Number Of Radio Frequency Chains Research Articles

Beam-space MIMO-NOMA Technique for mm-Wave Communication Systems

The multi-input-multi-output-antenna Scheme is regarded as the most promising method for next-generation wireless communication networks that employ millimeter-wave (mm-wave) frequencies and enable them to have a large information throughput. As a result of the large number of radio frequency chains, MIMO mm-wave technology has difficulty with energy consumption. The beam space MIMO (or BS-MIMO for short) reduces the total number of radio frequency components or chains (RF-Chains) without degrading performance significantly. Yeti, VS-MIMO restricts the number of clients that can be supported at the same time-frequency domain (cannot exceed the overall RF-chains number ). In order to cope with such problems, it has been intended to include the NOMA technique (which stands for Non-Orthogonal-Multiple-Access) with the concept of beam space to create the extra model of Non-Orthogonal beam space-MIMO (NOMA-BS-MIMO). With such a scheme the base station can support the service for more than one client (supposed to have correlated channels) through the same beam employing the same radio frequency chain, causing the total clients number greater than the number of employed radio chains in the system. In this research, the Spectral Efficiency of a proposed Non-Orthogonal beam space-MIMO has been considered. More specifically, the research proposes and develops a simple iterative algorithm with near-perfect performance in terms of system Spectral Efficiency. To achieve rate balancing across users, the proposed method, SLNR-IV, employs the signal-to-leakage-plus-noise ratio (SLNR) and the intermediate value method. Considering algorithm validity and a particular parameter setting scenario, the proposed strategy improves bandwidth efficiency by around 15% in comparison to the conventional approach based on OFDM and Zero-forcing digital precoder.

Energy‐efficient hybrid beamforming for millimeter‐wave‐based massive multiple‐input multiple‐output system

SummaryThe advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.

Low-complexity hybrid precoding for sub-connected millimeter wave massive MIMO systems

Hybrid analog-digital precoding architectures facilitate practical implementation of millimeter wave (mmW) massive multiple-input multiple-output (mmW-mMIMO) systems by reducing the number of radio frequency chains required. The spectral efficiency (SE) and energy efficiency (EE) optimization problems of these systems are typically non-convex and NP-hard, as the joint optimization that involves the analog and digital precoding functions along with the constant modulus constraints required by analog phase shifters, results in highly non-linear problems. To address such problems, we propose a decoupled two-stage design. More specifically, in the first stage, a closed-form approximation of the effective channel is proposed, and thus the SE maximization problem can be transformed to an effective channel gain maximization problem. Then, the analog precoding matrices can be generated to facilitate the second stage, where the digital precoding matrix is devised for maximizing the system’s EE. In addition, the circuit power consumption statistics of the system modules are exploited and a sub-connected mmW-mMIMO architecture is adopted during EE optimization. Finally, simulation results are provided to validate the effectiveness of our proposed mmW-mMIMO scheme with hybrid precoding.

Deep learning-based DOA estimation for hybrid massive MIMO receive array with overlapped subarrays

As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has 6^{circ } advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at textrm{SNR}=-8 dB and the number of snapshots N=100, this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.

Diagnosis of subarray‐structured base station antennas in a compact setup based on solving linear equations

AbstractSince the total number of the antenna elements can be up to hundreds in a massive multiple‐input multiple‐output (MIMO) system, subarray‐structured base station (BS) array configurations are widely adopted to achieve a good cell coverage and to reduce the required number of radio frequency chains at the same time. It is crucial for any BS product to ensure that the antenna elements perform correctly as expected. Therefore, the necessity of array diagnosis is evident, especially for large BS arrays. Furthermore, it is essential that the diagnosis can be achieved in a compact and cost‐effective setup with high measurement efficiency (i.e. only a few measurement samples are required). The principle of the diagnosis method presented in this article is to obtain the S‐parameters between the subarrays and the probe via solving linear equations. In the simulation, a BS array composed of 16 subarrays with each containing 3 elements is used to validate the diagnosis method at 3.5 GHz. An array composed of 4 subarrays with each containing 3 elements was used in the measurements to verify the diagnosis method with two different phase tuning matrices at 3 GHz. Successful diagnosis results have been achieved in both the simulations and the measurements.

Low-Resolution Hybrid Beamforming in Millimeter-Wave Multi-User Systems

A new hybrid amalgam of analog and baseband digital beamforming is conceived for millimeter-wave (mmWave) multi-user networks. The base station is equipped with a large-scale antenna-array, which however relies on a limited number of radio frequency chains for mitigating the severe path-loss of mmWave band transmission. Each user is also equipped with a multi-antenna array. The analog beamformer's weight-resolution is set low for the sake of power efficient implementation. The hybrid beamformer design relies on the novel optimization criterion of directly maximizing the geometric means of users' rates, which is shown to result in fair rate-distributions for the users without imposing a minimum user-rate constraint. Furthermore, new computationally efficient algorithms are developed, which are purely based on closed-form low-complexity expressions. Hence these low-complexity solutions are eminently suitable for large-scale mmWave arrays. Numerical examples are provided for demonstrating their efficiency.

Double-Phase-Shifter Based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bRamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein’s unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

Sparse Multiuser Receiver Design in Large Scale Array System

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method.

Power Distribution Based Beamspace Channel Estimation for mmWave Massive MIMO System With Lens Antenna Array

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with lens antenna array can achieve extremely high data rates with limited radio frequency (RF) chains. It brings challenges to channel estimation due to the gap between the number of RF chains and antennas. To solve this problem, by analyzing and exploiting the unique power distribution (PD) of the beamspace channel (BC) that differs from the general sparse signals, we propose a PD-based estimation scheme for the sparse BC. Specifically, we transform the issue into the direction of arrival (DOA) and complex gain estimation for each multipath component after PD-based support estimation. Meanwhile, we propose a method to reduce error propagation. Besides, we provide a lower bound for the error of the proposed PD-based scheme and explain the parameters that influence the performance. Finally, the numerical simulation results confirm the advantage of the proposed method over the conventional channel estimation ones in terms of accuracy and overhead. Meanwhile, the simulation results also prove the proposed proposition about the lower bound of normalized mean squared error.

Antenna Selection in Switch-Based MIMO Arrays via DOA Threshold Region Approximation

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.

Low RF-Complexity Digital Transmit Beamforming for Large-Scale Millimeter Wave MIMO Systems

Digital beamforming (DBF) with full Radio-Frequency (RF) complexity requires a separate RF chain per transmit antenna, which is not only difficult to realize for large-scale MIMO systems but also vulnerable to hardware imperfection. In this paper, we show that DBF architectures don’t necessarily require the number of RF chains to be the same as the number of transmit antennas. We propose a low RF-complexity transmit DBF architecture that enables to use a small number of RF chains to serve a large number of transmit antennas. In our architecture, the transmit antennas are divided into groups. All antennas in the same group share the same RF chain in a time-multiplexed manner to preserve the signal from baseband till the antenna aperture. A novel antenna grouping algorithm is proposed to dynamically group the transmit antennas in a way that each antenna group forms a close-to-rank-one channel matrix with the receive antennas. Under ideal hardware conditions, we show that our DBF architecture can achieve nearly the same performance as that of the conventional full RF-complexity DBF in terms of bandwidth and spectral efficiency. However, when hardware imperfection is considered, our architecture outperforms full RF-complexity DBF in both spectral and energy efficiency because it is more robust to inter RF-chain cross-talk effects due to the reduction on the number of required RF chains.

A Family of Hybrid Precoding Schemes for Millimeter-Wave Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems, hybrid precoding/combining is a promising way to reduce hardware cost and power consumption by decreasing the number of radio-frequency (RF) chains without causing a significant spectral-efficiency loss. Most of the existing hybrid precoding schemes involve iterative searching or alternating optimization, so their computational complexity is high and they do not take advantage of parallel computing to improve the real-time performance. In this article, considering the scenarios where the number of data streams equals to the number of RF chains, we propose a family of hybrid precoding schemes by leveraging the equivalent channel and singular value decomposition for a better tradeoff between spectral efficiency and computational complexity. Simulation results with different system settings show that our proposed schemes are superior to other existing schemes in terms of system spectral efficiency.

Machine Learning-Empowered Beam Management for mmWave-NOMA in Multi-UAVs Networks

We consider a mission-driven multiple unmanned aerial vehicles (multi-UAVs) network with millimeter-wave (mmWave) transmissions, where a leader UAV (LUAV) communicates with a large number of follower UAVs (FUAVs) simultaneously via a uniform planar array with only a limited number of radio frequency chains. Since only a few orthogonal beams are available and mission-driven UAV networks are autonomous and delay-sensitive, non-orthogonal multiple access (NOMA) over these beams is considered for agility and efficiency. In particular, aiming to address these challenges of highly dynamic mobility, we propose a machine learning framework to enable agile analog beam management for mmWave-NOMA transmissions. The proposed beam management aims to achieve efficient beam tracking and NOMA-grouping-aware analog beamforming optimization to facilitate mmWave-NOMA transmissions by judiciously utilizing angular domain information (ADI). More specifically, a Gaussian process machine learning-based ADI prediction scheme is proposed to track the angular dynamics of FUAVs, which facilitates fast beam-tracking for mmWave-NOMA transmissions. Moreover, by exploiting the predicted ADI, an unsupervised-learning-based FUAV grouping scheme is proposed to facilitate mmWave-NOMA transmissions with high radio-frequency chain efficiency, while a deep learning-based NOMA-grouping-aware fast transmit beamforming optimization scheme is proposed to improve the coverage of mmWave-NOMA transmissions in highly dynamic multi-UAVs networks. Simulation results validate the performance advantages of our proposed beam management scheme against state-of-the-art schemes.

