Millimeter-wave (mm-wave) communications require beamforming and consequent precise beam alignment between the gNodeB (gNB) and the user equipment (UE) to overcome high propagation losses. This beam alignment needs to be constantly updated for different UE locations based on beamsweeping radio frequency measurements, leading to significant beam management overhead. One potential solution involves using machine learning (ML) beam prediction algorithms that leverage UE position information to select the serving beam without the overhead of beam sweeping. However, the highly site-specific nature of mm-wave propagation means that ML models require training from scratch for each scenario, which is inefficient in practice. In this paper, we propose a robust cross-environment transfer learning solution for location-aided beam prediction, whereby the ML model trained on a reference gNB is transferred to a target gNB by fine-tuning with a limited dataset. Extensive simulation results based on ray-tracing in two urban environments show the effectiveness of our solution for both inter- and intra-city model transfer. Our results show that by training the model on a reference gNB and transferring the model by fine-tuning with only 5 % of the target gNB dataset, we can achieve 80 % accuracy in predicting the best beam for the target gNB. Importantly, our approach improves the poor generalization accuracy of transferring the model to new environments without fine-tuning by around 75 percentage points. This demonstrates that transfer learning enables high prediction accuracy while reducing the computational and training dataset collection burden of ML-based beam prediction, making it practical for 5G-and-beyond deployments.

Hybrid beamforming (HBF) multi-user multiple-input multiple-output (MU-MIMO) is a key technology for unlocking the directional millimeter-wave (mm-wave) nature for spatial multiplexing beyond current codebook-based 5G-NR networks. In order to suppress co-scheduled users' interference, HBF MU-MIMO is predicated on having sufficient radio frequency chains and accurate channel state information (CSI), which can otherwise lead to performance losses due to imperfect interference cancellation. In this work, we propose IABA, a 5G-NR standard-compliant beam pair link (BPL) allocation scheme for mitigating spatial interference in practical HBF MU-MIMO networks. IABA solves the network sum throughput optimization via either a distributed or a centralized BPL allocation using dedicated CSI reference signals for candidate BPL monitoring. We present a comprehensive study of practical multi-cell mm-wave networks and demonstrate that HBF MU-MIMO without interference-aware BPL allocation experiences strong residual interference which limits the achievable network performance. Our results show that IABA offers significant performance gains over the default interferenceagnostic 5G-NR BPL allocation, and even allows HBF MU-MIMO to outperform the fully digital MU-MIMO baseline, by facilitating allocation of secondary BPLs other than the strongest BPL found during initial access. We further demonstrate the scalability of IABA with increased gNB antennas and densification for beyond-5G mm-wave networks.

The spectrum-rich millimeter-wave (mm-wave) bands and exploiting multi-antenna technologies are envisioned as a key enabler for future high speed communication networks. 5G-NR in automation and industry requires supporting not only eMBB but also URLLC applications to meet the demands in production processes. Providing high-rate mm-wave coverage in real-world industrial environments is challenging and necessitates detailed and site-specific characterization of the directional link opportunities and beam management requirements for network planning of mm-wave factory deployments. In this paper, we present the results of our large-scale mm-wave measurement study using phased antenna arrays in a machine production hall. We systematically collect received signal strength data over fine-grained 3D TX/RX orientations for 31 spatially-dense RX positions in three typical factory scenarios in the 28 GHz and 60 GHz bands. We study the impact of transmitter placement and operating frequency band on the achievable data rate and the beam management effort considering the data rate demands of next-generation industrial networks. Our results show that the 28 GHz band provides sufficient connectivity to deliver data rates of up to 1 Gbps without the need for sophisticated beam management, which is in strong contrast to outdoor mobile mm-wave applications where active beam tracking is crucial to provide seamless connectivity.

Agile beam management is key to seamless high-speed mm-wave connectivity in the beyond-5G era, given the site-specific spatio-temporal variations of the mm-wave channel. Leveraging non-RF sensor inputs for environment awareness, e.g. via ML techniques, can greatly enhance RF-based beam management. To address the lack of diverse publicly available multi-modal mm-wave datasets for the design of novel beam management approaches and to enable their real-world, real-time evaluation, we present our SDR-based multi-band mm-wave experimental platform which integrates multi-modal sensors towards environment-aware beam management.

