In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.
In this paper, we propose one-bit feedback-based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet-of-Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one-bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on onebit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral.
Recently, wireless sensor networks have become a major focus in various machine-to-machine or Internet of Things (IoT) applications, such as home networking, military, and healthcare applications. Because sensor nodes are typically battery powered, energy-efficient data transmission schemes and routing protocols have been studied extensively ([1-3] and the references therein). Additionally, there has been significant interest in transferring energy wirelessly. To overcome the energy depletion of sensor nodes, wireless energy transfer networks have been deployed in combination with conventional wireless communication networks [4][5][6][7]. However, because the efficiency of wireless energy transfer decreases significantly over large transmission distances, multiple-antenna-based energy transmission techniques have been investigated extensively [8][9][10][11]. In [8], a radio frequency (RF) energy beamforming method with multiple antennas was developed. In [9], RF energy transmission using multiple antennas was demonstrated experimentally. However, these works assumed that multiple transmitting antennas can be collocated and processed coherently for RF energy beamforming. This assumption is not
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.