Power saving and optimal hybrid precoding in millimeter wave massive MIMO systems for 5G

The proliferation of wireless services emerging from use cases offifth-generation(5G) technology is posing many challenges on cellular communicationinfrastructure. They demand to connect a massive number of devices withenhanced data rates. The massive multiple-input multiple-output (MIMO)technology at millimeter-wave (mmWave) in combination with hybrid precodingemerges as a concrete tool to address the requirements of 5G networkdevelopments. But Massive MIMO systems consume significant power fornetwork operations. Hence the prior role is to improve the energy efficiency byreducing the power consumption. This paper presents the power optimizationmodels for massive MIMO systems considering perfect channel state information(CSI) and imperfect CSI. Further, this work proposes an optimal hybrid precodingsolution named extended simultaneous orthogonal matchingpursuit (ESOMP).Simulation results reveal that a constant sum-rate can be achieved in massiveMIMO systems while significantly reducing the power consumption. Theproposed extended SOMPhybrid precoder performsclose to the conventionaldigital beamforming method. Further, modulation schemes compatible withmassive MIMO systems are outlined and their bit error rate (BER) performance isinvestigate

Power Scaling and Antenna Selection Techniques for Hybrid Beamforming in mmWave Massive MIMO Systems

With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by 1/number of antennas and 1/square root (number of antennas) respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods