In this paper, we study the achievable rate and the energy efficiency of analog, hybrid, and digital combining (AC, HC, and DC) for millimeter wave (mmW) receivers. We take into account the power consumption of all receiver components, not just analog-to-digital converters (ADCs), determine some practical limitations of beamforming in each architecture, and develop performance analysis charts that enable comparison of different receivers simultaneously in terms of two metrics, namely, spectral efficiency (SE) and energy efficiency (EE). We present a multi-objective utility optimization interpretation to find the best SE-EE weighted tradeoff among AC, DC, and HC schemes. We consider an additive quantization noise model to evaluate the achievable rates with low resolution ADCs. Our analysis shows that AC is only advantageous if the channel rank is strictly one, the link has very low SNR, or there is a very stringent low power constraint at the receiver. Otherwise, we show that the usual claim that DC requires the highest power is not universally valid. Rather, either DC or HC alternatively results in the better SE versus EE tradeoff depending strongly on the considered power consumption characteristic values for each component of the mmW receiver.

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

Ultra wideband (UWB) is a popular technology to address the need for high-precision indoor positioning systems in challenging industry 4.0 use cases. In line-of-sight (LOS) environments, UWB positioning errors in the order of 1–10 cm can be achieved. However, in non-line-of-sight (NLOS) conditions, this precision drops significantly, with errors typically >30 cm. Machine learning (ML) has been proposed to improve the precision in such NLOS conditions, but is typically environment-specific and lacks generalization to new environments and UWB configurations. As such, it is necessary to collect large data sets to train a neural network (NN) for each new environment or UWB configuration. To remedy this, this article proposes automatic optimizations for transfer learning (TL) deep NNs toward new environments and UWB configurations. We analyze error correction and (N)LOS classification models, using either feature- or channel impulse response (CIR)-based input data. Our TL solutions show a 50% error improvement and 15% (N)LOS classification accuracy improvement (for both feature- and CIR-based approaches) compared to a model trained in a different environment. We also analyze the impact on TL using a limited number of samples (25 to 400 samples). The highest accuracy is typically achieved by the CIR-based approach, where with only 50 samples from the new mixed (N)LOS environment, we show ±10 cm precision after error correction with 93% (N)LOS detection. The presented results demonstrate high-precision UWB localization (from 643 to 245 mm) through ML with minimal data collection effort in challenging NLOS environments.

The accuracy and reliability of an Ultra- WideBand (UWB) Indoor Positioning System (IPS) are compromised owing to the positioning error caused by the Non-Line-of-Sight (NLoS) signals. To address this, Machine Learning (ML) has been employed to classify Line-of-Sight (LoS) and NLoS components. However, the performance of ML algorithms degrades due to the disproportion of the number of LoS and NLoS signal components. A Weighted Naive Bayes (WNB) algorithm is proposed in this paper to mitigate this issue. The performance of the proposed algorithm is compared with conventional state-of-the-art ML algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT) using the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC). The results prove that the WNB classifier can significantly reduce the impact of the limited number of NLoS components that are available for training the model. The proposed WNB algorithm also maintains a high classification accuracy and robustness in mixed LoS/NLoS conditions.

Sixth Generation (6G) transceivers are envisioned to feature massively large antenna arrays compared to its predecessor. This will result in even higher spectral efficiency (SE) and multiplexing gains. However, immense concerns remain about the energy efficiency (EE) of such transceivers. This work focuses on partially connected hybrid architectures, with the primary aim of enhancing the EE of the system. To achieve this objective, the study proposes a combined approach of joint antenna selection and precoding, which holds the potential to further optimize the system’s EE while maintaining a satisfactory SE performance levels. The proposed approach incorporates antenna selection based on a meta-heuristic cyclic binary particle swarm optimization algorithm along with successive interference cancellation-based precoding. The results indicate that the proposed solution, in terms of SE and EE, performs very close to the optimal exhaustive search algorithm. This study also investigates the trade-off between SE and EE in a low and high signal-to-noise ratio (SNR) regimes. The robustness of the proposed scheme is also demonstrated when the channel state information is imperfect. In conclusion, this work presents a lower complexity approach to enhance EE in 6G transceivers while maintaining SE performance and along with a reduction in power consumption.