Power Control of Multi-Layer Repeater Networks (POLARNet)

TL;DR

POLARNet introduces a gradient-free, layer-wise power control method for multi-layer repeater networks, optimizing SNR under various power constraints with proven monotonicity and superior performance over single-repeater selection.

Contribution

It presents a novel, locally optimized power control algorithm for multi-layer repeaters, inspired by neural network duality, applicable under diverse power constraints.

Findings

Significant SNR improvement over upper bounds.

Power distribution across multiple repeaters outperforms single repeater selection.

Method is gradient-free and monotonic in the objective.

Abstract

In this letter we introduce POLARNet -- power control of multi-layer repeater networks -- for local optimization of SNR given different repeater power constraints. We assume relays or repeaters in groups or layers spatially separated. Under ideal circumstances SISO narrow-band communication and TDD, the system may be viewed as a dual to a deep neural network, where activations, corresponding to repeater amplifications, are optimized and weight matrices, corresponding to channel matrices, are static. Repeater amplifications are locally optimized layer-by-layer in a forward-backward manner over compact sets. The method is applicable for a wide range of constraints on within-layer power/energy utilization, is furthermore gradient-free, step-size-free, and has proven monotonicity in the objective. Numerical simulations show significant improvement compared to upper bounds on the expected SNR. In addition, power distribution over multiple repeaters is shown to be superior to optimal selection of single repeaters in the layers.