Millimeter-wave Massive Multiple-input Multiple-output Research Articles

Energy Efficient Beamforming for Millimeter-Wave Massive MIMO Systems Under User-Wise Asymmetric Uplink–Downlink Traffic

In this paper, a beamforming scheme that aims to support user-wise asymmetric uplink-downlink (UL-DL) traffic in a more energy-efficient manner is proposed for time-division duplex (TDD) millimeter-wave massive multiple-input-multiple-output (MIMO) systems. Assuming that proper data links have been established during the initial access stage for DL or UL traffic, we consider the beamforming problem for UL or DL whose payload traffic is much lighter than the other link. Such asymmetric traffic allows part of the massive MIMO array to be deactivated in order to achieve a higher energy efficiency (EE) while still meeting the spectrum efficiency (SE) requirements for UL or DL. To deal with such a problem, we propose the corresponding phase shifter (PS) deactivation strategies based on different SE constraints for individual mobile stations or users independently, which select the PSs to be deactivated accordingly with low computational complexity. We then propose a novel codebook design method, which relies on the iteratively updated average main beam gain, in order to pursue a flatter beam under asymmetric DL-UL traffic that interacts with the corresponding PS deactivation strategy. The proposed codebook not only offers flexible beam width and flat main beam gain, but also can generate some good candidate codewords to account for the need of beam refinements, after some of the PSs have been deactivated. Simulation results demonstrated the superiority of the proposed energy efficient PS deactivation approach and the corresponding codebook design.

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of millimeter-wave V2X communications is, however, not exempt from challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an ultrasonic-aided tensor channel estimation for V2X millimeter-wave massive MIMO systems to improve the safety and traffic efficiency of cooperative automated driving. At the receiver, the dimension of multidimensional complex V2X information is reduced by the subspace tensor decomposition model. In order to quickly track beam angle changes caused by vehicle position changes, the ultrasonic-aided direction of arrival (DOA) tracking method is adopted to provide information about the surrounding environment. Based on the ultra-high resolution quantization grid and adaptive iterative update of the dictionary matrix, the DOA of the ultrasonic signal can be tracked. The angle update information of the millimeter-wave signal can be obtained by converting the estimated angle information. Using the cost function with global characteristics and ultrasonic-aided DOA tracking, a fast-layered alternating iterative tensor algorithm is proposed for joint iterative channel estimation. Simulation results show that the proposed solution outperforms some advanced alternative methods.

Time-Sequential Cooperative Localization for Moving Sensor in Millimeter-Wave Cell-Free Massive MIMO System

To address the moving-sensor localization problem in millimeter-wave (mm-wave) cell-free massive multiple-input multiple-output (MIMO) systems, a novel cooperative localization framework is proposed in this article. First, the angle of arrival (AoA) of the target sensor is extracted by each access point (AP) in the angle domain. Then, for the purpose of establishing the motion equation that is used to predict the location of the target sensor in the next period, a two-step velocity estimation algorithm is proposed to estimate the velocity and corresponding variance of the moving sensor simultaneously. Furthermore, based on horizontal dilution of precision (HDOP), an AP selection scheme is proposed to decide the subset of APs that participate in localization. On this basis, a time-sequential cooperative localization (TSCL) framework is constructed by jointly utilizing the estimated AoAs and the motion equation. The location estimation is converted to a joint posterior probability problem. The solution that maximizes the posterior probability is obtained through factor graph theory. The simulation results show that the proposed algorithm improves localization accuracy and robustness in time-varying environments.

