ARM: Adaptive Random-Selected Multi-Beamforming Estimation Scheme for Satellite-Based Internet of Things

Abstract

In this paper, we focus on the millimeter-wave (mmWave) band multiple-input and multipleoutput (MIMO) channel estimation in the satellite-based Internet of Things (S-IoT). At first, we establish a sparse geometric-based mmWave MIMO channel model between a high throughput satellite (HTS) and multiple terrestrial user equipments (UEs) for the S-IoT downlink system. By exploiting the sparsity inherent to the mmWave band channel, we propose an efficient adaptive random-selected multi-beamforming (ARM) estimation scheme, which can simultaneously estimate the mmWave MIMO channel state information (CSI) for multiple UEs in angle domain. The ARM estimation scheme measures of the CSI between the HTS and multiple UEs by utilizing a series of random combinations of transmitting beamforming vectors and receiving UEs' antennas and the required number of measurements can adaptively reduce as well as the signal-to-noise ratios (SNR) increases. To further improve the performance of ARM estimation scheme, the number of each beamforming been selected for the channel measurement is buffered, and modify the probability of the random selection of beams and UEs. Two improved ARM estimation schemes are proposed, one is ARM forcing adaptation beam selection (ARM-FABS) scheme, and the other is ARM partially estimated beam selection (ARM-PEBS) scheme. The simulation results show that the proposed ARM estimation schemes can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.INDEX TERMS Satellite-based Internet of Things, millimeter-wave band, multiple-input and multipleoutput, channel estimation.

A review on non-terrestrial wireless technologies for Smart City Internet of Things

“…At present, service demands on throughput and service quality are rising, and the traditional frequency band is difficult to meet the practical demand. Therefore, Wang et al 61 introduced the millimeter-wave (mm-Wave) frequency band. Combined with the massive MIMO assisted by beamforming, Wang et al proposed an effective adaptive random-selected multi-beamforming (ARM) estimation scheme.…”

Location Parameter Estimation of Moving Aerial Target in Space–Air–Ground-Integrated Networks-Based IoV

“…At the same time, the use of satellites is found to be of paramount importance in IoV, such as intelligent surveillance. Thus, satellite-based IoV is viewed as a vital part of space-air-ground integrated networks (SAGIN) to provide broadband access [3]- [5]. Data transmission and radar sensing are useful tools to extract valuable information from the environment [6]- [7].…”

Design and analysis of a multiple collaborative beamforming scheme in the realm of Wireless Sensor Networks featuring 3-dimension node configuration

“…In networks featuring multiple data sinks/receivers/base stations, multiple beamforming is of essence. Research in multiple beamforming entailing centralized antenna arrays can be found in [8] , [9] , [10] , [11] , [12] , [13] , [14] . In [8] , a multiple beamforming approach based on Butler matrices is presented.…”