Practical Load Balancing Algorithm for 5G Small Cell Networks Based on Real-World 5G Traffic and O-RAN Architecture

In the 5G network, small cells play a key role in enhancing network capacity and providing ultra-fast, low-latency data services. The traffic load per small cell is highly dynamic and suffers from rapid fluctuations, necessitating appropriate user association algorithms for load balancing. Nevertheless, existing researches often adopt overly tractable traffic models or rely on outdated datasets, making their sophisticated algorithms ineffective. In this paper, we take an improved approach: (i) we collect real-world 5G traffic data ourselves to accurately model the traffic load, and (ii) propose a low-complexity, one-shot user association algorithm, which makes a decision with a single computation without iterative operations. The proposed algorithm determines user association for cell-edge users based on the biased received power of small cells. Crucially, it dynamically adjusts the bias of each small cell in response to its traffic load. We have discovered that the 5G traffic load is dynamic, yet sufficiently predictable due to its daily pattern. Leveraging this characteristic, the proposed algorithm increases the bias value for small cells predicted to have lower traffic load in the near future and decreases it for those expected to have higher traffic load, thus achieving load balancing in 5G networks. The simulation results verify the superiority of our approach in terms of load fairness over the conventional static bias schemes, such as cell range expansion (CRE), although it slightly underperforms compared to the near-optimal results derived from an iterative solution with significantly higher computational complexity.