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Optimal hybrid precoder design for millimeter-wave massive MIMO systems

Millimeter-waves (mmWave) have emerged as a promising candidate for next-generation communication systems. However, they have severe degradation issues that can be overcome by multiple-input multiple-output (MIMO) technology. Still, high-power consumption and budget issues are associated with multiple antennas. Therefore, hybrid precoders are exploited to combat these inevitable issues. The prime objective of this manuscript is to develop a hybrid precoder for massive MIMO systems that can provide better performance than the existing ones in terms of bit error rate (BER) versus signal-to-noise ratio (SNR). In this context, we exploit iterative geometric mean decomposition (IGMD) method that presents asymptotically optimum performance not only in traditional MIMO systems but also in massive MIMO systems. Later, the outcomes of the proposed scheme are compared with a recent design exploiting generalized triangular decomposition (GTD) scheme. The simulation studies show that the proposed method provides more than 7 dB improvement according to other designs.

Ultra-Fast Accurate AoA Estimation via Automotive Massive-MIMO Radar

Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In this work, we propose two efficient methods, to accomplish fast subspace computation and accurate angle of arrival (AoA) acquisition. By leveraging randomized low-rank approximation, our fast multiple signal classification (MUSIC) methods, relying on random sampling and projection techniques, substantially accelerate the subspace estimation by orders of magnitude. Moreover, we establish the theoretical bounds of our proposed methods, which ensure the accuracy of the approximated pseudo-spectrum. As demonstrated, the pseudo-spectrum acquired by our fast-MUSIC would be highly precise; and the estimated AoA is almost as accurate as standard MUSIC. In contrast, our new methods are tremendously faster than standard MUSIC. Thus, our fast-MUSIC enables the high-resolution real-time environmental sensing with massive MIMO radars, which has great potential in the emerging unmanned systems.

Manifold Discriminative Learning Inspired Hybrid Beamforming for Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propose user clustering hybrid precoding to enable efficient and low-complexity operation in high-dimensional mmWave massive MIMO, where a large number of antennas are used in low-dimensional manifolds. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. The manifold discriminative learning seek to learn the embedding low-dimensional manifolds, where manifolds with different user cluster labels are better separated, and the local spatial correlation of the high-dimensional channels within each manifold is enhanced. Most of the high-dimensional channels are embedded in the low-dimensional manifolds by manifold discriminative learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into global and local nonlinearity to achieve dimensionality reduction. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions

Channel Estimation for Switch-Based Millimeter-Wave Communications via Atomic Norm

Channel estimation is a challenging issue in millimeter-wave massive multiple-input-multiple-output (MIMO) communication systems due to the large number of antennas in the transceiver. Existing methods are usually based on phase shifters which may not be a simple circuit at mmWave band. In this paper, we construct a switch-based architecture for analog processors from the coarray point of view and then propose an atomicℓ0-norm minimization problem. We then propose an efficient algorithm to solve this problem based on Wirtinger projection. Since the proposed method requires no angle discretization, it does not suffer from grid mismatch effect that greatly deteriorates the estimation performance of grid-based channel estimation methods. Compared to the atomic norm minimization (ANM) method, our method does not involve vectorization of the channel matrix and hence the dimensionality of the problem is much less than that of ANM. We show that our method is able to provide comparable estimation performance to ANM but with much less computational time. Extensive simulations are carried out to verify the effectiveness of our proposed method.

Extreme learning machine detector for millimeter-wave massive MIMO systems

In this paper, we present an extreme learning machine (ELM) neural network designed to perform multiple-input multiple-output (MIMO) detection for millimeter-wave (mm-wave) communications operating in the 28 GHz frequency band. The ELM strategy can perform online MIMO combining processing. This method does not require offline training like with deep neural networks. The proposed technique was compared in terms of the achievable bit error rate (BER) and spectral efficiency (SE) to the maximum ratio (MR) and minimum mean squared error (MMSE) MIMO detectors, considering an orthogonal frequency-division multiplexing (OFDM) uplink scheme based on the fifth generation (5G) New Radio standard. Numerical results show that the ELM strategy outperforms the MR and MMSE detectors since this method reduces the inter-user interference effects, specifically for low equivalent isotropic radiated power at the receiver during the uplink communication. Furthermore, the ELM method requires only 16 % of the floating-point operations required by the MMSE detector.

