AI 中文总结
将5G基站下行MIMO调度问题建模为QUBO，分析其可扩展性，并验证次优贪心算法在高用户低带宽下的有效性，为量子调度实现奠定基础。
AI 中文摘要
当问题首先被转化为二次无约束二元优化（QUBO）格式时，量子计算机有可能非常高效地解决大规模组合问题。第五代（5G）基站中的调度是一个实际的组合问题，使用经典计算无法实时最优求解。我们将5G基站中的下行（DL）多输入多输出（MIMO）调度建模为QUBO，并分析了QUBO建模相对于关键系统参数的可扩展性。单用户MIMO（SU-MIMO）的QUBO建模看起来很有前景，因为随着用户数量的增加，QUBO变量的数量线性增长，而问题搜索空间则呈指数增长。基于仿真结果，针对SU-MIMO的次优贪心算法在用户数量多且带宽低的情况下表现良好。一种混合方法，即根据系统参数选择量子求解器或次优低复杂度经典算法，在实践中似乎是明智的。这项工作为未来蜂窝系统中调度的量子及量子启发实现铺平了道路。
英文摘要
Quantum computers can potentially solve large-scale combinatorial problems very efficiently when the problems are first converted into the quadratic unconstrained binary optimization (QUBO) format. Scheduling in fifth generation (5G) base stations is a practical combinatorial problem that cannot be solved optimally in real-time using classical computing. We formulate the downlink (DL) multiple-input multiple-output (MIMO) scheduling at 5G base stations as QUBO and analyze the QUBO formulation scalability with respect to the key system parameters. The single-user MIMO (SU-MIMO) QUBO formulation looks promising because the number of QUBO variables grows linearly while the problem search space grows exponentially with increasing number of users. Based on the simulation results, the suboptimal greedy algorithm for the SU-MIMO performs well with a high number of users and low bandwidth. A hybrid approach, where either a quantum solver or a suboptimal low-complexity classical algorithm is selected based on the system parameters, seems sensible in practice. This work paves the way for future quantum and quantum-inspired implementations of scheduling in cellular systems.
