Smart Resource Allocation Scheme for Mimo Microstrip Wireless Communication

: In this paper we have proposed a minimum noise shortest path determination scheme considering the amount of delay and energy consumed with respect to each path. An artificial neural network has been employed for classifying the minimum noise shortest path from the source to destination. A simulation work has been carried out with respect to different Signal-to-Noise (SNR) values in a thirty-node network with one Internet node and 100 bits of message length. Also, a comparison has been made between plain Dynamic Source Routing (DSR) and integrating the minimum noise shortest path algorithm with DSR. The simulation results show that with the increase of SNR, noise constraint in the path reduces, and data throughput increases.

: Increase in global data traffic requirement from 2017 to 2022 [9] But, traditional MIMO is not capable of meeting such high data usage supported by 5G. That's why, a newer version of MIMO, known as massive MIMO has been considered for 5G technology. Where traditional MIMO uses 2 to 4 antennas, massive MIMO uses larger number of antennas. In addition to the characteristics of throughput and spectral efficiency increase of traditional MIMO, substantial increase in array gain is one of the fundamental characteristics of massive MIMO achieved using higher number of antennas [10].

Problem of user scheduling
In case of massive MIMO, concurrent transmission and receiving with large number of user devices can give rise to the problem of interference which consequently reduces the throughput. If by any chance, the number of user becomes more than the number of antennas, then the event of user scheduling can pose a threat to the system. So, a suitable user scheduling algorithm should be applied before precoding. A depiction of user scheduling has been given in figure 2 below. various research works that are now focusing on minimizing energy consumption in MIMO by employing less complex and low cost algorithms for precoding, user scheduling, signal detection, channel approximation, and antenna as well as power amplifier design. get connected with the Internet and how such a system functions when many systems are accessing the Internet  the Microstrip antenna that is being used here has been incorporated with soft computing, that is, the bandwidth and signal to noise (SNR) ratio are being adjusted depending upon the network parameters. Actually, various user devices are transmitting simultaneously and as a result there can be interference at the receiver end. Now, depending on the location of various sending devices, power, SNR and attenuation, a neural network-based classification algorithm is used to classify the received data in various suitable zones. So, the Microstrip antenna has been empowered with a simple decision-making soft computing system such that the antenna can switch to the most optimal configuration in order to maximize the throughput  a comparison has been made between the functions of the Microstrip antennas when it is with and without soft computing.

METHODOLOGY
As MIMO -OFDM systems are extensively used in LTE network, here we are considering a heterogeneous LTE network for our experiment.
In a heterogeneous network like LTE, communication via radio signal among user devices are basically managed by a base station along with the resource sharing and handover functions. 4G data carriers or WiMAX network is employed for connecting to the Internet.
When there is scarcity of resources, the base station determines the optimal subcarrier with respect to noise and power constraints for all channels and provides to the node. Current resource requirements and resource constraints actually determine the optimal path in deviceto-device communication.
Objective of this work is to find out the shortest path for transmission which has minimum noise constraint. Transmission throughput will be increased if the least noise and high SNR link is selected for each data transmission.

Block diagram
The block diagram of the proposed system has been illustrated below.

Figure 4: Minimum noise shortest path calculation
Before beginning of data transmission, the minimum noise shortest path has to be calculated.
For this purpose, probe packets are transmitted by the base station to the access points with respect to variable data load and SNR. Various parameters such as packet loss, bit error rate, and latency are used to determine the optimal shortest path with respect to each access point.
Then each access point forwards their findings to the base station. Using these outcomes, the base station utilizes ANN based minimum noise shortest path finding unit and determines the minimum noise shortest path at that instant. Depending on the noise interference, the ANN unit can suggest a new path or to retain the old path.

Algorithms for determining delay and energy consumed
The ANN unit determines the minimum noise shortest path by taking two factors in consideration: amount of delay and consumption of energy with respect to the current path of data transmission. Say, there are N nodes that are distributed over an area. The startup energy in each node is denoted as Es and energy consumed for communication to one-hop neighbors is indicated as E1h. It should be mentioned that total energy consumption for communicating to adjacent neighbors should be less than startup energy which can be expressed as Algorithm for calculating amount of delay has been highlighted below.  By employing a linear function of distance, the resulting delay is measured. So, the path with least ED value is the required path.
Next, algorithm for determining the energy consumption is detailed below.
 Say, there are m available paths from sender to receiver.
 C is the counter of available paths. Initially c = 1.
 Let, E denotes minimum energy requirement per bit with respect to m number of paths.
Initially, E is assigned maximum possible value where t is a threshold value defined earlier  Counter of paths is updated c = c + 1  Repeat four steps above  All the paths are compared with respect to energy consumption and the least one is selected The diagram below shows the ANN unit for determining minimum noise constraint path.

Simulation
Simulation has been carried out using MATLAB. Various network topologies of 30 nodes have been generated. At different SNR value, delay and energy consumption have been examined and ANN based model has been used to calculate throughput. Table -I   From the above graph, it can be seen that at -40dB, more energy consumption and more delay occur.

Figure 8: Simulation window at -20 dB
The simulation window above shows more noise and less signal strength at -20 dB. The graph below shows the status of delay and energy consumed at -20 dB.  The graph below demonstrates that average delay and energy consumption both are decreasing.  Bit error rate compared for theoretical and simulated values considering QAM-OFDM system and fifty nodes into consideration. It can be observed from the above figure that simulated values are perfectly aligned with theoretical values proving that our simulation is working properly.

CONCLUSION
Increase in the number of user devices can pose a problem in optimal resource allocation strategies. Determining the minimum noise shortest path from the source to destination is a necessity for high data rate. So, in this paper has proposed a ANN based scheme for finding minimum noise constraint path. The neural network takes delay and energy consumption as input and decides for the noise-optimal path. The Base Station(BS) keeps a watch on the network performance and suggests the noise-optimal path according to current requirement.
Simulation results show that the proposed scheme demonstrates better performance.