Advances in Distributed Graph Filtering

Graph filters are one of the core tools in graph signal processing. A central aspect of them is their direct distributed implementation. However, the filtering performance is often traded with distributed communication and computational savings. To improve this tradeoff, this paper generalizes state-of-the-art distributed graph filters to...

Distributed Rate-Constrained LCMV Beamforming

In this letter, we propose a decentralized framework for rate-distributed linearly constrained minimum variance (LCMV) beamforming in wireless acoustic sensor networks. To save the energy usage within the network, we propose to minimize the transmission cost and put a constraint on the noise reduction performance. Subsequently, we...

Adaptive and Distributed Beamforming for Cognitive Radio

Cognitive Radio (CR) is an energy efficient technique that is capable of optimizing the premium radio resources, such as power and spectrum. In this thesis, we focus on exploiting spatial diversity for CR. We have adopted two spatial signal processing techniques, i.e., Adaptive Beamforming (ABF) and Distributed Beamforming (DB) for CR users and...