自适应多码率语音编码流的可靠传输

自适应多码率语音编码已入选为第三代移动通信系统的语音压缩编码方案。本文提出了一种适合压缩语音传输的联合信源信道编码方法并对其性能进行了统计比较。利用压缩语音比特流中的固用冗余的信道译码算法是本文的研究重点。仿真结果表明利用信源冗余信息的信道译码器可以获得较大的编码增益。本文所用的信道编码方案为适合语音传输的卷积码。     

Robust Transmission of Multistage Vector Quantized Sources Over Noisy Communication Channels—Applications to MELP Speech Codec

Joint source-channel coding is an effective approach for the design of bandwidth efficient and error resilient communication systems with manageable complexity. An interesting research direction within this framework is the design of source decoders that exploit the residual redundancy for effective signal reconstruction at the receiver. Such source decoders are expected to replace the traditionally heuristic error concealment units that are elements of most multimedia communication systems. In this paper, we consider the reconstruction of signals encoded with a multistage vector quantizer (MSVQ) and transmitted over a noisy communications channel. The MSVQ maintains a moderate complexity and, due to its successive refinement feature, is a suitable choice for the design of layered (progressive) source codes. An approximate minimum mean squared error source decoder for MSVQ is presented, and its application to the reconstruction of the linear predictive coefficient (LPC) parameters in mixed excitation linear prediction (MELP) speech codec is analyzed. MELP is a low-rate standard speech codec suitable for bandwidth-limited communications and wireless applications. Numerical results demonstrate the effectiveness of the proposed schemes     

Joint MELP turbo code with unequal error protection

The mixed excitation linear prediction (MELP) algorithm has been recently selected as the new federal standard for 2.4 kbit/s coding of speech signals. The authors exploit the average residual inter-frame correlation and the error sensitivities of the bits in a MELP frame to enhance the robustness of the proposed joint MELP turbo coding schemes for operations over Rayleigh fading channels     

Sequence MAP decoding of trellis codes for Gaussian and Rayleighchannels

This paper considers the use of sequence maximum a posteriori (MAP) decoding of trellis codes. A MAP receiver can exploit any “residual redundancy” that may exist in the channel encoded signal in the form of memory and/or a nonuniform distribution, thereby providing enhanced performance over very noisy channels, relative to maximum likelihood (ML) decoding. The paper begins with a first-order two-state Markov model for the channel encoder input. A variety of different systems with different source parameters, different modulation schemes, and different encoder complexities are simulated. Sequence MAP decoding is shown to substantially improve performance under very noisy channel conditions for systems with low-to-moderate redundancy, with relative gain increasing as the rate increases. As a result, coding schemes with multidimensional constellations are shown to have higher MAP gains than comparable schemes with two-dimensional (2-D) constellations. The second part of the paper considers trellis encoding of the code-excited linear predictive (CELP) speech coder's line spectral parameters (LSPs) with four-dimensional (4-D) QPSK modulation. Two source LSP models are used. One assumes only intraframe correlation of LSPs while the second one models both intraframe and interframe correlation. MAP decoding gains (over ML decoding) as much as 4 dB are achieved. Also, a comparison between the conventionally designed codes and an I-Q QPSK scheme shows that the I-Q scheme achieves better performance even though the first (sampler) LSP model is used     

Boosting the error performance of suboptimal tailbiting decoders

Tailbiting is an attractive method to terminate convolutional codes without reducing the code rate. Maximum-likelihood and exact a posteriori probability decoding of tailbiting codes implies, however, a large computational complexity. Therefore, suboptimal decoding methods are often used in practical coding schemes. It is shown that suboptimal decoding methods work better when the slope of the active distances of the generating convolutional encoder is large. Moreover, it is shown that considering quasi-cyclic shifts of the received channel output can increase the performance of suboptimal tailbiting decoders. The findings are most relevant to tailbiting codes where the number of states is not small relative to the block length.     

The performance of incremental redundancy schemes based on convolutional codes in the block-fading Gaussian collision channel

The throughput performance of incremental redundancy (INR) schemes, based on short constraint length convolutional codes, is evaluated for the block-fading Gaussian collision channel. Results based on simulations and union bound computations are compared to estimates of the achievable throughput performance with random binary and Gaussian coding in the limit of large block lengths, obtained through information outage considerations. For low channel loads, it is observed that INR schemes with binary convolutional codes and limited block length may provide throughput close to the achievable performance for binary random coding. However, for these low loads, compared to binary random coding, Gaussian random coding may provide significantly better throughput performance, which prompts the use of larger modulation constellations. For high channel loads, a relatively large gap in throughput performance between binary convolutional codes and binary random codes indicates a potential for extensive performance improvement by alternative coding strategies. Only small improvements of the throughput have been observed by increasing the complexity through increased state convolutional coding.     

