Quantum Computing for Artificial Intelligence Based Mobile Network Optimization
This work addresses automation challenges in mobile networks for telecom operators, though it is incremental as it demonstrates a proof-of-concept without achieving quantum advantage.
The paper tackled mobile network optimization by modeling radio access problems as constraint satisfaction problems and solving them with quantum computing, specifically applying a quantum annealing solver to the root sequence index assignment problem using real network data, but found that classical heuristics outperformed it in solution quality and computation time.
In this paper, we discuss how certain radio access network optimization problems can be modelled using the concept of constraint satisfaction problems in artificial intelligence, and solved at scale using a quantum computer. As a case study, we discuss root sequence index (RSI) assignment problem - an important LTE/NR physical random access channel configuration related automation use-case. We formulate RSI assignment as quadratic unconstrained binary optimization (QUBO) problem constructed using data ingested from a commercial mobile network, and solve it using a cloud-based commercially available quantum computing platform. Results show that quantum annealing solver can successfully assign conflict-free RSIs. Comparison with well-known heuristics reveals that some classic algorithms are even more effective in terms of solution quality and computation time. The non-quantum advantage is due to the fact that current implementation is a semi-quantum proof-of-concept algorithm. Also, the results depend on the type of quantum computer used. Nevertheless, the proposed framework is highly flexible and holds tremendous potential for harnessing the power of quantum computing in mobile network automation.