Beamforming Techniques for Over-the-Air Computation in MIMO IoT Networks

Abstract

In this paper, a novel beamforming technique is proposed as the over-the-air computation (AirComp) framework in a multiple-input multiple-output (MIMO) Internet-of-things (IoT) network consisting of multiple IoT sensors (STAs) and a single access point (AP). We assume that each IoT device has the channel state information (CSI) from itself to the AP and the AP has the global CSI of all IoT devices. We consider the mean squared error (MSE), which represents the reliability of function computation, as a performance metric. In short, each IoT device exploits maximum-ratio transmission (MRT) as a transmit beamforming technique to improve MSE performance by taking full advantage of multiple transmit antennae. Moreover, for the receive beamforming, we first consider a receive antenna selection (RAS) technique as the simplest beamforming method at the AP. Then, a semi-definite relaxation (SDR) method and a successive convex approximation (SCA) algorithm are considered and compared with each other in terms of MSE. Finally, we propose a novel two-step beamforming algorithm to further improve the MSE performance of the aforementioned techniques. We have numerically verified through computer simulations that the proposed framework has an improved MSE performance of about 6dB compared to the conventional single-input multiple-output (SIMO) AirComp, even with only two transmit antennae, and the modified MRT outperforms the other transmit beamforming techniques. Furthermore, the proposed receive beamforming technique, a two-step algorithm, shows the best performance in terms of MSE compared to prior studies.

Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systems

“…Here, the term is the weight of link between the hidden layer and output layer from node to node, and represents the number of nodes in hidden layer. RNN 39–41 : The proposed channel estimation model for the MIMO‐OFDM system offers the demodulated signals by processing the extracted features of the channel coefficients in the RNN network. RNN contains a group of NNs with feedback.…”

“…The beamforming techniques in the MIMO system ensure the improved spectral efficiency and enhanced energy efficiency. But, it suffers from hardware complexity as it requires more antennas and diverse devices 40,43 . The accurate Channel State Information (CSI) is necessary to acquire in the MIMO wireless communication systems at the transmitter phase as the system performance is highly dependent on the CSI accuracy of the transmitter 3,4 .…”

“…But, it suffers from hardware complexity as it requires more antennas and diverse devices. 40,43 The accurate Channel State Information (CSI) is necessary to acquire in the MIMO wireless communication systems at the transmitter phase as the system performance is highly dependent on the CSI accuracy of the transmitter. 3,4 Particularly, the accurate CSI is obtained at the transmitter, which is further utilized for enhancing the MIMO system performance by employing the transmit precoding or beam forming.…”

Over-the-Air Computation Strategy Using Space–Time Line Code for Data Collection by Multiple Unmanned Aerial Vehicles

“…In addition, the channel effect cannot be removed from the received signal at the dUAV, as the channels are merged in the air. Accordingly, conventional OAC techniques using multiple transmit antennas, for example, [13], [17], [20], [23], are not directly applicable to sUAVs with a single antenna. In the next section, we propose a novel STLC-OAC strategy for single-antenna sUAVs such that the dUAV can readily estimate the average of the sensing data.…”

“…To prevent the abuse of the time resources in the TDMAbased protocol when the dUAV computes the statistics of the collected data, an efficient strategy called over-the-air computation (OAC) [15]- [23] can be employed in UAVbased data collection systems [13], [24]. In an OAC-based protocol, multiple sUAVs report the sensing data to the dUAV (or GBS) simultaneously using the same frequency band, that is, in a coordinated multi-point transmission [25], [26].…”