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Quantum-Enhanced Massive MIMO Beamforming for 6G IoT Networks: A QAOA-Based Optimization Framework
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DOI：10.1109/OJCOMS.2025.3645207.png)
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Abstract
En 中文
Massive MIMO beamforming for 6G networks faces a fundamental tradeoff between solution quality and computational complexity. Exhaustive search guarantees optimal antenna selection; however, this becomes prohibitively expensive for arrays exceeding 16 elements, while polynomial-time classical heuristics sacrifice 15-25% performance to achieve practical scalability. This paper introduces a quantum-enhanced optimization framework using the Quantum Approximate Optimization Algorithm (QAOA) to address this challenge for IoT-integrated 6G massive MIMO systems. Our approach combines quantum solution exploration with classical parameter optimization, integrating realistic 3GPP TR 38.901 channel models across 28-60 GHz bands and heterogeneous IoT device characteristics (mMTC, URLLC, eMBB). The framework incorporates an adaptive penalty mechanism that achieves constraint satisfaction within five iterations while maintaining polynomial complexity. Statistical validation across 50 independent channel realizations demonstrates significant advantages: 10-20% spectral efficiency improvement over classical heuristics (p < 0.001, Cohen's d = 1.24), 35-42% IoT energy reduction, and 90-95% near-optimal solution quality compared to 65-85% for polynomial-time classical methods. Hardware validation on IBM quantum platforms (127-133 qubits) confirms practical feasibility for medium-scale systems with M <= 16 antennas, achieving 89.3% of ideal performance with 22% measurement success rate. Current hardware limitations restrict deployment to proof-of-concept demonstrations, with full-scale 6G implementations requiring quantum error correction projected for 2030+.
Keywords:
6G mobile communication
Optimization
Array signal processing
Massive MIMO
Ultra reliable low latency communication
Internet of Things
Hardware
Quality of service
Channel models
Symbols
6G networks
IoT
massive MIMO
quantum computing
QAOA
beamforming optimization
Journal
I
IF:
6.1
Papers:
481
Citations:
0
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