This paper shows that deep neural network (DNN) can be used for efficient and distributed
channel estimation, quantization, feedback, and downlink multiuser precoding for a …

In this paper, the situation in which a receiver has to execute a task from a quantized version
of the information source of interest is considered. The task is modeled by the minimization …

Researchers across cognitive science, economics, and evolutionary biology have studied
the ubiquitous phenomenon of social learning—the use of information about other people's …

Management of bulk sensor data is one of the challenging problems in the development of
Internet of Things (IoT) applications. High volume of sensor data induces for optimal …

Estimation of a vector from quantized linear measurements is a common problem for which
simple linear techniques are suboptimal-sometimes greatly so. This paper develops …

In low-power wireless neural recording tasks, signals must be compressed before
transmission to extend battery life. Recently, compressed sensing (CS) theory has …

We consider the problem of optimizing the quantization intervals (or thresholds) of low-
resolution analog-to-digital converters (ADCs) via the minimization of a Cramér-Rao bound …

We study fixed data rate communication schemes for wireless relay-interference networks
with any number of transmitters, relays, and receivers. The transmitters and the relays have …

We consider the problem of distributed feature quantization, where the goal is to enable a
pretrained classifier at a central node to carry out its classification on features that are …

We design a distributed function-aware quantization scheme for distributed functional
compression. We consider 2 correlated sources and and a destination that seeks an …