Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem. In this paper, we propose a feature extraction method using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state. Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method. Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions.
The rising demands for network resources and quality of service are forcing operators of wireless cellular networks to continuously add capacities to their networks. One of the means to do so is densification: deploying heterogeneous networks with a multitude of smaller base stations, such as picobase stations and femtobase stations [
To determine an optimized state control strategy for a network, it is necessary to capture its traffic distribution, that is, the geographical distribution of mobile UE in the network [
Optimization algorithms were developed to determine control strategies while satisfying a set of conflicting requirements such as coverage and energy efficiency. In [
A MCPS is a combination of computation and communication systems. It is capable of sensing, processing, and responding to the dynamic changes of traffic distribution of a network [
In [
The remainder of the paper is organized as follows. The framework of the green MCPS is explained in Section
The framework of the green MCPS is shown in Figure
A green mobile cyberphysical system framework.
The off-line process is a learning process. Its input is a set of traffic distributions sampled at different times when the network is deployed. The outputs of this process are a set of traffic patterns and the corresponding optimized control strategies of the base stations. When the off-line process starts, a set of traffic distribution samples have been collected at various times and saved in the database of the system. Each sample contains the geographical locations of all mobile UE in the 2-dimensional target region at certain time. It must be noted that we must collect enough samples in order to capture the characteristics of the network’s traffic distribution during its daily operation. These samples are then transferred to the feature extraction module, in which if the target region is partitioned into equal-sized areas, a feature vector with a dimension equal to the number of areas is used to represent the UE densities in each area. The detailed feature extraction algorithm will be explained in the next section. After that, the feature vectors are transferred to a clustering module in order to group all traffic distribution samples into a certain number of traffic patterns using the clustering algorithm, as introduced in the following section. Each pattern is a traffic distribution that represents a group of samples with similar characteristics of geographical distribution. Each pattern has an optimal control strategy for the target region. The optimized strategy is decided using a heuristic method as introduced in previous papers [
The online process starts when a new traffic distribution is identified. In this case, the system starts the feature extraction module to extract and represent its distribution feature into a feature vector. Then the vector is imported into a classifier, which acts as a decision-making tool that is capable of matching the sample with the traffic patterns according to the Euclidean distance between feature vectors [
If we partition a 2-dimensional target region into equal-sized grids, that is, unit grid, and count the amount of mobile UE within each grid, we can represent the characteristics of the traffic distribution into a feature vector. However, elements of the feature vector are always independent of each other. Since the detail of the feature vector is limited by the grid size, the unit grid method is not effective in capturing the clustering characteristics of the traffic distribution.
The high density distribution of mobile UE forms cluster, a common and critical distribution form to affect the coverage and QoS of heterogeneous networks [
Feature extraction example.
Given a sample of traffic distribution in a region with area
In general,
(1) Determine the value of (2) (3) calculate the row and (4) count the number of mobile UEs (5) (6) (7) (8) count the number of mobile UEs (9) (10)
In Algorithm
Subsequently, we convert matrix
Traffic clustering module categorizes all traffic distributions samples into a limited set of traffic patterns. Clustering is an unsupervised classification approach, which is capable of analyzing the internal characteristic and mutual relationship of objects without label [
(1) Normalize Feature vectors to (2) Determine the number of traffic patterns (3) Construct an affinity matrix (4) Define the diagonal degree matrix (5) Compute the first (6) Construct a matrix (7) Treating each row of (8) Assign the original feature vector to the assigned label (9) Compute the features of traffic patterns, and
In Algorithm
In order to obtain the optimal
After traffic clustering, the optimal control strategy for each pattern is decided using the method presented in our previous work [
The presented method is demonstrated using a 1600 m × 1600 m target region. In order to verify the efficacy of the presented method in different traffic distributions, we create 1000 different traffic distributions including three distribution models: A A perfect lattice and a random perturbation [ Thomas process [
Each of these traffic distribution samples consists of a random amount of UE between 250 and 650. In order to differentiate the samples, the difference of the UE numbers between any two samples is at least 50. 113 base stations are in cochannel deployment in a two-tier heterogeneous cellular network, including one macro base station and 112 small base stations. In addition, other major network parameters, such as the bandwidth and transmission power of base stations, bit error rate, and outage probability, are set referring to [
According to the optimal control strategy, the amount of mobile UE served by the active base stations in each sample is counted. Hence, we select the rate of UE coverage as the performance metric of green MCPS.
We start by estimating an optimal number of traffic patterns. The sliding window size
Average silhouettes value for different number of traffic patterns.
It is observed that the average silhouettes value achieves their peak over a range of possible value for
In this experiment, we count the rate of traffic distribution samples with UE coverage over 98% based on the feature vectors extracted by sliding window with different step sizes and a unit grid. The number of grids partitioned is set to be
We can observe from Figure
Feature extraction using sliding window with different step sizes and a unit grid.
In order to discuss the effects of sliding window size and the number of traffic patterns on the coverage, we use
Coverage for different number of patterns.
It can be seen that the smaller the size of the sliding window is, the higher the percentage of samples achieves good coverage. This is because that more heterogeneous distribution features are preserved by using a smaller sliding window, which contributes to better coverage. However, smaller window size means larger feature vector for a sample, and this unavoidably reduces the operational speed. It is necessary to compromise between the coverage and operational speed when we decide the window size in practice. We can choose different window size according to different requirements for the rates of coverage. At the same time, we can observe that, by increasing the number of traffic patterns, the number of samples with good coverage rises slightly but is not significant. Therefore, we conclude that the number of traffic patterns is less important than other factors such as sliding step size and window size. In practice, we can decide the pattern number by the average silhouette method.
After adopting the optimal control strategy of the traffic pattern to its samples, we can observe the number of active base stations in the traffic distribution samples. Here, in the process of feature extraction,
As shown in Figure
Number of active base stations for different traffic clusters.
Finally, a SINR distribution example under a traffic pattern is depicted in Figure
SINR distributions.
Initial state
Final state
In this traffic pattern, UE is represented as dots, and base stations are uniformly distributed within the target region. As shown in Figure
In this paper, we present a feature extraction method using sliding window for traffic distributions in a green mobile cyberphysical system. The method has the advantages of reserving more clustering distribution features over using the grid method. In order to implement rapid base station state control and extend the lifespan of a heterogeneous network, we apply clustering analysis for all traffic distributions to obtain a limited set of traffic patterns. Numerical results demonstrate that the proposed method helps obtain better UE coverage comparing with using the grid method. It is worth noting that both smaller sliding step size and smaller sliding window size can lead to good UE coverage but slow the operational speed of the network.
The authors declare that they have no conflicts of interest.
This research was supported in part by the National Natural Science Foundation of China (Grant no. 61601482) and Guangdong Technology Project (2016B010125003 and 2016B010108010) and sponsored by the Foundation of Science and Technology on Information Transmission and Dissemination in Comm. Networks Lab, National Key Laboratory of Anti-Jamming Communication Technology, and State Joint Engineering Laboratory for Robotics and Intelligent Manufacturing funded by National Development and Reform Commission (no. 2015581).