Asymptotic Optimality of Massive MIMO Systems Using Densely Spaced Transmit Antennas

This paper investigates the performance of a massive multiple-input multiple-output (MIMO) system that uses a large transmit antenna array with antenna elements spaced densely. Under the assumption of idealized uniform linear antenna arrays without mutual coupling, precoded quadrature phase-shift keying (QPSK) transmission is proved to achieve the channel capacity of the massive MIMO system when the transmit antenna separation tends to zero. This asymptotic optimality is analogous to that of QPSK faster-than-Nyquist signaling.Comment: submitted to ISIT 2016. arXiv admin note: text overlap with arXiv:1601.0563

Asymptotic Optimality of Massive MIMO Systems Using Densely Spaced Transmit Antennas

This paper considers a deterministic physical model of massive multiple-input multiple-output (MIMO) systems with uniform linear antenna arrays. It is known that the maximum spatial degrees of freedom is achieved by spacing antenna elements at half the carrier wavelength. The purpose of this paper is to investigate the impacts of spacing antennas more densely than the critical separation. The achievable rates of MIMO systems are evaluated in the large-system limit, where the lengths of transmit and receive antenna arrays tend to infinity with the antenna separations kept constant. The main results are twofold: One is that, under a mild assumption of channel instances, spacing antennas densely cannot improve the capacity of MIMO systems normalized by the spatial degrees of freedom. The other is that the normalized achievable rate of quadrature phase-shift keying converges to the normalized capacity achieved by optimal Gaussian signaling, as the transmit antenna separation tends to zero after taking the large-system limit. The latter result is based on mathematical similarity between MIMO transmission and faster-than-Nyquist signaling in signal space representations.Comment: submitted to IEEE Trans. Inf. Theor

Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges

Precoding has been conventionally considered as an effective means of mitigating the interference and efficiently exploiting the available in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block- and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast-/multicast-/broadcast- precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial

Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning

Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. In the second approach, we develop a deep learning based solution where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.Comment: Submitted to IEEE Access. The code will be available soo

Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems

This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel distribution. While the existing techniques use statistical channel models, we utilize a model-free data-driven approach with foundations in machine learning to generate beamforming codebooks that adapt to the surrounding propagation conditions. The key technical contribution lies in reducing the codebook design problem to an unsupervised clustering problem on a Grassmann manifold where the cluster centroids form the finite-sized beamforming codebook for the channel state information (CSI), which can be efficiently solved using K-means clustering. This approach is extended to develop a remarkably efficient procedure for designing product codebooks for full-dimension (FD) multiple-input multiple-output (MIMO) systems with uniform planar array (UPA) antennas. Simulation results demonstrate the capability of the proposed design criterion in learning the codebooks, reducing the codebook size and producing noticeably higher beamforming gains compared to the existing state-of-the-art CSI quantization techniques

RF Lens-Embedded Massive MIMO Systems: Fabrication Issues and Codebook Design

In this paper, we investigate a radio frequency (RF) lens-embedded massive multiple-input multiple-output (MIMO) system and evaluate the system performance of limited feedback by utilizing a technique for generating a suitable codebook for the system. We fabricate an RF lens that operates on a 77 GHz (mmWave) band. Experimental results show a proper value of amplitude gain and an appropriate focusing property. In addition, using a simple numerical technique--beam propagation method (BPM)--we estimate the power profile of the RF lens and verify its accordance with experimental results. We also design a codebook--multi-variance codebook quantization (MVCQ)--for limited feedback by considering the characteristics of the RF lens antenna for massive MIMO systems. Numerical results confirm that the proposed system shows significant performance enhancement over a conventional massive MIMO system without an RF lens

Limited Feedback Designs for Machine-type Communications Exploiting User Cooperation

Multiuser multiple-input multiple-output (MIMO) systems are a prime candidate for use in massive connection density in machine-type communication (MTC) networks. One of the key challenges of MTC networks is to obtain accurate channel state information (CSI) at the access point (AP) so that the spectral efficiency can be improved by enabling enhanced MIMO techniques. However, current communication mechanisms relying upon frequency division duplexing (FDD) might not fully support an enormous number of devices due to the rate-constrained limited feedback and the time-consuming scheduling architectures. In this paper, we propose a user cooperation-based limited feedback strategy to support high connection density in massive MTC networks. In the proposed algorithm, two close-in users share the quantized version of channel information in order to improve channel feedback accuracy. The cooperation process is performed without any transmitter interventions (i.e., in a grant-free manner) to satisfy the low-latency requirement that is vital for MTC services. Moreover, based on the sum-rate throughput analysis, we develop an adaptive cooperation algorithm with a view to activating/deactivating the user cooperation mode according to channel and network conditions.Comment: 15 Pages, 9 figure

Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs

This letter revisits the channel estimation problem for MIMO systems with one-bit analog-to-digital converters (ADCs) through a novel algorithm--Amplitude Retrieval (AR). Unlike the state-of-the-art methods such as those based on one-bit compressive sensing, AR takes a different approach. It accounts for the lost amplitudes of the one-bit quantized measurements, and performs channel estimation and amplitude completion jointly. This way, the direction information of the propagation paths can be estimated via accurate direction finding algorithms in array processing, e.g., maximum likelihood. The upsot is that AR is able to handle off-grid angles and provide more accurate channel estimates. Simulation results are included to showcase the advantages of AR

A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. As a result, this paper is motivated to provide a comprehensive overview on MIMO transmission design with discrete input signals. We first summarize the existing fundamental results for MIMO systems with discrete input signals. Then, focusing on the basic point-to-point MIMO systems, we examine transmission schemes based on three most important criteria for communication systems: the mutual information driven designs, the mean square error driven designs, and the diversity driven designs. Particularly, a unified framework which designs low complexity transmission schemes applicable to massive MIMO systems in upcoming 5G wireless networks is provided in the first time. Moreover, adaptive transmission designs which switch among these criteria based on the channel conditions to formulate the best transmission strategy are discussed. Then, we provide a survey of the transmission designs with discrete input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, we discuss the transmission designs with discrete input signals for other systems using MIMO technology. Finally, technical challenges which remain unresolved at the time of writing are summarized and the future trends of transmission designs with discrete input signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE

Spatial Self-Interference Isolation for In-Band Full-Duplex Wireless: A Degrees-of-Freedom Analysis

The challenge to in-band full-duplex wireless communication is managing self-interference. Many designs have employed spatial isolation mechanisms, such as shielding or multi-antenna beamforming, to isolate the self-interference wave from the receiver. Such spatial isolation methods are effective, but by confining the transmit and receive signals to a subset of the available space, the full spatial resources of the channel be under-utilized, expending a cost that may nullify the net benefit of operating in full-duplex mode. In this paper we leverage an antenna-theory-based channel model to analyze the spatial degrees of freedom available to a full-duplex capable base station, and observe that whether or not spatial isolation out-performs time-division (i.e. half-duplex) depends heavily on the geometric distribution of scatterers. Unless the angular spread of the objects that scatter to the intended users is overlapped by the spread of objects that backscatter to the base station, then spatial isolation outperforms time division, otherwise time division may be optimal