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The Price of the Golden 6G Band: Evaluation of Beam Management Effort in FR3

Clémence Altmeyerhenzien, Ljiljana Simić, Marina Petrova

arXiv:2609.16839v1cs.NI

TL;DR

FR3 promises wider bandwidth than FR1 with more favorable propagation than FR2, but practical directional operation leaves the required beam-management effort unclear. The paper evaluates this effort with ray-traced urban mobility and codebook beamforming, finding that high and stable mobile throughput requires substantial management across FR3 bands. Lower frequencies ease tracking through wider beams, yet non-adjacent switching remains comparable to FR2.

  • Problem

    Existing FR3 evaluations assume ideal beamforming, leaving the practical beam-management effort required for mobile links insufficiently characterized.

  • Method

    The paper evaluates FR3 beam alignment, valid directional links, handovers, beam switches, and steering distance using ray-tracing, realistic urban mobility, codebook beamforming, and FR1 and FR2 baselines.

  • Results

    High and stable mobile throughput requires significant beam-management effort across all FR3 bands; lower frequencies relax tracking, but non-adjacent switching is relatively comparable to FR2.

  • Takeaways & Limitations

    FR3 can outperform FR1 mobile data rates, but exploiting that capacity for stable mobility still requires active directional beam management across the band.

Abstract

from arXiv · show

Frequency Range 3 (FR3), 7.125-24.25 GHz, regarded as the "golden band" for 6G networks, has less challenging propagation characteristics than FR2 while offering much wider bandwidth for high data rate applications than FR1. Reusing existing FR1 infrastructure for FR3 network deployments requires gNodeBs (gNBs) to employ antenna arrays and perform beam management, which has proven challenging at FR2. In this paper, we extensively study and characterize the beam management effort in an FR3 urban network, in terms of: beam alignment sensitivity, number of directional link opportunities, gNB handover and beam switch rates, and beam steering distance. Our results show that achieving a high and stable mobile throughput requires significant beam management effort across FR3 bands. While the beam tracking requirements are less stringent at the lower frequencies due to wider beams, the beam switching rate to a non-adjacent beam is relatively comparable at FR3 and FR2.

I. INTRODUCTION

FR3 offers more favorable propagation than FR2 and much wider bandwidth than FR1, but its directional operation introduces unresolved beam-management overhead. This paper evaluates that effort in realistic urban FR3 mobility and finds high stable throughput requires substantial management across bands.

  • I. INTRODUCTION: FR3 provides more favorable propagation than FR2 and substantially more bandwidth than FR1, motivating its use for high-rate 6G networks.
  • I. INTRODUCTION: Reusing FR1 infrastructure in FR3 requires directional antennas and beam acquisition or tracking, creating overhead for maintaining mobile links.
  • I. INTRODUCTION: Existing FR3 studies assume ideal beamforming, leaving practical codebook-based beam-management effort insufficiently characterized.
  • I. INTRODUCTION: The paper presents a comprehensive FR3 beam-management evaluation using ray-tracing, realistic urban mobility, codebook beamforming, and an FR2 baseline.It evaluates beam alignment, valid directional links, handovers, beam switches, and steering distance.
  • I. INTRODUCTION: FR3 can outperform FR1 mobile data rates, but high and stable throughput requires significant beam management across all FR3 bands.
  • I. INTRODUCTION: Lower FR3 frequencies relax beam tracking through wider beams, while non-adjacent beam-switch rates remain relatively comparable between FR3 and FR2.

II. SYSTEM MODEL

The study models an FR1-like Frankfurt cellular deployment and uses ray-tracing over a dense outdoor UE grid to generate spatially consistent propagation and realistic pedestrian mobility. It evaluates 8, 15, and 18 GHz FR3 against 2.1 GHz FR1 and 28 GHz FR2 baselines.

