Codebook Based Minimum Subspace Distortion Hybrid Precoding for Millimeter Wave Systems

© 2018 IEEE. Hybrid precoding is adopted for millimeter wave (mmWave) communications to offer a good trade-off between hardware complexity and system performance. In this paper, we investigate a codebook based hybrid precoder for single-user mmWave systems with large antenna arrays. We exploit the sparse nature of mmWave channels to transform the hybrid precoding design problem into a vector space distortion optimization problem which is only related to the radio frequency (RF) precoder. A near optimal solution for the RF optimization problem is derived with the assumption of the perfect channel state information (CSI) at the transmitter, which is practically very difficult to obtain. To reduce the requirement of the CSI at the transmitter, we propose the codebook based minimum subspace distortion (MSD) hybrid precoding algorithm, which obtains CSI at the combiner side and returns the index of optimal RF codewords and the baseband precoder through a limited feedback channel. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding algorithm

Parameter Estimation and Hybrid Precoding Design for Millimeter Wave Mobile Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.With the exponential rise of mobile data rates, millimeter wave (mmWave) mobile networks (mmWMNs) have become the trend in the 5th generation mobile cellular networks and beyond. In mmWMNs, the mmWave band can provide the ultra-high data rates due to its extremely wide frequency band resources, and the densely deployed base stations (BS) can significantly improve the network throughput per cell. However, the severe path loss and fading issues of the mmWave band dramatically impair the received signal-to-interference-plus-noise ratio and limit the network throughput. A revolution in the hardware architecture and the signal processing has been occurring for years. Numerous novel channel estimation and precoding techniques were proposed. In particular, angular sparsity is an intensified property for conducting mmWave channel estimations and hybrid precoding is a promising technique to realize mmWave communications. Existing hybrid precoding schemes either require full channel state information (CSI) or use codebook-based design. The former one requires highly accurate estimated channels while the latter has a degraded system performance. On the other hand, mmWave radar sensing has been successfully and commercially adopted for decades. With the number of electric devices increasing rapidly, there exist more and more demands to fuse the radar functions into the mmWave communication mobile networks. The primary issue is to realize a robust mmWave communication system. Issues following this include how to jointly estimate the communication channel and the radar channel, and how to address the interferences between radar waveforms and communication waveforms. Under this background, this doctoral thesis mainly focuses on signal processing techniques that can realize mmWave channel estimation for both radar and communication purposes, and hybrid beamforming/precoding algorithms that can increase the communication data rates. This thesis will include: 1) Subarray-based angle-of-arrivals (AoAs) estimation, where the AoAs can refer to both the line-of-sight (LOS) angles coming from users and the non-line-of-sight (NLOS) angles coming from targets; 2) Energy-efficient hybrid precoding and sparse precoding (virtual array), where both fully-connected and partially-connected hybrid precoders are optimized based on the metric of energy efficiency; 3) Adaptive hybrid precoding and the quantization of radio-frequency (RF) precoder using minimum subspace distortion (MSD), where the adaptive precoding aims to adjust the precoding matrix based on the transmit power, and the MSD quantization aims to improve the system performance loss caused by the quantization; 4) Uplink radar sensing fused in mmWMNs, where a radar sensing scheme is proposed without requiring synchronization between BS and user equipment

AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users

AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna User

User Grouping for the Uplink of Multiuser Hybrid mmWave MIMO

[Abstract] Hybrid analog/digital schemes for precoding/combining have proved to be a low-complexity and/or low-power strategy to obtain reasonable beamforming gains in multiuser millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Hybrid precoding/combining performs jointly baseband processing and analog processing in the radio frequency (RF) domain. In these systems, the number of RF chains limits the maximum number of streams simultaneously handled by the transceivers. In the uplink of a multiuser mmWave MIMO system, the hardware reduction based on hybrid transceivers is limited by the number of data streams that must be simultaneously served by the centralized node. Most works approach hybrid transceiver design by considering more RF chains than data streams, an unrealistic assumption when the number of nodes is large. On the other hand, statistically independent information is conventionally assumed in multiuser mmWave systems. This assumption does not hold in scenarios like wireless sensor networks (WSNs), where the sources produce correlated information. In this paper, by enabling inter-user correlation exploitation, we propose a grouping approach to handle a high number of individual sources with a limited number of RF chains through distributed quantizer linear coding (DQLC) mappings. The allocation of the users per group and the hybrid design of the combiner at the common central node to serve the grouped users is also analyzed. We also propose a hybrid minimum mean square error (MMSE) combining design in order to exploit the spatial correlation between the sources in a conventional uncoded mmWave uplink. Simulation results show the performance advantages of the proposed approaches in various hardware-constrained system settings.10.13039/501100010801-Xunta de Galicia (Grant Number: ED431G2019/01) 10.13039/501100011033-Agencia Estatal de Investigaci??n (Grant Number: TEC2016-75067-C4-1-R, RED2018-102668-T and PID2019-104958RB-C42) BES-2017-081955Xunta de Galicia; ED431G2019/0

Millimeter wave MIMO communications : high-resolution angle acquisition and low-resolution time-frequency synchronization

Knowledge of the propagation channel is critical to exploit the full benefit of multiple-input multiple-output (MIMO) techniques in millimeter wave (mmWave) cellular systems. Obtaining accurate channel state information in mmWave systems, however, is challenging due to high estimation overhead, high computational complexity and on-grid setting. It is also desirable to reduce the analog-to-digital converters (ADCs) resolution at mmWave frequencies to reduce power consumption and implementation costs. The use of low-precision ADCs, though, brings new design challenges to practical cellular networks. In the first part of this dissertation, we develop several new methods to estimate and track the mmWave channel's angle-of-departure and angle-of-arrival with high accuracy and low overhead. The key ingredient of the proposed strategies is custom designed beam pairs, from which there exists an invertible function of the angle to be estimated. We further extend the proposed algorithms to dual-polarized MIMO in wideband channels, and angle tracking design for fast-varying environments. We derive analytical angle estimation error performance of the proposed methods in single-path channels. We also use numerical examples to characterize the robustness of the proposed approaches to various transceiver settings and channel conditions. In the second part of this dissertation, we focus on improving the low-resolution time-frequency synchronization performance for mmWave cellular systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters (DACs). The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution ADCs. For low-resolution timing synchronization, we propose a new multi-beam probing based strategy, targeting at maximizing the minimum received synchronization signal-to-quantization-plus-noise ratio among all serving users. Regarding low-resolution frequency synchronization, we construct new sequences for carrier frequency offset (CFO) estimation and compensation. We use both analytical and numerical examples to show that the proposed sequences and the corresponding metrics used for retrieving the CFOs are robust to the quantization distortion.Electrical and Computer Engineerin

Enabling Efficient Communications Over Millimeter Wave Massive MIMO Channels Using Hybrid Beamforming

The use of massive multiple-input multiple-output (MIMO) over millimeter wave (mmWave) channels is the new frontier for fulfilling the exigent requirements of next-generation wireless systems and solving the wireless network impending crunch. Massive MIMO systems and mmWave channels offer larger numbers of antennas, higher carrier frequencies, and wider signaling bandwidths. Unleashing the full potentials of these tremendous degrees of freedom (dimensions) hinges on the practical deployment of those technologies. Hybrid analog and digital beamforming is considered as a stepping-stone to the practical deployment of mmWave massive MIMO systems since it significantly reduces their operating and implementation costs, energy consumption, and system design complexity. The prevalence of adopting mmWave and massive MIMO technologies in next-generation wireless systems necessitates developing agile and cost-efficient hybrid beamforming solutions that match the various use-cases of these systems. In this thesis, we propose hybrid precoding and combining solutions that are tailored to the needs of these specific cases and account for the main limitations of hybrid processing. The proposed solutions leverage the sparsity and spatial correlation of mmWave massive MIMO channels to reduce the feedback overhead and computational complexity of hybrid processing. Real-time use-cases of next-generation wireless communication, including connected cars, virtual-reality/augmented-reality, and high definition video transmission, require high-capacity and low-latency wireless transmission. On the physical layer level, this entails adopting near capacity-achieving transmission schemes with very low computational delay. Motivated by this, we propose low-complexity hybrid precoding and combining schemes for massive MIMO systems with partially and fully-connected antenna array structures. Leveraging the disparity in the dimensionality of the analog and the digital processing matrices, we develop a two-stage channel diagonalization design approach in order to reduce the computational complexity of the hybrid precoding and combining while maintaining high spectral efficiency. Particularly, the analog processing stage is designed to maximize the antenna array gain in order to avoid performing computationally intensive operations such as matrix inversion and singular value decomposition in high dimensions. On the other hand, the low-dimensional digital processing stage is designed to maximize the spectral efficiency of the systems. Computational complexity analysis shows that the proposed schemes offer significant savings compared to prior works where asymptotic computational complexity reductions ranging between $80\%$ and $98\%$. Simulation results validate that the spectral efficiency of the proposed schemes is near-optimal where in certain scenarios the signal-to-noise-ratio (SNR) gap to the optimal fully-digital spectral efficiency is less than $1$ dB. On the other hand, integrating mmWave and massive MIMO into the cellular use-cases requires adopting hybrid beamforming schemes that utilize limited channel state information at the transmitter (CSIT) in order to adapt the transmitted signals to the current channel. This is so mainly because obtaining perfect CSIT in frequency division duplexing (FDD) architecture, which dominates the cellular systems, poses serious concerns due to its large training and excessive feedback overhead. Motivated by this, we develop low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency (RF) analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to interference noise ratio for the single-user and multi-user cases, respectively. Mathematical analysis shows that the proposed algorithms are asymptotically optimal as the number of transmit antennas goes to infinity and the mmWave channel has a limited number of paths. Moreover, it shows that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the mmWave channel or equivalently the transmit channel covariance matrix when only the statistical channel knowledge is available at the transmitter. Further, we verify the performance of the proposed algorithms numerically where the obtained results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results illustrate that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art