Millimeter wave (mmWave) spectrum is associated with abundance of available spectrum and excellent spectrum reuse, but also has the significant downside of decreased resilience. Communication can be disrupted when the line of sight (LOS) path is blocked, or the link quality may degrade due to obstacles. These issues are exacerbated by mobility. One possible countermeasure against such signal shadowing and blockage is the use of intelligent reconfigurable surfaces (IRSs). However, while IRSs have the advantage to form a beam directly targeting the user, user-tracking and the need to orchestrate the IRS are very challenging. A practical solution towards improving the resilience is the use of non-reconfigurable reflecting surfaces (NRRSs), which enable communication through reflections by creating additional signal paths via a pre-configured reflection pattern. With sufficient number of reflections through NRRSs, the probability of communication outage is significantly reduced. The additional signal paths in NRRS-assisted networks can be very useful in scenarios characterized by repetitive mobility patters, especially since the cost and complexity are heavily reduced compared to IRS. While the benefits of IRS deployment have been extensively studied, most analyses are limited to the simulation domain. In contrast, this paper provides practical insights derived from measurements conducted on a mmWave band testbed. Results reveal that even with a few NRRSs, the resilience of a mmWave communication link could be dramatically improved resulting in close-to-zero packet loss. However, the channel becomes highly frequency-selective, which makes the use of advanced physical layers like IEEE 802.11be with adaptive modulation & coding per resource unit (RU) a necessity.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multiband splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications. All three algorithms are integrated into an experimental OFDM-based IEEE 802.11ac system. Our analysis centers on evaluating the performance of these algorithms under limited number of narrow-band measurements, demonstrating that accurate CIR estimation is achievable even using only 50% of the full wideband spectrum. Additionally, we analyze and compare the computational complexity of these algorithms to assess their practical feasibility

Adaptive beamforming is an enabling technology for millimeter-wave-based wireless communication which is used by many standards like 3GPP NR and IEEE 802.11ay. Supporting user mobility is challenging as efficient beam tracking is required. Therefore, a variety of beam tracking techniques have been proposed, many of which are machine learning (ML)-based. However, ML approaches are often not of practical use due to the long and complex learning phase. In this paper, we show the feasibility of virtual to real-world transfer learning. Our solution significantly speeds up the learning process as the learning happens mostly in a simulated environment and requires only little additional learning in the real-world deployment. As proof-of-concept, we implemented and evaluated a low-complexity beam tracking based on deep Q-network (DQN) reinforcement learning. The results reveal a substantial speed-up by a factor of 3× using transfer learning.

Abstract Coherent multi‐band splicing is an optimal solution for extending existing band‐limited communication systems to support high‐precision sensing applications. Conceptually, the communication system performs narrow‐band measurements at different centre frequencies, which are then concatenated to increase the effective bandwidth without altering the sampling rate. This can be done in parallel for multiple non‐contiguous subbands or by hopping across the different bands. However, multi‐band splicing poses significant challenges, particularly in compensating for phase offsets and hardware distortions before stitching the acquired samples, which can be distributed in contiguous or non‐contiguous manners. This survey paper studies the state of the art in coherent multi‐band splicing and identify open research questions. For beginners in the field, this review serves as a guide to the most relevant literature, enabling them to quickly catch up with the current achievements. For experts, open research questions that require further investigation are highlighted.

With the emerging 5G solutions for vehicular networking, the spectrum of radio communication technologies also extends into the mmWave band. This is further supported by the recent move towards RADar based COMmunication (RADCOM), i.e., the deeply integrated use of the 77 GHz band for communication and sensing. mmWave communication has widely been explored both analytically as well as in experiments, particularly for indoor usage and semi-stationary outdoor scenarios. In this paper, we explore the capabilities of existing simulators for the vehicular use case. We selected WinProp using deterministic ray-tracing techniques and NYUSIM relying on stochastic simulation of the channel. We compare both with a strong focus on simulation accuracy, usability, and computational performance.