On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems
Massive multiple-input-multiple-output (MIMO) technology has been proven to be a viable strategy for enhancing energy efficiency (EE) and achievable high data rates, which is the key to the design of the fifth-generation wireless cellular networks. The major challenge in massive MIMO systems i...
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TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
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TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Mohammed Ahmed, Adeeb Ali On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
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Massive multiple-input-multiple-output (MIMO) technology has been proven to be a
viable strategy for enhancing energy efficiency (EE) and achievable high data rates,
which is the key to the design of the fifth-generation wireless cellular networks. The
major challenge in massive MIMO systems is pilot contamination arising from large
numbers of pilot reuse sequences due to non-orthogonal pilot sequences between
different cells. Massive MIMO systems are affected by pilot contamination,
which influences the data rate of the system. In this thesis, highly interfering UEs
in adjacent cells were identified based on estimates of large-scale fading and then
included in the joint channel processing to achieve the desired tradeoff between the
effectiveness and the efficiency of channel estimation in order to increase the data
rate. The BS correlates the training signal with the established pilot reuse sequences
of every UE to obtain a high-quality channel estimation. The channel quality of the
users was enhanced by allocating orthogonal pilot reuse sequences to the center
user and the edge user according to different levels of pilot contamination based
on the large-scale fading that mitigated pilot contamination. Meanwhile, an
increase in the number of antenna arrays at BSs resulted in greater power
consumption due to the increased number of radio-frequency (RF) chains, which
could not be neglected and became a technical challenge. Achievable high data rate in
massive MIMO, depended on quality of channel and analyze the circuit power
consumption under power constraint for a limited number of RF chains for antennas
selection. The full knowledge of channel state information (CSI) and the
configuration channel selection, which used to prevent the major training that is
incurred in the channel estimation for all receiving antenna. The optimal antenna
could be chosen based on the transmitted power by selecting the preceding channel
estimation. Moreover, reducing the transmitted power from the BS depended on
selecting the optimal number of RF chains for choosing the best performing active
antenna selection. To evaluate the energy-efficient massive MIMO, we focused iv
not only on the joint antenna selection, optimal transmit power, and circuit
power consumption to balance the radiated EE but also on adjusting the length
of the pilot sequences to improve EE. The proposed Low–complexity iterative
algorithm for antenna selection and transmission power helped to choose an
accurate number of active RF chains to reduce circuit power consumption, and
minimize the reuse of pilot sequences to improve channel estimation. The
optimization of the antenna selection and optimal transmission power with impact of
pilot reuse sequences were achieved, by applying Newton’s method and the Lagrange
multiplier. This enabled the use of pilot reuse sequences and minimized the total
transmit power based on the proportional number of antenna selection and reduced
the number of RF chains at the receiver through efforts to allocate every RF chain.
From the simulation results, the channel quality of the users was enhanced by
allocating orthogonal pilot reuse sequences. From Fig.4.3, in chapter 4, the
maximal value of data rates = (17.4, 16.9,16.3) bits/s/Hz, when the optimal transmit
pilot reuse was = (14, 17, 20), with accounting channel estimation, when the
number of antennas was . The proposed low- complexity iterative algorithm
achieved the best maximal EE according Fig. 6.5 in chapter 6, which was 95 Mbits/j,
resulted from the large number of antennas at the BS, when the transmit power was
and transmit antennas was = 100 and user was = 20. In conclusion,
the proposed low-complexity iterative algorithm can be used to maximize the EE
based on the maximum transmit power , where the noise power is less
than the power of the received pilot sequence. |
format |
Thesis |
author |
Mohammed Ahmed, Adeeb Ali |
author_facet |
Mohammed Ahmed, Adeeb Ali |
author_sort |
Mohammed Ahmed, Adeeb Ali |
title |
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
title_short |
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
title_full |
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
title_fullStr |
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
title_full_unstemmed |
On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems |
