mmWave MIMO Systems Research Articles

THE PERFORMANCE OF A HYBRID BEAMFORMING mmWAVE MIMO SYSTEM WITH NON-IDEAL HARDWARE

With the increasing wireless data traffic and data rate demands, milimeter-wave band corresponding to the frequency range 30-300 GHz has much attention of the researchers. Using multiple antennas at the transmitter and the receiver with beamforming helps overcome high transmission loss. Therefore, millimeter-wave Multiple-Input Multiple-Output (mmWave MIMO) is a key technology for the next generation wireless communication system. However, due to hardware constrains when operating in the high frequency range, the performance of a mmWave MIMO system will decrease. In this paper, we analysis system performance of a hybrid beamforming mmWave MIMO system with non-ideal hardware (phase noise and quantization noise).

MmWave MIMO systems for 5G communications

: The next generation 5g communications are used with baseband precoders and beam forming. The technology is needed to update the all the available characteristics in the present wireless communication system. In the mmWave MIMO system the number of antenna are increased to improve the spectral efficiency of the system.

Towards Higher Spectral Efficiency: Spatial Path Index Modulation Improves Millimeter-Wave Hybrid Beamforming

The combination of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems and index modulation (IM) technique has recently constituted a novel form of mmWave hybrid beamforming. Lots of studies have been conducted to show that such system has the potential to outperform conventional mmWave-MIMOs with respect to spectral efficiency (SE). Most of the current works only focused on designing hybrid beamforming structures to empirically achieve higher SE performance. However, a fundamental question that whether IM technique can truly improve the SE of current mmWave hybrid beamforming is still left unanswered. Against such background, in this work we firstly extend the IM-assisted mmWave system to a more generalized version, i.e. a spatial path index modulation aided mmWave (SPIM-mmWave) system, which subsumes both IM-assisted and conventional mmWave-MIMO systems as its special cases. Based on the framework of SPIM-mmWave, a fundamental study is conducted towards a theoretic condition under which SPIM-mmWave guarantees to outperform conventional mmWave-MIMO schemes. It is demonstrated that, under a specific channel condition and noise level (which will be explicitly given by our work), SPIM-mmWave guarantees to outperform conventional mmWave-MIMO systems.

Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems

We propose an iterative channel estimation algorithm based on the Least Square Estimation (LSE) and Sparse Message Passing (SMP) algorithm for the Millimeter Wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli-Gaussian distribution and the channel matrix is sparse with only a few non-zero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of non-zero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cram\'er-Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations.

A Family of Practical Hybrid Precoding Algorithms for Rank-Deficient Millimeter-Wave MIMO Systems

Hybrid precoding (HP) techniques based on partially connected structure (PCS) are widely studied in millimeter-wave (mmWave) MIMO communication systems due to low hardware cost as well as low energy consumption. Many efficient HP schemes based on PCS have recently been proposed, which require that the baseband precoding (BP) matrix of the digital domain to be a diagonal or square matrix. To avoid the interference from multiple streams of a single-user, the number of data streams for a single-user should not exceed the rank of the equivalent baseband MIMO channel (EBMC) before BP. Hence, these schemes are only reliable to the case that the EBMC matrix is full rank. However, the EBMC matrix may be rank-deficient due to the geometrical change and misplacement of the transceiver in practice. In this paper, we focus on the HP with PCS and propose a family of effective iterative matrix factorization (IMF) algorithms for HP designs, which are suitable for rank-deficient EBMC. We firstly propose an IMF-HP algorithm based on traditional design, which approximates the performance of the fully digital precoder by utilizing an alternate optimization strategy. In particular, we present a successive interference cancelation combined with IMF for HP design (SiC-IMF-HP) to further achieve higher throughput and lower complexity. Simulation results show that the proposed SiC-IMF-HP algorithm performs better than other recently proposed hybrid precoding schemes based on PCS when the number of data streams equals to the number of RF chains. The proposed algorithms both enjoy an acceptable computational complexity.

