expresses the weighted moving average prediction where (hat {mathbf {X}}_{n + 1}^{k}) denotes the predicted value for the n+1-th step based on past L historical data of the k-th channel; α i s denote the weighting coefficients; and (mu _{mathbf {X}^{k}}) represents the intercept of the prediction.
Specifically, and from a discrete-time perspective, the WL regression problem was tackled in [16], the prediction problem in a complex autoregressive modeling setting was addressed in [17, 18] and later extended to autoregressive moving average prediction in [19].
The next stage, Mr Fadell reckons, involves moving from prediction to machine action.
The model is based on the cooperation of moving target prediction model and crowd control algorithm.
We illustrate the moving direction prediction scheme with Figure 2 as follows.
In the proposed moving direction prediction scheme, we adopt the Global Positioning System (GPS) to identify the position of each eNBS and UE [26].
The three processes are based on the cooperative control of the moving target prediction and crowd control algorithm to form a multi-objective optimization framework.
As shown in Fig. 3, the moving target prediction, optimization, and crowd optimization model can be realized by the mobile location and crowd control scheme.
Since the large amount of error inherited from orientation estimation leads to poor traveling distance calculation when driving, we build a moving distance prediction model by training the distance estimation model through linear regression to minimize the accumulated error.
Based on the cooperative control of the moving target prediction and crowd control algorithm, the ACO-DF algorithm can realize the intelligent time domain mapping control based on linear fusion.
