Scale-Free Beamforming using Swarm Arrays for Remote Sensing under Interference
This work addresses the scalability bottleneck in large-scale relay arrays for remote sensing under interference, though the swarm constraint is severe.
The paper presents a distributed algorithm for zero-forcing beamforming in a swarm array of relays that cancels interferers without prior channel knowledge, achieving computational and bandwidth overheads independent of array size.
We consider a swarm array of autonomous relays that seek to cooperatively forward a desired signal to a fusion center with the maximum possible fidelity while canceling out a number of interferers. We present a distributed algorithm for computing the optimal zero-forcing beamforming weights at the relays without requiring prior channel knowledge. Crucially, our algorithm is {\it scale-free} in the sense that the computational and bandwidth overheads are completely independent of the size of the array. We build on recent work that introduced the concept of a Collective Array that enables such {\it scale-free} computation by imposing a constraint that the array must always function as a {\it swarm} i.e. array elements can only ever communicate with external nodes collectively and never individually. While this is a very severe restriction, we show that it allows useful computations such as zero-forcing beamforming while being robust to noise and channel time-variations.