Emmanuel Abbe

Polar Codes for the m-User MAC

In this paper, polar codes for the m-user multiple access channel (MAC) with binary inputs are constructed. It is shown that Arikans polarization technique applied individually to each user transforms independent uses of an m-user binary input MAC into successive uses of extremal MACs. This transformation has a number of desirable properties: 1) the “uniform sum-rate” of the original MAC is preserved, 2) the extremal MACs have uniform rate regions that are not only polymatroids but matroids, and thus, 3) their uniform sum-rate can be reached by each user transmitting either uncoded or fixed bits; in this sense, they are easy to communicate over. A polar code can then be constructed with an encoding and decoding complexity of O(n log n) (where n is the block length), a block error probability of o(exp (- n1/2 - ε)), and capable of achieving the uniform sum-rate of any binary input MAC with arbitrary many users. Applications of this polar code construction to channels with a finite field input alphabet and to the additive white Gaussian noise channel are also discussed.

Polar coding schemes for the AWGN channel

This paper investigates polar coding schemes achieving capacity for the AWGN channel. The approaches using a multiple access channel with a large number of binary-input users and a single-user channel with a large prime-cardinality input are compared with respect to complexity attributes. The problem of finding discrete approximations to the Gaussian input is then investigated, and it is shown that a quantile quantizer achieves a gap to capacity which decreases like 1/q (where q is the number of constellation points), improving on the 1/log(q) decay achieved with a binomial (central limit theorem) quantizer.

Randomness and dependencies extraction via polarization

The basic polarization phenomenon for i.i.d. sources is extended to a framework allowing dependencies within and between multiple sources. In particular, it is shown that taking the polar transform of a random matrix with i.i.d. columns of arbitrary (correlated) distribution allows to extract the randomness and dependencies. This result is the used to develop polar coding schemes (having low complexity) for: (1) distributed data compression, i.e., Slepian-Wolf coding (without decomposing the problem into single-user problems), (2) compression of sources with memory, (3) compression of sources on finite fields, extending the polarization phenomenon for alphabets of prime cardinality to powers of primes.

Privacy-Preserving Methods for Sharing Financial Risk Exposures

Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that protect the privacy of all parties involved and without the need for a trusted third party. Our approach employs secure multi-party computation techniques from cryptography in which multiple parties are able to compute joint functions without revealing their individual inputs. In our framework, individual financial institutions evaluate a protocol on their proprietary data which cannot be inverted, leading to secure computations of real-valued statistics such a concentration indexes, pairwise correlations, and other single- and multi-point statistics. The proposed protocols are computationally tractable on realistic sample sizes. Potential financial applications include: the construction of privacy-preserving real-time indexes of bank capital and leverage ratios; the monitoring of delegated portfolio investments; financial audits; and the publication of new indexes of proprietary trading strategies.

MAC polar codes and matroids

In this paper, a polar code for the m-user multiple access channel (MAC) with binary inputs is constructed. In particular, Arikans polarization technique applied individually to each user polarizes any m-user binary input MAC into a finite collection of extremal MACs. The extremal MACs have a number of desirable properties: (i) the ‘uniform sum rate’1 of the original channel is not lost, (ii) the extremal MACs have rate regions that are not only polymatroids but matroids and thus (iii) their uniform sum rate can be reached by each user transmitting either uncoded or fixed bits; in this sense they are easy to communicate over. A polar code can then be constructed with an encoding and decoding complexity of O(n log n) (where n is the block length), a block error probability of o(exp(−n1/2-∊)), and capable of achieving the uniform sum rate of any binary input MAC with arbitrary many users. An application of this polar code construction to a coding scheme for the AWGN channel is also discussed.

A New Entropy Power Inequality for Integer-Valued Random Variables

The entropy power inequality (EPI) yields lower bounds on the differential entropy of the sum of two independent real-valued random variables in terms of the individual entropies. Versions of the EPI for discrete random variables have been obtained for special families of distributions with the differential entropy replaced by the discrete entropy, but no universal inequality is known (beyond trivial ones). More recently, the sumset theory for the entropy function yields a sharp inequality

Adaptive sensing using deterministic partial Hadamard matrices

H(X+X^{\prime})-H(X)\geq{{1}\over{2}}-o(1)

Coding along Hermite polynomials for Gaussian noise channels

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Universal source polarization and sparse recovery

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Randomness and Dependencies Extraction via Polarization, With Applications to Slepian–Wolf Coding and Secrecy

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