadband communication systems because of their ability to provide substantial coding gains. A key computational kernel in iterative decoders is the maximum a posteriori probability (MAP) decoder. The MAP decoder is recursive and complex, which makes high-speed implementations extremely difficult to realize. In this paper, we present block-interleaved pipelining (BIP) as a new high-throughput technique for MAP decoders. An area-efficient symbol-based BIP MAP decoder architecture is proposed by combining BIP with the well-known look-ahead computation. These architectures are compared with conventional parallel architectures in terms of speed-up, memory and logic complexity, and area. Compared to the parallel architecture, the BIP architecture provides the same speed-up with a reduction in logic complexity by a factor of M, where M is the level of parallelism. The symbol-based architecture provides a speed-up in the range from 1 to 2 with a logic complexity that grows exponentially with M and a state metric storage requirement that is reduced by a factor of M as compared to a parallel architecture. The symbol-based BIP architecture provides speed-up in the range M to 2M with an exponentially higher logic complexity and a reduced memory complexity compared to a parallel architecture. These high-throughput architectures are synthesized in a 2.5-V 0.25-/spl mu/m CMOS standard cell library and post-layout simulations are conducted. For turbo decoder applications, we find that the BIP architecture provides a throughput gain of 1.96 at the cost of 63% area overhead. For turbo equalizer applications, the symbol-based BIP architecture enables us to achieve a throughput gain of 1.79 with an area savings of 25%.     

Alternative structure for computing APPs of the Markov source

We introduce an alternative structure for computing the a posteriori probabilities (APPs) for state and transition sequences of a Markov source observed through a noisy output sequence. Compared to the well-established forward-backward recursion algorithm of Bahl et al. (1974), the proposed structure allows a reduction in computational complexity at the expense of increased memory requirements. Alternatively, for a similar complexity level, the proposed structure needs smaller memory when the input alphabet size is small.     

Modified maximum a posteriori decoding algorithm

A modified maximum a posteriori (MMAP) decoding algorithm that uses two extrinsic information values as the thresholds to determine the log-likelihood ratio, forward recursion probability and backward recursion probability is proposed. The MMAP requires less decoding time and complexity than an MAP decoder. Each probability of the proposed algorithm is derived and compared to that obtained using the MAP algorithm     

Turbo equalization with sequential sequence estimation overmultipath fading channels

We investigate the performance of a turbo equalization scheme over frequency-selective fading channels, where a soft-output sequential algorithm is employed as the estimation algorithm. The advantage of this scheme comes from the low computational complexity of the sequential algorithm, which is only linearly dependent on the channel memory length. Simulation results of an 8-PSK trellis-coded modulation (TCM) system show that the performance of this scheme suffers approximately 2-dB loss compared with that of the turbo max-log maximum a posteriori (MAP) probability equalizer after 5 iterations     

Low-complexity iterative decoding with decision-aided equalizationfor magnetic recording channels

Turbo codes are applied to magnetic recoding channels by treating the channel as a rate-one convolutional code that requires a soft a posteriori probability (APP) detector for channel inputs. The complexity of conventional APP detectors, such as the BCJR algorithm or the soft-output Viterbi algorithm (SOVA), grows exponentially with the channel memory length. This paper derives a new APP module for binary intersymbol interference (ISI) channels based on minimum mean squared error (MMSE) decision-aided equalization (DAE), whose complexity grows linearly with the channel memory length, and it shows that the MMSE DAE is also optimal by the maximum a posteriori probability (MAP) criterion. The performance of the DAE is analyzed, and an implementable turbo-DAE structure is proposed. The reduction of channel APP detection complexity reaches 95% for a five-tap ISI channel when the DAE is applied. Simulations performed on partial response channels show close to optimum performance for this turbo-DAE structure. Error propagation of the DAE is also studied, and two fixed-delay solutions are proposed based on combining the DAE with the BCJR algorithm     

Nondata-aided channel estimation for OFDM systems with space-frequency transmit diversity

This paper proposes a computationally efficient nondata-aided maximum a posteriori (MAP) channel-estimation algorithm focusing on the space-frequency (SF) transmit diversity orthogonal frequency division multiplexing (OFDM) transmission through frequency-selective channels. The proposed algorithm properly averages out the data sequence and requires a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates the complex channel parameters of each subcarrier iteratively, using the expectation maximization (EM) method. To further reduce the computational complexity of the proposed MAP algorithm, the optimal truncation property of the KL expansion is exploited. The performance of the MAP channel estimator is studied based on the evaluation of the modified Cramer-Rao bound (CRB). Simulation results confirm the proposed theoretical analysis and illustrate that the proposed algorithm is capable of tracking fast fading and improving overall performance.     

