Beamspace Channel Research Articles

Wideband Channel Estimation for Millimeter Wave Beamspace MIMO

Massive multiple-input multiple-output (MIMO) with lens antenna array, referred to as beamspace MIMO, is attractive for millimeter wave (mmWave) communications, since it can realize the adequate beamforming gain with a small number of radio frequency (RF) chains. This paper addresses wideband channel estimation for mmWave beamspace MIMO. Specifically, a two-stage channel estimation scheme is proposed. In the first stage, we utilize multi-task sparse Bayesian learning (MT-SBL) to coarsely estimate the beamspace channel vectors, each corresponding to one OFDM subcarrier. In the second stage, we re-formulate the channel estimation problem by artfully exploiting the characteristics of beamspace channels. Then we develop an expectation maximization (EM) based algorithm to estimate the parameters involved in the formulation, thereby refining the channel estimates. Compared with the state-of-the-art counterparts, the proposed scheme can achieve much better performance while requiring a lower training overhead.

Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the conditional transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.

3D Metallic Plate Lens Antenna based Beamspace Channel Estimation Technique for 5G Mmwave Massive MIMO Systems

Beamspace channel estimation mechanism for massive MIMO (multiple input multiple output) antenna system presents a major process to compensate the 5G spectrum challenges caused by the proliferation of information from mobile devices. However, this estimation is required to ensure the perfect channel state information (CSI) for lower amount of Radio Frequency (RF) chains for each beam. In addition, phase shifter (PS) components used in this estimation need high power to select the beam in the desired direction. To overcome these limitations, in this work, we propose Regular Scanning Support Detection (RSSD) based channel estimation mechanism. Moreover, we utilise a 3D lens antenna array having metallic plate and a switch in our model which compensates the limitation of phase shifters. Simulation results show that the proposed RSSD based channel estimation surpasses traditional technique and SD based channel estimation even in lower SNR area which is highly desirable in the millimeter wave (mmWave) massive MIMO systems.

3D Metallic Plate Lens Antenna based Beamspace Channel Estimation Technique for 5G Mmwave Massive MIMO Systems

3D Metallic Plate Lens Antenna based Beamspace Channel Estimation Technique for 5G Mmwave Massive MIMO Systems

Beamspace Channel Estimation With Beam Squint Effect for the Millimeter-Wave MIMO-OFDM Systems

In this paper, we investigate two novel beamspace channel estimation schemes for millimeter-wave MIMO-OFDM systems relying on a uniform linear antenna array. Based on the sparse nature of the beamspace channel, compressive sensing-based methods can be adopted. Most of the existing wideband beamspace channel estimation schemes take the common support assumption that the support of all sub-carriers from a single path component keeps unchanged. However, due to the beam squint effect caused by time delays among different antennas, this assumption is no longer satisfied. Based on the above, we analyze the frequency-dependent sparse structure of the beamspace channel due to beam squint and propose the block simultaneous orthogonal matching pursuit algorithm where support is jointly estimated by using beamspace windows. Moreover, considering the power focusing effect of beam squint, we propose the two-stage beamspace channel estimation scheme, where the first stage is for the angle of arrival estimation, and the second stage is for updating path gain to reconstruct the beamspace channel. Simulation results demonstrate that proposed schemes can estimate the channel under the beam squint effect with high accuracy but low pilot overheads.

Bayesian Learning-Based Doubly-Selective Sparse Channel Estimation for Millimeter Wave Hybrid MIMO-FBMC-OQAM Systems

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramér-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy.

Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO

In this letter, we present a lower performance bound for discrete Fourier transform (DFT)-based beamspace channel estimation (CE) in 64 antennas Massive Multiple-Input Multiple-Output (MIMO) receiver. The beamspace CE is implemented after antennas digital signal transformation to a priori estimated DFT sub-space resulting in less complexity. To estimate the performance, we calculate noise power after the beamspace CE unit by decomposing an ideal channel response into separate taps and transforming them into the beam domain. Then the minimum mean squared error (MMSE) method is employed with taps to estimate the CE error power. An artificial CE is calculated as a sum of the ideal beamspace channel and additive Gaussian noise with the same error power. The artificial CE is utilized in the MIMO detector and decoder to achieve performance bound. Simulation results are presented for the non-line-of-sight model of the 5G QuaDRiGa 2.0 channel.

Multi-Beam NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

Different from the conventional single-beam beamspace non-orthogonal multiple access (NOMA) scheme, where the users only within the same analog beam can be served by the NOMA technique, in this article, we propose a multi-beam NOMA scheme for the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with lens antenna array communication system. The proposed scheme can break the single-beam constraint and perform NOMA transmission within the different analog beams. Then, we design the power allocation for minimizing the total power consumption, which mainly includes two stages. In the first stage, after performing beam scanning and beam selection, we design the game-theory based user grouping method for minimizing the conditional total power consumption. In the second stage, with the achieved user grouping solution and the corresponding beamspace channel state information (CSI), we formulate and solve the power allocation problem for minimizing the total power consumption. In addition, given the user grouping solution for minimizing the total power consumption, for evaluating the sum rate performance of the proposed multi-beam beamspace MIMO-NOMA scheme, we formulate and solve the power allocation optimization problem for maximizing the sum rate. In simulations, compared with the single-beam beamspace MIMO-NOMA scheme, given the user grouping solution, the proposed scheme has inferior sum rate performance when the power budget is enough high. However, at the cost of a little higher transmit power consumption, the proposed scheme has lower total power consumption and can simultaneously serve more users with the same number of RF chains.

