RF Precoding Research Articles

Spotting Faults Over the Spectrum: Fast and Online Antenna Array Fault Diagnosis for Multi-Carrier Precoding Systems

We present a new online method to diagnose faulty elements in an antenna array quickly and reliably while a wireless communication system is under regular operation. Existing methods for fault diagnosis do not usually allow simultaneous operation and diagnosis, leading to downtime, which becomes a severe constraint for modern wireless systems. Our approach is based on exploiting an extra degree of freedom available in the frequency domain in modern multicarrier hybrid/digital precoding architectures by allocating a very small part of the available spectrum for diagnosis while the rest is used for regular operation. Measurements can be made in a single shot by using optimized inputs, thereby minimally disrupting regular operation. We address both fully and partially connected RF precoding architectures of hybrid precoders and introduce one and two-stage fault diagnosis procedures, respectively. While the faulty antennas for fully connected hybrid architectures or digital architectures are diagnosed employing ideas from sparse recovery, additional ideas from block-sparse recovery are used in the case of a partially connected architecture. Our results indicate that online array fault diagnosis is possible in both digital and hybrid precoding architectures by introducing simple digital operations into the system and by using a single fixed receiver measurement setup.

Design Optimization for UAV Aided Sustainable 3D Wireless Communication at mmWaves

Unmanned aerial vehicles (UAVs) operating at different altitudes will be integral to the 5th generation and beyond (5G+) communication network to provide ubiquitous coverage. Though 5G+ communications target to operate in sub-6 GHz as well as millimeter wave range (mmWaves), sub-6 GHz being already congested, mmWaves are seen as a viable technology that can support high data rates. To this end, in this paper, we study the feasibility of using UAVs at mmWaves deployed at low altitudes to serve a user population higher than the number of RF chains available at the UAV. Since the UAVs are energy constrained devices, solar harvesting is considered for the UAVs to act as access nodes for an extended period of time. Practical 3-dimensional antenna array radiation pattern and the resulting inter-beam interference from the sidelobes are taken into account in the analysis. We devise a sub-array hybrid precoder that provides minimum rate support to all users across the wideband mmWave channel. The RF precoder is designed according to the users' locations. In addition, we propose a low complexity iterative joint subcarrier allocation and baseband precoder optimization algorithm with faster convergence. In the proposed algorithm, we employ weighted minimum mean squared error (WMMSE) to design baseband precoder to reduce inter-beam interference while jointly optimizing subcarrier allocation to maintain minimum user rate constraint. Next, we determine the optimal number of beams at UAV to optimize throughput while guaranteeing minimum user rate support for energy sustainable UAV operation. Finally, we compare the performance gain achieved by low-altitude UAV deployed at mmWaves over the UAV deployed at sub-6 GHz frequency range.

Design and Development of Novel Hybrid Precoder for Millimeter-Wave MIMO System

Power consumption and hardware cost reduction with the use of hybrid beamforming in large-scale millimeter wave MIMO systems. The large dimensional analog precoding integrates with the hybrid beamforming based on the phase shifters including digital precoding with lower dimensionality. The reduction of Euclidean distance between the hybrid precoder and fully digital is the major problem to overcome the minimization of resultant spectral efficiency. The issue formulates as a fully digital precoder’s matrix factorization problem based on the analog RF precoder matrix and the digital baseband precoder matrix. An additional element-wise unit modulus constraint is imposed by the phase shifters on the analog RF precoder matrix. The traditional methods have a problem of performance loss in spectral efficiency. In the processing time and iteration, high complexities result in optimization algorithms. In this paper, a novel low complexity algorithm proposes which maximizes the spectral efficiency and reduces the computational processing time.

Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

Design and Development of Novel Hybrid Precoder for Millimeter-Wave MIMO System

Power consumption and hardware cost reduction with the use of hybrid beamforming in large-scale millimeter wave MIMO systems. The large dimensional analog precoding integrates with the hybrid beamforming based on the phase shifters including digital precoding with lower dimensionality. The reduction of Euclidean distance between the hybrid precoder and fully digital is the major problem to overcome the minimization of resultant spectral efficiency. The issue formulates as a fully digital precoder’s matrix factorization problem based on the analog RF precoder matrix and the digital baseband precoder matrix. An additional element-wise unit modulus constraint is imposed by the phase shifters on the analog RF precoder matrix. The traditional methods have a problem of performance loss in spectral efficiency. In the processing time and iteration, high complexities result in optimization algorithms. In this paper, a novel low complexity algorithm proposes which maximizes the spectral efficiency and reduces the computational processing time.

Machine Learning-Inspired Hybrid Precoding for mmWave MU-MIMO Systems with Domestic Switch Network.

Hybrid precoding is an attractive technique in MU-MIMO systems with significantly reduced hardware costs. However, it still requires a complex analog network to connect the RF chains and antennas. In this paper, we develop a novel hybrid precoding structure for the downlink transmission with a compact RF structure. Specifically, the proposed structure relies on domestic connections instead of global connections to link RF chains and antennas. Fixed-degree phase shifters provide candidate signals, and simple on-off switches are used to route the signal to antennas, thus RF adders are no longer required. Baseband zero forcing and block diagonalization are used to cancel interference for single-antenna and multiple-antenna users, respectively. We formulate how to design the RF precoder by optimizing the probability distribution through cross-entropy minimization which originated in machine learning. To optimize the energy efficiency, we use the fractional programming technique and exploit the Dinkelbach method-based framework to optimize the number of active antennas. Simulation results show that proposed algorithms can yield significant advantages under different configurations.

Performance Evaluation of Full-Duplex IAB Multi-Cell and Multi-User Network for FR2 Band

One of the approaches to support high data rates in beyond 5G cellular networks is the dense deployment of the small cell millimeter-wave (frequency range 2 (FR2)) ( $>24.5$ GHz) base stations. However, this dense deployment of base stations leads to high cost, because it requires fiber backhaul connection with the core network. One prominent solution is the integrated access and backhaul (IAB) network proposed in 3GPP Release 16, where some portion of wireless spectrum is used for backhaul to serve base stations instead of fiber, and the remaining portion of the spectrum is used for user equipment for communication. The partition of the spectrum into access and backhaul degrades the spectral efficiency of the IAB network. Thus, in this paper, we propose a full duplex (FD) enabled IAB network with large-scale array systems to enhance the spectral efficiency of the IAB network. In the FD IAB network, the IAB node is FD which transmits and receives at the same frequency/time resources. We consider a multi-cell multi-user IAB network with all the interferences due to FD transmission and evaluate the performance in terms of bit-error-rate and spectral efficiency. Moreover, an algorithm for choosing different users is proposed which is based on the cross-correlation of RF precoder weights. Further, identification of the optimal beam’s index is also proposed which is based on the index of synchronization signal block and half-frame bit of physical broadcast channel payload of 5G NR. Finally, successive interference cancellation is proposed for self-interference mitigation in the digital domain.

Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems.

Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm based scheme jointly evaluates the RF and digital pre-coder for a partially connected hybrid structure by taking into account the current RF chain for computations and therefore it is not based on interference cancellation from all other RF chains as in the case of successive interference cancellation (SIC). The evolutionary algorithm, i.e., Artificial Bee Colony (BEE) based pre-coding scheme outperforms other popular evolutionary algorithms as well as the SIC based pre-coding scheme in terms of spectral efficiency. In addition, the proposed algorithm is not overly sensitive to variations in channel conditions.