  • A. Network & Ray-Tracing Channel Model: The simulation uses K = 8 gNBs over an 800 m x 800 m Frankfurt area, with a 600 m x 600 m study region selected to avoid edge effects.The deployment density is approximately 13 gNB/km2, with rooftop gNBs at 12 m height.
  • A. Network & Ray-Tracing Channel Model: The evaluated carriers are 8, 15, and 18 GHz for FR3, with 2.1 GHz FR1 and 28 GHz FR2 as baselines.
  • A. Network & Ray-Tracing Channel Model: Ray-tracing uses a Frankfurt OpenStreetMap urban model, concrete buildings, and outdoor UE locations on a 5m × 5m grid, yielding L = 6651 locations.
  • A. Network & Ray-Tracing Channel Model: Ray-tracing provides propagation paths between each gNB and UE location, including path angles and path loss for subsequent beamforming calculations.
  • A. Network & Ray-Tracing Channel Model: The mobility evaluation samples realistic pedestrian trajectories generated by VisWalk, including M = 2000 paths with 17 to 117 UE locations each.

B. Beamforming & Antenna Array Model

The beamforming model combines three sectorized gNB panels, frequency-dependent uniform rectangular arrays, and 3GPP-style codebooks. Directional received power is computed by applying codeword gain to ray-traced propagation paths, with a Frankfurt-specific downtilt assumption.

  • B. Beamforming & Antenna Array Model: Each gNB uses three 120° sectorized panels and an N × N half-wavelength-spaced uniform rectangular array with 3GPP element patterns.The panels have azimuth orientations −120°, 0°, and 120°, and use Θ = 90° elevation down-tilt.
  • B. Beamforming & Antenna Array Model: The codebook contains evenly spaced per-sector beam directions whose count, beamwidth, and maximum gain vary with array size from 2 × 2 through 8 × 8.The corresponding beam counts are 3, 5, 10, and 21, with half-power beamwidths from 50.8° to 12.7°.
  • B. Beamforming & Antenna Array Model: Received power for a gNB, UE location, and codeword is formed by summing ray-traced path contributions weighted by directional codeword gain.The path loss comes from ray-tracing, while the codeword determines the directional gain applied to each path.
  • B. Beamforming & Antenna Array Model: The model uses a common Θ = 90° downtilt, and reports qualitatively consistent trends when other downtilts are considered.

C. Metrics & Definitions

The model computes beam-specific SNR and SINR from received power, thermal noise, bandwidth, and modeled interference from non-serving gNBs. These quantities support the paper’s throughput and beam-management evaluations.

  • C. Metrics & Definitions: Beam-specific SNR is computed as received power divided by noise power, where noise includes thermal noise over bandwidth B and a 7 dB noise figure.
  • C. Metrics & Definitions: SINR includes the serving-beam received power divided by noise plus average interference from non-serving gNBs.Interfering gNBs are modeled as selecting codewords uniformly at random from their codebooks.

3) Data Rate:

The paper estimates downlink throughput with an attenuated, truncated Shannon bound and defines valid beams using an SINR threshold.

  • 3) Data Rate:: Throughput is estimated using an attenuated and truncated Shannon bound.The model caps spectral efficiency at ψmax = 7.4 and sets rates below the −5 dB outage threshold to 0 Mbps.
  • 3) Data Rate:: A beam is valid when its SINR exceeds the selected threshold ρ for a given gNB–UE location pair.The valid-beam set supports evaluating directional link opportunities under target SINR requirements.

5) Optimal Beam and gNB:

The paper defines the optimal beam and serving gNB through the beam–gNB combination that maximizes SINR, and measures steering distance between codewords circularly.

  • 5) Optimal Beam and gNB:: The optimal beam and serving gNB are selected as the combination maximizing SINR.This selection identifies the best directional link for each UE location under the codebook-based beamforming model.
  • 5) Optimal Beam and gNB:: Steering distance between two codewords is defined using their circular distance within the codebook.The circular formulation accounts for wraparound between the first and last codewords.

A. Achievable Coverage at FR3 for Optimal Beam Alignment

With optimal serving gNB and beam selection, FR3 can approach FR1-like coverage using frequency-dependent antenna arrays and provide high data rates across most locations.

