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.

In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.

In this letter, we mathematically analyze the bit-error-rate (BER) and frame-error-rate (FER) performance of the resource hopping-based grant-free multiple access (RH-GFMA) systems that were recently proposed for 6G massive Internet-of-Things (mIoT) networks. In the RH-GFMA systems, each IoT device intermittently sends a short-packet to the access point (AP) based on a predetermined resource hopping pattern (HP). Each IoT device may experience HP collisions within the packet, but the effect of HP collisions on error performance can be reduced to be collision-friendly by exploiting multi-antenna-based signal detection and error-correction capability of channel codes. In this letter, hence, we rigorously analyze the effect of HP collisions on the error performance in the RH-GFMA system where repetition codes (RC) and tail-biting convolutional codes (TB-CC) are considered channel coding schemes for satisfying low-complexity and low-latency requirements of mIoT networks. Through computer simulations, we validate that our mathematical analysis is well-matched with the simulation results.

Recently, Internet of Things (IoT) devices have been installed everywhere, and these devices are connected through wireless communication networks. In particular, uncrewed aerial vehicles (UAVs), one of the most promising IoT devices, are expected to be used actively in Internet of Battlefield Things (IoBT) networks due to their flexible 3-D mobility. To react to enemy UAV attacks in the IoBT networks, the ground-to-air (G2A) or air-to-air (A2A) radio jamming can be an effective counterattack technique that disrupts the communication and control signals of adversary equipment. That is, it can be a very effective means of coping with attacks by UAVs in modern battlefields characterized by electronic warfare. Accordingly, this article proposes a hierarchical distributed deep reinforcement learning-based cooperative jamming (HDRL-CJ) method for secure air–ground integrated networks. The proposed method uses two types of jammers: ground jammers (GJs) and UAV jammers (UJs). The GJ optimizes the beamwidth to maximize the effectiveness of jamming, and the UJ tracks the malicious UAV (MU) and controls the jamming power to minimize the MU’s signal-to-jamming-plus-noise ratio (SJNR) while considering the UJ’s limited battery capacity. Moreover, to reduce the computational complexity of reinforcement learning (RL) method, we devise a hierarchical RL architecture that separates the UJ’s movement control and transmit power control. Through extensive simulations, we demonstrate that the proposed HDRL-CJ method converges to the optimal solution obtained by the optimal exhaustive search algorithm. Furthermore, by comparing the jamming performance with several benchmark methods, we validate the proposed method’s superior performance under various 3-D network environments.