Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems

Hybrid precoding has been recognized as a promising technology to combat the path loss of millimeter wave signals in massive multiple-input multiple-output (MIMO) systems. However, due to the joint optimization of the digital and analog precoding matrices as well as extra constraints for the analog part, the hybrid precoding design is still a tough issue in current research. In this paper, we adopt the thought of clustering in unsupervised learning and provide design schemes for fully-connected hybrid precoding (FHP) and adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems. For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based) scheme to explore the relevance among RF chains in optimal hybrid procoding design. The similar RF chains are merged into an individual RF chain when insufficient RF chains are equipped at the base station (BS). For AHP, we propose the modified-K-means-based (MKM-based) scheme to explore the relevance among antennas at the BS. The similar antennas are supported by the same RF chain to make full use of the flexible connection in AHP. Particularly, in proposed MKM-based AHP design, the clustering centers are updated by alternating-optimum-based (AO-based) scheme with a special initialization method, which is capable to individually provide feasible sub-connected hybrid precoding (SHP) design. Simulation results highlight the superior spectrum efficiency of proposed HAC-based FHP scheme, and the high power efficiency of proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified to effectively handle the inter-user interference and outperform the existing work

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

Cell-Free Millimeter-Wave Massive MIMO Systems with Limited Fronthaul Capacity

Network densification, massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) bands have recently emerged as some of the physical layer enablers for the future generations of wireless communication networks (5G and beyond). Grounded on prior work on sub-6~GHz cell-free massive MIMO architectures, a novel framework for cell-free mmWave massive MIMO systems is introduced that considers the use of low-complexity hybrid precoders/decoders while factors in the impact of using capacity-constrained fronthaul links. A suboptimal pilot allocation strategy is proposed that is grounded on the idea of clustering by dissimilarity. Furthermore, based on mathematically tractable expressions for the per-user achievable rates and the fronthaul capacity consumption, max-min power allocation and fronthaul quantization optimization algorithms are proposed that, combining the use of block coordinate descent methods with sequential linear optimization programs, ensure a uniformly good quality of service over the whole coverage area of the network. Simulation results show that the proposed pilot allocation strategy eludes the computational burden of the optimal small-scale CSI-based scheme while clearly outperforming the classical random pilot allocation approaches. Moreover, they also reveal the various existing trade-offs among the achievable max-min per-user rate, the fronthaul requirements and the optimal hardware complexity (i.e., number of antennas, number of RF chains)

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.Comment: 27 pages, 11 figure

Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mm-wave) communications, but its precoder design is highly complicated. In this paper, we propose a new hybrid precoder implementation, namely the double phase shifter (DPS) implementation, which enables highly tractable hybrid precoder design. Efficient algorithms are then developed for two popular hybrid precoder structures, i.e., the fully- and partially-connected structures. For the fully-connected one, the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator (LASSO) problem and a low-rank matrix approximation problem, respectively. In this way, computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency (RF) chains. On the other hand, the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem. To enhance the performance of this cost-effective structure, dynamic mapping from RF chains to antennas is further proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones. It shows that, with the proposed DPS implementation, the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.Comment: 32 pages, 6 figures, 1 table, submitted to Journal of Communications and Information Networks, Apr. 201

Investigation and Comparison of 3GPP and NYUSIM Channel Models for 5G Wireless Communications

Channel models describe how wireless channel parameters behave in a given scenario, and help evaluate link- and system-level performance. A proper channel model should be able to faithfully reproduce the channel parameters obtained in field measurements and accurately predict the spatial and temporal channel impulse response along with large-scale fading. This paper compares two popular channel models for next generation wireless communications: the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel model and the statistical spatial channel model NYUSIM developed by New York University (NYU). The two channel models employ different modeling approaches in many aspects, such as the line-of-sight probability, path loss, and clustering methodology. Simulations are performed using the two channel models to analyze the channel eigenvalue distribution and spectral efficiency leveraging the analog/digital hybrid beamforming methods found in the literature. Simulation results show that the 3GPP model produces different eigenvalue and spectral efficiency distributions for mmWave bands, as compared to the outcome from NYUSIM that is based on massive amounts of real-world measured data in New York City. This work shows NYUSIM is more accurate for realistic simulations than 3GPP in urban environments.Comment: 6 pages, 3 figures, in 2017 IEEE 86th Vehicular Technology Conference (VTC Fall), Toronto, Canada, Sep. 201

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

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

Large-scale Antenna Operation in Heterogeneous Cloud Radio Access Networks: A Partial Centralization Approach

To satisfy the ever-increasing capacity demand and quality of service (QoS) requirements of users, 5G cellular systems will take the form of heterogeneous networks (HetNets) that consist of macro cells and small cells. To build and operate such systems, mobile operators have given significant attention to cloud radio access networks (C-RANs) due to their beneficial features of performance optimization and cost effectiveness. Along with the architectural enhancement of C-RAN, large-scale antennas (a.k.a. massive MIMO) at cell sites contribute greatly to increased network capacity either with higher spectral efficiency or through permitting many users at once. In this article, we discuss the challenging issues of C-RAN based HetNets (H-CRAN), especially with respect to large-scale antenna operation. We provide an overview of existing C-RAN architectures in terms of large-scale antenna operation and promote a partially centralized approach. This approach reduces, remarkably, fronthaul overheads in CRANs with large-scale antennas. We also provide some insights into its potential and applicability in the fronthaul bandwidthlimited H-CRAN with large-scale antennas.Comment: To appear in IEEE Wireless Communications Magazine June 201

A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.Comment: to appear in IEEE JSAC, 201