Generalized Degrees of Freedom of Noncoherent Diamond Networks

TL;DR

This paper analyzes the generalized degrees of freedom (gDoF) of noncoherent diamond networks, deriving bounds, optimal signaling, and a novel train-scale quantize-map-forward scheme that demonstrates when partial network use or relay selection is optimal.

Contribution

It introduces a new achievability scheme, TS-QMF, and characterizes the regimes where partial network use or relay selection is optimal for noncoherent diamond networks.

Findings

Optimal signaling structure derived for the outer bound.

TS-QMF scheme achieves gDoF optimality in certain regimes.

Demonstrates tradeoff between channel learning and communication in network regimes.

Abstract

We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent diamond (parallel relay) wireless network with asymmetric distributions of link strengths, and a coherence time of T symbol duration. We first derive an outer bound for this channel and then derive the optimal signaling structure for this outer bound. Using the optimal signaling structure we solve the outer bound optimization problem in terms of its gDoF. Using insights from our outer bound signaling solution, we devise an achievability strategy based on a novel scheme that we call train-scale quantize-map-forward (TS-QMF). This uses training in the links from the source to the relays, scaling and quantizing at the relays combined with nontraining-based schemes. We show the optimality of this scheme with respect to the outer bound in terms of the gDoF. In noncoherent point-to-point multiple-input-multiple-output (MIMO) channels, where the fading channel is unknown to transmitter and receiver, an important tradeoff between communication and channel learning was revealed by Zheng and Tse, by demonstrating that not all the available antennas might be used, as it is suboptimal to learn all their channel parameters. Our results in this paper for the diamond network demonstrates that in certain regimes the optimal scheme uses a subnetwork, demonstrating a tradeoff between channel learning and communications. In some regimes, it is gDoF optimal to do relay selection, i.e, use a part of the network. In the other regimes, even when it is essential to use the entire network, it is suboptimal to learn the channel states for all the links in the network, i.e, traditional training-based schemes are suboptimal in these regimes.