Exploiting millimeter-wave (mm-wave) for high-capacity multi-user networks is predicated on jointly performing beam management for seamless connectivity and efficient resource sharing among all users. Beam management in 5G-NR actively monitors candidate beam pair links (BPLs) on the serving cell to simply select the user’s best beam, but neglects the multi-user resource sharing problem, potentially leading to severe throughput degradation on overloaded cells. We propose flexRLM, a coordinator-based flexible radio link monitoring (RLM) framework for multi-user downlink mm-wave networks. flexRLM enables flexible configuration of monitored BPLs on the serving and other candidate cells and beam selection jointly considering link quality and resource sharing. flexRLM is fully 5G-NR-compliant and uses the LTE coordinator in non-standalone mode to continuously update the monitored BPLs via measurement reports from periodic downlink control synchronization signals. We implement flexRLM in ns-3 and present full-stack simulations to demonstrate the superior performance of flexRLM over default 5G-NR RLM in multi-user networks. Our results show that flexRLM’s continuous updating of monitored BPLs improves both link quality and stability. By monitoring BPLs on candidate cells other than the serving one, flexRLM also significantly decreases handover decision delays. Importantly, flexRLM’s low-complexity coordinated load-balancing achieves a per-user throughput close to the single-user baseline.

We study the problem of joint user association and beam pair link (BPL) allocation in millimeter-wave (mm-wave) cellular networks. We propose two interference-aware strategies – a centralized and a distributed one – and evaluate their performance based on site-specific directional channel data and realistic antenna models. Our results show that using idealized sectored antenna models severely underestimates the spatial interference, considering the non-negligible sidelobes of realistic antenna arrays which strongly limit the achievable spatial separation of the allocated BPLs in mm-wave networks using beam codebooks. We also show that intra-cell interference is the dominant interference component for all allocated users, in contrast to assumptions in the prior literature. By exploiting non line-of-sight BPLs, our interference-aware strategies achieve significant performance gains over interference-agnostic 5G-NR default user association to the strongest base station and BPL, as well as outperforming a centralized, load-balancing literature benchmark. Our proposed strategies rely solely on downlink 5G-NR reference signals for channel state information updates, making them attractive for practical codebook-based mm-wave cellular networks.

The use of high-gain directional communications is key to enable the capacity enhancement of millimeter-wave (mm-wave) for 5G-and-beyond networks. However, robust mm-wave coverage using directional beams entails significant beamsteering effort for maintaining precise beam alignment between the base station and the user. Numerous mm-wave beamsteering algorithms have been proposed in the literature, but verified largely using statistical channel models or limited measurements. Importantly this still leaves open the question of whether these studies can be directly translated to real outdoor mm-wave network deployments. In this paper, we present the results of the first extensive comparative evaluation of eight state-of-the-art mm-wave beamsteering algorithms based on outdoor mm-wave measurements using phased antenna arrays. We collect received signal strength data over fine-grained 3D angular orientations for 78 spatially-dense user positions in a European city, comprising an open-source dataset of over 421,000 individual measurements. We then perform an empirical evaluation of the beamsteering algorithms in the context of link establishment for static and link maintenance for mobile users. Overall, our results show that these state-of-the-art mm-wave beamsteering algorithms, when tested on real measurement data, perform far from optimal and worse than originally reported. A key takeaway from our evaluation is that the practical feasibility of beamsteering algorithms strongly depends on the trade-off between the incurred beam training delay and the antenna gains facilitated in the initial beam training stage which limit the established link budget. Our study of link maintenance for mobile users showed significant deviations from the maximum achievable performance for all algorithms, including those that leverage correlation of the mm-wave sparse link opportunities or historical link information for link recovery. This shows that, to enable seamless connectivity in future mm-wave networks, more sophisticated beamsteering algorithms must be designed that intelligently adapt to the site-specific mm-wave channel while taking into account the realistic antenna beams of commercially-viable phased antenna arrays.

To address the end-to-end implications of realistic beam management control operations, in this paper we present a full-stack 5G-NR-compliant ns-3 framework which implements realistic scheduling, transmission, and reception of 5G-NR downlink control signals, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS). The framework is based on a non-standalone downlink 5G-NR millimeter wave (mm-wave) network, enabling control signal transmission via an LTE network connection for handover coordination and overcoming radio link failure (RLF). Our framework provides customizable interfaces to a threshold-based beam management operation. Furthermore, we propose three 5G-NR-compliant beam management strategies and analyze their performance using site-specific propagation and pedestrian mobility data. Overall, our results show the end-to-end implications of realistic beam management operations required to maintain high-rate mobile user performance, with frequent signal-to-noise ratio variations reflected as significant drops in the achievable throughput and high delay spikes. This highlights the importance of the presented ns-3 framework compared to the ideal, instantaneous beam scanning supported in existing ns-3 mm-wave modules, for realistic 5G-NR beam management evaluation. To this end, we provide open access to the ns-3 code to facilitate the research efforts of the wireless community.

