Reducing Initial Cell-search Latency in mmWave Networks

TL;DR

This paper proposes using random beamforming to significantly reduce initial cell-search latency in mmWave networks, especially in dense deployments, challenging the need for complex algorithms.

Contribution

It introduces a stochastic geometry framework to analyze random beamforming for cell-search, showing its advantages over exhaustive search in dense mmWave networks.

Findings

Random beamforming reduces cell-search latency compared to exhaustive search.

In dense networks, random beamforming achieves lower failure probability at reduced latency.

Complex cell-discovery algorithms may be unnecessary in dense mmWave deployments.

Abstract

Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility of random beamforming for cell-search. We develop a stochastic geometry framework to analyze the performance in terms of failure probability and expected latency of cell-search. Meanwhile, we compare our results with the naive, but heavily used, exhaustive search scheme. Numerical results show that, for a given discovery failure probability, random beamforming can substantially reduce the latency of exhaustive search, especially in dense networks. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave system design.