Considering a multi-user Downlink scenario, this paper studies transmit antenna selection (AS) for uniform rectangular arrays (URA). A key contribution of this work is the investigation of the trade-off between AS and the array directivity (or gain), and its impact on spectral efficiency (SE) and energy efficiency (EE). In this context, to improve EE of the system, a directivity-aware transmit AS and power allocation problem is formulated and solved using a sequential approach. For AS, two schemes are proposed: Block selection (BS) and L´evy flight based binary particle swarm optimization (LFBPSO), while for power allocation, an iterative quadratic transform method is employed in combination with Dinkelbach algorithm for EE maximization. The performance of the AS schemes is evaluated with respect to SE, EE and computational complexity. The results show that the array directivity varies with the subset of selected antennas and has a direct impact on the performance of the AS schemes in terms of achievable SE and EE. Furthermore, AS without considering the directivity can incur a loss in the array gain of up to 2 dB in a λ/2 spaced array. The results also show that LFBPSO outperforms other AS schemes with respect to SE and EE. Finally, another important contribution of this work is the experimental validation of the impact of array directivity on the AS process. This is demonstrated by replacing the isotropic antenna array with a measured 4×8 uniform rectangular array of patch antennas in the simulation framework.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for enabling seamless wireless connectivity, particularly in environments characterized by non-line-of-sight (NLoS) propagation and signal blockages. To enhance performance in such scenarios, densely deployed RIS configurations is a promising solution. In this context, this paper investigates the impact of inter-element spacing on the gain and the energy efficiency (EE) of RIS-assisted communication system. Unlike previous works, we formulate the received signal model that incorporates the array directivity and show how the array gain varies with the change in the inter-element spacing. The performance of the standard (where the spacing is half of the wavelength) and dense RIS (where the spacing is less than half of the wavelength) configurations is compared in terms of, 1) beam pattern and array gain while considering quantized and unquantized phase shifts, 2) variation in the number of RIS elements, and 3) the trade-off between EE and array gain. Results reveal that while dense RIS offers slightly improved array gain compared to standard RIS, it incurs higher power consumption and reduced EE.

Uniform planar arrays (UPAs) are widely employed in practical systems, yet the study of antenna selection for UPAs remains limited. In particular, the interplay between selected antenna subsets and directivity has been overlooked in the existing literature. This paper addresses this by exploring how different subsets of selected antennas affect directivity and developing effective antenna selection strategies for UPA with a rectangular lattice. Our findings reveal that different subsets of selected antennas can lead to significantly different values for directivity, influencing selection outcomes compared to scenarios where directivity is not considered. Furthermore, we propose and compare four antenna selection strategies: 1) Column Selection (RS), 2) Row Selection (CS), 3) Full Aperture Greedy Selection (FAGS), and 4) Hybrid Initialization Binary Particle Swarm Optimization (HBPSO). With careful initialization of the particles, HBPSO achieves a spectral efficiency performance close to exhaustive search and surpasses other strategies. Moreover, incorporating directivity into the selection process significantly affects the performance of these strategies.

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.

Beamforming techniques have a significant impact on the capacity of downlink transmission in cellular systems. Beamforming can offer significant performance enhancements for wireless systems by allowing directional signal transmission which can improve the Signal to Noise Ratio (SNR), co-channel interference, and network capacity. These benefits can be substantial in Massive MIMO systems but there are costs associated with the large number of antenna elements. In this paper, the trade-off between spectral efficiency and energy efficiency of digital and hybrid beamforming of Massive MIMO are analyzed for different numbers of RF chains. Based on simulation results, suitable ratios for the number of RF chains to the number of antennas are determined for the base station in order to achieve a good trade-off between spectral and energy efficiency.

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.

We consider a multihop network where a source node must reliably deliver a set of data packets to a given destination node. To do so, the source applies a fountain code and floods the encoded packets through the network, until they reach their destination or are lost in the process. We model the probability that the destination can recover the original transmissions from the received coded packets as a function of the network topology and of the code redundancy, and show that our analytical results predict the outcome of simulations very well. These results are employed to design distributed forwarding policies that achieve a good tradeoff between the success probability and the total number of transmissions required to advance a packet toward the destination. We finally develop in detail the case where intermediate relays can inject additional redundancy in the network, provided that they have successfully decoded the source packets.

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.