Secrecy rate analysis of hybrid precoders for mmWave communication systems

Millimeter-wave (mmWave) communication systems are realized to play a key role in next-generation communication systems to achieve high data rate requirements, allowing manifold connected devices and efficient bandwidth utilization. Although mmWave communication has severe degradation problems, security is considered as one of the most important issues. The objective of this study is to analyze and compare the effectiveness of secure communication among the transmitter, legitimate receiver and illegal user using different hybrid precoding and combining algorithms (phase extraction alternate minimizing (PE-AltMin), manifold optimization (MO-AltMin), spatially sparse precoding (SSP) and most recent reported algorithm in the literature). In this study, standard PE-AltMin and MO-AltMin algorithms are also modified in digital baseband domain to achieve better results. The security is achieved in two steps. First, cooperative analog precoder and combiner design algorithms are utilized to get RF precoder and combiner, and then, digital precoder and combiner are attained based on effective baseband digital channel. The simulation studies validate the impact of modified PE-AltMin algorithm that realizes better secrecy rate results than the other hybrid algorithms and ensures the most protected transmission. In simpler words, the utilization of proposed PE-AltMin hybrid algorithm offers the least information leakage towards Eavesdropper than others.

On the MMSE‐based multiuser millimeter wave MIMO hybrid precoding design

SummaryA hybrid MMSE‐based precoder and combiner design with low complexity is studied in this paper for both the downlink and uplink multiuser millimeter wave MIMO systems. At first, according to the underlying channel knowledge, the phases of the RF precoder and combiner are manipulated by using the MMSE criterion. On the basis of the MMSE‐based analog precoder design, the MMSE‐based digital baseband precoder can be readily derived. Thus, this method combines the advantage of using the MMSE precoding and phase extraction technique to obtain a reasonable solution. The proposed hybrid precoding design will be examined in both the uplink and downlink multiuser MIMO systems. Numerical results are presented to show that the proposed MMSE‐based hybrid precoding design is capable of attaining superior performance in terms of both the spectral efficiency and energy efficiency over the existing designs. Moreover, we will show that the MMSE‐based design can be readily extended to the multiuser millimeter wave MISO systems when only statistical channel state information is available.

Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27.6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62.5 % compared to the full CSI feedback.

Nonlinear Hybrid Precoding for Coordinated Multi-Cell Massive MIMO Systems

This paper examines nonlinear hybrid precoding with minimum mean square error (MMSE)-vector perturbation (VP) for multi-cell massive multiple-input multiple-output (MIMO) systems. Two-timescale channel state information (CSI) is assumed, which consists of short-term noisy observations of the RF-precoded MIMO channel, and perfect knowledge of the long-term channel temporal and spatial correlation. By exploiting the low-dimensional effective CSI, we propose to estimate the instantaneous realization of the high-dimensional CSI via Kalman filtering. The CSI estimate is then utilized for RF precoding in consideration of centralized and distributed MMSE-VP at baseband. By abstracting the effect of nonlinear baseband precoding, RF precoding is separately formulated as a solution to balance the error performance of signal detection with the accuracy of channel tracking. To solve such nonconvex problems, we develop Cayley transformation-based gradient descent algorithms. Numerical results demonstrate the benefits of incorporating CSI tracking into hybrid precoding from its superior bit error rate to other transmit spatial correlation-based baselines, and its improved resilience to the channel estimation errors over the fully digital counterpart.

Low-Complexity Subspace-Based Multi-User Hybrid Precoding

We propose a novel multi-user hybrid precoder design that imitates the bit error rate (BER) performance of a given fully digital precoder by minimizing the Frobenious norm between them. We show that the proposed design equivalently minimizes the chordal distance between the column spaces of the phase-only RF precoder and the given fully digital precoder. The proposed design, in the large antenna regime, has the same BER as that of an existing optimal hybrid precoder design but with much reduced complexity. Furthermore, the proposed design is also shown to approach the BER of a given fully digital precoder.

