Extremely large-scale (XL) antenna arrays and wideband transmission are critical enablers for next-generation mobile communication networks. These technologies offer substantial improvements in angular resolution, spatial degrees of freedom, and spectral efficiency (SE). However, the combination of large antenna apertures and ultra-wide bandwidth induces a frequency-dependent beam misalignment, known as the beam squint effect, which severely degrades spatial directivity and SE. Traditional frequency-independent analog phase shifters (APSs) cannot compensate for this phenomenon, necessitating the use of true-time delays (TTDs). Despite their potential, most existing TTD-based architectures rely on the assumption of ideal hardware, ignoring practical constraints such as limited delay range and finite delay resolution. This paper analyzes the performance of existing architectures under these practical hardware limitations and proposes a novel hardware-aware multi-stage delay-phase-precoding (MSDPP) architecture. The proposed MSDPP is designed to comply with commercially available device specifications, effectively minimizing the impact of quantization and clipping errors inherent in practical TTDs. Extensive simulations considering realistic hardware constraints demonstrate that the proposed MSDPP outperforms state-of-the-art solutions by approximately 3 dB and 55 % in SE and energy efficiency (EE), respectively, making it a robust solution for energy-efficient ultra-wide bandwidth extremely large-scale XL antenna array systems.

Current orthogonal frequency division multiplexing (OFDM) standards specify limited options for cyclic prefix (CP) duration, regardless of the wireless channel characteristics. These fixed options can result in significant overhead when the channel delay spread is very short. To address this, a more flexible approach to CP selection is needed, allowing for CP lengths that may be shorter than the delay spread. In this paper, we revisit the classical issue of waveform optimization for channels with short delay spreads, and investigate the potential to reduce the CP duration in OFDM. Building on our prior work in [1], we extend the analysis to multi-antenna systems and assess the impact of number of antennas on multiple-input single-output (MISO)-OFDM system with reduced CP durations. We first derive closed-form expressions for the desired signal power and inter-symbol interference (ISI) power in MISO-OFDM where the CP duration is shorter than the length of channel impulse response (CIR). Then, conditioned on the radio link reliability, defined by the link outage probability, we formulate an optimization problem to jointly determine the minimum CP duration and SNR values required for the system to satisfy that reliability condition. To solve the optimization problem, we use a weighted-sum approach combined with the Bisection method. Our analysis demonstrates that energy efficiency comparable to conventional OFDM systems can be maintained, while achieving increased spectral efficiency (SE) due to the reduced CP duration.

The conventional orthogonal frequency division multiplexing (OFDM) combats inter-symbol interference (ISI) by adding cyclic prefix (CP) at the beginning of OFDM block. Current standardization employs fixed CP duration irrespective of wireless channel characteristics, which is effective for channels with a long root mean-square (RMS) delay spread. However, this is impractical for industrial environments where the channel exhibits a shorter delay spread leading to a more pronounced CP overhead. Therefore, the classical problem of waveform design and optimization has to be revisited for industrial applications in the sixth generation (6G) of wireless communication systems. With the understanding of wave propagation characteristics in industrial environments, in this paper, we first analyze the impact of insufficient CP duration on OFDM and subsequently derive the closed-form expressions for ISI and desired power. Then, we design a multi-objective optimization problem (OP) which determines the minimum required signal-to-noise ratio (SNR) and CP duration conditioned on link reliability. To solve the proposed OP, we adopt the weighted-sum approach that transforms the multi-objective OP into a single-objective OP. We present numerical results for channels with various RMS delay spreads and confirm the spectral efficiency (SE) gains attainable by the proposed method with no need for additional equalization complexities.

Resource allocation has always been a challenging task in vehicular networks due to their dynamic nature. In this paper, we study the decentralized joint subchannel allocation and power control problem for a Cooperative Adaptive Cruise Control (CACC) system to satisfy string stability in a platoon of connected and autonomous vehicles. The developed optimization problem takes the string stability of the platoon as well as the reliability of all the Vehicle to Vehicle (V2V) links into account, aiming at maximizing the total ergodic capacity. We tackle the optimization problem from two different angles. The first approach is a centralized classical algorithm governed from the Base Station (BS) perspective, where we assume that the BS only knows the large-scale fading information of the V2V links due to the rapidly changing channel conditions in vehicular environments. In the second strategy, we devise a Federated Multi-Agent Reinforcement Learning (MARL) based algorithm where each transmitter vehicle in the platoon acts as an independent agent and tries to find an optimal policy to maximize its total expected reward. Finally, to better understand the policies each agent has learned, we also compare the performance of these algorithms in terms of per-link achievable capacity.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.