Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel, non-RF sensor inputs and environment awareness can be leveraged to greatly enhance beam management decisions, e.g. via machine learning (ML) techniques. However, the current literature lacks open platforms to gather datasets for the training of such ML techniques and to evaluate novel beam management approaches in real-time, real-world scenarios and full-stack endto-end networks. In this work, we present our SDR-based experimental platform based on OpenAirInterface and are the first to integrate popular low-cost antenna array transceivers, beam sweeping capabilities, and a highly-modular sensor framework and associated interfaces into such a full-stack experimental platform. This enables beam management experimentation in real-world, real-time scenarios and facilitates gathering datasets necessary for developing ML-based beam management protocols that incorporate environment awareness via sensor modalities.

Communication at millimeter-wave (mm-wave) enables unprecedented data rates for 5G-and-beyond cellular networks but relies on precise beam alignment to overcome the challenging propagation characteristics at higher frequencies. Leveraging additional sensor-aided context information for beam management, such as location data, has been proposed to reduce the search space of feasible directional beam-pair-links and reduce signaling overhead. So far, evaluation of such location-aided schemes has largely been based on simplified and limited-scope simulation scenarios, without accounting for the complexity and performance implications in real end-to-end cellular networks. In this work, we present REMLAB, a first-of-its-kind framework for REM-based location-aided beam management in the full-stack 5G-NR-compliant network simulator ns-3 and make our code publicly available. Our framework provides access to network performance metrics and parameters that can only be obtained in realistic end-to-end network simulation, such as the signaling overhead required for beam management. We evaluate REMLAB in an example urban mobility scenario and show an improvement in average IP throughput of at least 24% and up to 46% over default 5G-NR by eliminating the need for periodic control signaling. Furthermore, towards the evaluation of practical REM-based schemes, we implement a realistic location error model and show that localization errors increase the radio link failure duration. This highlights that the real-world feasibility of context-based beam management in 5G-and-beyond networks depends on the development and detailed evaluation of sensor-error-aware schemes.

5G-NR in FR2 relies on beamforming and accurate beam alignment between the gNodeB (gNB) and the user equipment (UE) to address challenging channel conditions. Maintaining beam alignment for a mobile UE requires frequent updates via beam-sweeping and directional radio frequency measurements, entailing high delay and signaling overhead. Location-aided beam management has emerged as a promising solution for reducing this overhead by combining UE location with historical directional beam training information to construct radio environment maps (REMs) for effective beam alignment. However, the highly site-specific nature of millimeter-wave (mm-wave) propagation makes REM-based beam management vulnerable to performance degradation due to errors in UE location information, e.g., obtained via global navigation satellite system (GNSS), affecting the accuracy of both REM construction and querying. This paper proposes a 5G-NR standard-compliant solution to mitigate GNSS location error in REM-based beam management, by constructing our rank-REM from multiple UE directional channel measurements and multi-beam monitoring for connected UEs. Our results, based on ray-tracing in an urban environment, show that our proposed REM construction and querying strategy effectively mitigates realistic GNSS location error, substantially enhancing link stability for a mobile UE by reducing radio link failures (RLFs) and handovers, thus enabling effective REM-based beam management for 5G-and-beyond.

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.