研究目的
To maximize the supportable aggregate data arrival rate in a cloud small cell network by jointly optimizing free space optical fronthaul and millimeter-wave access links while considering statistical quality-of-service constraints.
研究成果
The proposed joint optimization framework significantly improves the aggregate data arrival rate in cloud small cell networks with FSO fronthaul and mmWave access links under statistical QoS constraints. The algorithm converges with polynomial complexity, and simulations show substantial gains over benchmark schemes, highlighting the importance of buffer-aided SRRHs and load balancing.
研究不足
The optimization assumes ideal adaptive modulation and coding, ergodic block fading, and busy cycle for buffers, which may not hold in practical scenarios. The solution is sub-optimal due to non-convexity, and performance depends on accurate channel state information. Pointing errors in FSO links can degrade performance, requiring efficient acquisition-pointing-tracking mechanisms.
1:Experimental Design and Method Selection:
The study uses a mathematical optimization framework with Lagrangian dual decomposition and alternating optimization techniques to solve a non-convex combinatorial problem. It involves decomposing the problem into two sub-problems for fronthaul/access link power allocation and data rate/association optimization.
2:Sample Selection and Data Sources:
Simulations are conducted in a 200m x 200m square region with 4 SRRHs, UEs randomly distributed, and specific coordinates for BBU pool and FSO relay nodes. Channel states are generated based on Gamma-Gamma turbulence fading for FSO and Rician distribution for mmWave.
3:List of Experimental Equipment and Materials:
FSO transceivers, mmWave antennas, buffers at BBU pool and SRRHs, FSO relay nodes. Specific models and brands are not mentioned.
4:Experimental Procedures and Operational Workflow:
The algorithm iteratively solves sub-problems using convex optimization and matching techniques, with simulations run over 300 independent UE locations and channel gains to evaluate aggregate data arrival rate.
5:Data Analysis Methods:
Statistical analysis using ensemble averages over channel states, convergence proofs, and comparison with benchmark schemes through simulation results.
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