Conventionally, resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to obtain in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of a scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive MIMO systems shows that the proposed machine-learning-based resource allocation outperforms conventional methods.

中文翻译：

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云计算辅助的资源分配机器学习框架


传统上，资源分配被表述为优化问题，并利用即时场景信息在线求解。由于大多数资源分配问题不是凸的，因此很难实时获得最优解。然后经常采用拉格朗日松弛或贪婪方法，这会导致性能损失。因此，传统的资源分配方法在满足无线资源稀缺的用户日益增长的QoS需求方面面临着巨大的挑战。在云计算的辅助下，可以收集大量场景历史数据，利用机器学习提取场景之间的相似性。而且，可以离线搜索历史场景的最优或接近最优解并提前存储。当场景的测量数据到达时，将当前场景与历史场景进行比较，以找到最相似的场景。然后采用最相似的历史场景中的最优或接近最优的方案来为当前场景分配无线资源。为了促进新设计理念的应用，提出了云计算辅助的资源分配机器学习框架。多用户大规模 MIMO 系统中波束分配的示例表明，所提出的基于机器学习的资源分配优于传统方法。