ABSTRACT
Data collection with the help of modern wireless sensor-based networks is widely used in areas such as agriculture, health, the military, and so on. These networks are operating in a new scenario called cooperation. It is a well-known fact that when there are greater numbers of users and communication agents, not all of them will be in operation at any point in time. The limited number of operating users or elements in a network leads to limited data at any point in time. Thus there arises a need for operations on limited available data. Recent developments in image processing, with the help of limited available data, perform better with the help of a Compressive Sensing technique, a recent mathematical method developed to operate with limited data.
Channel information estimation in a wireless network is a major task for achieving successful communication. This can be made more successful with the employment of the compressive sensing technique. Wireless networks derive benefits from technologies such as Multiple input and multiple output and establish cooperation for satisfying its huge data rate requirements and coverage problems. The recent massive usage Multiple input and multiple output has resulted in more antennas for single communication. The compressive sensing technique aids in operating with limited data in such situations. Spatial modulation, another enabling technology for massive Multiple input and multiple output, when combined with compressive sensing, yields a more efficient communication system. Another breakthrough in the multiple antenna regime is the introduction of a kind of sparse data analysis called Principle Component Analysis. This use of compressive sensing algorithms on principal component analysis may reduce the bottlenecks in high speed communication.