Universal Source Controlled Channel Decoding with Nonsystematic Quick-Look-In Turbo Codes

Utilization of redundancy left in a channel coded sequence can improve channel decoding performance. Stronger improvement can usually be achieved with nonsystematic encoding. However, nonsystematic codes recently proposed for this problem are not robust to the statistical parameters governing a sequence and thus should not be used without prior knowledge of these parameters. In this work, decoders of nonsystematic quick-look-in turbo codes are adapted to extract and exploit redundancy left in coded data to improve channel decoding performance. Methods, based on universal compression and denoising, for extracting the governing statistical parameters for various source models are integrated into the channel decoder by also taking advantage of the code structure. Simulation results demonstrate significant performance gains over standard systematic codes that can be achieved with the new methods for a wide range of statistical models and governing parameters. In many cases, performance almost as good as that with perfect knowledge of the governing parameters is achievable.     

Optimal use of Markov models for DPCM picture transmission over noisy channels

Joint source/channel decoders that use the residual redundancy in the source are investigated for differential pulse code modulation (DPCM) picture transmission over a binary symmetric channel. Markov sequence decoders employing the Viterbi algorithm that use first-order source statistics are reviewed, and generalized for decoders that use second-order source statistics. To make optimal use of the source correlation in both horizontal and vertical directions, it is necessary to generalize the conventional Viterbi decoding algorithm for a one higher-dimensional trellis. The paths through the trellis become two-dimensional "sheets", thus, the technique is coined "sheet decoding". By objective [reconstruction signal-to-noise ratio (SNR)] and subjective measure, it is found that the sheet decoders outperform the Markov sequence decoders that use a first-order Markov model, and outperform two other known decoders (modified maximum a posteriori probability and maximal SNR) that use a second-order Markov model. Moreover, it is found that the use of a simple rate-2/3 block code in conjunction with Markov model-aided decoding (MMAD) offers significant performance improvement for a 2-bit DPCM system. For the example Lenna image, it is observed that the rate-2/3 block code is superior to a rate-2/3 convolutional code for channel-error rates higher than 0.035. The block code is easily incorporated into any of the MMAD DPCM systems and results in a 2-bit MMAD DPCM system that significantly outperforms the noncoded 3-bit MMAD DPCM systems for channel-error rates higher than 0.04.     

Quantization of memoryless and Gauss-Markov sources over binaryMarkov channels

Joint source-channel coding for stationary memoryless and Gauss-Markov sources and binary Markov channels is considered. The channel is an additive-noise channel where the noise process is an Mth-order Markov chain. Two joint source-channel coding schemes are considered. The first is a channel-optimized vector quantizer-optimized for both source and channel. The second scheme consists of a scalar quantizer and a maximum a posteriori detector. In this scheme, it is assumed that the scalar quantizer output has residual redundancy that can be exploited by the maximum a posteriori detector to combat the correlated channel noise. These two schemes are then compared against two schemes which use channel interleaving. Numerical results show that the proposed schemes outperform the interleaving schemes. For very noisy channels with high noise correlation, gains of 4-5 dB in signal-to-noise ratio are possible     

A novel product coding and recurrent alternate decoding scheme for image transmission over noisy channels

In this letter, we present a novel product channel coding and decoding scheme for image transmission over noisy channels. Two convolutional codes with at least one recursive systematic convolutional code are employed to construct the product code. Received data are decoded alternately in two directions. A constrained Viterbi algorithm is proposed to exploit the detection results of cyclic redundancy check codes so that both reduction in error patterns and fast decoding speed are achieved. Experiments with image data coded by the algorithm of set partitioning in hierarchical trees exhibit results better than those currently reported in the literature.     

Hybrid symbol- and bit-based source-controlled channel decoding for MELP speech parameters

A joint source channel coding (JSCC) scheme which exploits bit-level correlation as well as symbol-level correlation efficiently in a source-controlled channel decoding (SCCD) process is proposed and applied to the mixed-excitation linear prediction (MELP) parameters of speech. A modified BCJR algorithm is also proposed for use in the SCCD algorithm. Simulation results show that our proposed scheme performs better than other redundancy-based JSCC schemes such as bit-based SCCD, soft-bit speech decoding (SBSD) and iterative source-channel decoding.     

A joint source/channel coder with block constraints

The assumptions made about the source during source coder design result in a residual redundancy at the output of the source coder. This redundancy can be utilized for error protection without any additional channel coding. Joint source/channel coders obtained using this idea via maximum a posteriori probability decoders tend to fail at low probability of error. In this paper, we propose a modification of the standard approach which provides protection at low error rates as well     

Capacity, Cutoff Rate, and Coding for a Direct-Detection Optical Channel

It is shown that When Pierce's pulse-position modulation scheme with 2^Lpositions is used on a self-noise-limited directdetection optical communication channel, there results a 2^L-ary erasure channel that is equivalent to the parallel combination ofL"completely correlated" binary erasure channels. The capacity of the full channel is the sum of the capacities of the component channels, but the cutoff rate of the full channel is shown to be much smaller than the sum of the cutoff rates. An interpretation of the cutoff rate is given that suggests a complexity advantage in coding separately on the component channels. It is shown that if short-constraint length convolutional codes with Viterbi decoders are used on the component channels, then the performance and complexity compare favorably with the Reed-Solomon coding system proposed by McEliece for the full channel. The reasons for this unexpectedly fine performance by the convolutional code system are explored in detail, as are various facets of the channel structure.     

New VLSI architectures for fast soft-decision threshold decoders

New VLSI architectures for fast convolutional threshold decoders that process soft-quantized channel symbols are presented. The new architectures feature pipelining and parallelism and make it possible to fabricate decoders for data rates up to hundreds of Mbits per second. With these architectures, the data rate is shown to be independent of the memory of the code, implying that fast AAPP (approximate a posteriori probability) decoders can be built for long powerful codes. Furthermore, the architectures are convenient to use with low and high coding rates. Using a typical example it is shown that a soft-decision threshold decoder can provide a substantial coding gain while being less costly to implement than the hard-decision threshold decoder     

Parallel Viterbi decoding methods for uncontrollable andcontrollable sources

The parallel Viterbi decoding is discussed for two different cases: uncontrollable sources and controllable sources. For general, uncontrollable Markov processes, a previously known parallel method is extended to a hierarchical parallel decoding approach, which achieves a lower latency. For controllable Markov sources in telecommunications applications, new parallel decoding methods are obtained by controlling the source processes in appropriate ways. The focus is on the parallel decoding methods for controllable sources because these methods have zero processing overhead. Because the methods modify the coding process, they bring positive changes to framing and negative changes to latency and code performance. However, one can adjust the parameters of the methods to make the degradation negligible. Because of their low overhead, the methods are most attractive for high-speed decoders for convolutional and trellis codes, and they are also applicable to other sequential algorithms for suboptimal decoding and estimation of complex Markov sources     

Impact of Implicit Feedback Channel in Cooperative Relay Networks

In a multi-node network, cooperation among nodes is an effective means to enhance coverage and potentially increase the capacity. For such systems, schemes based on incremental relaying have great potential to improve the spectral efficiency by adapting the transmission to time varying channel conditions. The performance enhancement brought about by the presence of relays in such incremental relaying based cooperative systems is dependent on the level of cooperation (based on the relay information quality) and on coordination among the nodes. Coordination is achieved through the use of feedback channels, which incurs significant bandwidth penalty and brings down the spectral efficiency. In order to mitigate this, one can exploit an implicit feedback channel available due to broadcast nature of relay transmissions. Instead of using dedicated feedback channels, the implicit feedback channel is used to measure the relay information quality. Based on this information, the transmitter (source/relay) for the additional coded (redundancy) bits is determined. Such a mechanism enhances the reliability as it ensures the availability of correct information at the destination node for decoding. This paper studies the impact of such an implicit feedback channel by employing powerful codes which exhibit inherent incremental redundancy features, such as rate-compatible codes (rate-compatible punctured convolutional (RCPC) codes and punctured low-density parity-check (LDPC) codes) and rateless codes (Luby Transform (LT) codes). Theoretical analyses of the proposed scheme are presented, and supported with results from extensive simulation studies.     

Autocorrelation properties of channel encoded sequences-applicability to blind equalization

Many blind channel equalization/identification algorithms are derived assuming the transmitted information sequence to be white. In practical communication systems, redundancy is added to the source sequence in order to detect and correct symbol errors in the receiver. It is not obvious how channel encoding will affect the assumption of whiteness. The autocorrelation function of some commonly used channel codes is analyzed in order to study the validity of assumptions used in blind equalization. The codes are presented in terms of a Markov model for which the autocorrelation is analytically expressed. The various encoded sequences are used in a prediction error based blind equalizer, and the performance is empirically compared with the case of unencoded data. A blind equalization example using a practical GSM speech encoder combined with a convolutional channel encoder is also given.     

低速率语音通信中高效维特比译码的FPGA实现

维特比译码是卷积码勰码中一种最大似然的译码算法,文章给出了一种高效的卷积码编码及维特比解码FPGA硬件实现的结构,提出了一种采用（2,1,7）卷积码对2．4kbps MELP语音编码参数进行抗误码保护的信道编码方案。实验表明,能有效提舞噪声信道下传输的语音参数的抗误码性能。