Part 3: brain science, information science and associative memory model
New Generation Computing
Vector precoding in wireless communications: a replica symmetric analysis
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
IEEE Transactions on Communications
Gaussian process regressors for multiuser detection in DS-CDMA systems
IEEE Transactions on Communications
IEEE Transactions on Communications
Belief propagation with Gaussian priors for pilot-assisted communication over fading ISI channels
IEEE Transactions on Wireless Communications
A two-stage capacity-achieving demodulation/decoding method for random matrix channels
IEEE Transactions on Information Theory
Statistical-mechanical approach for multiple watermarks using spectrum spreading
ICITS'09 Proceedings of the 4th international conference on Information theoretic security
Multicode Sparse-Sequence CDMA: Approach to Optimum Performance by Linearly Complex WSLAS Detectors
Wireless Personal Communications: An International Journal
| Hi-index | 754.90 |
We present a theory to analyze the performance of the parallel interference canceller (PIC) for code-division multiple-access (CDMA) multiuser detection, applied to a randomly spread, fully synchronous baseband uncoded CDMA channel model with additive white Gaussian noise under perfect power control in the large-system limit. We reformulate PIC as an approximation to the belief propagation algorithm for the detection problem. We then apply the density evolution framework to analyze its detection dynamics. It turns out that density evolution for PIC is essentially the same as statistical neurodynamics, a theory to describe dynamics of a certain type of neural network model. Adopting this correspondence, we develop the density evolution framework for PIC using statistical neurodynamics. The resulting formulas, however, are only approximately correct for describing detection dynamics of PIC even in the large-system limit, because we ignore the Onsager reaction terms in the derivation. We then propose a modified PIC algorithm, in which we subtract the Onsager reaction terms algorithmically, for which the density evolution formulas give a correct description of the detection dynamics in the large-system limit.