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Welcome to the Learning-based-Power-Allocation-Strategy-in-Small-Cell-Network wiki! This repository is for the UCD EEC289Q 2018 Spring Quarter final project. The content of our project includes:

• data pre-processing
• feature selection
• learning algorithms
• Gibbs based optimization
• conclusion

Data Pre-processing

In this part, the data sets we get are some log files from Huawei Inst. We formalized the data sets with the target be the throughput of a base station and the feature be pilot power, data power, load, number of users, and its neighbor base station featres. A typical data frame is as below:

Time	Cell ID	Throughput	Load	Power	User	Neighbor
1	154	365	0.635	30	25	117,228,165,152,119

Finally, we store the data set into CSV files for the following usage.

Feature Selection

In this part, we do feature selection. By reducing the dimension of input feature, we can accelerate the computation in the base station and simplify the optimization in the following steps. To select the best features, we manually selection several subsets of the features. This table shows the subsets of selected features:

Index	Features
0	Power
1	Power, TimeAveLoad, TimeAveUsers
2	Power, Load, Users
3	Power, Load, Users,TimeAveLoad, TimeAveUsers
4	Power, Neighbor Power*5
5	Power, Load, Users, Neighbor: Power*5
6	Power, Load, Users,TimeAveLoad, TimeAveUsers, Neighbor Power*5, AveLoad, AveUser
7	Power, TimeAveLoad, TimeAveUser, Neighbor Power*5
8	Power, Load, Users, Neighbor Power*5, AveLoad, AveUser

We manually select the features instead of using some feature selection algorithms is because those features have explicit meaning. We use neuron networks to use features fit the throughput. We use two metrics to evaluate the result. First the $R$ value defined as $R(y,\hat y) = 1-\frac{\sum(y_i-\hat y)^2}{y_i-\bar y}$

Learning throughput of the base station

Optimization