On the partial MAP detection with applications to MIMO channels

We investigate a multidimensional detection problem with a partial information of the a posteriori probability, which is referred to as the partial maximum a posteriori probability (MAP) detection problem. We show that the maximum likelihood (ML) detection of a higher dimension can be reduced to the ML detection of a lower dimension with cancellation under a certain condition through the formulation of the partial MAP detection problem. Using this, we can propose a computationally efficient algorithm to apply to the detection problem for multiple input multiple output (MIMO) channels including multiple transmit and multiple receive antenna (MTMR) channels and intersymbol interference (ISI) channels. It is shown that the proposed method has less error propagation effect, and its performance is close to that of the full ML detection with a lower computational complexity.     

An improvement to the interacting multiple model (IMM) algorithm

Computing the optimal conditional mean state estimate for a jump Markov linear system requires exponential complexity, and hence, practical filtering algorithms are necessarily suboptimal. In the target tracking literature, suboptimal multiple-model filtering algorithms, such as the interacting multiple model (IMM) method and generalized pseudo-Bayesian (GPB) schemes, are widely used for state estimation of such systems. We derive a reweighted interacting multiple model algorithm. Although the IMM algorithm is an approximation of the conditional mean state estimator, our algorithm is a recursive implementation of a maximum a posteriori (MAP) state sequence estimator. This MAP estimator is an instance of a previous version of the EM algorithm known as the alternating expectation conditional maximization (AECM) algorithm. Computer simulations indicate that the proposed reweighted IMM algorithm is a competitive alternative to the popular IMM algorithm and GPB methods     

一种低复杂度双二元卷积Turbo码译码算法

双二元卷积Turbo码（ DB CTC)的非二进制编码使得译码复杂度增加,限制了其在某些实际通信工程中的使用。在最大后验概率（ MAP)译码算法的基础上,提出了一种优化算法,将译码的存储量和计算量降为原来的1/4。仿真结果表明：在不同编码长度和码率的情况下,优化算法与原算法性能相当;在误码率为10-5的条件下,两者的Eb/N0差异同样不大于0.1 dB。     

Low-power log-MAP decoding based on reduced metric memory access

Due to the powerful error correcting performance, turbo codes have been adopted in many wireless communication standards such as W-CDMA and CDMA2000. Although several low-power techniques have been proposed, power consumption is still a major issue to be solved in practical implementations. Since turbo decoding is classified as a memory-intensive algorithm, reducing memory accesses is crucial to achieve a low power design. To reduce the number of memory accesses for maximum a posteriori (MAP) decoding, this paper proposes an approximate reverse calculation method that can be implemented with simple arithmetic operations such as addition and comparison. Simulation results show that the proposed method applied to the W-CDMA standard reduces the access rate of the backward metric memory by 87% without degrading error-correcting performance. A prototype log-MAP decoder based on the proposed reverse calculation achieves 29% power reduction compared to a conventional decoder that does not use the reverse calculation.     

Complexity-reduced iterative MAP receiver for interference suppression in OFDM-based spatial multiplexing systems

In this paper, we propose a reduced-complexity iterative algorithm for joint maximum a posteriori (MAP) detection and the cochannel interferences (CCIs) suppression in orthogonal frequency-division multiplex (OFDM)-based spatial multiplexing systems, also known as multiple-input-multiple-output systems. The receiver employs an iterative architecture in which each iteration stage consists of channel/covariance estimation for CCIs and MAP detection, which suppresses interference while detecting the data. The interference suppression is performed in the MAP detection by whitening the interferences through a combination of the estimated covariance with the conventional MAP detection metric. Moreover, a complexity-reduction scheme is proposed for implementing an iterative MAP receiver without incurring performance degradation. Extensive simulations have demonstrated that the proposed scheme dramatically improves the performance compared to that of an iterative MAP scheme without interference suppression. The receiver complexity is significantly reduced with negligible performance degradation. Furthermore, it also can effectively suppress not only synchronous CCIs, but also all kinds of asynchronous CCIs without any a priori information on the CCIs.     

