Coordinates-based Resource Allocation Through Supervised Machine Learning
Abstract : Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies.Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions.Considering that future wireless technologies will be based on dense network deployment, where the mobile terminals are in line-of-sight of the transmitters, the terminals’ position information provides an alternative to estimate the channel condition.In this work, we propose a coordinates-based resource allocation scheme using supervised machine learning techniques, and investigate how efficiently this scheme performs in comparison to the traditional approach under various propagation conditions. We consider a simplistic system set up as a first step, where a single transmitter serves a single mobile user.The performance results show that the coordinates-based resource allocation scheme achieves a performance very close to the CSI-based scheme, even when the available user’s coordinates are erroneous.The proposed scheme performs consistently well with realistic system simulation, requiring only 4 s of training time, and the appropriate resource allocation is predicted in less than 90 µs with a learnt model of size
• For existing wireless systems assisted by cloud computing, a huge amount of data on historical scenarios may have been collected and stored at the cloud.
• The strong computing capability of the cloud is exploited to search the optimal or near-optimal solutions for these historical scenarios. By classifying these solutions, the similarities hidden in these historical scenarios are extracted as a machine learning based resource allocation scheme.
• The machine learning based resource allocation scheme will be forwarded to guide BS how to allocate radio resource more efficiently. When a BS is deployed in a new area, there is usually no available data about historical scenarios.
? We consider the maximization of transport capacity as the optimization problem in this work.
? The transport capacity at the terminal depends on the allocation of available system resources between the BS-terminal pair.
? In this work, we apply supervised machine learning to design a coordinates-based resource allocation scheme to solve the capacity maximization problem.
? One formulation is based on designing the dataset for binary classification problem, but this implies that the output variable can take one of the two possible values.
• At the cloud, a huge amount of historical data on scenarios are stored using the cloud storage.The historical data has a lot of attributes, including the user number, the CSI of users, international mobile subscriber identification numbers (IMSIs) of users, and so on.Some attributes, such as IMSIs of users, may be irrelevant for the specific resource allocation, i.e., these irrelevant attributes are not included in the parameter ve 7 problem.
• Learning from a large number of raw data with many attributes generally requires a large amount of memory and computation power, and it may influence the learning accuracy. Therefore, the irrelevant attributes can be removed without incurring much loss of the data quality.
? We present a detailed description of the coordinatesbased resource allocation scheme through machine learning proposed in, and discuss the different possibilities for dataset formulation and the associated challenges.
? We investigate the applicability of the proposed coordinates-based resource allocation scheme using the different dataset formulations.
? Based on the best possible choice for dataset formulation, we investigate the performance results of the proposed scheme with respect to the stochastic variations in system characterization.
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