Radio Frequency Chains Research Articles

Special issue on reconfigurable intelligent surface for B5G & 6G

Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) is recently recognized as one of the most promising technologies to effectively increase coverage and improve communication performance for future wireless communications beyond 5G and 6G (B5G & 6G). Specifically, the RIS is a planar surface consisting of an array of passive reflecting elements, each of which can independently induce a controllable reflection coefficient on the incident signal. By installing an RIS on the facade of buildings, an additional virtual line of sight (LoS) link can be established between the base station (BS) and the user. By judiciously adjusting the phase shifts of the reflecting elements, the reflected signal can be constructively superimposed with the signal from the direct path to enhance the desired signal power or destructively mitigate unfavourable signals such as multi-user interference or signal leakage to eavesdroppers. The RIS is less expensive to deploy than conventional active transmitters since its reflecting elements only passively reflect the incoming signal without any signal processing operations and does not require costly and power-hungry radio frequency (RF) chains. Moreover, it is lightweight and has limited layer thickness, so it can be easily integrated into the environment. Despite the above-mentioned appealing advantages of RIS, it also brings some new signal processing challenges such as channel estimation, robust transmission design, angle/position estimation, distributed algorithm design, etc.

Electromagnetic Based Communication Model for Dynamic Metasurface Antennas

Dynamic metasurface antennas (DMAs) arise as a promising technology in the field of massive multiple-input multiple-output (mMIMO) systems, offering the possibility of integrating a large number of antennas in a limited —and potentially large— aperture while keeping the required number of radio-frequency (RF) chains under control. Although envisioned as practical realizations of mMIMO systems, DMAs represent a new paradigm in the design of signal processing techniques (such as beamforming) due to the constraints inherent to their physical implementation, for which no complete models are available yet. In this work, we propose a complete and electromagnetic-compliant narrowband communication model for a generic DMA based system. Specifically, the model accounts for: i) the wave propagation and reflections throughout the waveguides that feed the antenna elements, ii) the mutual coupling both through the air and the waveguides, and iii) the insertion losses. Also, we integrate the electromagnetic model in the conventional digital communication model, providing a complete and useful framework to design and characterize the performance of these systems. Finally, the accuracy of the model is verified through full-wave simulations.

Optimal Design of Interleaved Subconnected Hybrid Structure for mmWave Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems based on millimeter-wave (mmWave) technology commonly utilize a combination of hybrid analog and digital signal processing to reduce the number of radiofrequency (RF) chains, cutting down the hardware costs and power consumption. The subconnected structure is one of the architectures that realize hybrid analog and digital processing, in which each RF chain is connected to a subset of antennas through phase shifters so as to reduce the implementation complexity. In this paper, we investigate the best subconnected structure design for mmWave massive MIMO systems. First, we formulate the optimization problem based on a simplified connection model. Next, we derive a suboptimal solution in closed form by leveraging the mmWave channel characteristics and discuss the impact of different connection parameters on the spectral efficiency. The spectral efficiency is closely related to the structure of the analog processing and the interantenna spacing. When the interantenna spacing is an even a multiple of half wavelength, the localized structure, i.e., i = 1 achieves the maximum spectral efficiency. When the interantenna spacing is an odd multiple of half wavelength and the analog processing is accomplished by i = 2 interleaved structure, the best performance is obtained. Numerical results demonstrate the correctness of the theoretical analysis.

Hybrid Beamforming in 5G NR Networks Using Multi User Massive MIMO at FR2 Frequency Bands

AbstractThe current Sub 6 GHz band is highly occupied and unable to meet the demands of future wireless data traffic. As the millimeter wave technology is a promising way of enhancing the performance of next-generation wireless networks, it is essential to explore FR2 frequency bands. Massive Multiple-Input Multiple Output (mMIMO) technology has been introduced to improve the capacity and coverage of 5G wireless networks. Hybrid beamforming (HB) is an efficient technique to improve the channel gain and minimize the interference at reasonable complexity. The aim of HB in mMIMO systems is to maximize the sum rate that can approach the fully-digital beamforming system performance. Combination of mMIMO systems, mmWave, and HB techniques achieve higher data rates and cell coverage in 5G wireless networks. It mitigates the hardware complexity, energy consumption, and cost as number of required radio-frequency chains at base station gets minimized. In this paper, we have investigated the performance of a multi user-mMIMO HB system at 28, 39 GHz of FR2 frequency bands as well as at 66 GHz by analyzing the effect of changing variables that include the number of users, transmitting and receiving antennas, and the type of modulation. Simulation results include RMS values of error vector magnitude (EVM) that gives quantitative analysis and receive constellation of the equalized symbols that shows qualitative analysis. EVM is computed for different scenarios and it is used as an indicator of performance to assess the suitable design configuration of mMIMO system.