  • Coverage baseline: 95% coverage requires a 2×2 array at 8 GHz, 4×4 at 15 GHz, and 6 × 6 at 18 GHz; 28 GHz cannot reach it with 8×8.FR1 provides full coverage above the −5 dB SNR threshold, while the FR3 array sizes are selected to match 95% network-location coverage.
  • Signal quality: Interference separates SINR from SNR for most users, except noise-dominated locations such as roughly the bottom 15th percentile at 18 GHz.The distributions use the smallest arrays that provide FR1-like coverage and evaluate the optimal gNB and beam.
  • Rate performance: More than 92% of FR3 UE locations achieve over 100 Mbps, approximately matching the top FR1 throughput through wide channel bandwidth.FR3 users reach the top FR1 rate in most cases, although at low spectral efficiency.
  • Beam alignment: Near a gNB in open space, optimal beam orientation follows the line-of-sight direction, so nearby locations tend to use adjacent beams and support straightforward tracking.The spatial beam-selection comparison covers 8 GHz with a 2×2 array and 18 GHz with a 6×6 array.

B. Valid Beam Availability at FR3

Valid-beam availability varies with frequency and SINR target, revealing increasingly selective beam choices at higher frequencies despite differences from channel sparsity being moderated by array gain and interference.

  • Valid-beam distributions: At a 10 dB SINR threshold, the median valid-beam fractions are 22% at 8 GHz, 13% at 15 GHz, 10% at 18 GHz, and 8% at 28 GHz.The corresponding medians are 2/9, 2/15, 3/30, and 5/63 beams, indicating more challenging selection at higher frequencies.
  • Valid-beam distributions: For a 10 dB target, the median valid-beam count is 2 at 8 GHz versus 4 at 28 GHz, while the distributions nearly overlap at ρmax.Thus, the lower-frequency advantage in valid-beam availability diminishes as the SINR requirement approaches the maximum rate threshold.
  • Propagation mechanisms: The valid-beam proportion declines with frequency consistently with increasing channel sparsity, but less sharply than the multipath-richness difference suggests.Around a building corner, dominant paths number roughly 15–20 at 8 GHz versus 5–12 at 18 GHz, while valid-beam proportions remain more comparable.
  • Propagation mechanisms: Higher-frequency array gain boosts weaker multipath components, while weaker inter-gNB interference helps explain comparable directional-link availability across FR3 bands.These two effects moderate the impact of increased channel sparsity on valid-beam availability.

C. Beam Management Effort at FR3 in a Mobility Scenario

A threshold-based policy evaluates the minimum beam-management effort needed to maintain target SINR along mobile UE paths. High-rate operation requires frequent beam switches, while relaxed thresholds reduce effort at the cost of stability and longer steering distances.

  • Beam management effort: 0.04–0.08 beam-switches/m occur at 8–28 GHz when maximizing mobile data rate, with median steering distance 1 beam and upper quartile 2–3 beams across FR3 and FR2.The higher switch rate at higher frequencies mainly reflects more frequent tracking to near-adjacent beams.
  • Threshold trade-offs: Lowering the SINR threshold relaxes FR3 beam-management effort, but average FR3 rates remain over 250, 550, and 800 Mbps across bands versus under 50 Mbps for FR1.The relaxed policy permits SINR and rate deterioration before switching.
  • Threshold trade-offs: At the relaxed threshold, median steering distances are 3, 4, 6, and 11 beams at 8, 15, 18, and 28 GHz, respectively.These non-adjacent switches suggest more expensive beam-sweep searches than adjacent-beam switches.
  • Threshold trade-offs: The rate of non-adjacent beam switches at FR3 is only 12–30% lower than at FR2.

IV. CONCLUSIONS

The study quantitatively evaluates FR3 beam-management effort using realistic mobility and urban ray-tracing. FR3 can outperform FR1 in data rates, but high and stable throughput still demands substantial management, including non-adjacent switching at rates comparable to FR2.

  • IV. CONCLUSIONS: FR3 spectrum can outperform FR1 mobile data rates, but high and stable mobile throughput requires significant beam-management effort across all FR3 bands.
  • IV. CONCLUSIONS: Lower FR3 frequencies relax beam tracking through smaller arrays and wider beams, while non-adjacent beam switching remains required at a relatively comparable rate to FR2.
  • IV. CONCLUSIONS: The findings motivate beam-management strategies that adapt to frequency band for spectrum-agile 6G networks.
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