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

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

Hardware Impairments Aware Transceiver Design for Bidirectional Full-Duplex MIMO OFDM Systems

In this paper we address the linear precoding and decoding design problem for a bidirectional orthogonal frequencydivision multiplexing (OFDM) communication system, between two multiple-input multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortion as well as the channel state information error are taken into account. In the first step, we transform the available time-domain characterization of the hardware distortions for FD MIMO transceivers to the frequency domain, via a linear Fourier transformation. As a result, the explicit impact of hardware inaccuracies on the residual selfinterference (RSI) and inter-carrier leakage (ICL) is formulated in relation to the intended transmit/received signals. Afterwards, linear precoding and decoding designs are proposed to enhance the system performance following the minimum-mean-squarederror (MMSE) and sum rate maximization strategies, assuming the availability of perfect or erroneous CSI. The proposed designs are based on the application of alternating optimization over the system parameters, leading to a necessary convergence. Numerical results indicate that the application of a distortionaware design is essential for a system with a high hardware distortion, or for a system with a low thermal noise variance.Comment: Submitted to IEEE for publicatio

Full-duplex MU-MIMO systems under the effects of non-ideal transceivers: performance analysis and power allocation optimization

Modern Technologies, particularly connectivity, increasingly support many facets of everyday life. The next generation of wireless communication systems aims to provide new advanced services and support new demands. These services are required to serve a massive number of devices and achieve higher spectral and energy efficiency, ultra-low latency, and reliable communication. The research community around the globe is still working on finding novel technologies to meet these requirements. Full duplex (FD) communications have been recognized as one of the promising wireless transmission candidates and gamechangers for the future of wireless communication and networking technologies, thanks to their ability to greatly improve spectral efficiency (SE) and dramatically enhance energy efficiency (EE). In this thesis, first, the influence of hardware impairment (HWI) on singleinput single-output (SISO) FD access point (AP) is studied. More precisely, the SE and EE when the system’s terminals have impaired transceivers are analyzed. Optimization problem for EE maximization is formulated to fulfill quality of service (QoS) and power budget constraints. An algorithm to solve the optimization problem by using the fractional programming theory and Karush–Kuhn–Tucker (KKT) conditions technique is proposed. [...