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.

Delay-Phase Precoding for Wideband THz Massive MIMO

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlike <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-independent</i> phase shifts, the time delay network introduced in the DPP can realize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frequency-dependent</i> phase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.

A novel angle of arrival tracking method in large-scale-array systems

In this paper, we investigate the problem of angle of arrival (AOA) tracking for the large-scale array in terahertz (THz) communication, which has a large size and a narrow beam, highly demanding an accurate angle estimation. On the one hand, the system usually adopts a hybrid structure with limited radio-frequency (RF) chains, which increases the difficulty of angle estimation; on the other hand, the rapid mobility of users also brings new challenges to angle estimation. To address the above challenges, a two-stage tracking framework is proposed in this paper, which employs the random phase matrix and orthogonal long pilots in the first stage to reduce the complicated multi-user estimation to multiple single-user estimations, followed by using both wide and narrow beams in the second stage to serve high-speed and low-speed users. Furthermore, a generalized-approximated-message-passing (GAMP) method is proposed for facilitating a low-accuracy estimation of the angles, followed by adopting a modified expectation-maximization (EM) algorithm based phase estimation to unbiased estimate the instantaneous angle with the help of high-gain characteristics of the beams. The proposed structure can not only simplify the estimation complexity, but also improve the estimation accuracy due to its capability of transferring the non-linear problem of angle observation into a linear gaussian model. In addition, the Kalman tracking framework is employed for performing a continuous angle tracking. Numerical results show that the angle estimation based on the random phase matrix in the initial stage can obtain a high enough estimation accuracy, while the GAMP algorithm implemented in the second stage can quickly capture the angle range under the Rayleigh limit. The performance of the proposed EM-based tracking method is shown to outperform the traditional extended Kalman filter (EKF) method.

Downlink Wideband Channel Estimation for Asymmetrical Full-Digital System

This letter considers downlink wideband channel estimation in an asymmetrical full-digital system where the number of uplink receive radio frequency chains is less than that of the downlink. This channel inconsistency will reduce downlink channel estimation accuracy. To deal with this problem, we adopt the coprime array topology in uplink receive antennas. Then, two iterative algorithms which are based on alternating direction method of multipliers and block-sparsity orthogonal matching pursuit, respectively, are developed to rebuild downlink channel. Simulation results demonstrate the effectiveness of the proposed scheme in the asymmetrical full-digital system.

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

Sparse Space Shift Keying Modulation with Enhanced Constellation Mapping.

For reducing the switching frequency between the radio frequency (RF) chain and transmit antennas, a class of new sparse space shift keying modulation (SSSK) schemes are presented. This new class is proposed to simplify hardware implementation, through carefully designing the spatial constellation mapping pattern. Specifically, different from traditional space shift keying modulation (SSK), the proposed SSSK scheme utilizes more time slots to construct a joint design of time and spatial domain SSK modulation, while maintaining the special structure of single RF chain. Since part of the multi-dimension constellations of SSSK concentrate the energy in less time slots, the RF-switching frequency is effectively reduced due to the sparsity introduced in the time domain. Furthermore, through theoretical analysis, we obtain the closed-form expression of the bit error probability for the SSSK scheme, and demonstrate that slight performance gain can be achieved compared to traditional SSK with reduced implementation cost. Moreover, we integrate transmit antenna selection (TAS) to achieve considerable performance gain. Finally, simulation results confirm the effectiveness of the proposed SSSK scheme compared to its traditional counterpart.

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.

Massive MIMO Precoding Methods That Minimize the Variation in Average Power and Active Impedance With Channel Conditions

This article investigates the impact of precoding on the performance of massive multiple-input multiple-output (mMIMO) transmitters exhibiting nonnegligible antenna crosstalk. For this, a new metric, called the average active impedance, is introduced to quantify the extent of load modulation as a function of the antenna S-parameters and the employed precoding. The new metric is then used to investigate the load modulation under conventional precoding schemes. It is shown that these precoders yield large disparities in average-power levels across the radio frequency (RF) chains, which results in substantial performance variations with channel conditions. Based on this investigation, we propose three new precoding schemes that yield equal average-power (EP) levels across all RF chains, independent of the channel conditions. Numerical simulations and experiments conducted on an mMIMO transmitter prototype confirmed that the proposed schemes improve the transmitter’s resilience to the variation in channel conditions.

Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems

In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate.

