Performance evaluation of downlink LTE system with QPP interleaver for channel encoder
The design of the LTE physical layer (PHY) is heavily influenced by the requirements for high peak transmission rate (100 Mbps DL/50 Mbps UL), spectral efficiency and multiple channel bandwidths (1.25–20 MHz). Unfortunately, the existing WCDMA turbo interleaver does not permit an efficient high throughput implementation. That is why the 3rd Generation Partnership Project (3GPP) has proposed the quadratic polynomial permutation (QPP) interleaver to rectify this situation. In this paper, we will study the performances of QPP interleaver for LTE encoder channel for different configurations of LTE system and we study the effect of LTE channel turbo encoder for the transmission over a wireless multipath channel.
We propose an embarrassingly parallel, memory ecient inference algorithm for latent variable models in which the complete data likelihood is in the exponential family. The algorithm is a stochastic cellular automaton and converges to a valid maximum a posteriori fixed point. Applied to latent Dirichlet allocation we find that our algorithm is over an order or magnitude faster than the fastest current approaches. A simple C++/MPI implementation on a 20-node Amazon EC2 cluster samples at more than 1 billion tokens per second. We process 3 billion documents and achieve predictive power competitive with collapsed Gibbs sampling and variational inference.