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.
翻译:在本文中,我们讨论了某些无线电接入网络优化问题如何使用人工智能中的限制满意度概念来模拟,并使用量子计算机在规模上解决。作为案例研究,我们讨论了根序列指数(RSI)分配问题――一个重要的LTE/NR物理随机访问频道配置与自动化使用情况相关的自动使用情况。我们将“RSI”分配作为使用商业移动网络数据构建的不限制的二次优化(QUBO)问题,并使用基于云的商用量子计算平台加以解决。结果显示,量子射线解解解解解码可以成功分配无冲突RSI。与众所周知的静态比较表明,某些经典算法在解决方案质量和计算时间方面甚至更有效。非量子优势在于,当前实施是一种半量子验证概念算法。此外,结果取决于所使用的量子计算机类型。然而,拟议的框架非常灵活,具有巨大的潜力,可以在移动网络自动化中利用量子计算能力。