Faster Projection in Sphere Decoding
arXiv:0906.0249 · doi:10.1109/TIT.2011.2143830
Abstract
Most of the calculations in standard sphere decoders are redundant, in the sense that they either calculate quantities that are never used or calculate some quantities more than once. A new method, which is applicable to lattices as well as finite constellations, is proposed to avoid these redundant calculations while still returning the same result. Pseudocode is given to facilitate immediate implementation. Simulations show that the speed gain with the proposed method increases linearly with the lattice dimension. At dimension 60, the new algorithms avoid about 75% of all floating-point operations.
Cited by in corpus (13)
- Learning to Detect
- RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
- Achieving a vanishing SNR-gap to exact lattice decoding at a subexponential complexity
- Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
- Low-complexity Voronoi shaping for the Gaussian channel
- Glued lattices are better quantizers than
- RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
- Optimization and Identification of Lattice Quantizers
- Robust Vector Perturbation Precoding Design for MIMO Broadcast Channel
- Deterministic Sampling Decoding: Where Sphere Decoding Meets Lattice Gaussian Distribution
- Modulus Zero-Forcing Detection for MIMO Channels
- Asymptotic Analysis of Random Lattices in High Dimensions
- Learning to Search for MIMO Detection