The use of high-gain directional communications is key to enable the capacity enhancement of millimeter-wave (mm-wave) spectrum for 5G-and-beyond networks. However, this entails extensive beam management to continuously track the mobile user to maintain precise transmitter (TX)/receiver (RX) beam alignment. Detailed characterization of mm-wave link opportunities in real-world urban environments and corresponding beam management requirements, are key to addressing the great challenge of mobility support for mm-wave urban deployments. In this paper, we present the results of our large-scale outdoor urban mm-wave measurements using phased antenna arrays, where we collected received signal strength data over fine-grained 3D TX/RX orientations for 78 spatially-dense RX positions in three sub-regions in Aachen, totaling over 421,000 individual measurements. Our results show 2–8 spatial link opportunities per RX position, where only up to 40% of all RX orientations result in a feasible mm-wave connection. Our results suggest that the beam management burden would fall heavily on the mobile user-side, and be moderate on the TX-side, for a typical walk in vicinity of the serving cell. For example, the required TX-side beam steering from one RX position to the next to maintain a minimum data rate of 1 Gbps is less than 12° (twice the half power-beamwidth), but up to 88° at the RX-side. Therefore, obtaining spatially-dense and fine-grained angular propagation data is a critical input for the design and evaluation of mm-wave beam management protocols.

Exploiting multi-antenna technologies for robust beamsteering to overcome the effects of blockage and beam misalignment is the key to providing seamless multi-Gbps connectivity in millimeter-wave (mm-wave) networks. In this paper, we present the first large-scale outdoor mm-wave measurement study using a phased antenna array in a typical European town. We systematically collect fine-grained 3D angle-of-arrival (AoA) and angle-of-departure (AoD) data, totaling over 50,000 received signal strength measurements. We study the impact of phased antenna arrays in terms of number of link opportunities, achievable data rate and robustness under small-scale mobility, and compare this against reference horn antenna measurements. Our results show a limited number of 2-4 distinct spatial link opportunities per receiver location, indicating that the mm-wave multipath richness in a European town is surprisingly similar to that of dense urban metropolises. The results for the phased antenna array reveal that significant losses in estimated data rate occur for beam misalignments in the order of the half-power beamwidth, with significant and irregular variations for larger misalignments. By contrast, the loss for horn antennas is monotonically increasing with the misalignment. Our results strongly suggest that the effect of non-ideal phased antenna arrays must be explicitly considered in the design of agile beamsteering algorithms.

The promise of millimeter-wave (mm-wave) frequencies for high capacity cellular networks depends on precise alignment of the narrow directional beams to either line-of-sight (LOS) or strong non-LOS links. Given this sensitivity of mm-wave communication to the spatial distribution of LOS/NLOS links, site-specific propagation data from ray-tracing simulations is an important tool for mm-wave networking studies. In this paper, we present the first detailed validation of mm-wave ray-tracing against large-scale outdoor measurements. We consider fine-grained angle-of-arrival (AoA), angle-of-departure (AoD) and received signal strength (RSS) data, using both horn and phased array antennas. We show that ray-tracing captures well the distribution of multipath clusters (MPCs) in terms of number of MPCs per receiver location, AoA, AoD and individual MPC structure. Moreover, our results indicate that ray-tracing provides accurate propagation data based on publicly available 3D building models which lack detailed material properties. Overall, our results show that individual propagation paths can be accurately identified in the ray-tracing data, with a median RSS prediction error within 5 dB of the measured RSS for all MPCs. This is an encouraging result which confirms the viability of ray-tracing propagation data as an input for mm-wave networking studies, on e.g. beam management protocols.

Energy harvesting has proved to be a viable solution for energy bottlenecks of wireless networks. This paper considers a single tier cellular network, in which the mobile nodes harvest energy from the aggregate downlink inter-cell interference and latter use it as uplink transmit power. A successful uplink transmission is assumed only when a user harvested enough energy in its battery and the received signal-to-interference ratio (SIR) is above a predefined threshold. We investigate the performance of the proposed system model in both downlink and uplink using stochastic geometry and provide theoretical analysis and numerical evaluation. Denser macro tiers show higher uplink successful transmission probability due to the increased number of available power extraction sources i.e macro base stations.