Abstract
In this work, we consider a multiple input multiple-output system with large-scale antenna array which creates unintended multiuser interference and increases the power consumption due to the large number of radio frequency (RF) chains. The antenna selective symbol level precoding design is developed by minimizing the symbol error rate (SER) with limits of available RF chains. The ℓ 0 -norm constrained nonconvex problem can be approximated as ℓ 1 -minimization, which is further solved by alternating direction method of multipliers (ADMM) approach. The basic ADMM scheme is mapped into iterative construction process where the optimum solution is obtained by taking deep learning network as building block. Moreover, because that the standard ADMM algorithm is sensitive to the selection of hyperparameters, we further introduce the back propagation process to train the parameters. Simulation results show that the proposed deep learning ADMM scheme can achieve significantly low SER performance with small activated subset of transmit antennas.
Highlights
With the severe spectrum shortage in conventional cellular network, the large-scale antenna system in the millimeterwave bands has been considered as a potential solution to meet the constantly growing demand by the users for higher data rate
We develop the AS-symbol-level precoding (SLP) design to reduce the power consumption of the radio frequency (RF) chains by jointly minimizing the achievable symbol error rate (SER) and the number of activated transmit antennas
In the SLP scheme, the optimization problem is formulated as the minimization of the average Euclidean distance between the received symbols and desired symbols with the constraint on the number of activated transmit antennas
Summary
With the severe spectrum shortage in conventional cellular network, the large-scale antenna system in the millimeterwave (mmWave) bands has been considered as a potential solution to meet the constantly growing demand by the users for higher data rate. In order to reduce the power consumption of RF chains, the proposed AS-SLP design is developed to minimize the achievable SER by using a subset of activated transmit antennas. The improper regularization parameters of the l1-norm optimization may degrade the SER performance, and the specification of the hyperparameters is a challenge To overcome this difficulty, a deep architecture dubbed as ADMM-Net is introduced to link the iterative algorithm to the deep learning architecture. (i) The SLP design is developed to jointly select the optimum subset of RF chains and minimize the SER between the desired and received symbols over large-scale MIMO system. Simulation results demonstrate that the proposed ADMM-Net scheme can reduce the achievable MSE and SER considerably and consume the low transmit power with optimum subset of transmit antennas. For a vector like a, kaki=1,2 are the Euclidean distance
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