Sparse regression codes
Sekhar Tatikonda, Yale University, USA
Abstract:
Many communication problems in networked control involve multi-terminal source and channel coding. Hence there is a need for designing computationally efficient coding schemes. In this talk we study a new class of codes called sparse regression codes. These codes are inspired by recent work in sparse high-dimensional linear regression.
We first review sparse regression codes; demonstrate how to implement random binning and superposition using these codes; and then show for a variety of multiterminal source and channel coding problems that these codes achieve the information theoretic limits and are computationally efficient. Joint work with Ramji Venkataramanan.