Abstract
Massive MIMO (also known as the “Large-Scale Antenna System”) enables a significant reduction of latency on the air interface with the use of a large excess of service-antennas over active terminals and time division duplex operation. For large-scale MIMO, several technical issues need to be addressed (e.g., pilot pattern design and low-antenna power transmission design) and theoretically addressed (e.g., channel estimation and power allocation schemes). In this paper, we analyze the ergodic spectral efficiency upper bound of a large-scale MIMO, and the key technologies including channel uplink detection. We also present new approaches for detection and power allocation. Assuming arbitrary antenna correlation and user distributions, we derive approximations of achievable rates with linear detection techniques, namely zero forcing, maximum ratio combining, minimum mean squared error (MMSE) and eigen-value decomposition power allocation (EVD-PA). While the approximations are tight in the large system limit with an infinitely large number of antennas and user terminals, they also match our simulations for realistic system dimensions. We further show that a simple EVD-PA detection scheme can achieve the same performance as MMSE with one order of magnitude fewer antennas in both uncorrelated and correlated fading channels. Our simulation results show that our proposal is a better detection scheme than the conventional scheme for LSAS. Also, we used two channel environment channels for further analysis of our algorithm: the Long Term Evolution Advanced channel and the Millimeter wave Mobile Broadband channel.
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Acknowledgements
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1014) supervised by the NIPA (National IT Industry Promotion Agency). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2007779).
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Appendix
Appendix
When the massive MIMO system adopts the detection, the approximately power allocation vector achieving the optimal PA
where
\(\left[ x \right]^{ + } = max\left\{ {0,x_{i} } \right\}, b = \left[ {b_{1} ,b_{2} , \ldots ,b_{K} } \right]^{T}\),and
where \(\tilde{H} = {\text{diag}}\left\{ {h_{1}^{2} ,h_{2}^{2} , \ldots ,h_{K}^{2} } \right\}/\upmu + 1_{K \times K} - I_{K \times K}\) and \({\text{b}} = [b_{1} ,b_{2} , \ldots ,b_{K} ]\), \(b_{K} = \frac{{\eta h_{K}^{2} }}{{\mu \eta_{PE}^{o} ln2}} - \frac{{\sigma^{2} }}{{\beta_{k} }}.\) η o PE unique globally optimal power allocation. As the independence of \({\text{h}}_{l}\,(l = 1,2, \ldots ,K)\), the rank of the matrix \(\tilde{H}\) is K. So, \(\tilde{H}\) is invertible and the power allocation expressed as
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Malik, S., Moon, S., Kim, B. et al. Novel Detection Scheme for LSAS Using Power Allocation in Multi User Scenario with LTE-A and MMB Channels. Wireless Pers Commun 95, 4425–4440 (2017). https://doi.org/10.1007/s11277-017-4093-7
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DOI: https://doi.org/10.1007/s11277-017-4093-7