A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems

In this paper, a novel covariance-based channel feedback mechanism is investigated for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The concept capitalizes on the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but not fully exploited so far. We here propose a hybrid statistical-instantaneous feedback mechanism where the users are separated into two classes of feedback design based on their channel covariance. Under the hybrid framework, each user either operates on a statistical feedback mode or quantized instantaneous channel feedback mode depending on their so-called statistical isolability. The key challenge lies in the design of a covariance-aware classification algorithm which can handle the complex mutual interactions between all users. The classification is derived from rate bound principles. A suitable precoding method is also devised under the mixed statistical and instantaneous feedback model. Simulations are performed to validate our analytical results and illustrate the sum rate advantages of the proposed feedback scheme under a global feedback overhead constraint.Comment: 31 pages, 9 figure

Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach

In this paper, we propose a novel channel feedback scheme for frequency division duplexing massive multi-input multi-output systems. The concept uses the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but not fully exploited so far. We here propose a hybrid statistical-instantaneous feedback scheme based on a user classification mechanism where the classification metric derives from a rate bound analysis. According to classification results, a user either operates on a statistical feedback mode or instantaneous mode. Our results illustrate the sum rate advantages of our scheme under a global feedback overhead constraint.Comment: 5 pages, 4 figures, conference paper, 2018 Asilomar Conference on Signals, Systems, and Computer

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications Magazin

Limited Feedback Channel Estimation in Massive MIMO with Non-uniform Directional Dictionaries

Channel state information (CSI) at the base station (BS) is crucial to achieve beamforming and multiplexing gains in multiple-input multiple-output (MIMO) systems. State-of-the-art limited feedback schemes require feedback overhead that scales linearly with the number of BS antennas, which is prohibitive for $5$G massive MIMO. This work proposes novel limited feedback algorithms that lift this burden by exploiting the inherent sparsity in double directional (DD) MIMO channel representation using overcomplete dictionaries. These dictionaries are associated with angle of arrival (AoA) and angle of departure (AoD) that specifically account for antenna directivity patterns at both ends of the link. The proposed algorithms achieve satisfactory channel estimation accuracy using a small number of feedback bits, even when the number of transmit antennas at the BS is large -- making them ideal for $5$G massive MIMO. Judicious simulations reveal that they outperform a number of popular feedback schemes, and underscore the importance of using angle dictionaries matching the given antenna directivity patterns, as opposed to uniform dictionaries. The proposed algorithms are lightweight in terms of computation, especially on the user equipment side, making them ideal for actual deployment in $5$G systems

Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges

As we make progress towards the era of fifth generation (5G) communication networks, energy efficiency (EE) becomes an important design criterion because it guarantees sustainable evolution. In this regard, the massive multiple-input multiple-output (MIMO) technology, where the base stations (BSs) are equipped with a large number of antennas so as to achieve multiple orders of spectral and energy efficiency gains, will be a key technology enabler for 5G. In this article, we present a comprehensive discussion on state-of-the-art techniques which further enhance the EE gains offered by massive MIMO (MM). We begin with an overview of MM systems and discuss how realistic power consumption models can be developed for these systems. Thereby, we discuss and identify few shortcomings of some of the most prominent EE-maximization techniques present in the current literature. Then, we discuss "hybrid MM systems" operating in a 5G architecture, where MM operates in conjunction with other potential technology enablers, such as millimetre wave, heterogenous networks, and energy harvesting networks. Multiple opportunities and challenges arise in such a 5G architecture because these technologies benefit mutually from each other and their coexistence introduces several new constraints on the design of energy-efficient systems. Despite clear evidence that hybrid MM systems can achieve significantly higher EE gains than conventional MM systems, several open research problems continue to roadblock system designers from fully harnessing the EE gains offered by hybrid MM systems. Our discussions lead to the conclusion that hybrid MM systems offer a sustainable evolution towards 5G networks and are therefore an important research topic for future work.Comment: IEEE Wireless Communications, under revie

