Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 38, Issue 9, Pages 2041-2056Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2020.3000876
Keywords
Precoding; Radio frequency; Computer architecture; Antenna arrays; Power demand; Wireless communication; Gain; Terahertz band; dynamic array-of-subarrays; hybrid precoding
Funding
- Ministry of Education of China [6141A02033350]
- National Natural Science Foundation of China (NSFC) [61701300]
- Australian Research Council [DP190101363, LP170101196]
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Terahertz (THz) communications are envisioned as a key technology for 6G wireless systems, owing to an unprecedented promised multi-GHz bandwidth. While THz band suffers from huge propagation losses, large arrays of sub-millimeter wavelength antennas can be realized in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the received power and overcome the distance limitation. In this paper, a dynamic array-of-subarrays (DAoSA) hybrid precoding architecture is proposed to reduce the power consumption while meeting the data rate requirement in THz UM-MIMO systems. The connections between RF chains and subarrays are intelligently adjusted through a network of switches. First, to solve the intractable DAoSA hybrid precoding problem, element-by-element (EBE) and vectorization-based (VEC) algorithms are derived. Moreover, to determine the connections of the switches, near-optimal progressive stage-by-stage (PSBS), low-complexity alternating-selection (AS) and block-diagonal-search (BDS) algorithms are developed. Extensive simulation results show that both the EBE and VEC algorithms have higher spectral efficiency than existing hybrid precoding algorithms. Furthermore, the power consumption of the DAoSA architecture is substantially lessened, with PSBS, AS and BDS algorithms, respectively. The developed DAoSA architecture associated with proposed hybrid precoding and switch network design algorithms demonstrates a superior capability on balancing the spectral efficiency and power consumption.
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