Lapidoth's talk: Matched filter done right
Today as part of EPFL annual research day, there were 3 interesting talks. In the morning Prakash Narayan gave a very interesting talk titled "Common randomness, multiuser secrecy and tree packing". Essentially it covered three distinct problems and he showed a connection among the three. The first problem setup is the following: A set of terminals observe separate but correlated signals. The classical Slepian and Wolf formulation of the data compression then is essentially the problem where a subset of the given terminals seeking to acquire the signals observed by all the terminals. And this is done by means of efficiently compressed interterminal communication. This is a problem of generating common randomness. This ofcourse does not involve any secrecy constraints. Now suppose a secret key generation problem. There the same subset of terminals seek to devise ``secret'' common randomness or a secret key through public communication. Assume here that an eavesdropper can observe this. So the setup is such that the key is concealed from the eavesdropper. Such a secret key can be used for subsequent encryption. Prakash's talk was then to explain the connection between the two problems. He went on to establish the connection to a problem in computer science namely the maximal packing og Steiner trees in an associated multi graph. I dont think I figured out the details that well, but it triggered some curiosity to read the work a little more detail. I hope to do that sometime soon.
The afternoon session had two talks. One was by Shamai who talked about Broadcast approach in communication systems. It went over time. I thought I focussed well in the beginning to follow him, but partly because of the post lunch effect and partly because of the tiredeness I lost the flow. From what I understood, he outlined a lot of communication scenarios incorporating the broadcast strategy. Some examples were MIMO rate diversity trade off, ARQ, multilayer schemes etc. A lot the work seems to have gone in this direction, especially Suhas and Sanked etc (from the citation) and David Tse, Zheng Al-Dahir and Shamey himself. I am somewhat amazed by the areas Shamai worked on. He seems to have covered a broad spectrum of research and yet produced some stellar work.
After Shamai, it was an interesting talk by Amos Lapidoth. He presented handsomely. I was attentive enough to follow this. Also, it happened to be a talk of different kind. He talked about the well known Matched filter used in communication. He sort of started with a little story. The story of a man from a village, venturing out of that place with a mission to find the meaning of life. So he goes to the mountains with a resolve not to come back until he finds the meaning of life. So days passed, months passed and years passed. Even after 10 years no sign of him. Finally he comes back after 11 years or so. The whole village feels curious: Aha he has come back. They ask him, wow, you have figured out the meaning of life. Please share us what is it? He says, with a pause: Life is (he pauses again).... : Villages out of patience ask him, : " You please go on .. life is ...". The man completes and says " Life is like a train!". Then they ask what you mean by "life is like a train". Then to the surprise of the entire village he says, "may be not!".
That was simply amazing a prelude for the talk. The talk abstract is the following:
One of the key results of Digital Communications can be paraphrased very roughly as follows: "in guessing which of two deterministic signals is being observed in white Gaussian noise, the inner products between the observed waveform and each of the signals form a sufficient statistic. Consequently, it is optimal to base one's decision on these two inner products." It is surprising that this basic result is never formulated as a theorem in any of the textbooks on the subject. This may be because of the difficulties in defining white Gaussian noise, in defining sufficient statistics for waveform observations, and in relating sufficiency to optimal detection. In this talk I shall describe a number of approaches to formulating the above statement as a theorem and point out some of their shortcomings. I will finally describe my proposed approach, formulate the theorem, and prove it from first principles. The proposed approach does not rely on the Ito Calculus, on Brownian Motion, or on generalized stochastic processes. It does not introduce non-physical infinite-power noise processes. Moreover, it is suitable for rigorously treating colored noise.
He gave a counter example where we can do better than matched filter. He says a Gaussian noise, but choose a point at random where the noise is made zero. Since it is randomly chosen (the null point) he claims it is still Gaussian. To me, that will result in SNR to blow up to infinity. So, are we missing something. I cant wait to read the full paper presentation of this. Otherwise, it seem to be a very very interesting way to look at matched filter, without needing the sojourn mathematical machinery.
Anyway all these talks are available (schedule at the moment) at [1]
[1]http://ic.epfl.ch/page65253-fr.html
The afternoon session had two talks. One was by Shamai who talked about Broadcast approach in communication systems. It went over time. I thought I focussed well in the beginning to follow him, but partly because of the post lunch effect and partly because of the tiredeness I lost the flow. From what I understood, he outlined a lot of communication scenarios incorporating the broadcast strategy. Some examples were MIMO rate diversity trade off, ARQ, multilayer schemes etc. A lot the work seems to have gone in this direction, especially Suhas and Sanked etc (from the citation) and David Tse, Zheng Al-Dahir and Shamey himself. I am somewhat amazed by the areas Shamai worked on. He seems to have covered a broad spectrum of research and yet produced some stellar work.
After Shamai, it was an interesting talk by Amos Lapidoth. He presented handsomely. I was attentive enough to follow this. Also, it happened to be a talk of different kind. He talked about the well known Matched filter used in communication. He sort of started with a little story. The story of a man from a village, venturing out of that place with a mission to find the meaning of life. So he goes to the mountains with a resolve not to come back until he finds the meaning of life. So days passed, months passed and years passed. Even after 10 years no sign of him. Finally he comes back after 11 years or so. The whole village feels curious: Aha he has come back. They ask him, wow, you have figured out the meaning of life. Please share us what is it? He says, with a pause: Life is (he pauses again).... : Villages out of patience ask him, : " You please go on .. life is ...". The man completes and says " Life is like a train!". Then they ask what you mean by "life is like a train". Then to the surprise of the entire village he says, "may be not!".
That was simply amazing a prelude for the talk. The talk abstract is the following:
One of the key results of Digital Communications can be paraphrased very roughly as follows: "in guessing which of two deterministic signals is being observed in white Gaussian noise, the inner products between the observed waveform and each of the signals form a sufficient statistic. Consequently, it is optimal to base one's decision on these two inner products." It is surprising that this basic result is never formulated as a theorem in any of the textbooks on the subject. This may be because of the difficulties in defining white Gaussian noise, in defining sufficient statistics for waveform observations, and in relating sufficiency to optimal detection. In this talk I shall describe a number of approaches to formulating the above statement as a theorem and point out some of their shortcomings. I will finally describe my proposed approach, formulate the theorem, and prove it from first principles. The proposed approach does not rely on the Ito Calculus, on Brownian Motion, or on generalized stochastic processes. It does not introduce non-physical infinite-power noise processes. Moreover, it is suitable for rigorously treating colored noise.
He gave a counter example where we can do better than matched filter. He says a Gaussian noise, but choose a point at random where the noise is made zero. Since it is randomly chosen (the null point) he claims it is still Gaussian. To me, that will result in SNR to blow up to infinity. So, are we missing something. I cant wait to read the full paper presentation of this. Otherwise, it seem to be a very very interesting way to look at matched filter, without needing the sojourn mathematical machinery.
Anyway all these talks are available (schedule at the moment) at [1]
[1]http://ic.epfl.ch/page65253-fr.html
posted by Rethnakaran Pulikkoonattu at 12:12 PM
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