Terahertz (THz) communication is widely deemed the next frontier of wireless networks owing to the abundant spectrum resources in the THz band. Whilst THz signals suffer from severe propagation losses, a massive antenna array can be deployed at the base station (BS) to mitigate those losses through beamforming. Nevertheless, a very large number of antennas increases the BS’s hardware complexity and power consumption, and hence it can lead to poor energy efficiency (EE). To surmount this fundamental problem, we propose a novel array design based on superdirectivity and nonuniform inter-element spacing. Specifically, we exploit the mutual coupling between closely spaced elements to form superdirective pairs. A unique property of them is that all require the same excitation amplitude, and thus can be driven by a single radio frequency chain akin to conventional phased arrays. Moreover, they facilitate multi-port impedance matching, which ensures maximum power transfer for any beamforming angle. After addressing the implementation issues of superdirectivity, we show that the number of BS antennas can be effectively reduced without sacrificing the achievable rate. Simulation results demonstrate that our design offers huge EE gains compared to uncoupled arrays with uniform spacing, and hence could be a radical solution for future THz systems.

Dense arrays can facilitate the integration of multiple antennas into finite volumes. In addition to the compact size, sub-wavelength spacing enables superdirectivity for endfire operation, a phenomenon that has been mainly studied for isotropic and infinitesimal radiators. In this work, we focus on linear dipoles of arbitrary yet finite length. Specifically, we first introduce an array model that accounts for the sinusoidal current distribution (SCD) on very thin dipoles. Based on the SCD, the loss resistance of each dipole antenna is precisely determined. Capitalizing on the derived model, we next investigate the maximum achievable rate under a fixed power constraint. The optimal design entails conjugate power matching along with maximizing the array gain. Our theoretical analysis is corroborated by the method of moments under the thin-wire approximation, as well as by full-wave simulations. Numerical results showcase that a super-gain is attainable with high radiation efficiency when the dipole antennas are not too short and thin.

Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies over the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.

We study the uplink performance of massive multiple-input multiple-output (MIMO) when users are equipped with multiple antennas. To this end, we consider a generalized channel model that accounts for line-of-sight propagation and spatially correlated multipath fading. Most importantly, we employ the Weichselberger correlation model, which has been shown to alleviate the deficiencies of the popular Kronecker model. The main contribution of this paper is a rigorous closed-form expression for the uplink spectral efficiency using maximum-ratio combining and minimum mean square error channel estimation. Our result is a non-trivial generalization of previous results on massive MIMO with spatially correlated channels, thereby enabling us to have suitable designs for future massive MIMO systems. Numerical simulations corroborate our analysis and provide useful insights on how different propagation conditions affect system performance.

A fundamental problem arising in dense wireless networks is the high co-channel interference. Interference alignment (IA) was recently proposed as an effective way to combat interference in wireless networks. The concept of IA, though, is originated by the capacity study of interference channels and as such, its performance is mainly assessed under ideal assumptions, such as instantaneous and perfect channel state information (CSI) at all nodes, and a homogeneous signal-to-noise ratio (SNR) regime, i.e., all users have the same average SNR. Consequently, the performance of IA under realistic conditions has not been completely investigated yet. In this paper, we aim at filling this gap by evaluating the performance of spatial IA in practical systems. Specifically, we derive a closed-form expression for the IA average sum-rate when CSI is acquired through training and users have heterogeneous SNR. A main insight from our analysis is that IA can indeed provide significant spectral efficiency gains over traditional approaches in a wide range of dense network scenarios. To demonstrate this, we consider the examples of linear, grid and random network topologies.

The Internet of Things (IoT) is fueling innovation in nearly every part of our lives. From smart homes, cars, and cities, the Internet of Things is creating a more convenient, secure, intelligent, and personalized experience. While for any final user this IoT vision is a substantial innovation step, for communication providers is a compelling thread with massive number of devices connected to the Internet. Multiple connected devices sharing common wireless resources might create interference if they access the channel simultaneously. Medium access control protocols generally regulate the access of the devices to the shared channel to limit signal interference. In particular, irregular repetition slotted ALOHA (IRSA) techniques can achieve high-throughput performance when interference cancellation methods are adopted to recover from collisions. In this work, we study the finite length performance of IRSA schemes by building on the analogy between successive interference cancellation and iterative belief-propagation on erasure channels. We use a novel combinatorial derivation based on the matrix-occupancy theory to compute the error probability and we validate our method with simulation results.