Radio Frequency Chains Research Articles

HYBRID BEAMFORMING FOR MULTIBEAM PHASED ARRAY RECEVIERS

In the search for enhanced wireless communication systems, multibeam phased array receivers have gained prominence. They promise to significantly boost data rates, reduce latency, and improve reliability. However, implementing multibeam receivers with traditional beamforming techniques can be challenging due to the high computational demands and the need for multiple radio frequency (RF) chains. Hybrid beamforming combines the advantages of digital and analog beamforming to strike a balance between performance and complexity. Our research delves into the following aspects of Hybrid beamforming for multibeam phased array receivers: Reduced Hardware Complexity: By blending digital and analog beamforming, we reduce the number of required RF chains, making multibeam receivers more cost-effective. Enhanced Beam Steering: Hybrid beamforming maintains precise control over multiple beams, ensuring efficient signal reception and transmission. Real-World Applications: We discuss practical applications in 5G and beyond, satellite communications, and radar systems. The paper delves into the fundamental concepts of analog and digital beamforming, illustrating their respective strengths and limitations. It then presents the architecture of hybrid beamforming systems, emphasizing the synergistic integration of analog and digital components. Special attention is given to the design considerations of beamforming networks, antenna arrays, and the beam steering mechanism. KEYWORDS Hybrid Beamforming , Phased Array , Multi-Beam , Analog Beamforming , Digital Beamforming , Beamforming Networks , Antenna Array , Beam Steering , Spatial Multiplexing , Precoding , Channel Estimation , Interference Mitigation , Power Consumption , Millimeter Wave (mmWave) , 5G and Beyond .

Attention-Aided Autoencoder-Based Channel Prediction for Intelligent Reflecting Surface-Assisted Millimeter-Wave Communications

Sixth-generation (6G) wireless communication networks will provide larger coverage and capacity with lower energy consumption and hardware costs than 5G. Intelligent reflecting surface (IRS)-aided millimeter-wave massive MIMO OFDM communication is a new technology that intelligently manipulates electromagnetic waves. This has recently attracted much attention given its potential to manage the wireless propagation environment at low hardware costs and with minimal energy usage. However, channel prediction is complicated by the fact that IRS is rarely equipped with power amplifiers, various radio frequency chains, or a significant number of reflecting components. In this paper, we propose a convolutional denoising autoencoder model and investigate a joint attention mechanism for channel prediction. Then, we employ the attention mechanism to identify features of channel subcarrier interference to improve the channel prediction performance. Long-range dependent specificity is captured through the attention mechanism to generate useful features from the input signal. The encoder-decoder design of the autoencoder serves as a dimensionality reduction method that enables the autoencoder to predict the spatial and temporal distribution features of continuous signals by exploiting the extraction of sequence features from the model. Numerical results show that the proposed algorithm significantly improves the performance of IRS-aided millimeter-wave massive MIMO OFDM communication systems compared with previous methods.

Multi-User Downlink Beamforming Using Uplink Downlink Duality With CEQs for Frequency Selective Channels

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">per sub-carrier</i> version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quadriga</i> demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.

SE Analysis and GEE Optimization of Hardware-Impaired Multi-Cell Massive MIMO Systems With Rician Fading and Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase shift. The multi-cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BS), which enables us to independently vary the resolution of ADC connected to its each antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the UEs uplink data power and LSFD vectors to maximize the global energy efficiency metric. The proposed optimization, which is practically implementable due to its closed-form updates, is derived by combining the quadratic and Dinkelbach transforms, and generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

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.

Accurate channel estimation and hybrid beamforming using Artificial Intelligence for massive MIMO 5G systems

In a large-scale massive Multi User-Multiple Input Multiple Output (MU-MIMO) environment channel estimation and beamforming is a breathtaking task for enhancing the array gain without utilizing the many Radio Frequency (RF) chains. Somehow, several state-of-the-art works perform channel estimation and Hybrid Beamforming (HB) using Artificial Intelligence (AI) Algorithms but the massive computation intricacy and power consumption hindered the performance of the existing system. By considering the existing issues, we designed a DL-based Hybrid Beamformer for the MIMO environment with 5G communication technology (DLHB-MIMO 5G). The proposed HB design centers on three progressive processes such as accurate channel estimation, hybrid beamformer design, and hybrid beamforming. In the accurate channel estimation phase, the noise and interference-free channels are estimated using the Improved Extreme Learning Machine-Adaptive Orthogonal Matching Pursuit (IELM-AOMP) algorithm based on channel parameters and user feedback. In the HB design stage, the shortcoming of prior DL models is resolved by adopting Transfer Learning Lite Convolutional Neural Network (TL-LiteCNN) for designing a hybrid beamformer. Beforehand, we select the appropriate antenna numbers using Stackelberg Game Theory (StGT) using adequate parameters. In the hybrid beamforming stage, the problem of less Spectral Efficiency (SE) during low SNR conditions is fixed by adopting the Improved Proximal Policy Optimization (IPPO) algorithm with several beamforming parameters to generate highly resourceful hybrid beams. The realization of the proposed research is carried out using the MATLAB R2020a simulation tool and the performance of the proposed work is compared with the major state-of-the-art works in terms of useful performance metrics. The comparative results show that the proposed work beats the existing works.