title_sort |
on iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive mimo systems |
publishDate |
2020 |
url |
http://eprints.uthm.edu.my/879/1/24p%20ADEEB%20ALI%20MOHAMMED%20AHMED.pdf http://eprints.uthm.edu.my/879/2/ADEEB%20ALI%20MOHAMMED%20AHMED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/879/3/ADEEB%20ALI%20MOHAMMED%20AHMED%20WATERMARK.pdf http://eprints.uthm.edu.my/879/ |
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my.uthm.eprints.8792021-09-06T07:36:56Z http://eprints.uthm.edu.my/879/ On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems Mohammed Ahmed, Adeeb Ali TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Massive multiple-input-multiple-output (MIMO) technology has been proven to be a viable strategy for enhancing energy efficiency (EE) and achievable high data rates, which is the key to the design of the fifth-generation wireless cellular networks. The major challenge in massive MIMO systems is pilot contamination arising from large numbers of pilot reuse sequences due to non-orthogonal pilot sequences between different cells. Massive MIMO systems are affected by pilot contamination, which influences the data rate of the system. In this thesis, highly interfering UEs in adjacent cells were identified based on estimates of large-scale fading and then included in the joint channel processing to achieve the desired tradeoff between the effectiveness and the efficiency of channel estimation in order to increase the data rate. The BS correlates the training signal with the established pilot reuse sequences of every UE to obtain a high-quality channel estimation. The channel quality of the users was enhanced by allocating orthogonal pilot reuse sequences to the center user and the edge user according to different levels of pilot contamination based on the large-scale fading that mitigated pilot contamination. Meanwhile, an increase in the number of antenna arrays at BSs resulted in greater power consumption due to the increased number of radio-frequency (RF) chains, which could not be neglected and became a technical challenge. Achievable high data rate in massive MIMO, depended on quality of channel and analyze the circuit power consumption under power constraint for a limited number of RF chains for antennas selection. The full knowledge of channel state information (CSI) and the configuration channel selection, which used to prevent the major training that is incurred in the channel estimation for all receiving antenna. The optimal antenna could be chosen based on the transmitted power by selecting the preceding channel estimation. Moreover, reducing the transmitted power from the BS depended on selecting the optimal number of RF chains for choosing the best performing active antenna selection. To evaluate the energy-efficient massive MIMO, we focused iv not only on the joint antenna selection, optimal transmit power, and circuit power consumption to balance the radiated EE but also on adjusting the length of the pilot sequences to improve EE. The proposed Low–complexity iterative algorithm for antenna selection and transmission power helped to choose an accurate number of active RF chains to reduce circuit power consumption, and minimize the reuse of pilot sequences to improve channel estimation. The optimization of the antenna selection and optimal transmission power with impact of pilot reuse sequences were achieved, by applying Newton’s method and the Lagrange multiplier. This enabled the use of pilot reuse sequences and minimized the total transmit power based on the proportional number of antenna selection and reduced the number of RF chains at the receiver through efforts to allocate every RF chain. From the simulation results, the channel quality of the users was enhanced by allocating orthogonal pilot reuse sequences. From Fig.4.3, in chapter 4, the maximal value of data rates = (17.4, 16.9,16.3) bits/s/Hz, when the optimal transmit pilot reuse was = (14, 17, 20), with accounting channel estimation, when the number of antennas was . The proposed low- complexity iterative algorithm achieved the best maximal EE according Fig. 6.5 in chapter 6, which was 95 Mbits/j, resulted from the large number of antennas at the BS, when the transmit power was and transmit antennas was = 100 and user was = 20. In conclusion, the proposed low-complexity iterative algorithm can be used to maximize the EE based on the maximum transmit power , where the noise power is less than the power of the received pilot sequence. 2020-06 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/879/1/24p%20ADEEB%20ALI%20MOHAMMED%20AHMED.pdf text en http://eprints.uthm.edu.my/879/2/ADEEB%20ALI%20MOHAMMED%20AHMED%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/879/3/ADEEB%20ALI%20MOHAMMED%20AHMED%20WATERMARK.pdf Mohammed Ahmed, Adeeb Ali (2020) On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems. Doctoral thesis, Universiti Tun Hussein Onn Malaysia. |
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