Naftali Sommer

Low-Density Lattice Codes

Low-density lattice codes (LDLC) are novel lattice codes that can be decoded efficiently and approach the capacity of the additive white Gaussian noise (AWGN) channel. In LDLC a codeword x is generated directly at the n-dimensional Euclidean space as a linear transformation of a corresponding integer message vector b, i.e., x = Gb-1, where H = G-1 is restricted to be sparse. The fact that H is sparse is utilized to develop a linear-time iterative decoding scheme which attains, as demonstrated by simulations, good error performance within ~0.5 dB from capacity at block length of n =100,000 symbols. The paper also discusses convergence results and implementation considerations.

Low Density Lattice Codes

Low density lattice codes (LDLC) are novel lattice codes that can approach the capacity of the additive white Gaussian noise (AWGN) channel and be decoded efficiently. In LDLC a codeword x is generated directly at the n-dimensional Euclidean space as a linear transformation of a corresponding integer message vector b, i.e., x = Gb, where H = G -1 is restricted to be sparse. The fact that H is sparse is utilized to develop a linear-time iterative decoding scheme which attains, as demonstrated by simulations, good error performance within ~ 0.5 dB from capacity at block length of n = 100,000 symbols. The paper also discusses convergence results and implementation considerations

Shaping methods for low-density lattice codes

Low density lattice codes (LDLC) are recently-proposed lattice codes that can be decoded efficiently and approach the capacity of the additive white Gaussian noise (AWGN) channel. In LDLC a codeword x is generated directly at the n-dimensional Euclidean space as a linear transformation of a corresponding integer message vector b, i.e., x = Gb, where H = G−1 is restricted to be sparse. In order to design practical lattice codes, the infinite lattice should be combined with a shaping algorithm, that maps information bits to lattice points and ensures that the power of the lattice codewords is properly constrained. This work proposes several efficient and practical shaping algorithms for LDLC.

Signal Codes: Convolutional Lattice Codes

The coded modulation scheme proposed in this paper has a simple construction: an integer sequence, representing the information, is convolved with a fixed, continuous-valued, finite impulse response (FIR) filter to generate the codeword - a lattice point. Due to power constraints, the code construction includes a shaping mechanism inspired by precoding techniques such as the Tomlinson-Harashima filter. We naturally term these codes “convolutional lattice codes” or alternatively “signal codes” due to the signal processing interpretation of the code construction. Surprisingly, properly chosen short FIR filters can generate good codes with large minimal distance. Decoding can be done efficiently by sequential decoding or for better performance by bidirectional sequential decoding. Error analysis and simulation results indicate that for the additive white Gaussian noise (AWGN) channel, convolutional lattice codes with computationally reasonable decoders can achieve low error rate close to the channel capacity.