A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems

In millimeter wave (mmWave) communication systems, existing hybrid beamforming solutions generally require a large number of high-resolution phase shifters (PSs) to realize analog beamformers, which still suffer from high hardware complexity and power consumption. Targeting at this problem, this article introduces a novel hardware-efficient hybrid precoding/combining architecture, which only employs a limited number of simple phase over-samplers (POSs) and a switch (SW) network to achieve maximum hardware efficiency while maintaining satisfactory spectral efficiency performance. The POS can be realized by a simple circuit and simultaneously outputs several parallel signals with different phases. With the aid of a simple switch network, the analog precoder/combiner is implemented by feeding the signals with appropriate phases to antenna arrays or RF chains. We analyze the design challenges of this POS-SW-based hybrid beamforming architecture and present potential solutions to the fundamental issues, especially the precoder/combiner design and the channel estimation strategy. Simulation results demonstrate that this hardware-efficient structure can achieve comparable spectral efficiency but much higher energy efficiency than that of the traditional structures

Channel Estimation and Hybrid Precoding for Distributed Phased Arrays Based MIMO Wireless Communications

Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is a newly introduced architecture that enables both spatial multiplexing and beamforming while facilitating highly reconfigurable hardware implementation in millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on channel state information (CSI) acquisition and hybrid precoding. As benefited from a coordinated and open-loop pilot beam pattern design, all the sub-arrays can perform channel sounding with less training overhead compared with the traditional orthogonal operation of each sub-array. Furthermore, two sparse channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP) and joint sparse Bayesian learning with $\ell_2$ reweighting (JSBL-$\ell_2$), are proposed to exploit the hidden structured sparsity in the beam-domain channel vector. Finally, successive interference cancellation (SIC) based hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO system, which decomposes the joint sub-array RF beamformer design into an interactive per-sub-array-group handle. Simulation results show that the proposed two channel estimators fully take advantage of the partial coupling characteristic of DPA-MIMO channels to perform channel recovery, and the proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays architecture with satisfactory performance and low complexity.Comment: accepted by IEEE Transactions on Vehicular Technolog

Interleaved Training and Training-Based Transmission Design for Hybrid Massive Antenna Downlink

In this paper, we study the beam-based training design jointly with the transmission design for hybrid massive antenna single-user (SU) and multiple-user (MU) systems where outage probability is adopted as the performance measure. For SU systems, we propose an interleaved training design to concatenate the feedback and training procedures, thus making the training length adaptive to the channel realization. Exact analytical expressions are derived for the average training length and the outage probability of the proposed interleaved training. For MU systems, we propose a joint design for the beam-based interleaved training, beam assignment, and MU data transmissions. Two solutions for the beam assignment are provided with different complexity-performance tradeoff. Analytical results and simulations show that for both SU and MU systems, the proposed joint training and transmission designs achieve the same outage performance as the traditional full-training scheme but with significant saving in the training overhead.Comment: 16 Pages (double column), 11 figures. This work has been accepted by the IEEE Journal of Selected Topics in Signal Processing (JSTSP), Special Issue on Hybrid Analog - Digital Signal Processing for Hardware-Efficient Large Scale Antenna Arrays. This version is different from the former one due to the revisions made for the comments of 1st and 2nd round revie

Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray

Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.Comment: This paper has been accepted by 2018 GLOBECOM workshop. arXiv admin note: text overlap with arXiv:1809.0336

IEEE 802.11ay based mmWave WLANs: Design Challenges and Solutions

Millimeter-wave (mmWave) with large spectrum available is considered as the most promising frequency band for future wireless communications. The IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave are the two most expected wireless local area network (WLAN) technologies for ultra-high-speed communications. For the IEEE 802.11ay standard still under development, there are plenty of proposals from companies and researchers who are involved with the IEEE 802.11ay task group. In this survey, we conduct a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, some cross-layer between physical layer (PHY) and MAC technologies are also included. We start with MAC related technologies in the IEEE 802.11ad and discuss design challenges on mmWave communications, leading to some MAC related technologies for the IEEE 802.11ay. We then elaborate on important design issues for IEEE 802.11ay. Specifically, we review the channel bonding and aggregation for the IEEE 802.11ay, and point out the major differences between the two technologies. Then, we describe channel access and channel allocation in the IEEE 802.11ay, including spatial sharing and interference mitigation technologies. After that, we present an in-depth survey on beamforming training (BFT), beam tracking, single-user multiple-input-multiple-output (SU-MIMO) beamforming and multi-user multiple-input-multiple-output (MU-MIMO) beamforming. Finally, we discuss some open design issues and future research directions for mmWave WLANs. We hope that this paper provides a good introduction to this exciting research area for future wireless systems.Comment: 27 pages, 33 figures. Accepted for publication in IEEE Communications Surveys and Tutorial