Impact of Overlapped AoAs on the Achievable Uplink Rate of Hybrid Beamforming for Massive MIMO mm-Wave Systems for Industrial Environments

In this paper, we develop novel precoder and combiner schemes for multi-user hybrid digital-to-analog (D-A) beamforming for uplink massive multiple-input multiple-output millimeter wave (mm-Wave) systems, where the number of radio frequency chains is much smaller than the number of antennas each transceiver is equipped with. Industry 4.0 targets to accelerate the digitalization of manufacturing processes by allocating fixed and mobile robotics which use wireless communication. Such development requires high data throughput for which the utilization of short distance wireless communication such as mm-Wave system is crucially required. Based on our measurements, the probability of mm-Wave propagation waves to be reflected and refracted from metallic surfaces are shown to be significantly high. Consequently, uplink transmissions from different users such as robotics, machines, and sensors can go through paths sharing the same physical scatters, some transmission paths of different users may have overlapped angle of arrivals (AoAs) at the base station. Under such circumstance, the correlation between the channel vectors also increases considerably, which affects the achievable uplink rate severely. Therefore, the intrinsic focus is on substantially maximizing the desired signal while reducing the system interference. The proposed analog precoders and combiners are designed by using the power iteration and the Riemannian optimization method based on Stiefel manifold algorithms, respectively. The proposed digital combiner adapts the minimum mean-square error by judiciously exploiting the effective uplink analog channel gains. Furthermore, the channel estimation is investigated through a newly designed two-step procedure. In each scattering point, there is the strongest power point which is detected and then used to improve the accuracy by employing an angular domain scheme. The impact of this paper will be the boosting of the achievable uplink rate in industrial environments.

MMSE algorithm based two stages hybrid precoding for millimeter wave massive MIMO systems

Millimeter wave (mm-wave) communications has been sighted as solution becoming strong technology for the fifth generation networks. That is because, it brings orders of degree higher spectrum than current cellular bands and helps applications that require weak latency. Massive MIMO is used to minimize the severe constraints enjoined by the excessive propagation loss for millimeter wave frequencies. Where, normal implementation of antenna arrays is transmitted to multiple users simultaneously. Due to hardware constraints in mm-wave systems, classical lower frequency precoding techniques are difficult to be applied at mm-wave. A proposed modified hybrid precoding is suggested for downlink massive MIMO mm-wave systems in this paper. The hybrid precoding consists of mixed of analog and digital processing which is based on minimum mean square error (MMSE) algorithm. Where, quantized beamstearing codebooks are used to produce the analog and digital beamforming vectors with small training and feedback overhead. Extensive simulation programs are executed to compare the performance of the proposed precoding scheme with analog beamforming performance at different digital baseband precoders such as zero forcing precoder. Moreover, studies on the impact of modified hybrid beamforming parameters on the system performance are done. These parameters such as; the number of BS antennas, the number of mobile station antennas, number of effective channel quantization bits and number of RF beamforming quantization bits. Besides, the impact of rate threshold on the coverage probability is studied under different values of NBS antennas. It is shown that the performance of the modified hybrid precoding using MMSE is very close to the single user performance with only 0.5 b/s/Hz performance degradation at SNR = − 5 dB.

Distributed compressed sensing‐based multiuser channel estimation for frequency‐selective hybrid mmWave MIMO system

SummaryThe multiuser millimeter wave (mmWave) MIMO system has been considered as a promising technology for future 5G cellular networks since high volume of data rates can be achieved by simultaneously supporting multiple users. However, the channel estimation is challenging due to the multiuser interference in frequency‐selective hybrid mmWave MIMO system. In this paper, we focus on the multiuser uplink wideband channel estimation with frequency selectivity. Specifically, a layer‐precoder reuse strategy is proposed, in which each transmission frame is divided into several subframes and the users share a common precoder at each subchannel of different subcarriers. Then, instead of only estimating channel of one user, multiuser frequency‐selective channel can be jointly estimated via a sparse recovery formulation. Furthermore, by exploiting the common sparsity among subchannels in frequency‐selective mmWave system, a distributed compressed sensing (DCS)–based channel estimation is proposed to jointly acquire the channel state information of all users via weighted least square to further reduce the pilot overhead. In addition, the Cramer‐Rao lower bound (CRLB) is derived for multiuser frequency‐selective channel estimation. Simulation results demonstrate that the proposed DCS‐based method can asymptotically approach the CRLB of channel estimation.

RF Lens-Embedded Antenna Array for mmWave MIMO: Design and Performance

The requirement of high data rate in 5G calls for utilization of the mmWave frequency band. Researchers seeking to compensate for mmWave's high path loss have proposed that mmWave MIMO systems make use of beamforming. Hybrid beamforming systems demonstrate promising performance in achieving high gain and directivity by using phase shifters. The actual implementation, however, is costly and complex. To reduce such cost and complexity, this article presents actual prototypes of the lens antenna to be used in 5G mmWave beamforming systems. Using a lens as a passive phase shifter enables beamforming without the heavy network of phase shifters, while gain and directivity are achieved by the energy-focusing property of the lens. Proposed in this article are two types of lens antennas, one for static and the other for mobile usage. The lens antennas' sizes are varied to also discuss the lens design. Their performance is evaluated using measurements and simulation data along with link- and system-level analysis. Results show the lens antenna yields high gain, directivity, and improved beam-switching feasibility compared to when a lens is not used.

Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems

Future cellular systems based on the use of millimeter waves will heavily rely on the use of antenna arrays both at the transmitter and at the receiver. For complexity reasons and energy consumption issues, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated. This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for a cellular multiuser MIMO (MU-MIMO) system with large number of antennas. Analytical formulas for the asymptotic achievable spectral efficiency and for the global energy efficiency of several beamforming structures are derived in the large number of antennas regime. Using the most recently available data for the energy consumption of phase shifters and switches, we show that fully-digital beamformers may actually achieve a larger energy efficiency than lower-complexity solutions, as well as that low-complexity beam-steering purely analog beamforming may in some cases represent a good performance-complexity trade-off solution.

Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems

Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.

Low RF-Complexity Technologies to Enable Millimeter-Wave MIMO with Large Antenna Array for 5G Wireless Communications

mmWave MIMO with large antenna array has attracted considerable interest from the academic and industry communities, as it can provide larger bandwidth and higher spectrum efficiency. However, with hundreds of antennas, the number of RF chains required by mmWave MIMO is also huge, leading to unaffordable hardware cost and power consumption in practice. In this article, we investigate low RF-complexity technologies to solve this bottleneck. We first review the evolution of low RF-complexity technologies from microwave frequencies to mmWave frequencies. Then, we discuss two promising low RF-complexity technologies for mmWave MIMO systems in detail, that is PAHP and LAHP, including their principles, advantages, challenges, and recent results. We compare the performance of these two technologies to draw some insights about how they can be deployed in practice. Finally, we conclude this article and point out some future research directions in this area.

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.

AMLSA Algorithm for Hybrid Precoding in Millimeter Wave MIMO Systems

In this paper, an effective algorithm will be proposed for hybrid precoding in mmWave MIMO systems, referred to as alternating minimization algorithm with the least squares amendment (AMLSA algorithm). To be specific, for the fully-connected structure, the presented algorithm is exploited to minimize the classical objective function and obtain the hybrid precoding matrix. It introduces an orthogonal constraint to the digital precoding matrix which is amended subsequently by the least squares after obtaining its alternating minimization iterative result. Simulation results confirm that the achievable spectral efficiency of our proposed algorithm is better to some extent than that of the existing algorithm without the least squares amendment. Furthermore, the number of iterations is reduced slightly via improving the initialization procedure.

In-Phase and Quadrature Timing Mismatch Estimation and Compensation in Millimeter-Wave Communication Systems

The emerging millimeter-wave (mm-wave) MIMO systems are subject to strong radio frequency (RF) distortions and their compensation is crucial to realize such systems. In the existing literature, several estimation and compensation schemes have been proposed for RF distortions, such as carrier frequency offset, phase noise, and in-phase and quadrature amplitude and phase imbalance (IQI). However, in-phase and quadrature timing mismatch (IQTM) is largely ignored. This paper investigates the effect of the IQTM on mm-wave system performance and reveals that IQTM causes a specific image rejection ratio characteristic, which is substantially different from the regular frequency-dependent IQI and it substantially prolongs the effective channel length. If not compensated, the IQTM can degrade system performance significantly. As a solution to this IQTM problem, this paper proposes novel pilot designs for transmit and receive IQTM estimation, and develops corresponding estimators, transmission protocol, and compensation schemes. MIMO averaging is also proposed, which substantially enhances the IQTM estimation performance. Simulation results show that our proposed pilot designs and estimators offer an efficient solution to the IQTM problem.

Channel Estimation and Hybrid Precoding for Millimeter-Wave MIMO Systems: A Low-Complexity Overall Solution

To enable multi-stream transmission and increase the achievable rate, a hybrid digital/analog precoding structure is usually adopted in millimeter-wave (mmWave) MIMO systems. However, it may require matrix operations with a scale of antenna size, which is generally large in mmWave communications. Moreover, the channel estimation is also rather time-consuming due to the large number of antennas at both Tx/Rx sides. In this paper, a low-complexity overall channel estimation and hybrid precoding approach is proposed. In the channel estimation phase, a hierarchical multi-beam search scheme is proposed to fast acquire $N_{\mathrm {S}}$ (the number of streams) multipath components (MPCs)/clusters with the highest powers. In the hybrid precoding phase, the analog and digital precodings are decoupled. The analog precoding is designed to steer along the $N_{\mathrm {S}}$ acquired MPCs/clusters at both Tx/Rx sides, shaping an $N_{\mathrm {S}}\times N_{\mathrm {S}}$ baseband effective channel, while the digital precoding is performed in the baseband with the reduced-scale effective channel. Performance evaluations show that, compared with the state-of-the-art scheme, while achieving a close or even better performance when the number of radio frequency chains or streams is small, both the time complexity of the channel estimation and the computational complexity of the hybrid precoding are reduced.

Low Complexity and Robust Codebook-Based Analog Beamforming for Millimeter Wave MIMO Systems

Millimeter-wave (mm-Wave) MIMO systems have been proposed to achieve higher spectral efficiency via the hybrid beamforming structure, which consists of analog beamforming and digital beamforming. In analog beamforming, each antenna subarray generates a codebook-based directional beam, which determines the equivalent MIMO channel. Then, digital beamforming can be applied to fully exploit the spatial multiplexing gain of MIMO channels. In this paper, we propose a low-complexity analog beam selection scheme to achieve near-optimal spectral efficiency for the split hybrid beamforming system. The core of our scheme is the beam selection criterion, which is derived from capacity analysis. Furthermore, we propose a beam switching algorithm to ensure the robustness when blockage occurs. Through simulations under different channel conditions, the near-optimal performance of our scheme is demonstrated, and the beam switching algorithm is able to ensure robust network connectivity and achieve higher channel capacity when the blockage is relived.

Spatiotemporal-MIMO Channel Estimator and Beamformer for 5G

With requirements of spiraling data rates and limited spectrum availability, there is an increased interest in mm-wave beamformer-based communications for 5G. For upcoming cellular networks, the critical point is to exploit the increased number of employable antennas at both Tx and Rx to: 1) combat increased path loss; 2) tackle higher interference due to higher user density; and 3) handle multipath effects in frequency selective channels. Toward this, a multi-beam spatiotemporal superresolution beamforming framework is proposed in this paper as a promising candidate to design beampatterns that mitigate/suppress co-channel interference and deliver massive gain in the desired directions. Initially, channel and signal models suitable for the mm-wave MIMO system are presented using the manifold vectors of both Tx and Rx antenna arrays. Based on these models, a novel subspace-based channel estimator is employed, which estimates delays, directions, velocities, and fading coefficients of the desired signal paths. This information is then exploited by the proposed spatiotemporal beamformer to provide a massive array gain that combats path loss without increasing the number of antenna array elements and to be tolerant to the near-far problem in a high interference environment. The performance of the proposed channel estimator and beamformer is examined using computer simulation studies.

Transmitter-Centric Channel Estimation and Low-PAPR Precoding for Millimeter-Wave MIMO Systems

The small wavelength at millimeter wave (mmWave) allows to pack antenna arrays in a very small area enabling practical large-scale MIMO. However, wideband and high-precision analog-to-digital converters (ADCs) are very expensive and power-hungry. In this paper, we propose a mmWave MIMO communication system that employs simple one-bit ADCs at the receiver and MIMO precoding at the transmitter. One significant challenge associated with this system is efficient channel estimation based on one-bit quantized channel output. We propose a novel continuous-time channel estimator based on level-triggered sampling, a nonuniform sampling scheme that offers estimation performance similar to that of the estimator based on full-precision channel output. We also develop the transmitter precoding scheme that reduces the transmit peak-to-average power ratio (PAPR) and, at the same time, equalizes the antenna cross-talks so that receiver demodulation can be implemented simply by symbol-rate slicing. Extensive simulation results are provided to demonstrate the effectiveness of the proposed channel estimation and precoding techniques for mmWave systems.

Subspace Estimation and Decomposition for Large Millimeter-Wave MIMO Systems

Channel estimation and precoding in hybrid analog-digital millimeter-wave (mmWave) MIMO systems is a fundamental problem that has yet to be addressed, before any of the promised gains can be harnessed. For that matter, we propose a method (based on the well-known Arnoldi iteration) exploiting channel reciprocity in TDD systems and the sparsity of the channel's eigenmodes, to estimate the right (resp. left) singular subspaces of the channel, at the BS (resp. MS). We first describe the algorithm in the context of conventional MIMO systems, and derive bounds on the estimation error in the presence of distortions at both BS and MS. We later identify obstacles that hinder the application of such an algorithm to the hybrid analog-digital architecture, and address them individually. In view of fulfilling the constraints imposed by the hybrid analog-digital architecture, we further propose an iterative algorithm for subspace decomposition, whereby the above estimated subspaces, are approximated by a cascade of analog and digital precoder / combiner. Finally, we evaluate the performance of our scheme against the perfect CSI, fully digital case (i.e., an equivalent conventional MIMO system), and conclude that similar performance can be achieved, especially at medium-to-high SNR (where the performance gap is less than 5%), however, with a drastically lower number of RF chains (4 to 8 times less).