Energy-Efficient Optimization of mm-Wave Communication Using a Novel Approach of Beamspace MIMO-NOMA

The new concept of Beamspace Multiple Input Multiple Output system (BS-MIMO) was developed to address the issue of reduced Energy Efficiency (EE) of the traditional MIMO in millimeter-Wave (mm-Wave) wireless communication systems by decreases the large number of radio frequency chains (RF-chains) while maintaining the same number of antennas in those systems. On the other hand, a fewer RF-Chains leads to a smaller number of users that the system can serve as a result of the number of users that can be served must be equal to/or less than the RF-chains’ number. To overcome the above issue, the BS-MIMO is proposed to be integrated with Non-Orthogonal Multiple-Access (NOMA) scheme to produce the novel approach of BS-MIMO-NOMA. As a result, the novel scheme is capable of serving a group of users with correlated channels via a single RF-chain. In this paper, we will address the issue of EE of the above-mentioned communication systems. Specifically, we propose and develop an iterative algorithm with a low complexity that achieves near-perfect performance. The proposed scheme, named MSE-DPA (for Mean-Square-Error-Based Dynamic Power Allocation Algorithm), is checked to ensure this validity of Figure-of-Merit. The simulation results indicate that the EE is approximately 85% greater than that of traditional (fully-digital) MIMO systems for a certain fair system and environment scenario.

Physical Beam Sharing Multiuser Systems: Beamforming Designs and OTA Testings

This paper proposes a novel physical beam sharing system to use few radio-frequency (RF) chains to support more users. The concept of beam sharing is to design a beam pattern to simultaneously benefit all users. In this proposed system, every user can receive a number of data streams equal to the number of RF chains at the base station (BS) and achieve full multiplexing gain. A special design example is that the BS can support multiple users simultaneously even with only one RF chain. As a result, the computational and hardware costs of the BS can be significantly reduced due to the decreased RF chains in deploying a dense communications network. We first derive beamforming designs for this proposed system in a MISO channel with one data stream. Then, the results are further extended to MIMO channels with multiple data streams. Simulation results show that the proposed schemes with the derived solutions outperform conventional systems in several interested parameter settings, and have advantages in terms of fairness and complexity compared with NOMA (non-orthogonal multiple access) systems. Moreover, we also implemented 28 GHz hardware platforms with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> antenna array to verify the feasibility and advantages of the proposed schemes as well as measure the resulting beam patterns. The over-the-air (OTA) testings validate the concept of the proposed physical beam sharing and show that it outperforms conventional physical beamforming schemes.

An Irregular Tiled Array Technique for Massive MIMO Systems

An irregular tiled array technique is proposed for the application of massive MIMO systems, where the uniformly spaced antenna array elements are irregularly divided into subarrays fed by only one radio frequency (RF) chain separately. As compared to conventional beamforming architecture, the number of RF chains is reduced to one-half or one-quarter of the original number by using domino-shaped subarrays or tetromino-shaped subarrays, respectively. Two beamforming architectures for such a framework are proposed and a minimum mean square error (MMSE) method is applied to design the layouts of irregular arrays efficiently. In the framework of single-user massive MIMO systems, numerical simulation results show that the proposed architectures outperform conventional beamforming architectures and antenna selection architecture under the same number of RF chains conditions. With saving half of the transceivers, the loss of gain of the state-of-the-art sparsely sampled array is 1.9dB in the broadside and 2.2 dB for scanning to 30° in comparison with reference array (i.e., one transceiver per element). By contrast, there is no loss of gain of domino-shaped irregular array (i.e., one transceiver per two elements) in the broadside as compared to reference array and the loss of gain will be about 1.5dB for scanning to 30°.

Machine Learning-Based Vision-Aided Beam Selection for mmWave Multiuser MISO System

In this letter, we propose a machine learning-based vision-aided beam selection (ML-VBS) for millimeter-wave indoor multi-user communications. The proposed scheme is aimed at addressing the beam selection overhead with narrow beams in a multi-user scenario. The proposed scheme relies on a base station (BS) equipped with a single camera to observe the scene and estimates the angles to the multiple users. Given the estimated angle information and the limited number of radio frequency chains at the BS, two serial deep neural network structures are employed for joint user and beam selection subject to a minimum rate constraint. The numerical evaluation shows that the proposed ML-VBS scheme achieves a good performance in terms of the multi-user angle estimation, achievable sum rate and low computational complexity compared to conventional beam selection techniques.

Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.

A Low-Complexity Soft-Output Detector for Differential Spatial Modulation

Differential spatial modulation (DSM) can avoid pilot overhead and transmit additional data bits without increasing the number of radio frequency chains. If DSM is concatenated with a channel code, which can improve DSM’s error performance, the detector’s output for DSM ought to be soft-decision. The complexity of the MAP (maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> ) and Max-Log-MAP detectors increases exponentially with the number of transmit antennas, and the only existing reduced-complexity soft-output detector for DSM is derived from the Max-Log-MAP detector. In this letter, we propose a new low-complexity soft-output detector, which simplifies the MAP detector. The complexity of the proposed detector increases only linearly with the number of transmit antennas. Simulation results show that the error performance of the proposed detector is close to that of the MAP detector. Besides, the proposed detector outperforms the Max-Log-MAP detector and the existing detector with respect to both complexity and error performances.