On iterative low-complexity algorithm for optimal antenna selection and joint transmit power allocation under impact pilot contamination in downlink 5g massive MIMO systems

• Main Author: Mohammed Ahmed, Adeeb Ali
• Format: Thesis
• Language: English; English; English
• Published: 2020
• Subjects: TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
• Online Access: 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/
• Institution: Universiti Tun Hussein Onn Malaysia

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.