Chun-Feng Wu

Perceptual Optimization of Playout Buffer in Voip Applications

Packet delay and loss are two essential problems to real-time voice transmission over IP networks. In the proposed system, the playout delay is adaptively adjusted based on a simplified version of the conversational-quality E-model. Perceptual-based buffer design is formulated as an unconstrained optimization problem leading to a better balance between end-to-end delay and packet loss. Experimental results show that the proposed playout buffer algorithm can achieve the optimum perceived speech quality under various network conditions.

Adaptive second-order control of transmitter power in wireless communication systems

The authors consider transmitter power control of wireless communication systems. They propose an adaptive second-order distributed power control algorithm in which the relaxation factors are adaptively adjusted to improve the rate of convergence. The algorithm updates power using a weighted combination of the distributed power control algorithm and the second-order power control (SOPC) algorithm. Simulation results show performance improvement over distributed constrained power control and constrained SOPC.

Iterative source-channel decoder using symbol-level extrinsic information

An iterative source-channel decoding scheme which exploits the source residual redundancies on symbol-basis is proposed. We first derive a modified BCJR algorithm based on the sectionalized code trellis for symbol decoding of the convolutional codes. This is used in conjunction with a softbit source decoder that computes the interpolative a posteriori probabilities of quantizer indexes. Simulation results indicate that our proposed scheme can achieve significant improvement over the conventional bit-level schemes.

Iterative Decoding of Convolutionally Encoded Multiple Descriptions

Transmission of convolutionally encoded multiple descriptions over noisy channels can bene¿t from the use of iterative source-channel decoding methods. This paper investigates the combined use of time-dependencies and inter-description correlation incurred by the multiple description scalar quantizer. We ¿rst modi¿ed the BCJR algorithm in a way that symbol a posteriori probabilities can be derived and used as extrinsic information to help iterative decoding between channel and source decoders. Also proposed is a recursive implementation for the source decoder that exploits the inter-description correlation to jointly decode multiple descriptions. Simulation results indicate that our proposed scheme can achieve signi¿cant improvement over the bit-level iterative decoding schemes.

Iterative symbol decoding of convolutionally-encoded variable-length codes

In this paper, we present a symbol-level iterative source-channel decoding algorithm for variable-length codes (VLCs). First a soft-input source decoder for VLC-encoded data is derived by modification of the BCJR forward-backward recursion and adaptation to the nonstationary VLC trellis. Also proposed is a recursive implementation based on sectionalized code trellises for MAP symbol decoding of binary convolutional codes. This allows to use a merged trellis representation for a VLC and a convolutional channel code and exploit the residual source redundancies as a priori information on a code trellis. Simulation results indicate that the proposed iterative decoder allows to exchange between its constituent decoders the symbol-level extrinsic information and achieves high robustness against channel noises.

Joint playout and FEC control for multi-stream voice over IP networks

Packet loss and delay are the major network impairments for transporting real-time voice over internet protocol (IP) networks. In the proposed system, multiple descriptions of the speech are used to take advantage of packet path diversity. A new objective method is presented for predicting the perceived quality of multi-stream voice transmission. Also proposed is a joint playout buffer and forward error control (FEC) adjustment scheme that maximizes the perceived speech quality via delay-loss trading. Experimental results showed that the proposed multi-stream voice transmission scheme achieves significant reductions in delay- and packet-loss rates as well as improved speech quality.

Symbol-based iterative decoding of convolutionally encoded multiple descriptions

Transmission of convolutionally encoded multiple descriptions over noisy channels can benefit from the use of iterative source-channel decoding. The authors first modified the BCJR algorithm in a way that symbol a posteriori probabilities can be derived and used as extrinsic information to improve the iterative decoding between the source and channel decoders. The authors also formulate a recursive implementation for the source decoder that processes reliability information received on different channels and combines them with inter-description correlation to estimate the transmitted quantiser index. Simulation results are presented for two-channel scalar quantisation of Gauss - Markov sources which demonstrate the error-resilience capabilities of symbol-based iterative decoding.

Multiple Description Quantization for Recognizing Voice Over Packet Networks

The practical design of multiple description vector quantizers for robust distributed speech recognition over packet networks is investigated. In the proposed system, speech parameters are quantized and mapped to multiple descriptions for transmission over independent channels. A new approach to the index assignment optimization is presented on the basis of a linear programming framework. Also, a fast local search algorithm is proposed to find the optimal index assignment without compromising the speech recognition accuracy. Experiments with random packet loss in a range of loss conditions are conducted on the Mandarin digit string recognition task. Simulation results indicate that the proposed multiple description scheme provides more robust performance than the ETSI standardized split vector quantization scheme with a single description.