In this paper, we investigate multilevel coding and multistage decoding for satellite broadcasting with moderate decoding complexity. An unconventional signal set partitioning is used to achieve unequal error protection capabilities. Two possibilities are shown and analyzed for practical systems: (i) linear block component codes with near optimum decoding, (ii) punctured convolutional component codes with a common trellis structure.

Channel coding for bandwidth limited channels based on multilevel bit-interleaved channels is discussed in this paper. This coding and decoding structure has the advantage of simplified design, and naturally incorporates flexible and powerful design of unequal error protection (UEP) capabilities, especially over time-varying channels to be often found in mobile radio communications. Multilevel coded modulation with multistage decoding, and bit-interleaved coded modulation are special cases of the proposed general framework. Simulation results verify the usefulness of the system considered.

Unequal error protection (UEP) is a very promising coding technique for satellite broadcasting, as it gradually reduces the transmission rate. From the viewpoint of bandwidth efficiency, UEP should be achieved in the context of multilevel coded modulation. However, the conventional mapping between encoded bits and modulation signals, usually realized for multilevel block modulation codes and multistage decoding, is not very compatible with UEP coding because of the large number of resulting nearest neighbor codewords. In this paper, new coded modulation schemes for UEP based on unconventional partitioning are proposed. A linear operation referred to as interlevel combination is introduced. This operation generalizes previous partitioning proposed for UEP applications and provides additional flexibility with respect to UEP capabilities. The error performance of the proposed codes are evaluated both by computer simulations and a theoretical analysis. The obtained results show that the proposed codes achieve good tradeoff between the proportion and the error performance of each error protection level.

This letter presents ternary convolutional codes and their punctured codes with optimum distance spectrum.

This letter treats lossy source coding of binary sources with short linear block codes. It is numerically shown that very simple basis-reprocessing approach yields good rate-distortion performance with manageable computational complexity for small block lengths. The result for code lengths up to 100 outperforms the message-passing based encoding for low-density codes which are several times longer.

This letter presents numerical results of lossy source coding for non-uniformly distributed binary source with trellis codes. The results show how the performance of trellis codes approaches the rate-distortion function in terms of the number of states.

We present numerical results on the rate-distortion performance of convolutional coding for the binary symmetric source, and show how convolutional codes approach the rate-distortion bound by increasing the trellis states.

We analyze the error performance of coding for ordered random variables with side information at the decoder, where encoder input as well as side information are deliberately extracted from the output of correlated sources. A tight upper bound on error probability is derived for linear encoding, and minimum distance decoding for a binary source and Gaussian distributed side information. Some features caused by the signal extraction are discussed.

We study the use of the additive white Gaussian noise channel to achieve a cryptographic primitive that is important in secure multiparty computation. A protocol for unconditionally secure oblivious transfer is presented. We show that channel input alphabets with a certain algebraic structure and their partitions are useful in achieving the requirements on the primitive. Signal design for a protocol with high information rate is discussed.

Low-density parity-check (LDPC) codes are widely used in communication systems for their high error-correcting performance. This survey introduces the elements of LDPC codes: decoding algorithms, code construction, encoding algorithms, and several classes of LDPC codes.

Performance of network coded cooperation over the Gaussian channel in which multiple communication nodes send each one's message to a common destination is analyzed. The nodes first broadcast the message, and subsequently relay the XOR of subset of decoded messages to the destination. The received vector at the destination can be equivalently regarded as the output of a point-to-point channel, except that the underlying codes are drawn probabilistically and symbol errors may occur before transmission of a codeword. We analyze the error performance of this system from coding theoretic viewpoint.

We consider the use of the additive white Gaussian noise channel to achieve information theoretically secure oblivious transfer. A protocol for this primitive that ensures the correctness and privacy for players is presented together with the signal design. We also study the information theoretic efficiency of the protocol, and some more practical issues where the parameter of the channel is unknown to the players.

In this paper we look at the serial concatenation of short linear block codes with a rate-1 recursive convolutional encoder, with a goal of designing high-rate codes with low error floor. We observe that under turbo-style decoding the error floor of the concatenated codes with extended Hamming codes is due to detectable errors in many cases. An interleaver design addressing this is proposed in this paper and its effectiveness is verified numerically. We next examine the use of extended BCH codes of larger minimum distance, resulting in an improved weight spectrum of the overall code. Reduced complexity list decoding is used to decode the BCH codes in order to obtain low decoding complexity for a negligible loss in performance.

In this paper, we introduce a framework of distributed orthogonal approximate message passing for recovering sparse vector based on sensing by multiple nodes. The iterative recovery process consists of local computation at each node, and global computation performed either by a particular node or joint computation on the overall network by exchanging messages. We then propose a method to reduce the communication cost between the nodes while maintaining the recovery performance.

Granular gain of low-dimensional lattices based on binary linear codes is estimated using a quantization algorithm which is equivalently a soft-decision decoding of the underlying code. It is shown that substantial portion of the ultimate granular gain is achieved even in limited dimensions.

Reliability-based decoding algorithm in multistage decoding of multilevel codes is discussed. Through theoretical analyses, effects of soft reliability information are examined for different types of partitionings.