Channel Path Identification in mmWave Systems With Large-Scale Antenna Arrays

We consider the uplink channel estimation problem in a millimeter wave (mmWave) system with large-scale antenna arrays. Unlike many existing works which estimate the channel assuming that the number of channel paths is known a priori , we address the problem of channel estimation with an unknown number of channel paths. The spatial channel is transformed into the beamspace channel by the discrete Fourier transform (DFT). Based on the sparsity property of the beamspace channel, we propose three algorithms to estimate the number of paths, direction of arrivals (DoAs) and path gains. The first one is the Spectrum Weighted Identification of Signal Sources (SWISS) for the case when the channel statistics are unknown, which introduces a weight vector to amplify the desired signal and suppress the noise. The second one is the Neyman-Pearson criterion based-Detector (NPD) based on the Rician channel model, which adopts the Neyman-Pearson criterion to decide whether there exists a path on each DFT point. In practice, the DoAs are continuously distributed, leading to the power leakage problem. We solve this leakage problem by proposing the combined algorithm with leakage (CAL). Simulation results show that the proposed algorithms perform better than the conventional spatial smoothing.

Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design

Millimeter-wave (mmWave) communication in combination with massive multiuser multiple-input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple users in the same time-frequency resource. The strong path loss of wave propagation at such high frequencies necessitates accurate channel state information to ensure reliable data transmission. We propose a novel channel estimation algorithm called BEAmspace CHannel EStimation (BEACHES), which leverages the fact that wave propagation at mmWave frequencies is predominantly directional. BEACHES adaptively denoises the channel vectors in the beamspace domain using an adaptive shrinkage procedure that relies on Stein's unbiased risk estimator (SURE). Simulation results for line-of-sight (LoS) and non-LoS mmWave channels reveal that BEACHES performs on par with state-of-the-art channel estimation methods while requiring orders-of-magnitude lower complexity. To demonstrate the effectiveness of BEACHES in practice, we develop a very large-scale integration (VLSI) architecture and provide field-programmable gate array (FPGA) implementation results. Our results show that adaptive channel denoising can be performed at high throughput and in a hardware-friendly manner for massive MU-MIMO mmWave systems with hundreds of antennas.

Hybrid Transceiver Design for Beamspace MIMO-NOMA in Code-Domain for MmWave Communication Using Lens Antenna Array

As a hybrid MIMO architecture, 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, in which, however, the number of users supported cannot be larger than that of RF chains. To break this fundamental limit, we introduce the concept of code-domain non-orthogonal multiple access (NOMA) into beamspace MIMO. A beam selection scheme is proposed first to maximize the sum-rate by utilizing the quasi-orthogonality of the beamspace channel. Furthermore, a low-complexity detection algorithm is developed to realize the transceiver design in mmWave communications using lens antennas. Finally, numerical results of the decoding complexity at receiver side are analyzed. Simulation results show that the proposed beamspace MIMO-NOMA in code domain can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution.

User Selection for Millimeter Wave Non-Uniform Full Dimensional MIMO

The millimeter wave (mmWave) based full-dimensional (FD) MIMO communication is one of the promising technology to fulfill the demand of high data rate for the sixth generation (6G) services including 6D hologram, haptic and multi-sensory communications. In order to satisfy the requirements of 6G applications, we investigate a non-uniform rectangular array (NURA) structure with FD-MIMO antenna systems for the multiuser mmWave communications. For the dense scenarios where the number of users to be served is high, we propose user selection algorithms for both digital and hybrid transceiver designs in FD-MIMO with NURA for the multiuser mmWave communications. For the digital transceivers, the users are selected based on their channel correlation considering FD-MIMO with NURA structures. For the hybrid transceivers, sequential user and beam selection is performed using the correlation between the beamspace channels in FD-MIMO with NURA case. The superiority of the NURA compared to uniform antenna structure is shown through the performance evaluations in the multiuser mmWave communications. Besides, the sum data rate results and complexity analysis denote the feasibility of the proposed algorithms compared to the joint user and beam selection schemes.

