This paper proposes a new scheme to assist the base station to predict a wide range of frequency bands and path loss. In a practical scenario, however, the receiver has no access to the actual bands, and is supposed to predict it. The proposed system is a single-input single-output (SISO) wireless communication system with a macro urban environment. The system has a radio frequency bandwidth of 800 MHz with a transmitter power of 30 dBm, and the transmit and receive array type are URA. Due to the weakness of higher MmWave bands, other effects related to weather have been considered, such barometric pressure 1013.25 mbar, humidity 50%, the temperature is 20 degrees Celsius, and rain rate was assumed to be zero [
20]. Other system parameters can be summarized in
Table 1.
Theorem
Investigating the effects of higher mmWave can be driven with the usage of the Friis Theorem [
21] by assuming an environment with only a transmitter
and a receiver
and no obstacles between them to create free space. The distance is
d and a transmitted power of
and omnidirectional antennas at at both
and
. Therefore, the received power
is calculated using
here
is the gain of the transmit antenna. Assuming the antenna at the receiver side has an effective aperture
. Then (
1) becomes.
The effective aperture of the antenna can be:
where
,
is the wavelength in meters,
c is the speed of light and
is the frequency in GHz [
22]. Moreover, Friis theory in (
4) can lead to the same conclusion that path loss is proportional to the frequency bands.
From (
5), we can infer the frequency bands as shown below.
The above equation proves that with in creasing the frequency, the received power reduced which means the path loss will increase as well as shown in the below equations.
Understanding the behavior of the path loss (L) concerning distance and other CSI features with continuous dependent variables leads to the use of the proper procedure of the ML categories. Therefore, it is clear in our case that the ML strategy will be a supervised regression problem based on a data-driven approach. One technique to measure the energy loss is by using the path loss model, which measures the reduction of the energy during the propagation. By using path loss models, energy loss can be governed. The complexity and the accuracy of the path loss models can vary with many factors, such as the environment, interference level, energy, distance, and so on. The wireless bands have a high impact on the amount of loss and coverage following the equation of that is different from the lower bands. CSI features that are used in our system are frequency bands (GHz), T-R separation distance (m), received power (dBm), phase (rad), azimuth AoD (degree), elevation AoD (degree), azimuth AoA (degree), elevation AoA (degree), path Loss (dB) and root mean square (RMS) delay spread (ns). Frequency band (GHz) and path Loss (dB) will be used as targets where other CSI will be used to feed the NNs to assist the base station to predict the bands or the path loss.
In the macro environment, user equipment (UEs) are assumed to be non-stationary and uniformly distributed. In our model, we applied deep learning NNs techniques such as MLP as supervised learning to predict the dependent variable. Having a dataset consisting of the dependent variable and CSI , features of the mmWave bands can be predicted. In this paper, we will focus on predicting the path loss and the frequency bands based on previous data . The data are split into training and testing where .
The ML algorithms learn the pattern and assist base stations to make a decision due to the complexity of the network structures and wireless services that are recommended for the new radio techniques [
23]. ANNs are widely used nowadays to learn the complex pattern of the wireless channel to avoid complex and unreliable mathematical formulations. Since the presented data is nonlinear and multivariable characteristics, ANN could be involved to predict the frequency bands and path loss to assist the base stations as alternative model structures for received power. The ANN’s structure consists of at least three main layers, input (i), hidden (j
), and output (k) layers, and for simplification, we assume the system only consists of only one hidden layer. Each layer composes of one or more number of neuron
, where
l to represent which layer and
i for identify the specific neurons which considered the main component and the processing unit.
neurons in layer j
feeds from
neurons in the previous layer by a weight vector. Input of CSI
are fed to networks and then multiplied by the weight vector
and with addition of bias variable
then summed at the hidden layer. Then, activation function
is used for every node in the hidden layers to produce an output where more details of the activation functions will be elaborated later in this manuscript. The summation of the hidden and output layer of the NNs denoted by
and
respectively and can be formulated as follows.
Figure 3 is a simplified systems model, where the training data samples
are feed into the system. Forward, backward, and optimization techniques are implemented in this system. Deep learning algorithms will be compared with ML methods and then optimization techniques will be applied to reduce the loss of the prediction.