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The capacity of communication channe...

Yang, Shaohua.

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The capacity of communication channels with memory.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The capacity of communication channels with memory./
作者:	Yang, Shaohua.
面頁冊數:	115 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2565.
Contained By:	Dissertation Abstracts International65-05B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3132033
ISBN:	0496792730

The capacity of communication channels with memory.
Yang, Shaohua.

The capacity of communication channels with memory. - 115 p.

Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2565.

Thesis (Ph.D.)--Harvard University, 2004.

For an inter-symbol-interference state-machine channel, a simple form of the feedback-capacity-achieving source distribution is revealed. A Markov source, whose memory length equals the channel memory length, achieves the feedback capacity. Given the posterior channel-state distribution, the optimal source Markov transition probabilities become independent of the whole history of past channel outputs. Further, when the feedback is delayed, the delayed feedback capacity is achieved by a Markov source whose memory length equals the sum of the channel memory length and the feedback delay. The Markov source optimization is formulated as a standard stochastic control problem and is solved by dynamic programming. The (delayed) feedback capacity is an upper-bound on the feed-forward channel capacity, and this bound can be made tight by increasing the feedback delay.

ISBN: 0496792730Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

The capacity of communication channels with memory.
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245 1 4 $a The capacity of communication channels with memory.
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2565.
500 $a Adviser: Aleksandar Kavcic.
502 $a Thesis (Ph.D.)--Harvard University, 2004.
520 $a For an inter-symbol-interference state-machine channel, a simple form of the feedback-capacity-achieving source distribution is revealed. A Markov source, whose memory length equals the channel memory length, achieves the feedback capacity. Given the posterior channel-state distribution, the optimal source Markov transition probabilities become independent of the whole history of past channel outputs. Further, when the feedback is delayed, the delayed feedback capacity is achieved by a Markov source whose memory length equals the sum of the channel memory length and the feedback delay. The Markov source optimization is formulated as a standard stochastic control problem and is solved by dynamic programming. The (delayed) feedback capacity is an upper-bound on the feed-forward channel capacity, and this bound can be made tight by increasing the feedback delay.
520 $a The linear Gaussian channel with an average input power constraint can be equivalently modelled as a state machine channel. When the channel has feedback, by following similar procedures as developed for the state machine channel, it is shown that Gauss-Markov sources achieve the feedback capacity and a Kalman-Bucy filter is optimal for processing the feedback. By introducing an auxiliary shadow price variable, the average signal power constraint is eliminated from the optimization problem. The source optimization and capacity computation problem becomes a standard stochastic control problem and is solved by dynamic programming. Dynamic programming further reveals the structure of the optimal signal, i.e., Xt=dTt St-1 -ESt-1&vbm0; yt-11, s0 +etVt, Kalman filterinnovation where vector d&barbelow;t and scaler et are coefficients and random variable Vt represents new information. When the optimal Kalman-Bucy filter for processing the feedback is asymptotically stationary, the asymptotic feedback capacity exists and is determined by simple non-linear programming. The non-linear programming problem is explicitly solved for first-order autoregressive moving-average Gaussian noise channels.
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