A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems

The precoding scheme based on codebooks is used to save the same set of codebook in advance at the transmitter and the receiver, then, the receiver selects the most appropriate precoding matrix from codebooks according to different channel state information (CSI). Therefore, the design of codebook plays an important role in the performance of the whole scheme. The symmetry-based hybrid precoder and combiner is a highly energy efficient structure in the millimeter-wave massive multiple-input multiple-output (MIMO) system, but at the same time, it also has the problems of high bit error rate and low spectral efficiency. In order to improve the spectral efficiency, we formulate the codebook design as a joint optimization problem and propose an iteration algorithm to obtain the enhanced codebook by combining the compressive sampling matching pursuit (CoSaMP) algorithm with the dictionary learning algorithm. In order to prove the validity of the proposed algorithm, we simulate and analyze the change of the spectral efficiency of the algorithm with the signal-to-noise ratio (SNR) and the number of radio frequency (RF) chains of different precoding schemes. The simulation results demonstrate that the spectral efficiency of the algorithm is obviously outstanding compared with that of the OMP-based joint codebook algorithm and the hybrid precoding algorithm with quantization algorithm under low SNR and different numbers of RF chains. Particularly, when SNR is lower than 0 dB, the proposed algorithm performs very close to the optimal unconstrained precoding algorithm.

An Optimal Low-Complexity Energy-Efficient ADC Bit Allocation for Massive MIMO

Fixed low-resolution Analog to Digital Converters (ADC) help reduce the power consumption in millimeter-wave Massive Multiple-Input Multiple-Output (Ma-MIMO) receivers operating at large bandwidths. However, they do not guarantee optimal Energy Efficiency (EE). It has been shown that adopting variable-resolution (VR) ADCs in Ma-MIMO receivers can improve performance with Mean Squared Error (MSE) and throughput while providing better EE. In this paper, we present an optimal energy-efficient bit allocation (BA) algorithm for Ma-MIMO receivers equipped with VR ADCs under a power constraint. We derive an expression for EE as a function of the Cramer-Rao Lower Bound on the MSE of the received, combined, and quantized signal. An optimal BA condition is derived by maximizing EE under a power constraint. We show that the optimal BA thus obtained is exactly the same as that obtained using the brute-force BA with a significant reduction in computational complexity. We also study the EE performance and computational complexity of a heuristic algorithm that yields a near-optimal solution.

A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback

Hybrid beamformer design plays very crucial role in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. Previous works assume the perfect channel state information (CSI) which results heavy feedback overhead. To lower complexity, channel statistics can be utilized such that only infrequent update of the channel information is needed. To reduce the complexity and provide robustness, in this work, we propose a deep learning (DL) framework to deal with both hybrid beamforming and channel estimation. For this purpose, we introduce three deep convolutional neural network (CNN) architectures. We assume that the base station (BS) has the channel statistics only and feeds the channel covariance matrix into a CNN to obtain the hybrid precoders. At the receiver, two CNNs are employed. The first one is used for channel estimation purposes and the another is employed to design the hybrid combiners. The proposed DL framework does not require the instantaneous feedback of the CSI at the BS. We have also investigated the online deployment of DL for channel estimation. We have shown that the proposed approach has higher spectral efficiency with comparison to the conventional techniques. The trained CNN structures do not need to be re-trained due to the changes in the propagation environment such as the deviations in the number of received paths and the fluctuations in the received path angles up to 4 degrees. Also, the proposed DL framework exhibits at least 10 times lower computational complexity as compared to the conventional optimization-based approaches.

Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems.