3D Localization With a Single Partially-Connected Receiving RIS: Positioning Error Analysis and Algorithmic Design

In this paper, we introduce the concept of partially-connected receiving reconfigurable intelligent surfaces (R-RISs), which refers to metasurfaces designed to efficiently sense electromagnetic waveforms impinging on them, and perform localization of the users emitting them. The presented R-RIS hardware architecture comprises subarrays of meta-atoms, with each of them incorporating a waveguide assigned to direct the waveforms reaching its meta-atoms to a reception radio-frequency (RF) chain, enabling signal/channel parameter estimation. We particularly focus on the scenarios where the user is located in the far-field of all the R-RIS subarrays, and present a three-dimensional (3D) localization method which is based on narrowband signaling and angle of arrival (AoA) estimates of the impinging signals at each single-receive-RF R-RIS subarray. For the AoA estimation, which relies on spatially sampled versions of the received signals via each subarray's phase configuration of meta-atoms, we devise an off-grid atomic norm minimization approach, which is followed by subspace-based root multiple signal classification (MUSIC). The AoA estimates are finally combined via a least-squared line intersection method to obtain the position coordinates of a user emitting synchronized localization pilots. Our derived theoretical Cramér Rao lower bounds (CRLBs) on the estimation parameters, which are compared with extensive computer simulation results of our localization approach, verify the effectiveness of the proposed R-RIS-empowered 3D localization system, which is showcased to offer cm-level positioning accuracy. Our comprehensive performance evaluations also demonstrate the impact of various system parameters on the localization performance, namely the training overhead and the distance between the R-RIS and the user, as well as the spacing among the R-RIS's subarrays and its partitioning patterns.

UAV-Assisted Wideband Terahertz Wireless Communications with Time-Delay Phased UPA under Beam Squint

Future Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems are expected to utilize wide bandwidths available at terahertz (THz) frequencies to enhance system throughput. To compensate for the severe path loss in the THz band, it is essential to have a multitude of antennas in the UAV to generate narrow beams for directional transmission. However, narrow beams severely limit its spatial coverage, which greatly affects the efficiency of large-scale access UAV-assisted THz systems. Moreover, the combination of massive antennas and large bandwidth at THz makes the misalignment of the beams caused by beam squint non-negligible and also high energy consumption. UAV-assisted communication technology can effectively increase spatial coverage and provide reliable LoS communication links. In addition, reducing the number of radio frequency (RF) chains while ensuring the number of transmitted data streams and space division multiplexing capability is also an effective way to reduce energy consumption in the UAV communication. In this paper, a single RF chain uniform planar array (UPA) with true-time-delays (TTDs) is equipped on the UAV to achieve two dimensional (2D) beams and split spatial beams to improve transmission efficiency. We analyze the 2D beam squint of the UPA and design a time-delay phased UPA for UAV-assisted THz communication systems. By introducing TTDs between the single RF chain and phase shifters, the beam squint can be controlled flexibly by introducing the delay between each antenna. When TTDs are arranged in both the horizontal and vertical dimensions, the coverage of the beams becomes more complicated compared to uniform linear arrays (ULA). Simulation results show that the proposed time-delay phased UPA can achieve better performance in both single-beam and multi-beam modes for single user and multi-user scenarios compared with conventional phased UPA, respectively. In addition, we propose frequency division beam multiple access (FDBMA) multi access technology, which achieves more efficient multi access by reusing resources from different frequency beam pairs. Finally, the results also show that the enlargement of the beamwidth through the proposed FDBMA strategy can also increase the performance in multi-user scenarios.

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.

