In a time-division duplex (TDD) multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multicell multiuser massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing subspace approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared to the in-cell users. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. The proposed approach is illustrated via simulation examples.
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Cell-edge Interferometry: Reliable Detection of Unknown Cell-edge Users via Canonical Correlation Analysis
A key challenge in 4G and emerging 5G systems is that of reliably detecting the uplink transmissions of users close to the edge between cells. These users are subject to significant signal attenuation due to path loss, and frequent hand-off from one cell to the other, making channel estimation very challenging. Even multiuser detection using base station cooperation often fails to detect such users, due to channel estimation errors and the sensitivity of multiuser detection to near-far power imbalance. Is it even possible to reliably decode the cell-edge users' signals under these circumstances? This paper shows, perhaps surprisingly, that with a suitable base station `interferometry' strategy, the cell-edge users' signals can be reliably decoded at low SNR under mild conditions. Exploiting the fact that cell-edge users' signals are weak but common to both base stations, while users close to a base station are unique to that base station, reliable detection is enabled by Canonical Correlation Analysis (CCA) - a machine learning technique that reliably estimates a common subspace, even in the presence of strong individual interference. Free from cell-center interference, the resulting mixture of cell-edge signals can then be unraveled using well-known algebraic signal processing techniques. Simulations demonstrate that the proposed detector achieves order of magnitude BER improvement compared to an `oracle' zero-forcing with successive interference cancellation that assumes perfect knowledge of all channels. The paper also includes proof of common subspace identifiability for the assumed generative model, which was curiously missing from the machine learning / CCA literature.
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- Award ID(s):
- 1807660
- NSF-PAR ID:
- 10176284
- Date Published:
- Journal Name:
- 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SPAWC.2019.8815536
