Song-Nam Hong

Sequential message-passing decoding of LDPC codes by partitioning check nodes

In this paper, we analyze the sequential message- passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results indicate that as the number of partitioned subsets of check nodes increases, the BER performance is improved. We also derive the recursive equations for mean values of messages at check and variable nodes by using density evolution with a Gaussian approximation. From these equations, the mean values are obtained at each iteration of the sequential decoding algorithm and the corresponding BER values are calculated. They show that the sequential decoding algorithm converges faster than the conventional one. Finally, the analytical results are confirmed by the simulation results.

Analysis of Check-Node Merging Decoding for Punctured LDPC Codes with Dual-Diagonal Parity Structure

In this paper, the authors propose new decoding scheme of punctured LDPC codes with dual-diagonal parity structure by merging check nodes connected to the punctured parity nodes. This check-node merging decoding not only needs smaller number of operations at each iteration but also shows faster decoding convergence speed than the conventional erasure decoding. For the binary erasure channel (BEC) and AWGN channel, the authors analyze and compare the check-node merging and the conventional erasure decoding schemes of the punctured LDPC codes with dual-diagonal parity structure using density evolution. Analytical results show that the check-node merging decoding gives faster convergence speed for both BEC and AWGN channel. Also, simulation results are provided to confirm the analytical results.

Optimal rate-compatible punctured concatenated zigzag codes

In the next-generation mobile communication systems for various high-speed data services, the error correcting codes are required to have rate-compatibility, low decoding complexity and good performance for various frame lengths. In this paper, new rate-compatible punctured concatenated zigzag (RCPCZ) codes are proposed and analyzed by using the density evolution. As their application, type-II HARQ using RCPCZ code is shown to have better throughput at short frame lengths than yype-II HARQ using turbo code.

Adaptive Bit-Reliability Mapping for LDPCCoded High-Order Modulation Systems

In this paper, an adaptive bit-reliability mapping is proposed for the bit-level Chase combining in LDPC-coded high-order modulation systems. Contrary to the previously known bit-reliability mapping that assigns the information (or parity) bits to more (or less) reliable bit positions, the proposed mapping flexibly assigns codeword bits to the bit positions of various reliabilities by considering the characteristics of code and protection levels. Compared with the symbol-level Chase combining and the constellation rearrangement bit mapping, the proposed mapping gives 0.7 - 1.3 dB and 0.1 - 1.0 dB performance gain at FER = 10-3 with no additional complexity, respectively. The adaptive bit-reliability mappings are derived for various environments and the validity of them is confirmed through simulation.

Coded compressive sensing: A compute-and-recover approach

In this paper, we propose coded compressive sensing that recovers an n-dimensional integer sparse signal vector from a noisy and quantized measurement vector whose dimension m is far-fewer than n. The core idea of coded compressive sensing is to construct a linear sensing matrix whose columns consist of lattice codes. We present a two-stage decoding method named compute-and-recover to detect the sparse signal from the noisy and quantized measurements. In the first stage, we transform such measurements into noiseless finite-field measurements using the linearity of lattice codewords. In the second stage, syndrome decoding is applied over the finite-field to reconstruct the sparse signal vector. A sufficient condition of a perfect recovery is derived. Our theoretical result demonstrates an interplay among the quantization level p, the sparsity level k, the signal dimension n, and the number of measurements m for the perfect recovery. Considering 1-bit compressive sensing as a special case, we show that the proposed algorithm empirically outperforms an existing greedy recovery algorithm.

Uplink Multiuser Massive MIMO Systems with One-Bit ADCs: A Coding-Theoretic Viewpoint

This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which

ICI self cancellation - Golay complementary Reed-Muller code scheme for OFDM systems

K

Uplink Multiuser Massive MIMO Systems with Low-Resolution ADCs: A Coding-Theoretic Approach.

users with a single-antenna communicate with one base station (BS) with

Uplink Massive MIMO Systems with One-Bit ADCs: A Low-Complexity Weighted Minimum Distance Decoding

n_r

Analysis of Complexity and Convergence Speed of Sequential Schedules for Decoding LDPC Codes

antennas. In this system, we propose a novel MIMO detection framework, which is inspired by coding theory. The key idea of the proposed framework is to create a non-linear code