Channel Estimation Overhead Reduction For Downlink Fdd Massive Mimo Systems

Massive multiple-input multiple-output (MIMO) is the concept of deploying a very large number of antennas at the base stations (BS) of cellular networks. Frequency-division duplexing (FDD) massive MIMO systems in the downlink (DL) suffer significantly from the channel estimation overhead. In this thesis, we propose a minimum mean square error (MMSE)-based channel estimation framework that exploits the spatial correlation between the antennas at the BS to reduce the latter overhead. We investigate how the number of antennas at the BS affects the channel estimation error through analytical and asymptotic analysis. In addition, we derive a lower bound on the spectral efficiency of the communication system. Close form expressions of the asymptotic MSE and the spectral efficiency lower bound are obtained. Furthermore, perfect match between theoretical and simulation results is observed, and results show the feasibility of our proposed scheme.

There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells. The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression. In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequency-specific channel parameters are estimated via downlink training using a very short training signal. To efficiently estimate the channel parameters in the uplink, the underlying distribution of the channel parameters is incorporated as a prior into our estimation algorithm. This distribution is captured using deep generative models (DGMs). The proposed channel estimation technique significantly outperforms the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR).

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. With massive antenna arrays at the BS, for most propagation environments, the channels become favorable, i.e., the channel vectors between the users and the BS are (nearly) pairwisely orthogonal, and hence, linear processing is nearly optimal. A huge throughput and energy efficiency can be achieved due to the multiplexing gain and the array gain. In particular, with a simple power control scheme, Massive MIMO can offer uniformly good service for all users. In this dissertation, we focus on the performance of Massive MIMO. The dissertation consists of two main parts: fundamentals and system designs of Massive MIMO. In the first part, we focus on fundamental limits of the system performance under practical constraints such as low complexity processing, limited length of each coherence interval, intercell interference, and finite-dimensional channels. We first study the potential for power savings of the Massive MIMO uplink with maximum-ratio combining (MRC), zero-forcing, and minimum mean-square error receivers, under perfect and imperfect channels. The energy and spectral efficiency tradeoff is investigated. Secondly, we consider a physical channel model where the angular domain is divided into a finite number of distinct directions. A lower bound on the capacity is derived, and the effect of pilot contamination in this finite-dimensional channel model is analyzed. Finally, some aspects of favorable propagation in Massive MIMO under Rayleigh fading and line-of-sight (LoS) channels are investigated. We show that both Rayleigh fading and LoS environments offer favorable propagation. In the second part, based on the fundamental analysis in the first part, we propose some system designs for Massive MIMO. The acquisition of channel state information (CSI) is very importantin Massive MIMO. Typically, the channels are estimated at the BS through uplink training. Owing to the limited length of the coherence interval, the system performance is limited by pilot contamination. To reduce the pilot contamination effect, we propose an eigenvalue-decomposition-based scheme to estimate the channel directly from the received data. The proposed scheme results in better performance compared with the conventional training schemes due to the reduced pilot contamination. Another important issue of CSI acquisition in Massive MIMO is how to acquire CSI at the users. To address this issue, we propose two channel estimation schemes at the users: i) a downlink "beamforming training" scheme, and ii) a method for blind estimation of the effective downlink channel gains. In both schemes, the channel estimation overhead is independent of the number of BS antennas. We also derive the optimal pilot and data powers as well as the training duration allocation to maximize the sum spectral efficiency of the Massive MIMO uplink with MRC receivers, for a given total energy budget spent in a coherence interval. Finally, applications of Massive MIMO in relay channels are proposed and analyzed. Specifically, we consider multipair relaying systems where many sources simultaneously communicate with many destinations in the same time-frequency resource with the help of a massive MIMO relay. A massive MIMO relay is equipped with many collocated or distributed antennas. We consider different duplexing modes (full-duplex and half-duplex) and different relaying protocols (amplify-and-forward, decode-and-forward, two-way relaying, and one-way relaying) at the relay. The potential benefits of massive MIMO technology in these relaying systems are explored in terms of spectral efficiency and power efficiency.

Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems, where the base station (BS) is equipped with a large number of antenna elements to serve multiple user equipments. With the large number of antenna elements, the BS can perform multi-user beamforming with much narrower beamwidth, thereby simultaneously serving more users with less interference among them. Furthermore, the large antenna array results in large array gain which lowers the radiated energy. However, efficient beamforming relies on the availability of channel state information at the BS. In a frequency-division duplexing massive MIMO system, the channel estimation is challenging due to the need to estimate a high dimensional unknown channel vector, which requires large training and feedback overhead for the conventional channel estimation algorithms. Moreover, massive MIMO system with fully digital architecture, where a dedicated radio frequency chain and a high-resolution analog-to-digital converter (ADC) are connected to each antenna element, will cause too much power and hardware cost as the size of the antenna array becomes large. To reduce the training and feedback overhead, compressive sensing methods and sparse recovery algorithms are proposed to robustly estimate the downlink and uplink channel by exploiting the sparse representation of the massive MIMO channel. Previous works model this sparse representation by some predefined matrix, while in this dissertation, a dictionary learning based channel model is proposed which learns an efficient and robust representation from the data. Furthermore, a joint uplink/downlink dictionary learning framework is proposed by observing the reciprocity between the angle of arrival in uplink and the angel of departure in downlink, which enables a joint channel estimation algorithm. To save the power and hardware cost, a hardware-efficient architecture which contains both hybrid analog-digital processing and low-resolution ADCs is proposed. This hardware-efficient architecture poses significant challenges to channel estimation due to the reduced dimension and precision of the measured signal. To address the problem, the sparse nature of the channel is exploited and the transmitted data symbols are utilized as the "virtual pilots", both of which are treated in a unified Bayesian formulation. We formulate the channel estimation into a quantized compressive sensing problem utilizing the sparse Bayesian learning framework, and develop a variational Bayesian algorithm for inference. The performance of the compressive sensing can be further improved by applying a well structured sensing matrix, and we propose a sensing matrix design algorithm which can exploit the partial knowledge of the support.

