Massive multiple-input multiple-output MIMO is one of themost promising technologies for the next generation of wirelesscommunication networks because it has the potential to providegame-changing improvements in spectral efficiency SE and energyefficiency EE. This monograph summarizes many years ofresearch insights in a clear and self-contained way and providesthe reader with the necessary knowledge and mathematical toolsto carry out independent research in this area. Starting froma rigorous definition of Massive MIMO, the monograph coversthe important aspects of channel estimation, SE, EE, hardwareefficiency HE, and various practical deployment considerations.From the beginning, a very general, yet tractable, canonical systemmodel with spatial channel correlation is introduced. This modelis used to realistically assess the SE and EE, and is later extendedto also include the impact of hardware impairments. Owing tothis rigorous modeling approach, a lot of classic "wisdom" aboutMassive MIMO, based on too simplistic system models, is shownto be questionable.

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Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency

Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

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Nonorthogonal Multiple Access for 5G and Beyond

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Linear And Nonlinear Programming

This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users is active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-free two-phase access scheme in which device activity detection and channel estimation are performed jointly using pilot sequences in the first phase and data is transmitted in the second phase. In order to accommodate a large number of simultaneously transmitting devices, this paper studies an asymptotic regime where the BS is equipped with a massive number of antennas. The main contributions of Part I of this paper are as follows. First, we note that as a consequence of having a large pool of potentially active devices but limited coherence time, the pilot sequences cannot all be orthogonal. However, despite the nonorthogonality, this paper shows that in the asymptotic massive multiple-input multiple-output regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern. Part II of this paper further characterizes the achievable rates using the proposed scheme and quantifies the cost of using nonorthogonal pilot sequences for channel estimation in achievable rates.

Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation

In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.

Deep Learning for Massive MIMO CSI Feedback

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance.

Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.

Interference Alignment for Partially Connected MIMO Cellular Networks

Cloud Radio Access Network (CRAN) is proposed as a promising network architecture for future mobile communications In this paper, we consider the topic of active user detection (AUD) and channel estimation (CE) in uplink CRAN systems with sparse active users Different from conventional AUD and CE approaches which require the length of uplink pilots to scale with the number of users times the number of antennas per user, a novel algorithm will be proposed to substantially reduce the uplink training overhead by leveraging the technique of compressive sensing (CS) To achieve this goal, we first transform the problem of AUD and CE into standard CS problems We then propose a modified Bayesian compressive sensing (BCS) algorithm to conduct AUD and CE in CRAN, which exploits not only the active user sparsity, but also the innate heterogeneous path loss effects and the joint sparsity structures in multi-antenna uplink CRAN systems

Active user detection and channel estimation in uplink CRAN systems

In this paper, we consider the problem of compressive sensing (CS) recovery with a prior support and the prior support quality information available. Different from classical works which exploit prior support blindly, we shall propose novel CS recovery algorithms to exploit the prior support adaptively based on the quality information. We analyze the distortion bound of the recovered signal from the proposed algorithm and we show that a better quality prior support can lead to better CS recovery performance. We also show that the proposed algorithm would converge in ${\cal O}(\log{\rm SNR})$ steps. To tolerate possible model mismatch, we further propose some robustness designs to combat incorrect prior support quality information. Finally, we apply the proposed framework to sparse channel estimation in massive MIMO systems with temporal correlation to further reduce the required pilot training overhead.

Compressive Sensing With Prior Support Quality Information and Application to Massive MIMO Channel Estimation With Temporal Correlation

Interference alignment is degree of freedom optimal on $K$ -user MIMO interference channels and many previous works have studied the transceiver designs. However, these works predominantly focus on networks with perfect channel state information at the transmitters and symmetrical interference topology. In this paper, we consider a limited feedback system with heterogeneous path loss and spatial correlations and investigate how the dynamics of the interference topology can be exploited to improve the feedback efficiency. We propose a novel spatial codebook design and perform dynamic quantization via bit allocations to adapt to the asymmetry of the interference topology. We bound the system throughput under the proposed dynamic scheme in terms of the transmit SNR, feedback bits, and the interference topology parameters. It is shown that when the number of feedback bits scales with SNR as $C_{s}\cdot \log {\hbox{SNR}}+ {\cal O}(1)$ , the sum degrees of freedom of the network are preserved. Moreover, the value of scaling coefficient $C_{s}$ can be significantly reduced in networks with asymmetric interference topology.

Xiongbin Rao

Papers

Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems

Interference Alignment for Partially Connected MIMO Cellular Networks

Active user detection and channel estimation in uplink CRAN systems

Compressive Sensing With Prior Support Quality Information and Application to Massive MIMO Channel Estimation With Temporal Correlation

Limited Feedback Design for Interference Alignment on MIMO Interference Networks With Heterogeneous Path Loss and Spatial Correlations