# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 6 Conclusions
(1) Full automation dominance will prevail in 5G/6G networks, driven by AI/ML for real-time adaptability. (2) Persistence of semi-automation will endure in high-risk scenarios (e.g., ethics, compliance). (3) Hybrid algorithms will continue to be transition enablers, reconciling legacy and next-generation infrastructures. (4) Context-aware automation will prioritize explainability in safety-critical domains. (5) Sustainability-driven automation will optimize energy efficiency in the context of green grid mandates. Our analysis identifies inherent limitations of current load balancing algorithms that affect the feasibility of next-generation deployment. Automatic approaches incur latency penalties due to computational overhead during dynamic state transitions, while semi-automatic/hybrid methods exhibit accuracy degradation when high-quality training data is not available. These drawbacks, compounded by poor explainability and lack of alignment with latency targets, highlight the importance of trade-offs between algorithmic sophistication and operational deployability in 5G/6G networks. Furthermore, case studies demonstrate the operational effectiveness of the proposed taxonomy. Automatic algorithms achieve fewer handover failures in smart factory URLLC scenarios, meeting low latency objectives. Semi-automatic algorithms resolve congestion in large scenarios (e.g., stadiums) with higher access success rates. In contrast, hybrid models ensure high reliability during critical network outage transitions in 5G testing. While existing taxonomies classify load balancing algorithms by technical attributes, our framework introduces an operational autonomy axis aligned with zero-touch industrial paradigms. This reveals critical insights for the deployment of next-generation networks. Our taxonomy transcends incremental evolution by revealing how autonomy-centric load balancing will pioneer 6G's cognitive networks [64]. Quantum-secure hybrids [65] and holographic projection leveraging electromagnetic information theory [66] will transform balancing into a physics-aware service. These disruptions--grounded in 3 GPP Release 19 studies--position our framework as the governance backbone for zero-touch 6G ecosystems [67]. Although this systematic review provides a comprehensive taxonomy of load balancing algorithms for next-generation networks, we acknowledge the limitation regarding experimental validation. The absence of an empirical testing framework is due to the conceptual nature of this work, focusing on classification and gap analysis rather than performance benchmarking. However, this limitation reveals a fundamental research avenue: future studies should rigorously validate these algorithmic categories through controlled simulations and field testing. Specifically, we propose the following: comparative tests between algorithms in simulated ultra-dense urban scenarios (e.g., using NS-3/5G-LENA) with QoS metrics (latency, throughput, handover failure rate) as key variables [68], followed by performing real-world validations through partnerships with mobile operators, deploying the best performing algorithms in standalone 5G networks. These experiments will quantitatively establish the various deployment contexts while addressing practical constraints such as computational overhead and scalability. Our proposed experimental framework (see the specifically in Supplementary S3) employs 12 normalized KPIs, including energy per bit and PRB variance, to holistically evaluate the trade-offs between QoS, resource efficiency, and sustainability. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/fil7070290/s1](https://www.mdpi.com/article/10.3390/fil7070290/s1)]([https://www.mdpi.com/article/10.3390/fil7070290/s1](https://www.mdpi.com/article/10.3390/fil7070290/s1)). Conceptualization, J.O.-A., C.S.-C., R.T., J.I.G. and S.F. ; methodology, J.O.-A., C.S.-C., R.T., J.I.G. and S.F. ; software, J.O.-A., C.S.-C., R.T., J.I.G. and S.F. ; validation, J.O.-A., C.S.-C., R.T., J.I.G. and S.F.