A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

Abstract

Conventionally, the 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 be obtained 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 current 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 cloudcomputing. An example of beam allocation in multi-user massive multiple-input-multiple-output (MIMO) systems shows that the proposed machine-learning based resource allocation outperforms conventional methods. Index TermsResource allocation, machine learning, cloud computing, k-nearest neighbour (k-NN), beam allocation algorithm, massive MIMO.

Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey

“…It has also been increasingly used to solve challenging (in many cases, non-linear non-convex) problems in wireless communication systems, e.g., Polar decoding [214], [215] and massive MIMO channel estimation [216], [217]. Deep neural networks (DNNs) are able to solve sophisticated non-convex problems without explicit mathematical formulations [218]- [220]. Several recent works have investigated the EH-based wireless communication systems where deep learning is utilized as an optimization method [221]- [223].…”

20 Years of Evolution From Cognitive to Intelligent Communications

“…Depending on different kinds of purposes, only the related radio environment data should be used, which is usually selected through a trial and error manner. For example, the number of users, CSI, and interference level are related to the beam selection scheme [42]. The power strength of the received primary signal samples can indicate spectrum occupancy and thus can be used as the core radio environment data for the spectrum detection in CR networks [43].…”

Energy-Efficient Subchannel and Power Allocation for HetNets Based on Convolutional Neural Network

“…• Considering an OFDM-based HetNet, we formulate the resource allocation task as a joint subchannel and power allocation problem, which maximizes the energy efficiency (EE) of the network while satisfying the requirement of the spectrum efficiency (SE). • Different from [11]- [13], which either solve a regression problem or a classification problem for resource allocation by deep learning, the proposed CNN, for the first time, decomposes the original problem into a classification subproblem and a regression subproblem, to infer the energy-efficient decisions on joint subchannel and power allocation. • Extensive numerical experiments are conducted to demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while substantially reduce the computational time.…”