Multiple-input, multiple-output (MIMO), which transmits multiple data streams via multiple antenna elements, is one of the most attractive technologies in the wireless communication field. Its extension, called ‘massive MIMO’ or ‘large-scale MIMO’, in which base station has over one hundred of the antenna elements, is now seen as a promising candidate to realize 5G and beyond, as well as 6G mobile communications. It has been the first decade since its fundamental concept emerged. This Special Issue consists of 19 papers and each of them focuses on a popular topic related to massive MIMO systems, e.g. analog/digital hybrid signal processing, antenna fabrication, and machine learning incorporation. These achievements could boost its realization and deepen the academic and industrial knowledge of this field.

Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems. In massive MIMO, a base station (BS) is equipped with a large antenna with potentially hundreds of antennas elements, allowing many users to be served simultaneously. Unfortunately, the hardware complexity and power consumption will scale with the number of antennas. The use of one-bit analog-to-digital converters (ADCs) provides an attractive solution to solve the above issues, since a one-bit ADC consumes negligible power and complex automatic gain control (AGC) can be removed. However, the signal distortion from the severe quantization poses significant challenges to the system designer. One bit quantization effectively removes all amplitude information, which is not recoverable by an increase in signal strength. This places a bound on channel estimation performance. Since the channel model is highly nonlinear, linear detector is suboptimal compared to more sophisticated nonlinear techniques. To reduce the impairment caused by one-bit quantization, a novel antithetic dithering scheme is developed. Antithetic dither is introduced into the system to generate negative correlated noise. Efficient channel estimation algorithms are developed to exploit the induced negative correlated noise in the system. A statistical framework is developed to validate the noise reduction from negative correlated quantized output. To improve the performance of data detection, feed forward neural network based detectors are developed, performance of these detectors are analyzed, architectural modification and training techniques are employed to partially resolve issues that prevent the networks from reaching ideal maximum likelihood performance. Next, model based approaches are evaluated and the shortcomings of iterative methods that rely on the exact likelihood are identified. Iterative methods based on the exact likelihood is shown to diverge due to the increasingly large gradient at high SNR. The constant gradient induced by the sigmoid approximation is shown to increase the robustness of these methods. A structured deep learning detector based on stochastic variational inference is proposed. Stochastic estimate of the gradient is introduced to reduce complexity of the algorithm. Damping is added to improve the performance of mean field inference. Parallel processing is proposed to reduce inference time. The proposed detector is shown to outperform existing methods that do not employ a second candidate search step.

We propose a novel limited feedback scheme for massive multiple-input multiple-output (MIMO) systems in frequency-division duplexing (FDD) wideband system. We assume that the user (UE) has knowledge of a downlink (DL) channel estimate. In order for massive MIMO systems to achieve high capacity, the base station (BS) must have the DL channel state information. Traditional feedback methods cannot work because channels for massive MIMO systems are usually too large to feedback within the coherence time. Our goal is to feedback the DL channel estimate from the UE back to the BS with as little information as possible. Our method uses two different tensor decompositions, the canonical polyadic decomposition (CPD) and the rank-(L [subscript r], L [subscript r], 1) or LL-1 block decomposition, on the DL frequency channel to estimate its parameters. By feeding back only the channel parameters, we show through simulations that our method is able to efficiently and accurately reconstruct the DL channel

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel state information at transmitter side (CSIT) is essential. Frequency division duplex (FDD) is widely employed by the most cellular systems today. However, it requires unaffordable pilot overhead and has high computational complexity. On the other hand, by exploiting the channel reciprocity using uplink pilots, the time division duplex (TDD) can overcome the overwhelming pilot training as well as the pilot feedback overhead. Considering these advantages, we propose a subsampling algorithm that can be implemented in a TDD mode. Particularly, we first exploit the intrinsic sparsity of CSIT, and then employ the Walsh-Hadamard Transform (WHT), which will subsample the received signal at BS, to perform channel estimation. Additionally, we discuss the proposed channel estimation scheme in a multicell scenario. Simulation results demonstrate that the proposed algorithm can accurately estimate channels with reduced computational complexity.

This book gathers selected papers presented at the 5th International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI 2021), organized by JCT College of Engineering and Technology, Coimbatore, Tamil Nadu, India during 27 – 28 August 2021. This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy.