Abstract
In the Modern Wireless Communication Systems, there is a direct need for the enhancement of the network capacity, in order to support more and more data. This paper describes the data reduction methodology for enhancement of channel capacity based on Principal Component Analysis (PCA) of the Multiple-Input-Multiple-Output (MIMO) based untether communication system. The communication system has employed 16 Quadrature Amplitude Modulation (16-QAM) scheme with LDPC for channel coding method for error correction. The proposed method here utilizes the Principal Component Analysis (PCA) for data reduction which transmits only the feature vectors i.e. Eigen Vector for the set of composite data for large numbers of signals data over time-stamped. The effectiveness of the proposed work is evident through the simulation results of the systems incorporating 1×1, 2×2, 3×3, 4×4 MIMO networks. We have presented RMSE analysis for the PCA regression for verification of the signal fidelity and the BER for confirmation of the recovery of the data over MIMO channel over LDPC coding. Here we observe that our proposed work has achieved much better results.
Highlights
Wireless Sensors Networks (WSNs) are employed to gather variety of information which includes light, humidity, air quality, wind speed, temperature, and other vital signs
We have presented the bit error rate (BER) for confirmation of the recovery of the data over MIMO channel over Low Density Parity Check (LDPC) coding
From perspective of the capacity of the channel, QPSK performance is better compared to BPSK but overall QAM is most suitable for massive MIMO system
Summary
Wireless Sensors Networks (WSNs) are employed to gather variety of information which includes light, humidity, air quality, wind speed, temperature, and other vital signs. Monitoring systems are being developed in health care, industries, ecological system, governmental as well as military applications. These monitoring systems need to transmit data on continuous basis over sufficiently prolonged periods. Energy efficiency becomes the prime consideration as well as the lifetime longevity of WSNs. The major chunk of the collected data usually renders itself redundant and generally can be mined from other observations. Reducing or preventing altogether, the unnecessary transmissions in a WSN has a very significant influence on reducing the overall energy consumption. These results in lifelong longevity since most of the energy is spent in the data transmissions [1]
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