Optimal Antenna Selection and Beamforming for an IRS Assisted System

An intelligent reflecting surface (IRS) is a cost and energy-efficient solution to improve wireless system performance. Transmit antenna selection (AS) harnesses the benefits of multiple antennas with a smaller number of radio frequency (RF) chains. We focus on joint optimization of antenna subset and transmit beamforming at the transmitter (Tx) and passive beamforming at the IRS to maximize the receive signal power. We derive a closed-form optimal AS rule for a Tx and receiver (Rx) equipped with single RF chain each and ideal IRS. We analyze its performance with a correlated channel model and then extend it to non-ideal IRS. We also propose a simpler rule that significantly reduces the number of computations and pilots. For an Rx that performs maximal ratio combining, we propose a manifold optimization algorithm and a low-complexity selection rule. For a Tx with multiple RF chains, we propose a subset selection algorithm that yields a locally optimal solution and an alternating optimization algorithm that reduces complexity. Our simulations study the impact of estimation errors, discrete phase shifts, and channel correlation on the proposed selection rules, which perform better than the existing AS rules. They also show that the proposed low-complexity rules are near-optimal.

One-Bit Downlink Precoding for Massive MIMO OFDM System

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.

1-Bit Programmable Metasurface-Based 2-D Direction Finding

A novel two-dimensional (2-D) direction finding method is proposed, relying on a 1-bit programmable metasurface and the time modulation method. Both the azimuth and elevation angles of far-field signals are estimated simultaneously by analyzing the harmonic characteristic of received signals in the frequency domain. Low hardware cost and computational complexity can be achieved owing to the intrinsic nature of the single radio frequency (RF) chain in the proposed method. Extensive numerical results confirm that the direction finding attains a near-zero mean square error (MSE), when the signal-to-noise ratio (SNR) attains 10 dB. A Ka-band reflective programmable metasurface with 400 elements is fabricated and tested to verify its effectiveness partly.

L-Shape Array-Based Technique to Reduce Cross User Correlation in Massive MIMO Systems

This paper proposes a new L-shape array based technique for massive Multiple Input Multiple Output (MIMO) systems to overcome the cross user correlation (CUC) of tightly coupled users in a near line-of-sight scenario in fifth-generation massive MIMO systems without changing signal processing complexity. The performance of the scheme is validated using standard performance metrics like CUC and achievable sum-rate. The results reveal that CUC reduces from 0.8 to around 0.05, and the scheme achieves CUC and sum-rates very close to an independently identically distributed Gaussian channel even under the tightest coupling of mobile terminals. The technique requires a few additional components per radio frequency (RF) chain at the base station.

RIS-Based Opposite Phase Index Modulation and Its DNN-Assisted Joint Sequence Detection

Reconfigurable intelligent surface (RIS), a key technology for future wireless communications, allows intelligent reflection of signals and information transmission. The RIS-based modulation is considered a prospective information transmission mechanism. In this paper, a RIS-based opposite phase index modulation system is proposed, in which RIS can enhance the received power of user signals while transmitting additional data bits for near-end internet of things devices via opposite phase reflection pattern index without any additional radio frequency chain. To reduce the computational complexity of receiver detection, a deep neural network-assisted joint sequence detection method is proposed, in which the reflection pattern index and the symbol index are jointly detected. In addition, we derive the theoretical upper bound performance of the proposed modulation scheme in terms of the average bit error rate (BER) with the maximum likelihood detection. The theoretical and simulation results show that the proposed scheme has better BER performance and lower detection complexity than existing RIS-based modulation schemes.

Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology.

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G

A revolutionary technology relying on Stacked Intelligent Metasurfaces (SIM) is capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave regime. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counterparts, such as reconfigurable intelligent surfaces (RIS) and metasurface lenses. We harness this new SIM for implementing holographic multiple-input multiple-output (HMIMO) communications without requiring excessive radio-frequency (RF) chains, which is a substantial benefit compared to existing implementations. First of all, we propose an HMIMO communication system based on a pair of SIM at the transmitter (TX) and receiver (RX), respectively. In sharp contrast to the conventional MIMO designs, SIM is capable of automatically accomplishing transmit precoding and receiver combining, as the EM waves propagate through them. As such, each spatial stream can be directly radiated and recovered from the corresponding transmit and receive port. Secondly, we formulate the problem of minimizing the error between the actual end-to-end channel matrix and the target diagonal one, representing a flawless interference-free system of parallel subchannels. This is achieved by jointly optimizing the phase shifts associated with all the metasurface layers of both the TX-SIM and RX-SIM. We then design a gradient descent algorithm to solve the resultant non-convex problem. Furthermore, we theoretically analyze the HMIMO channel capacity bound and provide some fundamental insights. Finally, extensive simulation results are provided for characterizing our SIM-aided HMIMO system, which quantifies its substantial performance benefits, e.g., 150% capacity improvement over both conventional MIMO and its RIS-aided counterparts.

