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Consider an ambient modulated backscatter communication (AmBC) system adopting binary phase shift keying modulation that the receiver is to decode the backscatter device induced message without knowledge of the channel state information, the statistical channel covariance matrices, and the noise variance at the receiver antennas. In this paper, we apply the fact that the ambient orthogonal frequency-division multiplexing (OFDM) signals with a large number of subcarriers contain repetitive elements inducing time correlation.


In this paper, we consider a simple downlink channel with a multi-antenna base station and two single-antenna receivers. We assume that the channel is deterministic and known to all the nodes. Our contribution is two-fold. First, we show that linear precoding with private streams can have unbounded gap to the capacity of the channel. Second, we show that using rate-splitting with a simple power allocation one can achieve the sum capacity to within a constant gap for any channel realization.


Massive MIMO relies on nearly orthogonal user channels to achieve unprecedented spectral efficiency. But in LoS (line-of-sight) environment, some users can be subjected to similar channel vectors. Serving users with similar channel vectors simultaneously can severely compromise the throughput performance to all users. We propose a scheduler that identifies users with similar channels and serves them in separate time slots with properly assigned data rates, while aiming to provide fair service to all users and maximize the system spectral efficiency at the same time.


When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning.


Communication at mmWave frequencies is one of the major innovations of the fifth generation of cellular networks, because of the potential multi-gigabit data rate given by the large amounts of available bandwidth. The mmWave channel, however, makes reliable communications particularly challenging, given the harsh propagation environment and the sensitivity to blockage. Therefore, proper modeling of the mmWave channel is fundamental for accurate results in system simulations of mmWave cellular networks.


In this paper, we design a self-powering dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node adaptively controls single tone or multi-tone communication mode. To this end, we introduce duty cycle operation for the dual mode SWIPT with self-powering, which considers nonlinear energy harvesting (EH) model for both single tone and multi-tone waveforms. We formulate an adaptive mode switching (MS) problem which maximizes the achievable rate under the energy causality condition.