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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Shengchu Yunzhou Li Jing Wang |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Wang Shengchu; Yunzhou Li; Jing Wang) |
| Abstract | This paper proposes a new large-scale antenna system (LSAS) where the base station (BS) is equipped with several classical radio-frequency (RF) chains but massive one-bit integrated information and energy receivers (Rxs). Multiple users with multiple antennas are served simultaneously. This new LSAS is much more energy efficient than massive MIMO because it requires much less classical power-hungry RF chains, and its massive integrated Rxs can harvest new energy. For the uplink multiuser detection (MUD), we develop a practical generalized approximate message passing detector (GAMPD), which exploits the signal prior probability distribution, and the hybrid BS measurements (full ones with both amplitudes and phases from the classical Rxs, and the one-bit amplitude measurements from the integrated Rxs) in a Bayesian manner. GAMPD involves matrix-vector multiplications, small-scale matrix inversions, and parallelized nonlinear operations, so it is suitable for hardware implementation. Our numerical results indicate that the proposed LSAS retains the advantages of massive MIMO, and can even perform similarly as the latter after introducing sufficient number of integrated Rxs. |
| Starting Page | 2024 |
| Ending Page | 2029 |
| File Size | 232501 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467364324 |
| DOI | 10.1109/ICC.2015.7248623 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-08 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | MIMO Radio frequency Receivers Antennas Multiuser detection Baseband Signal to noise ratio |
| Content Type | Text |
| Resource Type | Article |
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