Reduced-complexity MAP-based iterative multiuser detection for coded multicarrier CDMA systems

In recent years, combining multiuser detection (MUD) and channel decoding has received considerable attention. The maximum a posteriori (MAP) criterion-based iterative multiuser detector greatly improves the system performance and can approach the performance of single-user coded systems. However, its complexity increases exponentially with the number of users and can become prohibitive for systems with a medium-to-large number of users. In this paper, a reduced complexity MAP-based iterative MUD based on the use of a soft sensitive bits algorithm is proposed for coded multicarrier code-division multiple-access systems. It is shown that it can greatly reduce the computational complexity with a minimal penalty in performance compared to the conventional optimal scheme.     

Expectation maximization algorithms for MAP estimation of jumpMarkov linear systems

In a jump Markov linear system, the state matrix, observation matrix, and the noise covariance matrices evolve according to the realization of a finite state Markov chain. Given a realization of the observation process, the aim is to estimate the state of the Markov chain assuming known model parameters. Computing conditional mean estimates is infeasible as it involves a cost that grows exponentially with the number of observations. We present three expectation maximization (EM) algorithms for state estimation to compute maximum a posteriori (MAP) state sequence estimates [which are also known as Bayesian maximum likelihood state sequence estimates (MLSEs)]. The first EM algorithm yields the MAP estimate for the entire sequence of the finite state Markov chain. The second EM algorithm yields the MAP estimate of the (continuous) state of the jump linear system. The third EM algorithm computes the joint MAP estimate of the finite and continuous states. The three EM algorithms optimally combine a hidden Markov model (HMM) estimator and a Kalman smoother (KS) in three different ways to compute the desired MAP state sequence estimates. Unlike the conditional mean state estimates, which require computational cost exponential in the data length, the proposed iterative schemes are linear in the data length     

BAD: bidirectional arbitrated decision-feedback equalization

The bidirectional arbitrated decision-feedback equalizer (BAD), which has bit-error rate performance between a decision-feedback equalizer (DFE) and maximum a posteriori (MAP) detection, is presented. The computational complexity of the BAD algorithm is linear in the channel length, which is the same as that of the DFE, and significantly lower than the exponential complexity of the MAP detector. While the relative performance of BAD to those of the DFE and the MAP detector depends on the specific channel model, for an error probability of 10/sup -2/, the performance of BAD is typically 1-2 dB better than that of the DFE, and within 1 dB of the performance of MAP detection.     

AWGN信道下Turbo码解码算法的选择

在加性高斯白噪声（AWGN）信道下,采用最大后验概率(MAP)算法的Turbo码解码是误比特率最低的算法。为了降低运算量实现快速解码,Log-MAP算法、Max-Log-Map算法和线性Max-Log-Map算法分别对MAP算法进行了不同程度的简化。本文简单介绍了基于MAP算法的Turbo码解码原理,从纠正函数的角度出发归纳和比较了三种MAP类简化算法,通过纠正函数从理论上对算法性能以及对信噪比估计误差的敏感度进行了分析,对分析结果进行了仿真验证。综合解码性能和运算量,提出了Turbo码解码的算法选择方案,以及实用,简易的Turbo码解码参数设置建议。      

Low-complexity iterative channel estimation for serially concatenated systems over frequency-nonselective Rayleigh fading channels

A low-complexity iterative maximum a posteriori (MAP) channel estimator is proposed whose complexity increases linearly with the symbol alphabet size 'M. Prediction-based MAP channel estimation is not appropriate with a high-order prediction filter or a large modulation alphabet size, since the computational complexity increases with M^L , where L is the predictor order. In contrast, the proposed channel estimator has a constant number of trellis states regardless of the prediction filter order, and is shown to provide comparable error performance to the prediction-based MAP estimator     

A Memory‐Efficient Block‐wise MAP Decoder Architecture

Next generation mobile communication system, such as IMT‐2000, adopts Turbo codes due to their powerful error correction capability. This paper presents a block‐wise maximum a posteriori (MAP) Turbo decoding structure with a low memory requirement. During this research, it has been observed that the training size and block size determine the amount of required memory and bit‐error rate (BER) performance of the block‐wise MAP decoder, and that comparable BER performance can be obtained with much shorter blocks when the training size is sufficient. Based on this observation, a new decoding structure is proposed and presented in this paper. The proposed block‐wise decoder employs a decoding scheme for reducing the memory requirement by setting the training size to be N times the block size. The memory requirement for storing the branch and state metrics can be reduced 30% to 45%, and synthesis results show that the overall memory area can be reduced by 5.27% to 7.29%, when compared to previous MAP decoders. The decoder throughput can be maintained in the proposed scheme without degrading the BER performance.