Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data

Abstract

Beam training in dynamic millimeter-wave (mmwave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Exploiting the fact that mobile devices will typically integrate multiple chipsets, we study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mmwave devices. We evaluate SLASH both with simulations and experiments in a 60-GHz testbed. SLASH can increase the data rate by more than 64% for link establishment and 67% for link maintenance with respect to prior work.

Augmenting mmWave localization accuracy through sub-6 GHz on off-the-shelf devices

“…The authors of [11] show how to improve mmWave beam search using sub-6 GHz information, simulating a sub-6 GHz antenna with 4 elements and a 32-element mmWave antenna. A similar approach is taken in [36] which focuses on sub-6 GHz assisted mmWave beam steering and link maintenance. The authors experimentally demonstrate how to use sub-6 GHz ToF and an external gyroscope to estimate the position of the AP.…”

“…Only few prior works study the joint use of mmWave and sub-6 GHz bands. The most common use case is to reduce the mmWave beam-training overhead with the help of sub-6 GHz information [11,28,36,41] to infer the optimal steering direction of the mmWave antenna array. The only system that combines mmWave and sub-6 GHz information for localization purposes "fuses" mmWave ToFbased ranging with sub-6 GHz AoA [23] but does not in fact provide an independent location system at either band.…”

Beam searching for mmWave networks with sub-6 GHz WiFi and inertial sensors inputs: An experimental study

“…In [30] we already introduced SLASH algorithm, showing through extensive simulations and experiments its capability to significantly increase the data rate compared to prior work. More specifically, [30] computed the ToF distance estimate using 802.11 data/ACK handshake.…”

“…In [30] we already introduced SLASH algorithm, showing through extensive simulations and experiments its capability to significantly increase the data rate compared to prior work. More specifically, [30] computed the ToF distance estimate using 802.11 data/ACK handshake. It fixed the AoA estimation based on the median angle error presented in [10], and it assumed that rotation information is subjected to an error of a few degrees.…”

“…It fixed the AoA estimation based on the median angle error presented in [10], and it assumed that rotation information is subjected to an error of a few degrees. For tests in mobility, [30] adopted the well known ray-tracing based signal propagation tool WinProp. On the contrary, this work uses the new FTM protocol for the estimation of the distance and it investigates CSI data at sub-6 GHz for AoA estimation.…”