Energy-efficient base station transmission design for green 5G massive MIMO and hybrid networks

Massive multiple-input-multiple-output (MaMIMO) is considered as the promising technology for 5th generation (5G) wireless communication systems since it can considerably improve energy efficiency (EE). Besides, the integration of conventional MaMIMO with other technology, including simultaneous wireless information and power transfer (SWIPT) and Heterogeneous Networks (HetNets), has shown prominent potentials to satisfy the Quality of Service (QoS) of 5G systems. However, existing research studies concentrated on system EE enhancement, leaving opportunities on new roads to be identified. Therefore, further research problems can be determined to propose new research directions for better energy-efficient system design. This thesis addresses state-of-the-art MaMIMO technology and its integration with SWIPT and HetNets. Hence, this work aims to recognize new opportunities to achieve effective energy-efficient system design that can be divided into three parts. The first part investigates energy-efficient downlink power transmission in multicell MaMIMO systems. A new base station (BS) transmit power adaptation model named BSTPA is proposed under zero-forcing beamforming (ZF-BF) scheme and perfect channel state information (CSI). The analytical closed-form expression of the BSTPA is derived in which the BS transmitted power is adapted to channel condition and user-level QoS, including data rate requirement and maximum allowable outage probability to minimize the total BS radiated power. Then, a new corresponding iterative EE optimization algorithm is proposed based on the BSTPA model to further improve the system’s EE. The proposed algorithm maximizes the EE by jointly optimizing the minimum data rate requirement, the number of BS antennas and users. The results indicate that the proposed BSTPA model achieves better EE improvei ment up to 32% compared to the energy-efficient equal power allocation (EE-EPA) algorithm as the conventional scheme, especially for small per-antenna circuit power consumption. The second part of the thesis focuses on the energy-efficient system design of the downlink MaMIMO enabled SWIPT based on power splitting (PS) and ZF-BF techniques. A new system model is proposed in which each user equipment (UE) utilizes the harvested power for pilot transmission. Closed-form expressions of UE’s energy harvesting (EH) and achievable data rate are first derived. Then, the EE maximization problem is formulated to jointly optimize the CE time duration, the PS ratios, and the BS transmit power allocation and antennas number concerning the data rate requirement and the maximum BS power transmission constraints. However, a new low-complex and alternative optimization (LCAO) algorithm is proposed to tackle the non-linear and non-convex characteristics of the original optimization problem with an acceptable computational complexity. The results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) and max-min algorithms up to 15% and 4% better EE improvement. In the last part, MaMIMO enabled SWIPT system is integrated with HetNets technology. Therefore, a new system model is proposed based on separated SWIPT where only macro UEs (MUEs) exploit the harvested energy from received signal power and cross-tier interference for pilot transmission. The analytical closed-form expressions of MUEs’ EH and data rate are derived. An EE maximization problem is then formulated with respect to the required data rate and MBS transmission capacity. Hence, a new iterative EE optimization (IEEO) algorithm is proposed that individually optimize pilot transmission duration, PS coefficients, macro BS (MBS) transmit power and antennas number, respectively. The results demonstrate that IEEO improves EE up to 6.7% and 9.9% compared to EPA and max-min algorithms.

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