|
|
|
|
LEADER |
02361 am a22002413u 4500 |
001 |
76664 |
042 |
|
|
|a dc
|
100 |
1 |
0 |
|a Ivan, Ioana Elisabeta
|e author
|
100 |
1 |
0 |
|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
|
100 |
1 |
0 |
|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
|e contributor
|
100 |
1 |
0 |
|a Ivan, Ioana Elisabeta
|e contributor
|
100 |
1 |
0 |
|a Yuen, Henry
|e contributor
|
700 |
1 |
0 |
|a Mitzenmacher, Michael
|e author
|
700 |
1 |
0 |
|a Thaler, Justin
|e author
|
700 |
1 |
0 |
|a Yuen, Henry
|e author
|
245 |
0 |
0 |
|a Continuous time channels with interference
|
260 |
|
|
|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2013-01-30T17:20:32Z.
|
856 |
|
|
|z Get fulltext
|u http://hdl.handle.net/1721.1/76664
|
520 |
|
|
|a Khanna and Sudan [2] studied a natural model of continuous time channels where signals are corrupted by the effects of both noise and delay, and showed that, surprisingly, in some cases both are not enough to prevent such channels from achieving unbounded capacity. Inspired by their work, we consider channels that model continuous time communication with adversarial delay errors. The sender is allowed to subdivide time into an arbitrarily large number M of micro-units in which binary symbols may be sent, but the symbols are subject to unpredictable delays and may interfere with each other. We model interference by having symbols that land in the same micro-unit of time be summed, and we study k-interference channels, which allow receivers to distinguish sums up to the value k. We consider both a channel adversary that has a limit on the maximum number of steps it can delay each symbol, and a more powerful adversary that only has a bound on the average delay. We give precise characterizations of the threshold between finite and infinite capacity depending on the interference behavior and on the type of channel adversary: for max-bounded delay, the threshold is at D[subscript max] = Θ (M log (min{k, M})), and for average bounded delay the threshold is at D[subscript avg] = Θ (√(M min{k, M})).
|
520 |
|
|
|a Massachusetts Institute of Technology. Presidential Fellowship
|
546 |
|
|
|a en_US
|
655 |
7 |
|
|a Article
|
773 |
|
|
|t Proceedings of the 2012 IEEE International Symposium on Information Theory Proceedings (ISIT)
|