Browsing by Subject "Hybrid beamforming"
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Item Compressed-sensing based channel state information acquisition in mmWave hybrid beamforming communication systems(2020-06-24) Sung, Junmo; Evans, Brian L. (Brian Lawrence), 1965-; Al-Dhahir, Naofal; Dimakis, Georgios-Alex; Vikalo, Haris; Zhu, HaoFifth-generation (5G) cellular communications promises 10x higher data rate, 10x reduced latency and high reliability compared against the fourth-generation (4G). The higher data rate is primarily enabled by the use of higher frequency millimeter wave (mmWave) bands. MmWave bands experience high signal power attenuation over distance, which can be overcome by employing large antenna arrays and advanced signal processing techniques to focus radiated power in a beam. Massive number of antennas and accompanying radio frequency (RF) circuitry, however, excessively increase system operating power. Hybrid analog and digital beamforming (HB) architectures, which can significantly scale down the number of RF transceivers, and low resolution analog-to-digital converters (ADCs) are attractive in reducing power consumption for wireless communication systems with large antenna arrays. However, reducing power consumption comes at the expense of reducing communication performance. The HB architectures, due to fewer dimensions of the digital beamforming stage and hardware constraints in the analog beamforming stage, suffer fewer degrees of freedom compared with the all digital architecture. Low resolution quantizers inevitably produce higher quantization noise than high resolution quantizers do. Conventional channel state information (CSI) acquisition algorithms employed in all-digital beamforming architectures generally yield degraded performance in such power saving architectures. Therefore I consider compressed sensing techniques to acquire millimeter wave (mmWave) CSI in HB architectures. Compressed sensing is able to exploit the sparsity in angular mmWav channel responses. In the point-to-point mmWave communications, I develop a deterministic HB codebook design framework for compressed sensing (CS) based channel estimation. The framework is versatile to be applicable to various HB architectures including phase shifters, switches and RF lens. The design approach is to configure analog and digital beamformers in the most favorable forms to CS techniques under the hybrid beamforming constraints. When one tries to reduce measurement overhead of CS-based channel estimation, extra randomness is usually considered: random RF precoder permutation. I propose a computationally efficient algorithm to find a deterministic order of RF precoders that can reduce the overhead down to a half without significant performance loss. Low-resolution ADC is another means for further power consumption reduction along with HB architectures. However, the combination of a HB architecture and low-resolution ADCs makes channel estimation in such systems more challenging. Adopting the extremely low resolution, i.e., one-bit ADC, in the HB communication systems, I develop a CS-based channel estimation algorithm that is suitable for one-bit quantization. In developing 5G NR, a new challenge has been arisen: beam management. Since beamforming became an essential component in 5G, beam search and detection are performed even in the initial access. I investigate CS-based downlink beam detection for mmWave HB systems taking the 3GPP standard into account. With the exhaustive search being a benchmark, the CS approach is evaluated using the random and the discrete Fourier transform (DFT) RF codebooks in terms of the beam detection probability. Through the research contributions I present in this dissertation, it is shown that compressed sensing is the key to exploit sparsity in angular mmWave channel responses. Compressed sensing is beneficial in not only improving accuracy and reducing latency of CSI acquisition, but also the overall communication performance of the hybrid analog/digital beamforming system.Item Optimizing communication performance of low-resolution ADC systems with hybrid beamforming(2019-09-19) Choi, Jinseok; Evans, Brian L. (Brian Lawrence), 1965-; Andrews, Jeffrey G; Baldick, Ross; Caramanis, Constantine; Hajj, HazemLow-resolution analog-to-digital converter (ADC) systems and hybrid analog-and-digital beamforming systems have drawn extensive attention as a promising receiver architecture for millimeter wave (mmWave) communications by reducing hardware cost and power consumption. In this dissertation, hybrid beamforming systems that employ low-resolution ADCs are considered to achieve a better trade-off between communication performance and power consumption. Due to non-negligible quantization errors, however, existing state-of-the-art hybrid beamforming techniques cannot be directly applied to such systems as they ignore the impact of the quantization error. In this regard, I propose new receiver architectures and algorithms for hybrid beamforming with low-resolution ADC systems to enhance spectral efficiency under coarse quantization in different layers of the network stack, and provide subsequent analyses. First, problems of optimizing the number of ADC bits and designing analog combiners with fixed-resolution ADCs are tackled to design an energy-efficient receiver architecture with phase shifter-based hybrid beamforming. A hybrid receiver architecture with resolution-adaptive ADCs for mmWave communications is proposed to optimize the power distribution over ADCs. For the proposed architecture, a near-optimal bit-allocation solution is derived in closed form. In addition, the performance lower bound of the proposed receiver architecture is derived in ergodic rate. For a fixed-resolution ADC system, a new analog combining architecture is proposed for mmWave communications. The proposed analog combiner consists of two consecutive analog combiners that maximize channel gain and minimize effective quantization error. An approximated ergodic rate of the proposed receiver is also derived in closed form. Next, considering switch-based analog beamforming, antenna selection at a base station is investigated for low-resolution ADC systems. Unlike downlink transmit antenna selection problems, a quantization-aware antenna selection criterion is necessary and derived to incorporate quantization error for uplink receive antenna selection problems. Leveraging the criterion, a quantization-aware antenna selection algorithm is proposed and analyzed for uplink. Last, in a higher layer of the network stack, a user scheduling problem is investigated for hybrid beamforming systems with low-resolution ADCs. New user scheduling criteria are derived to maximize scheduling gain under coarse quantization and efficient scheduling algorithms are proposed accordingly. Subsequent analysis for the proposed algorithm provides closed-form ergodic rates