Spatio-temporal Correlated Channel Feedback for Massive MIMO Systems

In a massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The user equipment (UE) needs to estimate CSI and then feeds it back to the BS in the frequency division duplexing (FDD) mode. Effective compression of CSI will significantly reduce the cost of channel feedback, and many deep learning (DL) based channel compression schemes have been proposed to achieve this goal. In this paper, rather than viewing CSI as images as in most existing works, we propose a new perspective of viewing CSI as an information sequence and analogize CSI feedback to a machine translation task. Further, we propose a novel sequence to sequence (Seq2Seq) model for CSI feedback composed of only recurrent neural networks and a small-scale fully connected layer, avoiding the convolution and pooling structure commonly used in current DL-based works. The advantage of this scheme is that it fully integrates the physical characteristics of the MIMO channel in the spatial domain into the structure of the neural network and avoids the loss of some prominent unsmooth or discontinuous features caused by the inappropriate convolution and pooling operation. Simulation results show that the proposed Seq2Seq model outperforms other DL-based CSI compression techniques under various communication scenarios.

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 5G massive MIMO. This paper proposes novel limited feedback algorithms that lift this burden by exploiting the inherent sparsity in double-directional MIMO channel representation using overcomplete dictionaries. These dictionaries are associated with angle of arrival and angle of departure 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 5G 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 5G systems.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

The fifth generation cellular communication is all about high speed internet access with devices consuming less amount of power. Millimeter wave Multiple Input Multiple Output (MIMO) systems is a promising technology that offers high data rates due to a large bandwidth. These systems are compact with minimal antenna heights which allows us to deploy these systems more widely. The mm-Wave Massive MIMO systems enhances the data rates to a large extent. But due to the increasing number of antennas to support high data rates, the power consumption of the system goes high. In this paper, a limited channel state information (CSI) based precoding technique has been used to optimize the power consumption and to reduce the computational complexity of the system. The use of Hybrid Precoder architecture with sub array of precoding blocks helps in reducing the computational complexity by many folds. The conditional switching between two stages minimizes the number of computations required. The estimation of Angle of arrival (AoA) enables the precoder to steer the beam at the transmitter for higher throughput. The smart antenna selection algorithm optimizes the power consumption of the system. The two – staged precoding algorithm proposed employs all these techniques to optimize the performance of mm-Wave MIMO systems.

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.

Massive multiple-input multiple-output (MIMO) systems hold the potential to be an enabling technology for 5G cellular. Uniform planar array (UPA) antenna structures are a focus of much commercial discussion because of their ability to enable a large number of antennas in a relatively small area. With UPA antenna structures, the base station can control the beam direction in both the horizontal and vertical domains simultaneously. However, channel conditions may dictate that one dimension requires higher channel state information (CSI) accuracy than the other. We propose the use of an additional one bit of feedback information sent from the user to the base station to indicate the preferred domain on top of the feedback overhead of CSI quantization in frequency division duplexing (FDD) massive MIMO systems. Combined with variable-rate CSI quantization schemes, the numerical studies show that the additional one bit of feedback can increase the quality of CSI significantly for UPA antenna structures.

Visible light communication (VLC) building on the existing illumination infrastructure utilizes light emitting diode (LED) luminaries as wireless transmitters. To achieve data rates on the order of gigabits per second in VLC systems, multiple-input multiple-output (MIMO) transmission has been identified as a key technology. In this paper, we consider a centralized network architecture where the ceiling LED luminaries act as transmitter elements of a distributed massive MIMO VLC system. To obtain the full benefits of massive MIMO, channel state information must be available at the transmitter side. Downlink (DL) channel estimation in a massive MIMO system requires huge pilot overhead in DL as well as feedback overhead in uplink. This makes the implementation of massive MIMO system infeasible if not properly designed. In our study, we address the design of massive MIMO VLC systems with a limited number of DL pilot signals. We explore different DL pilot patterns (PPs) in frequency, time, and spatial domains where interpolation is performed to obtain the global channel matrix taking advantage of the indoor environment geometry and layout of luminaries. We then present data rate results for different DL PPs under the consideration of varying number of active user equipments through Monte-Carlo simulations.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication system is expected to achieve enormous transmission rate, provided that the transmit and receive beams are properly aligned with the MIMO channel. However, existing beam alignment techniques rely on either channel estimation or beam sweeping, which incur prohibitively high training overhead, especially for future wireless systems with further increased antenna dimensions and more stringent requirement on cost-effective hardware architectures. In this paper, we propose a new beam alignment technique, which is environment-aware and training-free, by utilizing the emerging concept of channel knowledge map (CKM), together with the user location information that is readily available in contemporary wireless systems. CKM is a site-specific database, tagged with the transmitter/receiver locations, which contains useful channel information to facilitate or even obviate real-time channel state information (CSI) acquistion. Two instances of CKM are proposed for beam alignment in mmWave massive MIMO systems, namely channel path map (CPM) and beam index map (BIM). It is shown that compared with existing training-based beam alignment schemes, the proposed CKM-enabled environment-aware beam alignment is able to drastically improve the effective communication rate, even with moderate user location errors, thanks to its significant saving of the prohibitive training over-head.

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.

Feedback overhead for channel state information (CSI) acquisition and accordingly backhaul overhead for base station (BS) cooperation can be significantly reduced by channel statistics based codebook in cooperative massive multiple-input multiple-output (MIMO) systems. When utilizing the skewed codebook based on the channel covariance matrices, the effect of the quantization error on the achievable rate depends on the channel covariance matrices of the serving and interfering channels. Leveraging the analysis, we propose channel statistics based adaptive feedback by deriving optimal feedback bit allocation to the serving and interfering channels in a closed-form according to their channel covariance matrices.

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output (MIMO) systems employing single- carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced-rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead.

기존 MIMO 시스템 대비 수십 또는 수백 배 많은 송신 안테나들이 설치되어 있는 주파수 분할(frequency division duplex, FDD) 방식의 거대 다중 안테나(massive multiple-input mulitple-output, Massive MIMO)에선 송신 안테나 별 CSI-RS(channel state information reference signal) 전송을 위해 기존 MIMO 시스템 대비 너무 많은 하향 및 상향링크 자원 요소가 필요하다는 한계를 갖는다. 이러한 단점들을 극복하기 위해 본 논문에선 주파수 분할 방식의 거대 다중 안테나 시스템에서 많은 송신 안테나를 사용하더라도 자원요소(resource element, RE)를 재사용함으로써 송신 안테나 수에 비례하지 않는 제한된 양의 하향링크 자원만을 사용하여 CSI-RS를 효과적으로 전송하는 기술 및 자원격자 구조를 제안하였다. Since FDD massive MIMO (multiple-input multiple-output) system deploys hundreds of transmit antennas at base station (BS) compared to conventional MIMO system, the overhead of transmitting channel state information reference signal (CSI-RS) increases proportionally to the number of transmit-antennas. To overcome these disadvantages, we proposed beamforming based CSI-RS transmission technique for FDD massive MIMO system which transmit CSI-RS by limited amount of downlink resources.