Sorry, you need to enable JavaScript to visit this website.

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers.

The document presents the multi-linear algebra framework to the RFI excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for efficient RFI excision in single-input multiple-output (SIMO) systems.

In a frequency division duplex (FDD) massive MIMO system, downlink channel estimation poses several challenges with limited training duration being one impediment. Our previous work developed an algorithm to learn a dictionary in which the channel can be sparsely represented, and then leveraged compressed sensing framework to estimate the downlink channel. In this work, we extend the sparse channel representation framework to joint uplink and downlink channel modeling exploiting the similar scattering environment experienced by the signal during uplink and downlink transmission.

The paper investigates a new framework for spectrum sharing between a MIMO-MC radar (MIMO radar using matrix completion) and a MIMO communication system, based on radar transmit precoding. The radar transmit precoder is jointly designed with the communication codewords so that the SINR at the radar receiver is maximized while meeting certain rate and power constraints at the communication system. By shaping the transmit beam, the proposed approach results in enhanced SINR at the receive antennas.

Recent channel measurement campaigns have revealed that at high carrier frequencies (10GHz and above) MIMO channels exhibit sparsity structure, i.e., a few dominant propagation paths and, in some cases, it can also reduce to a single path, yet very directive, wireless channel. In this work, we leverage the sparsity feature into the development of sparse-MIMO channel estimator and show, how an adaptive l1-optimization method can greatly improve the estimation error.