DOI: 10.5281/zenodo.18593401
title: Reconfigurable Intelligent Surface 6G URLLC Single-User QoS Dataset

description: Description

This dataset provides 800 realistic ray-tracing-based channel realizations and URLLC QoS metrics, generated using DeepMIMO v2 [1]. It is designed to support research on predicting the minimal number of active RIS (Reconfigurable Intelligent Surface) reflective elements (N) that satisfy stringent 6G URLLC requirements in single-user scenarios:



End-to-end latency ≤ 1 ms (propagation delay + 0.3 ms processing delay)  

Reliability ≥ 99.9999% (physical-layer SER ≤ 10⁻⁶)  

Achievable data rate≥ 300 Mbps 


For each user position and RIS configuration, the dataset includes:



Complex channel coefficients:  



h_{TU}: direct BS-to-UE channel (real & imaginary parts)  

h_{TR}: BS-to-RIS channel  

h_{RU}: RIS-to-UE channel  



User position coordinates (x, y, z) in meters  

Optimal beam index (beam_id) from UPA codebook search  

Optimal RIS phase-shift vector Φ (real & imaginary parts)  

Optimal BS beamforming vector w (real & imaginary parts)  

Minimal sufficient N, achievable data rate, latency, reliability, best linear SNR

Feasibility link & optimal_N (the smallest N that meets all constraints, or 0 if infeasible)


The objective is to jointly optimize the BS beamforming vector w, the RIS phase-shift matrix Φ, and the number of active RIS elements N, to minimize hardware complexity while meeting 6G URLLC QoS requirements.

Generation Details



DeepMIMO version: v2  

Scenarios & Parameters:





RIS Size (N)
DeepMIMO Scenario
Carrier Frequency
Transmit Power


64
O1_3p5
3.5 GHz
10 kW


256
O1_28 
28 GHz
100 kW


512
O1_28 
28 GHz
100 kW


1024
O1_60
60 GHz
100 kW






Bandwidths (B): 500 MHz, 200 MHz, 100 MHz, 50 MHz (applied across all RIS sizes)

Users per scenario: 50 user requests (different positions)  

Total combinations: 4 RIS sizes × 4 bandwidths × 50 users = 800 scenario–user combinations

Codebook generation: 3D UPA codebook with oversampling factor of 2 [2], steering angles uniformly quantized over [0, π), phases constructed via exponential terms and Kronecker products.  

Reliability calculation: Physical-layer reliability ρ = 1 − Q(√(SINR / 2)) (approximation for Gray-coded 64-QAM SER)  

Latency model: end-to-end latency = propagation delay + 0.3 ms fixed processing delay  

Other key parameters: LoS-dominant paths, antenna spacing 0.5λ, noise PSD −174 dBm/Hz, MRT beamforming at BS, unit-modulus constraint on RIS phases.


Files



dataset_nn_single_user_labeled_2.csv  → Channel coefficients, positions, beam_id, Φ, w, feasibility link, optimal_N 

dataset_nn_single_user_urllc.csv  → QoS metrics (achievable data rate, latency, reliability, best_linear_snr) per user and RIS size


 

References

[1] A. Alkhateeb, "DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications," arXiv, 2021.[2] A. Taha, Y. Zhang, F. B. Mismar and A. Alkhateeb, "Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation," in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020.