Channel Coding Bits Research Articles

IJARCCE - Computer and Communication Engineering

In wireless communication system, limited bandwidth and power is the primary restriction. The existing wireless systems involved in transmission of speech visualized that efficient and effective methods be developed to transmit and receive the same while maintaining quality of speech, especially at the receiving end. Speech coding technique is a material of research for the scientific and academic community since the era of digitization (digital). Amongst all elements of the communication systems (transmitter, channel and receiver), transmission channel is the most critical and plays a key role in the transmission and reception of information. The quality of speech at receiver end decides by channel conditions. Modelling a channel is a multifarious task. A number of techniques are adopted to alleviate the effect of the channel. Adaptive Multi Rate is one of the techniques that neutralize the deleterious effect of the channel on speech. This technique utilizes variable bit rate that dynamically switches to specific modes of operation depending upon the channel conditions. For example, Low bit rate mode of operation is selected in adverse channel conditions, this helps to provide more error protection bits for channel coding and vice versa. Therefore, in this paper, application of Code Excited Linear Prediction (CELP) source codec on speech followed by AMR codec is studied. Further, higher the bit rate used, the better is the quality of speech. In this paper apart from speech codec about AMR is also studied that why the AMR is proposed for the GSM, how the bits rates are reduced in AMR, operation of AMR and other applications of AMR.

Cooperative Source and Channel Coding for Wireless Multimedia Communications

Past work on cooperative communications has indicated substantial improvements in channel reliability through cooperative transmission strategies. To exploit cooperation benefits for multimedia transmission over slow fading channels, we propose to jointly allocate bits among source coding, channel coding and cooperation to minimize the expected distortion of the reconstructed signal at the receiver. Recognizing that, not all source bits are equally important in terms of the end-to-end distortion, we further propose to protect the more important bits through user cooperation. We compare four modes of transmission that differ in their compression and error protection strategies (single layer or multiple layer source coding with unequal error protection, with versus without cooperation). Our study includes an i.i.d. Gaussian source as well as a video source employing an H.263+ codec. We present an information theoretic analysis for the Gaussian source to investigate the effects of the modulation scheme, bandwidth ratio (number of channel uses per source sample), and average link signal-to-noise ratios on the end-to-end distortion of the four modes studied. The information theoretic observations are validated using practical channel coding simulations. Our study for video considers error propagation in decoded video due to temporal prediction and jointly optimizes a source coding parameter that controls error propagation, in addition to bits for source coding, channel coding and cooperation. The results show that cooperation can significantly reduce the expected end-to-end distortion for both types of source and that layered cooperation provides further improvements and extends the benefits to a wider range of channel qualities.

Error-resilient transmission of 3D models

In this article, we propose an error-resilient transmission method for progressively compressed 3D models. The proposed method is scalable with respect to both channel bandwidth and channel packet-loss rate. We jointly design source and channel coders using a statistical measure that (i) calculates the number of both source and channel coding bits, and (ii) distributes the channel coding bits among the transmitted refinement levels in order to maximize the expected decoded model quality. In order to keep the total number of bits before and after applying error protection the same, we transmit fewer triangles in the latter case to accommodate the channel coding bits. When the proposed method is used to transmit a typical model over a channel with a 10% packet-loss rate, the distortion (measured using the Hausdorff distance between the original and the decoded models) is reduced by 50% compared to the case when no error protection is applied.

Bit allocation for joint source and channel coding of progressively compressed 3-D models

This work presents a joint source and channel coding method for transmission of progressively compressed three-dimensional (3-D) models where the bit budget is allocated optimally. The proposed system uses the Compressed Progressive Mesh (CPM) algorithm to produce a hierarchical bitstream representing different levels of detail (LODs). In addition to optimizing the transmitted bitstream with respect to the channel characteristics, we also choose the optimum combination of quantization and tessellation to maximize the expected decoded model quality. That is, given a 3-D mesh, a total bit budget (B) and a channel packet-loss rate (P/sub LR/), we determine the optimum combination of: 1) the geometry coordinates quantizer step size (l-bit); 2) the number of transmitted batches (L); 3) the total number of channel coding bits (C); and 4) the distribution of these channel coding bits among the transmitted levels (C/sub L/). Experimental results show that, with our unequal error protection approach that uses the proposed bit-allocation algorithm, the decoded model quality degrades more gracefully (compared to either no error protection or equal error protection methods) as the packet-loss rate increases.

Bit allocation for joint source/channel coding of scalable video

We propose an efficient bit allocation algorithm for a joint source/channel video codec over noisy channels. The approach is to distribute the available source and channel coding bits among the subbands in such a way that the expected distortion is minimized. The constructed distortion curves bound the performance degradation should the channel be estimated incorrectly. The algorithm can be used in other similar distortion minimization problems with two constraints, such as power or complexity.

Joint source and channel decoding of compressed still images

A joint source-channel decoding approach is described, which utilizes the residual redundancy in the bit stream of a source-coded image by employing a modified APRI-SOVA (a priori soft-output Viterbi algorithm) at the channel decoder. The source coders used are the nonadaptive discrete cosine transform (DCT) coding and subband coding with Lloyd-Max quantization. The log-likelihood ratio describing the a priori information is linearized. The reliability of the current bit is computed as a proper linear combination of the soft values characterizing statistical dependency of the zeroth order (probability of 1's or 0's) and the first order (bit correlation among neighboring samples). Substantial subjective and objective performance gains can be obtained by using this method. It is demonstrated that especially in a noisy environment, it may be better to keep the residual redundancy in the data and use it at the receiver side in order to achieve a more reliable decoding, rather than to further remove it by using a variable length coding. The trade-off between the allocation of bits for source and channel coding is also addressed.