Uplink Downlink Rate Balancing in Cooperating Cellular Networks

TL;DR

This paper analyzes the tradeoff between uplink feedback rate and downlink capacity in cellular networks, showing how adjusting feedback can optimize overall network throughput under various conditions.

Contribution

It quantifies the uplink-downlink rate tradeoff in cellular networks, providing bounds and insights for both finite and infinite network models.

Findings

Higher channel coherence time improves feedback efficiency.

Constant feedback fraction can increase downlink multiplexing gain.

In infinite networks, rate scales doubly logarithmically with SNR.

Abstract

Broadcast MIMO techniques can significantly increase the throughput in the downlink of cellular networks, at the price of channel state information (CSI) feedback from the mobiles, sent over the uplink. Thus, it creates a mechanism that can tradeoff some uplink capacity for increased downlink capacity. In this work we quantify this tradeoff and study the exchange ratio between the feedback rate (over the uplink) and the downlink rate. We study both finite and infinite networks, and show that for high enough (but finite) SNR, the uplink rate can be exchanged for increased downlink rate with a favorable exchange ratio. This exchange ratio is an increasing function of the channel coherence time, and a decreasing function of the number of measured base stations. We also show that devoting a constant fraction of the uplink to CSI feedback can increase the downlink multiplexing gain continuously from 0 to 1, in finite networks. On the other hand, in infinite networks (with infinite connectivity) our bounds can only show doubly logarithmic scaling of the rate with SNR. The presented results prove that the adaptation of the feedback rate can control the balance between the uplink and downlink rates. This capability is very important in modern cellular networks, where the operators need to respond to continuously changing user demands.