Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks

In wireless acoustic sensor networks (WASNs), sensors typically have a limited energy budget as they are often battery-driven. Energy efficiency is, therefore, essential for the design of algorithms in WASNs. One way to reduce energy costs is to select only the sensors that are most informative, a problem known as sensor selection. In this way, only sensors that significantly contribute to the task at hand will be involved. In this paper, we consider a more general approach, which is based on rate-distributed spatial filtering. Depending on the distance over which a transmission takes place, the hit rate directly influences the energy consumption. We try to minimize the battery usage due to transmission, while constraining the noise reduction performance. This results in an efficient rate allocation strategy, which depends on the underlying signal statistics, as well as the distance from sensors to a fusion center (FC). Through the utilization of a linearly constrained minimum variance beamformer, the problem is derived as a semidefinite program. Furthermore, we show that rate allocation is more general than sensor selection, and sensor selection can be seen as a special case of the presented rate-allocation solution, e.g., the best microphone subset can be determined by thresholding the rates. Finally, numerical simulations for estimating several target sources in a WASN demonstrate that the proposed method outperforms the sensor-selection-based approaches in terms of energy usage, and we find that the sensors close to the FC and point sources are allocated with higher rates.