Researchers have developed a system-level simulation framework for evaluating ultra-dense indoor 5G and future 6G wireless networks. The framework combines standardized 3GPP channel modeling, beamforming, receiver processing, channel uncertainty, link adaptation, and throughput evaluation in a unified platform. The simulator closely matches 3GPP Phase-2 calibration results. Under imperfect channel information, interference rejection combining achieves about 42% higher average cell throughput and more than 160% higher 5th-percentile user throughput than maximum ratio combining.
Ultra-dense wireless networks can increase network capacity by deploying many small base stations close to users. However, as networks become denser, interference between neighboring cells also becomes stronger, making system performance more difficult to predict.
To study these effects before practical deployment, researchers commonly use system-level simulation. Many existing simulation platforms, however, simplify physical-layer behavior in order to reduce computational complexity. Important effects such as imperfect channel information, beam selection, interference-aware receiver processing, and link adaptation may therefore not be considered together.
Researchers have developed a unified system-level simulation framework for evaluating ultra-dense indoor wireless networks. The framework integrates standardized 3GPP indoor channel modeling, user association, directional beamforming, channel-state-information uncertainty, receiver processing, link adaptation, block-error prediction, and throughput evaluation.
The study considers a 120 m × 50 m indoor hotspot containing 12 base stations and 120 users operating at 30 GHz. The framework models both large-scale and small-scale wireless propagation and evaluates how receiver design and imperfect channel information affect network performance.
The researchers first validated the simulator against 3GPP Phase-2 system-level calibration results. The simulated signal-to-interference-plus-noise ratio distribution closely follows the reference results reported by multiple 3GPP participants, showing that the framework reproduces standardized system-level behavior.
The framework was then used to compare maximum ratio combining (MRC) and interference rejection combining (IRC). MRC mainly strengthens the desired signal, while IRC also suppresses interference from neighboring transmissions.
Under imperfect channel-state information, IRC increased the average cell throughput from 83.22 Mbps to 117.94 Mbps, corresponding to an improvement of about 42%. For users in the lowest 5% of the throughput distribution, throughput increased from 1.2 Mbps to 3.2 Mbps, representing a gain of more than 160%.
These results show that interference-aware receiver processing can provide substantial benefits in dense multi-cell networks, particularly for users experiencing difficult interference conditions.
The proposed framework provides a modular platform for evaluating receiver algorithms, beamforming strategies, and link-adaptation methods before more costly hardware or field testing. It may also support future studies on intelligent beamforming and machine-learning-assisted wireless communication for beyond-5G and 6G networks.
This paper, “A system-level simulation framework for performance evaluation of ultra-dense wireless networks” was published in Advanced Information and Communication .
Le T, Dinh N, Nguyen N, Luong N. A system-level simulation framework for performance evaluation of ultra-dense wireless networks. Adv. Inf. Commun. 2026(3):0013, https://doi.org/10.55092/aic20260013.
Experimental study
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A system-level simulation framework for performance evaluation of ultra-dense wireless networks
18-Sep-2026