Novel Detection Scheme for LSAS Using Power Allocation in Multi User Scenario with LTE-A and MMB Channels

Novel Detection Scheme for LSAS Using Power Allocation in Multi User Scenario with LTE-A and MMB... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Novel Detection Scheme for LSAS Using Power Allocation in Multi User Scenario with LTE-A and MMB Channels

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4093-7
Publisher site
See Article on Publisher Site

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.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Mar 16, 2017

References

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