Digital Twin for Ultra-Reliable & Low-Latency 6G Wireless Communications in Dense Urban City

TL;DR

Dense urban 6G deployment requires realistic planning tools to account for local morphology and interference. The paper builds a geometric Digital Twin of Sunway City from geo-referenced 3D data, embeds seven rooftop base stations at $f_c=10$ GHz with $B=400$ MHz, and uses GPU-accelerated ray tracing to produce path-gain and SINR fields across a $3\text{ km} \times 3\text{ km}$ grid. The link between physics and service is given by $C(i,j)=\log_{2}(1+\mathrm{SINR}(i,j))$ and $R(i,j)=B\,C(i,j)$, enabling application-level targets for XR, V2X, and URLLC. Key contributions include a detailed DT geometry with ITU-based material modeling, maps of XR/V2X/URLLC feasibility, and macro-diversity analysis that reveals dual-connectivity opportunities in URLLC-capable cells. The study demonstrates that a city-scale DT can translate ray-tracing outputs into actionable planning insights, complementing analytical models and measurement campaigns, and guiding where to add sites, refine beams, or enable multi-connectivity in dense urban 6G deployments.

Abstract

High-frequency deployments in dense cities are difficult to plan because coverage, interference, and service reliability depend sensitively on local morphology. This paper develops a geometric Digital Twin (DT) of the Sunway City and uses it to study the service implications of a multi-site mmWave deployment. The DT is constructed from geo-referenced three-dimensional meshes of buildings, roads, and open areas, assembled in Blender and exported as a mesh scene. A seven-transmitter downlink at 10 GHz is then embedded into this geometry and evaluated using a GPU accelerated ray tracing engine that returns path-gain and Signal-to-Interference-plus-Noise Ratio (SINR) fields over a dense grid of user locations. These fields are mapped to achievable throughput and compared against representative target rates for immersive extended reality (XR), vehicle-to-everything (V2X) services, and ultra-reliable low-latency communication (URLLC). The resulting maps show that favourable streets and courtyards form narrow high rate corridors surrounded by deep shadows, even within a dense area. In the baseline deployment, one fifth of the simulated area can maintain 100 Mbps URLLC rates, and less than 10% of cells can reach 1.7 Gbps for XR, despite the presence of several rooftop sites. By exploiting the DT, we further quantify the macro-diversity margin between the best and second best serving sites and show that most URLLC-feasible cells have several decibels of SINR headroom that could be harvested through dual connectivity. The study shows how a city DT can translate ray tracing output into service centric metrics and planning insights, complementing both analytical models and expensive measurement campaigns.

Figures (6)

• Figure 1: Overview of the Sunway City DT.
• Figure 2: Sunway City digital twin with the considered mmWave deployment. Red markers denote the seven transmitter locations and the coloured beams illustrate ray samples used to populate the coverage grid over the campus.
• Figure 3: Large–scale downlink field over the Sunway grid. For each cell, the best–serving transmitter is selected and the corresponding (a) received signal strength, (b) signal–to–interference–plus–noise ratio, and (c) path gain are plotted. Red markers indicate the transmitter sites.
• Figure 4: Service-level view over the Sunway City region of interest. Black dots show grid cells whose achievable throughput meets the indicated minimum rate threshold which is: (a) XR requirement $\ge 30$ Mbps and (b) V2X requirement $\ge 700$ Mbps. The maps show that higher rate service is focused in localized areas around well served streets and open areas, while large portions of the region is still below these minima under the assumed seven site hotspot deployment.
• Figure 5: Empirical CDF of per–cell achievable throughput over the Sunway layout. Vertical dashed lines mark the XR, V2X, and URLLC operating points, together with the percentage of area that meets each target.