Dual-Signal Transmission Using RF Precoding and Analog Beamforming With TMAs

Time-modulated arrays (TMAs) receiving beamforming provide a noteworthy hardware simplicity given the ability of this multi-antenna technique to transform spatial diversity into frequency diversity. However, the dual behavior at transmission seems to be as simple as limited: a given signal is simultaneously transmitted over all the different TMA harmonic patterns. We investigate the efficient and simultaneous transmission of two different signals over the same physical antenna using two independent harmonic beam-patterns of the TMA. For that purpose, we propose an innovative dual-signal TMA transmitter based on two complementary operations: the complex mixing of the baseband signals and the TMA processing with quadrature and time-delayed periodic pulses.

Two-Timescale Hybrid RF-Baseband Precoding With MMSE-VP for Multi-User Massive MIMO Broadcast Channels

This paper explores joint design of two-timescale hybrid RF-baseband precoding with minimum-mean-square-error (MMSE)-vector perturbation (VP) for multi-user massive multiple-input multiple-output systems, where users on the downlink are separated into geographical clusters, and each user cluster experiences identical transmit spatial correlation. Considering the perfect effective channel state information-based MMSE-VP at baseband, the spatial correlation-based RF precoder design is formulated as orthonormality-constrained stochastic optimization problems, where the objective functions cannot be characterized in closed form. RF eigen-beamforming is shown as an optimal solution for single-cluster transmission. In multi-cluster scenarios, mathematically tractable lower bounds are proposed and numerically optimized by trust-region Newton methods on Riemannian manifolds. Additionally, constant-modulus RF precoding based on the discrete Fourier transform (DFT) codebook is addressed. By recognizing the objective functions as a difference of increasing functions, branch-reduce-and-bound techniques are developed to find the globally optimal solutions to such combinatorial problems with reduced computational complexity. Simulation results demonstrate that the proposed nonlinear hybrid schemes deliver a superior bit error rate to other state-of-the-art baselines. The effectiveness of the suboptimal DFT-based RF solutions is also verified.

Stochastic Successive Convex Optimization for Two-Timescale Hybrid Precoding in Massive MIMO

Hybrid precoding, which consists of an RF precoder and a baseband precoder, is a popular precoding architecture for massive MIMO due to its low hardware cost and power consumption. In conventional hybrid precoding, both RF and baseband precoders are adaptive to the real-time channel state information (CSI). As a result, an individual RF precoder is required for each subcarrier in wideband systems, leading to high implementation cost. To overcome this issue, two-timescale hybrid precoding (THP), which adapts the RF precoder to the channel statistics, has been proposed. Since the channel statistics are approximately the same over different subcarriers, only a single RF precoder is required in THP. Despite the advantages of THP, there lacks a unified and efficient algorithm for its optimization due to the non-convex and stochastic nature of the problem. Based on stochastic successive convex approximation (SSCA), we propose an online algorithmic framework called SSCA-THP for general THP optimization problems, in which the hybrid precoder is updated by solving a quadratic surrogate optimization problem whenever a new channel sample is obtained. Then we prove the convergence of SSCA-THP to stationary points. Finally, we apply SSCA-THP to solve three important THP optimization problems and verify its advantages over existing solutions.

Progressive Hybrid Precoding and Combining for Massive MIMO ARQ Systems

In this paper, we consider a progressive transceiver design for point-to-point massive multiple input multiple output (MIMO) with automatic repeat request (ARQ). As an alternative to address the difficulties such as prohibitive hardware complexity, cost, and power consumption encountered by a fully digital implementation of massive MIMO, a hybrid architecture, which introduces RF analog processing together with baseband digital processing, is employed. Based on the perfect channel state information, the hybrid RF-baseband transceiver is derived, aiming at increasing the achievable rate through exploiting the time diversity inherent in ARQ packet retransmissions. Due to the intractability of joint optimization of the hybrid precoder and combiner, we heuristically assume that the fully digital linear minimum-mean-square-error (MMSE) filter is perfectly realizable by its hybrid counterpart at the receiver, and decouple the precoding from the combining design, where the previous failed retransmissions are sequentially incorporated into the joint RF-baseband precoding/combining optimization. Specifically, for each ARQ round, we construct the RF precoder either from the transmit array response vectors or from a discrete Fourier transform-based codebook. In consideration of the RF precoding effect, the optimal baseband precoder is analytically derived. To minimize the performance loss incurred by the decoupled precoder/combiner optimization, the proposed two-step design technique is further applied to derive the hybrid combiner as an approximation of the optimal linear MMSE solution in terms of error performance. The efficacy of the proposed progressive hybrid solutions is numerically evaluated by performance comparison with various baselines. The effect of RF precoding/combining with finite-resolution elements is also examined.

