To improve the efficiency of SWIPT, transmit beamforming has been recognized as a key technique, where multiple antennas are used at the transmitter to adaptively form transmit beams toward multiple information and energy user receivers to effectively compensate the path loss as well as mitigate the inter-user interference.
Our model exploits multiple information resources.
In medical encounters, there has been a chronic complaint of a disconnection between information providers' and information receivers' perspectives.
For these types of applications, point-to-multipoint services (i.e., the same information is headed to multiple selected receivers in a certain group) offer a significant improvement compared with unicast services, since they allow the simultaneous transmission of packets to multiple destinations which enables to use radio resource more efficiently [1, 2].
In the context of the setting discussed in this article, the index coding problem [17], which also considers only a single sender with multiple receivers (having side information), is also unsolved in general.
To tackle this issue, we leverage the fact that the correlation of the observed frequency change across multiple receivers provides valuable information on the location of the nodes involved.
These include networks with multiple transmitters, multiple receivers, decoder side information, intermediate nodes, and any combination of these features.
Wireless communication of multiple correlated information sources to a common receiver has become an important research problem due to potential applications in emerging information gathering systems such as wireless sensor networks (WSNs)[1].
In the first part of the paper (Section 2), we extend the water-filling strategy [11] to multiple receivers considering parallel multicast channels with perfect channel state information (CSI) at the transmit side.
Multiple-antenna receivers are considered, and unlike [15], only statistical channel state information (CSI) is assumed at the transmitter side, which reduces the amount of feedback required to implement the proposed algorithms.