Most of the previous work on hybrid transmit and receive beamforming focused on narrowband channels. Because the millimeter wave channels are expected to be wideband, it is crucial to propose efficient solutions for frequency-selective channels. In this regard, this paper proposes an iterative analog–digital multi-user equalizer scheme for the uplink of wideband millimeter-wave massive multiple-input-multiple-output (MIMO) systems. By iterative equalizer we mean that both analog and digital parts are updated using as input the estimates obtained at the previous iteration. The proposed iterative analog–digital multi-user equalizer is designed by minimizing the sum of the mean square error of the data estimates over the subcarriers. We assume that the analog part is fixed for all subcarriers while the digital part is computed on a per subcarrier basis. Due to the complexity of the resulting optimization problem, a sequential approach is proposed to compute the analog phase shifters values for each radio frequency (RF) chain. We also derive an accurate, semi-analytical approach for obtaining the bit error rate (BER) of the proposed hybrid system. The proposed solution is compared with other hybrid equalizer schemes, recently designed for wideband millimeter-wave (mmWave) massive MIMO systems. The simulation results show that the performance of the developed analog–digital multi-user equalizer is close to full-digital counterpart and outperforms the previous hybrid approach.

Joint measure matrix and channel estimation for millimeter-wave massive MIMO with hybrid precoding

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with hybrid precoding is a promising technology for future 5G wireless communications. Channel estimation for the millimeter-wave (mmWave) MIMO systems with hybrid precoding can be performed by estimating the path directions of the channel and corresponding path gains. This paper considers joint measure matrix and channel estimation for a massive MIMO system. By exploiting the sparsity of a massive MIMO system, a channel estimation scheme based on a Toeplitz-structured measure matrix and complete complementary sequence (CC-S) is proposed. Moreover, analytic studies show that the measurement matrix based on CC-S yields either optimal performance or feasibility in practice than an independent identically distributed Gaussian matrix. The performance of the scheme is shown with numerical examples.

Hybrid Precoding Design for Adaptive Subconnected Structures in Millimeter-Wave MIMO Systems

Hybrid precoding design for an adaptive subconnected structure offers a compromise between hardware complexity and spectral efficiency in massive multiple-input multiple-output (MIMO) systems at millimeter-wave (mmWave) frequencies. This paper focuses on the impacts of the partial antenna connections on the achievable sum-rate (ASR) performance for the uniform linear array and uniform square planar array configurations in mmWave networks. The subarray architectures consist of the overlapped, interlaced, and dynamically selective subset schemes in mmWave MIMO systems. Notably, on the basis of the ASR maximization, the Gram–Schmidt (GS)-based antenna selection (AS) algorithm is used to acquire an appropriate antenna subset and magnificently simplifies the radio frequency chain–antenna mapping operation in the sequel. Finally, simulation results demonstrate that an adaptive subarray architecture in conjunction with the GS-based AS search strategy is able to accomplish the ASR performance approaching the fully connected architecture with an alleviated demand on both computation and hardware complexities.

Digital Beamforming-Based Massive MIMO Transceiver for 5G Millimeter-Wave Communications

A 64-channel massive multiple-input multiple-output (MIMO) transceiver with a fully digital beamforming (DBF) architecture for fifth-generation millimeter-wave communications is presented in this paper. The DBF-based massive MIMO transceiver is operated at 28-GHz band with a 500-MHz signal bandwidth and the time division duplex mode. The antenna elements are arranged as a 2-D array, which has 16 columns (horizontal direction) and 4 rows (vertical direction) for a better beamforming resolution in the horizontal plane. To achieve half-wavelength element spacing in the horizontal direction, a new sectorial transceiver array design with a bent substrate-integrated waveguide is proposed. The measured results show that an excellent RF performance is achieved. The system performance is tested with the over-the-air technique to verify the feasibility of the proposed DBF-based massive MIMO transceiver for high data rate millimeter-wave communications. Using the beam-tracking technique and two streams of QAM-64 signals, the proposed millimeter-wave MIMO transceiver can achieve a steady 5.3-Gb/s throughput for a single user in fast mobile environments. In the multiple-user MIMO scenario, by delivering 20 noncoherent data streams to eight four-channel user terminals, it achieves a downlink peak data rate of 50.73 Gb/s with the spectral efficiency of 101.5 b/s/Hz.

Spectrum and Energy-Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper, we propose a new spectrum and energy-efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. In particular, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero forcing is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.