Michael Christoph Gastpar

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We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of f-Divergences. The results are then applied to specific settings of interest and compared to other notable results i ...

Polar codes are introduced for discrete memoryless broadcast channels. For m-user deterministic broadcast channels, polarization is applied to map uniformly random message bits from m-independent messages to one codeword while satisfying broadcast constraints. The polarization-based codes achieve rates on the boundary of the private-message capacity region. For two-user noisy broadcast channels, polar implementations are presented for two information-theoretic schemes: 1) Cover's superposition codes and 2)

We present a novel unified framework of compute- forward achievable rate regions for simultaneous decoding of multiple linear codeword combinations. This framework covers a wide class of discrete and continuous-input channels, and computation over finite f ...

In the problem of coded caching for media delivery, two separate coding opportunities have been identified. The first opportunity is a multi-user advantage and crucially hinges on a public broadcast link in the delivery phase. This has been explored in a p ...

A new scheme for the problem of centralized coded caching with non-uniform demands is proposed. The distinguishing feature of the proposed placement strategy is that it admits equal sub-packetization for all files while allowing the users to allocate more ...

This paper develops a new communication strategy, ergodic interference alignment, for the K-user interference channel with time-varying fading. At any particular time, each receiver will see a superposition of the transmitted signals plus noise. The standard approach to such a scenario results in each transmitter-receiver pair achieving a rate proportional to 1/K its interference-free ergodic capacity. However, given two well-chosen time indices, the channel coefficients from interfering users can be made t

Neurons in the brain form complicated networks through synaptic connections. Traditionally, functional connectivity between neurons has been analyzed using simple metrics such as correlation, which do not provide direction of influence. Recently, an information theoretic measure known as directed information has been proposed as a way to capture directionality in the relationship, thereby moving towards a model of effective connectivity. This measure is grounded upon the concept of Granger causality and can

When is uncoded transmission optimal? This paper derives easy-to-check necessary and sufficient conditions that do not require finding the rate-distortion and the capacity-cost functions. We consider the symbol-by-symbol communication of discrete-time memoryless sources across discrete-time memoryless channels, using single-letter coding and decoding. This is an optimal communication system if and only if the channel input cost function and the distortion measure can be written in a form that we explicitly

We study communication over a Gaussian multiple-access channel (MAC) with two types of transmitters: Digital transmitters hold a message from a discrete set that needs to be communicated to the receiver. Analog transmitters hold sequences of analog values, ...

Email Print Request Permissions Save to Project Linear receivers are often used in multiple-antenna systems due to ease of implementation. However, traditional linear receivers such as the Decorrelator and the linear minimum-mean squared error (MMSE) receiver often have a significant performance loss compared to the optimal joint maximum likelihood (ML) receiver. In previous work, we proposed the Integer-Forcing linear receiver, which bridges the rate gap between traditional linear receivers and the joint M

How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stat ...

A new achievable rate region is given for the Gaussian cognitive many-to-one interference channel. The proposed novel coding scheme is based on the compute-and-forward approach with lattice codes. Using the idea of decoding sums of codewords, our scheme im ...

In this work we consider the cognitive many-to-one interference network. We first extend existing coding schemes from the two-user case to this network scenario. Then we present a novel coding scheme using compute-and-forward and show it can enlarge the achievable rate region considerably for a wide range of parameters. Numerical evaluations are given to compare the performance of different schemes. Specializing the results to symmetric settings, for a range of parameters, our achievable rate region is show

Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to the interested party, the goal being not to make any errors. This coding paradigm is inspired by Shannon’s separation theorem for point-to-point communication, but it leads to suboptimal performance in general n

We study a generalization of Wyner’s Common Information to Watanabe’s Total Correlation. The first minimizes the description size required for a variable that can make two other random variables conditionally independent. If independence is unattainable, W ...

A source produces i.i.d. vector samples from a Gaussian distribution, but the user is interested in only one component. In the cache phase, not knowing which component the user is interested in, a first compressed description is produced. Upon learning the ...

Inspired by the connection between classical regret measures employed in universal prediction and Rényi divergence, we introduce a new class of universal predictors that depend on a real parameter α≥1 . This class interpolates two well-known predictors, the mixture estimators, that include the Laplace and the Krichevsky-Trofimov predictors, and the Normalized Maximum Likelihood (NML) estimator. We point out some advantages of this new class of predictors and study its benefits from two complementary viewpoi

Compute-and-Forward (CF), also known as reliable physical layer network coding, is a technique that provides the possibility of exploiting the features of broadcast and superposition in wireless networks. It has been shown that the throughput for multiple ...

We consider a fading AWGN 2-user 2-hop network in which the channel coefficients are independently and identically distributed (i.i.d.) drawn from a continuous distribution and vary over time. For a broad class of channel distributions, we characterize the ...

We study a 2-user 2-hop network with 2 relays in which channel coefficients are independently drawn from continuous distributions and vary over time. For a broad class of channel distributions, we characterize the ergodic sum capacity within a constant number of bits/sec/Hz, independent of signal- to-noise ratio. Specifically, we characterize the ergodic sum capacity within 4 bits/sec/Hz for independent and identically distributed (i.i.d.) uniform phase fading and approximately 4.7 bits/sec/Hz for i.i.d. Ra

Michael Christoph Gastpar

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Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Polar Codes for Broadcast Channels

A Discretization Approach to Compute-Forward

Relaxed Wyner's Common Information

The Optimal Memory-Rate Trade-Off for the Non-Uniform Centralized Caching Problem With Two Files Under Uncoded Placement

Ergodic Interference Alignment

Assessing directed information as a method for inferring functional connectivity in neural ensembles

To Code, Or Not To Code: On the Optimality of Symbol-by-Symbol Communication

Simultaneous Computation and Communication over MAC

Integer-Forcing architectures for MIMO: Distributed implementation and SIC

A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs

Lattice Codes for Many-to-One Interference Channels With and Without Cognitive Messages

Lattice codes for many-to-one cognitive interference networks

Source-Channel Communication in Sensor Networks

Total Correlation of Gaussian Vector Sources on the Gray–Wyner Network

Caching (a pair of) Gaussians

Alpha-NML Universal Predictors

Maximum Throughput Gain of Compute-and-Forward for Multiple Unicast

Approximate Ergodic Capacity of a Class of Fading 2-user 2-hop Networks

Approximate Ergodic Capacity of a Class of Fading 2x2x2 Networks