Compressed sensing techniques for dynamic resource allocation in wideband cognitive networks

Abstract

For multi-user cognitive networks, joint dynamic resource allocation (DRA) and waveform adaptation techniques have been developed that effectively represent, manipulate and utilize the physical-layer radio resources by synthesizing both transmitter and receiver waveforms from generalized signal expansion functions. To effect distributed DRA games, this paper discusses the intertwined sensing task and develops compressed sensing techniques that simultaneously estimate all the channel and interference links using only a small number of samples collected from a sparse set of expansion functions. By properly identifying and utilizing the sparsity properties of a wideband environment, the proposed schemes considerably reduce both sensing time and implementation costs.