研究目的
To address the unfair load distribution and lower throughput for picocells’ users equipments (UEs) in heterogeneous networks (HetNets) by proposing a downlink coordinated cell range expansion for mobility management (CCREMM) strategy that computes the joint optimal bias at picocells and macrocells, mitigating interference and improving load balancing and UE QoS satisfaction.
研究成果
The proposed CCREMM strategy, combined with MTS scheduling, significantly improves LTE HetNets performance in terms of user and network throughput, SINR, and fairness. It outperforms dynamic and fixed uncoordinated range expansion techniques, demonstrating its effectiveness in load balancing and interference mitigation.
研究不足
The study acknowledges that aggressive CRE may degrade network capacity if not properly chosen, leading to severe intercell interference at ERC-UEs. The computational complexity increases with the number of parameters to be optimized, potentially limiting real-time implementation.
1:Experimental Design and Method Selection:
The study employs a CCREMM strategy based on a combined objective function (COF) to compute optimal biases for macrocells and picocells. The methodology includes utility functions for MUE and PUE to derive analytical expressions for optimal range expansion offsets (REOs).
2:Sample Selection and Data Sources:
The simulation scenario is based on a two-layer LTE HetNet with macrocells and picocells, following configuration 4b in 3GPP TR 36.814. Users are randomly distributed within macrocell and picocell coverage areas.
3:Users are randomly distributed within macrocell and picocell coverage areas.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: The simulation involves macrocells and picocells with specified transmission powers, bandwidth, and resource blocks (RBs).
4:Experimental Procedures and Operational Workflow:
The CCREMM strategy is implemented by computing optimal REOs for each cell, scheduling users based on the maximum throughput scheduling (MTS) technique, and evaluating performance metrics like throughput, SINR, and fairness.
5:Data Analysis Methods:
Performance is evaluated through system-level simulations, comparing CCREMM with benchmarks like FNCCRE-10, FNCCRE-6, and DNCCRE in terms of user throughput, SINR, and cell load balancing.
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