Hybrid Precoding via Successive Refinement for Millimeter Wave MIMO Communication Systems

We propose a hybrid precoding design for spatial multiplexing in millimeter wave massive multiple-input multiple-output systems. The proposed design relies on the popular orthogonal matching pursuit (OMP) algorithm. The OMP algorithm uses necessary columns of the right/left (for receiver/transmitter) unitary matrix of the channel (obtained via a singular value decomposition) to obtain the initial baseband (digital) precoder using principles of basis pursuit. The resulting baseband precoder is then used to refine the RF (analog) precoder. This iterative refinement improves the quality of hybrid preocders. Quantization of the RF and baseband precoding matrices is also discussed for limited feedback systems, where the high-dimensional RF precoder is quantized using codebooks generated using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K-$ </tex-math></inline-formula> means clustering algorithm. On the other hand, the low-dimensional baseband precoder is quantized using well-known Grassmannian codebooks. Simulation results show that the proposed scheme performs close to the fully digital precoding scheme.

Impact of CSI Knowledge on the Codebook-Based Hybrid Beamforming in Massive MIMO

Hybrid beamforming, which consists of an RF precoder and a baseband precoder, has been proposed to reduce the number of RF chains at the massive multiple input multiple output (MIMO) base station (BS). This paper studies the impact of channel state information (CSI) on the capacity of massive MIMO systems with codebook-based hybrid beamforming , where the RF precoder is selected from a finite size codebook. Two types of CSI at the BS (CSIT) are commonly assumed: full instantaneous CSIT and hybrid CSIT (channel statistics plus the low-dimensional effective channel matrix). With full instantaneous CSIT, both the RF and baseband precoders are adaptive to the full instantaneous CSI at the fast timescale. With hybrid CSIT, the RF precoder is adaptive to channel statistics only, and the baseband precoder is adaptive to the instantaneous effective channel, yielding lower implementation complexity by sacrificing some capacity. We derive asymptotic sum capacity expressions under these two types of CSIT. We find that, in codebook-based hybrid beamforming systems, exploiting the full instantaneous CSIT can only achieve a marginal SNR gain and hybrid CSIT is sufficient to achieve the first-order gain provided by the massive MIMO for most of the cases. We also propose fast and slow timescale RF precoding algorithms, which asymptotically achieve the capacity under full instantaneous CSIT and hybrid CSIT, respectively.

Hybrid Block Diagonalization for Massive Multiuser MIMO Systems

For a massive multiple-input multiple-output (MIMO) system, restricting the number of RF chains to far less than the number of antenna elements can significantly reduce the implementation cost compared to the full complexity RF chain configuration. In this paper, we consider the downlink communication of a massive multiuser MIMO (MU-MIMO) system and propose a low-complexity hybrid block diagonalization (Hy-BD) scheme to approach the capacity performance of the traditional BD processing method. We aim to harvest the large array gain through the phase-only RF precoding and combining and then digital BD processing is performed on the equivalent baseband channel. The proposed Hy-BD scheme is examined in both the large Rayleigh fading channels and millimeter wave (mmWave) channels. A performance analysis is further conducted for single-path channels and large number of transmit and receive antennas. Finally, simulation results demonstrate that our Hy-BD scheme, with a lower implementation and computational complexity, achieves a capacity performance that is close to (sometimes even higher than) that of the traditional high-dimensional BD processing.