A Precoding Approach for Dual-Functional Radar-Communication System With One-Bit DACs

In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimiza...

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Vydáno v:IEEE journal on selected areas in communications Ročník 40; číslo 6; s. 1965 - 1977
Hlavní autoři: Yu, Xiaoyou, Yang, Qi, Xiao, Zhu, Chen, Hongyang, Havyarimana, Vincent, Han, Zhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
alternating minimization
Antennas
Bit error rate
Communications systems
Digital to analog converters
digital-to-analog converter
Downlink
Dual-functional radar-communication
Massive MIMO
massive multiple-input multiple-output
MIMO communication
Optimization
Particle swarm optimization
Precoding
Quantization (signal)
Radar
Radio frequency
Simulated annealing
Simulation
Waveforms
ISSN:0733-8716, 1558-0008
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Shrnutí:In this paper, we investigate the precoder design for multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) system with one-bit digital-to-analog converters (DACs). In order to form the dual-functional beam-pattern, we formulate the precoding problem as a weighted optimization problem with the constant modulus constraint, which aims at minimizing the average error power and guaranteeing radar waveform similarity. The problem is divided into three sub-problems corresponding to the multiple variables, i.e., the precoding factor, transmit signal matrix, and radar waveform matrix. Due to the discrete and non-convex properties of the optimization problem, we propose a multi-variable alternating minimization (MVAM) framework to achieve the near-optimal solutions. The precoding factor and radar waveform can be solved in closed-forms. For the transmit signal matrix, we devise a binary particle swarm optimization-simulated annealing (BPSO-SA) algorithm to obtain it under the MVAM framework. Extensive simulations validate the effectiveness of the proposed approach under various scenarios, including the case without perfect channel state information. The simulation results show that, compared with existing non-linear precoders, the proposed approach achieves 7dB SNR gain at the bit error rate of 10 −4 in the 8-antenna system, and the gain of SNR is 0.2dB in the massive MIMO system with 128 antennas.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2022.3155532