Design and Optimization of Hardware Impaired Multi-Cell Rician-Faded mMIMO Systems With Pilot Decontamination Precoding

We consider a hardware-impaired multi-cell Rician-faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). We derive a closed-form spectral efficiency (SE) expression by assuming a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture, and hardware-impaired radio frequency chains at the base stations (BSs) and user equipments. The dynamic ADC/DAC architecture enables us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize SE. We design a distortion-aware minimum mean squared error (DA-MMSE) precoder, and investigate its usage by combining it with the two-layer LSFP, and conventional single-layer precoding (SLP). We show that the DA-MMSE precoder with SLP outperforms its distortion-unaware counterpart, which is used with LSFP. We analytically show that for pure LoS channels, the LSFP reduces to SLP, and its implementation can thus be avoided. It is shown that the LSFP can tolerate high hardware impairments at the BS, but is extremely sensitive to the ADC resolution of the user. We also optimize the global energy efficiency by using a minorization-maximization based algorithm, and show its improved performance over the conventional SE optimization techniques.

Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment

With the development of the Internet of Things (IoT), more and more devices are connected in edge-cloud environment, and spectrum scarcity has become a major bottleneck for the employment of IoT devices in the end side of cloud computing. In addition, due to the broadcast nature of RF link, the potential eavesdropper may be presented in the wireless environment. To improve spectrum efficiency (SE) and ensure safe transmission of the users, in this paper, the two-way relay cooperative Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) with massive multiple-in multiple-out (MIMO) is proposed. Our objective is to maximize the security energy efficiency (SEE) of the cognitive system subject to the quality of service (QoS) of users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e., the optimization of transmit power, power allocation, and antenna selection, respectively. The Lagrange dual algorithm based on the first-order Taylor series expansion function is proposed to solve the non-convex problem. Moreover, a novel orthogonal antenna selection algorithm is proposed to decrease the cost of radio frequency chains and limit the interference to the primary user. The simulation results show that the proposed scheme has significant performance gain on SEE. It can effectively increase the number of access for cloud computing edge IoT users. In addition, due to the impact of correlation between the antennas, as the number of relay antennas increases, the SEE converges to a specific value, which exists the optimal number of antenna.

Joint Precoding and Array Design for Broadcast in the Internet of Unmanned Aerial Vehicles

To promote energy and spectrum efficient communications in multi-antenna channels, broadcast can provide a substantial gain in system throughput. However, the hardware constraints and strong line-of-sight (LoS) limit the implementation and performance of multi-antenna broadcast in the Internet of unmanned aerial vehicles (UAVs). Based on the pseudo-Doppler principle, we propose a joint precoding and antenna array design to reduce the number of radio frequency (RF) chains required by the broadcast and free the LoS path from the inter-stream interference. The reduction of RF chains is realised by designing the precoding matrix that makes at least one of the transmit antennas have null inputs during any broadcast and, moreover, the LoS path is formed to match the obtained precoding matrix through antenna array design at the broadcasting UAV. The algorithms with low computational complexity for optimising this design are developed to minimise the transmit power within the UAV broadcast paradigms. Theoretical formulation and numerical results in the metrics of sum data rate and bit error rate substantiate the validity of our proposed design, specifically in the Internet of UAVs with strong LoS.

Low‐complexity unsupervised learning‐based hybrid precoding for massive MIMO systems

AbstractHybrid precoding can significantly reduce the number of required radio frequency (RF) chains in massive MIMO systems. However, due to the large number of antennas and the high‐dimensional channel, the existing hybrid precoding algorithms usually require very high complexity. In this paper, we propose a convolutional neural network (CNN)‐based hybrid precoding scheme by utilizing unsupervised learning to reduce both the computational complexity and space complexity. Specifically, we first design a low‐complexity CNN structure to achieve the analog precoder, where the size of the convolution kernel and the number of channels are reduced by the further improved inception network. Then, we obtain the digital precoder by the classical zero‐forcing (ZF) algorithm. Simulation results show that, the proposed CNN‐based hybrid precoding can approach the sum‐rate performance of the classical two‐stage hybrid precoding but has much lower complexity.