Capacity Achieving Distributions and Separation Principle for Feedback Gaussian Channels With Memory: the LQG Theory of Directed Information

2018 ◽  
Vol 64 (9) ◽  
pp. 6384-6418 ◽  
Author(s):  
Charalambos D. Charalambous ◽  
Christos K. Kourtellaris ◽  
Sergey Loyka
Keyword(s):  
Separation Principle ◽  
Gaussian Channels ◽  
Directed Information ◽  
With Memory

Optimal Encoders Maximizing Directed Information of Channels with Memory and Feedback: Stochastic Control and Dynamic Programming

2011 ◽  
Vol 44 (1) ◽  
pp. 6598-6603
Author(s):  
Charalambos D. Charalambous ◽  
Christos K. Kourtellaris ◽  
Christoforos Hadjicostis
Keyword(s):  
Dynamic Programming ◽  
Stochastic Control ◽  
Directed Information ◽  
With Memory

scholarly journals Improving the Performance of Viterbi Decoder using Window System

2018 ◽  
Vol 8 (1) ◽  
pp. 611
Author(s):  
Rekkal Kahina ◽  
Abdesselam Bassou
Keyword(s):  
The Other ◽  
Viterbi Decoder ◽  
Decoding Algorithms ◽  
Window System ◽  
Gaussian Channels ◽  
The Matrix ◽  
Simulation Results ◽  
Convolutional Encoder ◽  
Map Decoder ◽  
With Memory

An efficient Viterbi decoder is introduced in this paper; it is called Viterbi decoder with window system. The simulation results, over Gaussian channels, are performed from rate 1/2, 1/3 and 2/3 joined to TCM encoder with memory in order of 2, 3. These results show that the proposed scheme outperforms the classical Viterbi by a gain of 1 dB. On the other hand, we propose a function called RSCPOLY2TRELLIS, for recursive systematic convolutional (RSC) encoder which creates the trellis structure of a recursive systematic convolutional encoder from the matrix “H”. Moreover, we present a comparison between the decoding algorithms of the TCM encoder like Viterbi soft and hard, and the variants of the MAP decoder known as BCJR or forward-backward algorithm which is very performant in decoding TCM, but depends on the size of the code, the memory, and the CPU requirements of the application.

scholarly journals INFORMATION TRANSMISSION VIA ENTANGLED QUANTUM STATES IN GAUSSIAN CHANNELS WITH MEMORY

2006 ◽  
Vol 04 (03) ◽  
pp. 439-452 ◽  
Author(s):  
NICOLAS J. CERF ◽  
JULIEN CLAVAREAU ◽  
JÉRÉMIE ROLAND ◽  
CHIARA MACCHIAVELLO
Keyword(s):  
Quantum Channel ◽  
Quantum States ◽  
Gaussian Channel ◽  
Hard Problem ◽  
Quantum Channels ◽  
Classical Capacity ◽  
Gaussian Channels ◽  
Memoryless Channels ◽  
Entangled Quantum States ◽  
With Memory

Gaussian quantum channels have recently attracted a growing interest, since they may lead to a tractable approach to the generally hard problem of evaluating quantum channel capacities. However, the analysis performed so far has always been restricted to memoryless channels. Here, we consider the case of a bosonic Gaussian channel with memory, and show that the classical capacity can be significantly enhanced by employing entangled input symbols instead of product symbols.

Capito-Lunate Fusion with Memory Staples

2015 ◽  
Vol 04 (S 01) ◽  
Author(s):  
M. Solomons
Keyword(s):  
With Memory
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