405 research outputs found
Impact of User Mobility on Optimal Linear Receivers in Cellular Networks
We consider the uplink of non-cooperative multi-cellular systems deploying
multiple antenna elements at the base stations (BS), covering both the cases of
conventional and very large number of antennas. Given the inevitable pilot
contamination and an arbitrary path-loss for each link, we address the impact
of time variation of the channel due to the relative movement between users and
BS antennas, which limits system's performance even if the number antennas is
increased, as shown. In particular, we propose an optimal linear receiver (OLR)
maximizing the received signal-to-interference-plus-noise (SINR). Closed-form
lower and upper bounds are derived as well as the deterministic equivalent of
the OLR is obtained. Numerical results reveal the outperformance of the
proposed OLR against known linear receivers, mostly in environments with high
interference and certain user mobility, as well as that massive MIMO is
preferable even in time-varying channel conditions.Comment: 3 figures, 6 pages, accepted in ICC 201
Deep Learning-aided TR-UWB MIMO System
This paper presents a novel deep learning-aided scheme dubbed PRρ-net for improving the bit error rate (BER) of the Time Reversal (TR) Ultra-Wideband (UWB) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI). The designed system employs Frequency Division Duplexing (FDD) with explicit feedback in a scenario where the CSI is subject to estimation and quantization errors. Imperfect CSI causes a drastic increase in BER of the FDD-based TR-UWB MIMO system, and we tackle this problem by proposing a novel neural network-aided design for the conventional precoder at the transmitter and equalizer at the receiver. A closed-form expression for the initial estimation of the channel correlation is derived by utilizing transmitted data in time-varying channel conditions modeled as a Markov process. Subsequently, a neural network-aided design is proposed to improve the initial estimate of channel correlation. An adaptive pilot transmission strategy for a more efficient data transmission is proposed that uses channel correlation information. The theoretical analysis of the model under the Gaussian assumptions is presented, and the results agree with the Monte-Carlo simulations. The simulation results indicate high performance gains when the suggested neural networks are used to combat the effect of channel imperfections
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