We investigate the uplink non-orthogonal multiple access (NOMA)-based Internet-of-Things (IoT) networks, where each IoT device (station: STA) equipped with a single antenna exploits the constellation-rotated space-time line code (CR-STLC) to send signals to an access point (AP) equipped with two receive antennas. The AP decodes the signals transmitted from the STAs with the optimal joint maximum-likelihood (JML) detector. As a main result, we mathematically analyze both the bit-error-rate (BER) performance and the spatial diversity order of each STA in the two-user uplink CR-STLC NOMA system. In particular, it is shown that the two-user uplink CR-STLC NOMA system achieves the optimal diversity order regardless of the constellation rotation angle in the high signal-to-noise ratio (SNR) regime. We also consider two different rotation angle optimization techniques (dynamic rotation & fixed rotation) to improve the BER performance in the practical SNR regime, and found that the fixed rotation approach is simple but yields almost the same performance as the dynamic approach. Finally, we show that the uplink CR-STLC NOMA system achieves the spatial diversity order of 1 even when more than two STAs send packets simultaneously. It is worth noting that all STAs obtain an improved spatial diversity gain compared to that without constellation rotation.

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 investigate the performance of uplink non- orthogonal multiple access (NOMA) systems for internet- of-things (IoT) applications, which exploits a space-time line code (STLC) and a binary phase shift keying (BPSK) modulation at each sensor (transmitter). A single transmit antenna is assumed to be equipped at the sensor and two receive antennas are assumed to be equipped at a fusion center (FC). The STLC enables the FC to decode the transmitted symbols from multiple sensors without channel state information (CSI) of each sensor. In particular, we mathematically analyze the symbol error rate (SER) of the super-imposed symbol received at the FC and obtain the diversity order of the uplink NOMA-IoT network. We also propose a phase steering (PS)-based STLC technique to improve the SER performance, in which certain sensors rotate its constellation. The proposed PS-STLC technique significantly outperforms the conventional STLC scheme in the uplink NOMA-IoT network.

In this paper, we propose a novel uplink non-orthogonal multiple access (NOMA) for massive internet-of-things (IoT) networks where each device exploits a space-time line code (STLC) to send a small data packet to fusion center (FC) and the FC decodes the received packet without channel state information (CSI). In particular, we mathematically analyze the bit error ratio (BER) performance of the NOMA-based massive IoT network with the STLC by assuming the optimal joint maximum-likelihood (JML) detector. In addition, we propose a phase-steering based STLC (PS-STLC) technique to further improve the BER performance. Through extensive simulations, it is shown that the proposed PS-STLC technique achieves the optimal diversity and significantly improves the BER performance of the NOMA-based massive IoT network.

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.