Cross Z-Complementary Sets for Training Design in Spatial Modulation

Spatial modulation (SM) is one special type of multiple-input multiple-output (MIMO) technique whose advantage is that only one radio-frequency (RF) chain is needed. Recently, the so-called cross Z-complementary pair (CZCP) was proposed as the SM training sequence. Optimal channel estimation performance over frequency-selective channels can be achieved since the CZCPs have the specific zero correlation zone (ZCZ) for auto-correlation and cross-correlation sums. However, the ZCZ width of the CZCP is theoretically upper bounded by half sequence length. In this paper, the CZCP is extended to the cross Z-complementary set (CZCS) to have larger ZCZ width which can be used in SM to combat larger delay spread. Several generic constructions of CZCSs with large ZCZ widths and flexible lengths are proposed in this paper. Even the perfect CZCS, whose ZCZ width has the maximum value, can be obtained. Numerical simulations demonstrate that the proposed CZCS-based training sequence can tolerate large delay spreads to improve the channel estimation performance in SM.

Editorial Introduction to the Issue on Advanced Signal Processing for Reconfigurable Intelligent Surface-Aided 6G Networks

The papers in this special issue aim to report on the latest advances in signal processing for reconfigurable intelligent surface (RIS)-aided 6G networks with focus on theoretical development, algorithmic design, and future applications.RIS or intelligent reflecting surface (IRS) technology has been widely regarded as one of the most promising techniques to deal with the blockage issue in millimeter-wave (mmWave) communications. An RIS is a planar surface consisting of an array of nearlypassive reflecting elements, each of which can independently induce an controllable phase shift on the incident signal. By installing RIS on walls or ceilings, a virtual line-of-sight (LoS) link between a mmWave base station (BS) and the users can be established so as to bypass any blockage between them. In addition, RIS is a promising technology for applications in conventional sub-6 GHz communications. Specifically, by judiciously adjusting the phase shifts of the reflecting elements, the reflected signals can be constructively superimposed (even with the direct path if it is available) to enhance the desired signal power or can be cancelled out to mitigate the impact of co-channel interference or signal leakage to eavesdroppers. Since the elements of an RIS reflect the incoming signals without any signal processing operations that require radio-frequency (RF) chains, an RIS has a lower implementation cost than conventional active receivers and transmitters. An RIS can be fabricated with light weight and small-thickness layers, and thus can be readily integrated into the environment.

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.

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions.

An intelligent reflecting surface (IRS) can intelligently configure wavefronts such as amplitude, frequency, phase, and even polarization through passive reflections and without requiring any radio frequency (RF) chains. It is predicted to be a revolutionizing technology with the capability to alter wireless communication to enhance both spectrum and energy efficiencies with low expenditure and low energy consumption. Similarly, unmanned aerial vehicle (UAV) communication has attained a significant interest by research fraternity due to high mobility, flexible deployment, and easy integration with other technologies. However, UAV communication can face obstructions and eavesdropping in real-time scenarios. Recently, it is envisaged that IRS and UAV can combine together to achieve unparalleled opportunities in difficult environments. Both technologies can achieve enhanced performance by proactively altering the wireless propagation through maneuver control and smart signal reflections in three-dimensional space. This study briefly discusses IRS-assisted UAV communications. We survey the existing literature on this emerging research topic for both ground and airborne scenarios. We highlight several emerging technologies and application scenarios for future wireless networks. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on open challenges and future research directions for IRS-assisted UAV communication.

The dynamic power allocation to maximize the achievable sum rate for massive MIMO‐NOMA systems

The demand of mobile networks and quality of service have recently increased to higher Spectral Efficiency (SE) or Energy Efficiency (EE) and massive connectivity for 5G wireless communications. The concept of beamforming Multiple-Input Multiple-Output (MIMO) is capable of significantly reducing the amount of required Radio Frequency Chains (RFCs) used by massive MIMO systems without remarkable performance loss. However, in existing beamformed MIMO, the amount of supported devices cannot be higher than the amount of RFCs using the same time-frequency resources, and it is the basic limit for these systems. To address this issue, non-orthogonal multiple access (NOMA) has been recently recommended, which can accommodate higher covered using via non-orthogonal resource allocation. Nevertheless, power allocation is a core factor of the NOMA scheme. However, maximizing the sum rate problem based on power allocation in Massive MIMO-NOMA scenarios is non-convex and non-linear, which creates a very challenging situation to acquire the closed-form approach. Thus, in this paper, an approach to this difficulty is outlined for Massive MIMO-NOMA systems to maximize the sum rate as a convex and linear problem. Simulation results of the suggested Beamformed MIMO-NOMA (BMN) algorithm show that a higher achievable sum rate is achieved compared with the usual beamformed MIMO.

Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.