WSDSBL Method for Wideband Channel Estimation in Millimeter-Wave MIMO Systems with Lens Antenna Array

In millimeter wave (mmWave) communication systems, Channel State Information(CSI) is extremely essential for beamforming. The traditional Successive Support Detection (SSD) algorithm can achieve high wideband channel estimation accuracy, but it only used least square (LS) algorithm to recover the detected channel part, the estimation accuracy is low under low SNR regions. To tackle this problem, in this paper, inspired by the classic Support Detection (SD) channel estimation scheme in narrowband, we propose an efficient Wideband Support Detection Sparse Bayesian Learning (WSDSBL) channel estimation scheme. For every subcarrier, we first detect the support of the wideband beamspace channel of the subcarrier, then we use the Sparse Bayesian Learning (SBL) scheme to recover it. Simulation results show that the proposed WSDSBL channel estimation algorithm is better than conventional wideband channel estimation schemes in MSE performance and achievable sum-rate performance, especially in low SNR regions.

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

Beamspace multiple-input multiple-output (MIMO) systems employ beam selection algorithms based on the sparsity of the beamspace channel to reduce the number of radio-frequency chains required in multi-user systems. In this article, we address the problem of selecting multiple beams to each user. Specifically, we propose a beam selection scheme subject to signal to interference plus noise ratio (SINR) maximization. The proposed beamspace beam selection algorithm is developed in two stages. In the first stage, each user is assigned a non-overlapping beam determined by Kuhn-Munkres assignment algorithm. In the second stage, the selected beams in the previous stage are augmented with additional beams from an M -dominant beam set subject to a sum-rate maximization criterion. The performance of the proposed scheme is verified by simulations, and the results show a near-optimal performance in terms of the achievable sum-rate. Also, a significant improvement in terms of energy efficiency is achieved compared to the conventional beam selection scheme.

Wideband Beamspace Channel Estimation for Millimeter-Wave MIMO Systems Relying on Lens Antenna Arrays

Beamspace channel estimation is indispensable for millimeter-wave MIMO systems relying on lens antenna arrays for achieving substantially increased data rates, despite using a small number of radio-frequency chains. However, most of the existing beamspace channel estimation schemes have been designed for narrowband systems, while the rather scarce wideband solutions tend to assume that the sparse beamspace channel exhibits a common support in the frequency domain, which has a limited validity owing to the effect of beam squint caused by the wide bandwidth in practice. In this paper, we investigate the wideband beamspace channel estimation problem without the common support assumption. Specifically, by exploiting the effect of beam squint, we first prove that each path component of the wideband beamspace channel exhibits a unique frequency-dependent sparse structure. Inspired by this structure, we then propose a successive support detection (SSD) based beamspace channel estimation scheme, which successively estimates all the sparse path components following the classical idea of successive interference cancellation. For each path component, its support at different frequencies is jointly estimated to improve the accuracy by utilizing the proved sparse structure, and its influence is removed to estimate the remaining path components. The performance analysis shows that the proposed SSD-based scheme can accurately estimate the wideband beamspace channel at a low complexity. Simulation results verify that the proposed SSD-based scheme enjoys a reduced pilot overhead, and yet achieves an improved channel estimation accuracy.

On the Power Leakage Problem in Millimeter-Wave Massive MIMO With Lens Antenna Arrays

The emerging millimeter-wave massive multiple-input multiple-output (MIMO) with lens antenna arrays, which is also known as “beamspace MIMO,” can effectively reduce the required number of power-hungry radio frequency (RF) chains. Therefore, it has been considered as a promising technique for the upcoming 5G communications and beyond. However, most current studies on beamspace MIMO have not taken into account the important power leakage problem in beamspace channels, which possibly leads to a significant degradation in the signal-to-noise ratio (SNR) and the system sum-rate. To this end, in this paper, we propose a beam-aligning precoding method to handle the power leakage problem. First, a phase shifter network (PSN) structure is proposed, which enables each RF chain in beamspace MIMO to select multiple beams to collect the leakage power. Then, a rotation-based precoding algorithm is designed based on the proposed PSN structure, which aligns the channel gains of the selected beams toward the same direction for maximizing the received SNR at each user. Furthermore, we reveal some system design insights by analyzing the sum-rate and energy efficiency (EE) of the proposed beam-aligning precoding method. In simulations, the proposed approach is found to achieve the near-optimal sum-rate performance compared with that of the ideal case of no power leakage, and obtains a higher EE than those of the existing schemes with either a linear or planar array.

An AMP-Based Network With Deep Residual Learning for mmWave Beamspace Channel Estimation

Beamspace channel estimation in millimeter-wave (mmWave) massive MIMO system is a very challenging task, especially when the number of radio-frequency chains is limited. To address this problem, we present a novel approximate message passing (AMP)-based network with deep residual learning, referred to as LampResNet. It mainly consists of two components: 1) a learned AMP (LAMP) network and 2) a deep residual learning network (ResNet). The former utilizes the sparsity property of beamspace channel matrix and is employed to obtain a preliminary estimation result, while the latter is designed to reduce the impact of channel noise and further refine the coarse estimation obtained by the LAMP network. Simulation results validate the efficiency of the proposed network.

User Scheduling for Millimeter Wave Hybrid Beamforming Systems With Low-Resolution ADCs

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions.