Utilizing the mmWave band can potentially achieve the high data rate needed for realistic and seamless interaction within a virtual reality (VR) application. To this end, beamforming in both the access point (AP) and head-mounted display (HMD) sides is necessary. The main challenge in this use case is the specific and highly dynamic user movement, which causes beam misalignment, degrading the received signal level and potentially leading to outages. This study examines mmWave-based coordinated multi-point networks for VR applications, where two or multiple APs cooperatively transmit the signals to an HMD for connectivity diversity. Instead of using omni-reception, we propose dual-beam reception based on the analog beamforming at the HMD, enhancing the receive beamforming gain towards serving APs while achieving diversity. Evaluation using actual HMD movement data demonstrates the effectiveness of our approach, showcasing a reduction in outage rates of up to 13% compared to quasi-omnidirectional reception with two serving APs, and a 17% decrease compared to steerable single-beam reception with a serving AP. Widening the separation angle between two APs can further reduce outage rates due to head rotation as rotations can still be tracked using the steerable multi-beam, albeit at the expense of received signal levels reduction during the non-outage period.

Multi-connectivity is considered a key enabler for 5G networks and beyond, aiming to enhance capacity by combining multiple communication links in the same or different bands. Similarly, in cell-free networks all \acpap jointly serve users in the same band, boosting capacity through enhanced spectral efficiency. Both approaches can be very effective in \acmmwave networks by addressing key issues of reliability and robustness due to the multiple simultaneous links. Furthermore, the use of narrow directional beams in \acmmwave spatially separates the signals, allowing for in-band multi-connectivity through local beamtraining. Such in-band multi-connectivity would be an alternative design to traditional cell-free networks that does not rely on phase-coherent processing or centralized methods for interference suppression. The physical layer processing and resource allocation problem then simplifies to a local beamtraining challenge, making these networks easier and simpler to implement and deploy, as any connection just has to train and maintain the local beam. We validate this approach by designing a multi-connectivity \acmmwave network with minimal network synchronization, relying solely on analog beamforming for spatial separation. Our evaluation results demonstrate that in-band multi-connectivity with 4 asynchronous and independent links can provide uninterrupted service even in dense, high-traffic scenarios, compared to up to 20% of service loss in a standard single-connectivity deployment. Distributing the traffic across multiple \acpap also had throughput gains of up to 30%, showing that multi-connectivity \acmmwave networks can provide a high-throughput, reliable and stable service for next-generation applications.

Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing blockage types, is an initial step towards signal blockage avoidance. In this context, we investigate the feasibility of using human motion recognition as a surrogate task for blockage type recognition through a set of hypothesis validation experiments using both qualitative and quantitative analysis (visual inspection and hyperparameter tuning of deep learning (DL) models, respectively). A surrogate task is useful for DL model testing and/or pre-training, thereby requiring a low amount of data to be collected from the eventual JCAS environment. Therefore, we collect and use a small dataset from a 26 GHz cellular multi-user communication device with hybrid beamforming. The data is converted into Doppler Frequency Spectrum (DFS) and used for hypothesis validations. Our research shows that (i) the presence of domain shift between data used for learning and inference requires use of DL models that can successfully handle it, (ii) DFS input data dilution to increase dataset volume should be avoided, (iii) a small volume of input data is not enough for reasonable inference performance, (iv) higher sensing resolution, causing lower sensitivity, should be handled by doing more activities/gestures per frame and lowering sampling rate, and (v) a higher reported sampling rate to STFT during pre-processing may increase performance, but should always be tested on a per learning task basis.

Human blockage is one of the main challenges for mmWave communication networks in dynamic environments. The shadowing by a human body results in significant received power degradation and could occur abruptly and frequently. A shadowing period of hundred milliseconds might interrupt the communication and cause significant data loss, considering the huge bandwidth utilized in mmWave communications. An even longer shadowing period might cause a long-duration link outage. Therefore, a blockage prediction mechanism has to be taken to detect the moving blocker within the vicinity of mmWave links. By detecting the potential blockage as early as possible, a user equipment can anticipate by establishing a new connection and performing beam training with an alternative base station before shadowing happens. This paper proposes an early moving blocker detection mechanism by leveraging an extra guard beam to protect the main communication beam. The guard beam is intended to sense the environment by expanding the field of view of a base station. The blockage can be detected early by observing received signal fluctuation resulting from the blocker's presence within the field of view. We derive a channel model for the pre- shadowing event, design a moving blockage detection algorithm for the guard beam, and evaluate the performance of the guard beam theoretically and experimentally based on the measurement campaign using our mmWave testbed. Our results demonstrate that the guard beam can extend the detection range and predict the blockage up to 360 ms before the shadowing occurs.

We exploit a dense cell-free mmWave network where User Equipments (UEs) are served by multiple highly directional beams provided by multiple Base Stations (BSs) simultaneously. Such multi-beam scenarios can either offer high spectral efficiency when different information is transmitted through each beam or a diversity gain when each beam transmits the same information. However, this increased spectral efficiency or diversity gain costs a more complex network association phase. A UE requires finding multiple nearby serving BSs and determining the optimal beam pair for each one. Thus, an efficient association process is urgently needed. In this work, we propose a UE-initiated association method for dense cell-free mmWave networks. We design an efficient beam training mechanism with multiple BSs using hybrid beamforming. We evaluate the proposed association method under different network configurations. The simulation results show that compared to traditional solutions, our proposed association method can lead to maximally 100% faster beam training and reduce energy consumption by up to 77%. The proposed UE-initiated association method is also scalable to the number of RF chains and antennas at BSs and UEs, making it very suitable for dense cell-free networks.