Beamforming Scheme Research Articles

User fairness-enhanced beamforming for IRS-assisted cognitive radio networks based on prior judgment mechanism

This research proposes a novel approach, namely the user fairness-enhanced beamforming (UFEB) scheme, to improve the user fairness in intelligent reflecting surface (IRS) assisted multiuser cognitive radio networks (CRNs). The proposed scheme takes into account the bounded channel state information (CSI) error of the primary user (PU) related channel and establishes a sum α-fair utility maximization problem, where α denotes the fairness adjustment index. This problem optimizes the system for fairness and rate, subject to constraints, such as the quality-of-service requirement of secondary users (SUs), limited interference on PUs, maximum transmit power of cognitive base station (CBS), and unit modulus of the IRS. Firstly, we design a prior judgment mechanism that makes preliminary assessments of the optimized requirements of the system in terms of fairness and rate, whose rationality can be proved in the simulation results. Then, with the prior selection of α, by exploiting the successive convex approximation and Taylor series expansion, we present an alternating iterative algorithm to solve the α-fair utility maximization problem. Simulation results show that the prior selection of α can accurately reflect the difference in the optimization requirements for sum rate and fairness of the system after the beamforming of CBS and the phase of IRS are determined, and validate the effectiveness and superiority of our proposed UFEB scheme in improving the fairness among SUs.

Robust Beamforming for IRS-Aided SWIPT in Cognitive Satellite and Terrestrial Networks

This paper proposes a robust beamforming (BF) scheme for intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a cognitive satellite and terrestrial network (CSTN). The satellite network serves multiple earth stations through the multicast transmission, while the terrestrial network operating at the same spectrum implements the SWIPT through IRS-aided multicast technology. Assuming that the imperfect channel state information (CSI) is available, we formulate an optimization problem to maximize the minimum achievable rate of the information receivers (IRs), subject to the transmit power budget, achievable rate and harvesting energy requirements. To address this nonconvex problem, we propose a tight bound robust BF algorithm based on Lagrange duality and alternating optimization (AO) to jointly optimize the active and passive beamformers for the base station, satellite and IRS, respectively. Simulation results confirm the robustness and superiority of our proposed BF scheme over other related works.

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

Robust beamforming for dual‐function radar‐communication with imperfect channel state information

AbstractDual‐function radar and communication (DFRC) have recently received significant attention as an effective way to alleviate spectrum congestion and reduce system costs. Joint transmit beamforming plays a critical role in DFRC systems, as it can effectively reduce mutual interference and improve the performance of both the radar and communication systems. This paper investigates the problem of robust beamforming design with imperfect channel state information (CSI) to ensure communication performance while optimizing radar performance. Considering elliptically bounded CSI errors, the objective function is designed to minimize the radar loss function, which is related to the radar transmit beam pattern, subject to worst‐case signal‐to‐interference‐plus‐noise ratio constraints at each user, which ensure fairness between users. An efficient algorithm based on semidefinite relaxation and the S‐procedure is proposed to solve the non‐convex problem. Simulation results are presented to illustrate the effectiveness of the proposed robust beamforming scheme, which can reduce the impact of channel errors.

Roadside IRS-Aided Vehicular Communication: Efficient Channel Estimation and Low-Complexity Beamforming Design

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation environment for enhancing the communication performance cost-effectively. However, the rapidly time-varying channel in high-mobility communication scenarios such as vehicular communication renders it challenging to obtain the instantaneous channel state information (CSI) efficiently for IRS with a large number of reflecting elements. In this paper, we propose a new roadside IRS-aided vehicular communication system to tackle this challenge. Specifically, by exploiting the symmetrical deployment of IRSs with inter-laced equal intervals on both sides of the road and the cooperation among nearby IRS controllers, we propose a new two-stage channel estimation scheme with off-line and online training, respectively, to obtain the static/time-varying CSI required by the proposed low-complexity passive beamforming scheme efficiently. The proposed IRS beamforming and online channel estimation designs leverage the existing uplink pilots in wireless networks and do not require any change of the existing transmission protocol. Moreover, they can be implemented by each of IRS controllers independently, without the need of any real-time feedback from the user’s serving BS. Simulation results show that the proposed designs can efficiently achieve the high IRS passive beamforming gain and thus significantly enhance the achievable communication throughput for high-speed vehicular communications.

Development of an Optimum Beamforming Strategy for Outdoor Communication in Millimeter Wave Wireless Sensor Network

Development of an Optimum Beamforming Strategy for Outdoor Communication in Millimeter Wave Wireless Sensor Network

A novel adaptive beamforming scheme for array signal data processing

When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal cancelation phenomenon, resulting in a sharp decline in performance. To address the occurrence of desired signal cancelation, an improved matrix projection-based efficient beamforming method is proposed. Firstly, based on spatial partitioning (SP) technology, a significant projection matrix for interference-plus-noise space (INS) is constructed. Secondly, using the constructed key projection matrix, the sample data covariance matrix is projected into the INS to achieve the goal of suppressing the desired signal data information. Finally, the weight data vector is calculated by Capon beamformer. The proposed algorithm does not require an iterative search for the optimal solution, which has the advantage of a small amount of calculation. Simulation experiments have verified that the proposed method has significant advantages in suppressing the desired data signals. Especially when the desired data signal has large power, the signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm is better than that of the compared algorithms under the conditions of random directionality errors or local scattering errors between the desired signal and interference.

Evaluation of multi‐user multiple‐input multiple‐output digital beamforming algorithms in B5G/6G low Earth orbit satellite systems

SummarySatellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever‐increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co‐channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed‐form solution, we propose a beamforming algorithm based on maximizing the users' signal‐to‐leakage‐and‐noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi‐beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per‐user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to‐leakage‐plus‐noise ratio beamforming technique is able to outperform both minimum mean square error and multi‐beam schemes in the presented satellite communication scenario.

Secrecy Performance Analysis for MIMO-DF Relay Systems With MRT/MRC and TZF/MRC Schemes

Transmit beamforming is a diversity technique in multiple-input multiple-output (MIMO) systems. It improves the diversity order and enhances the information security of the MIMO systems. In this paper, we mathematically analyze the secure performance of a MIMO decode-and-forward (DF) relay system under the presence of an eavesdropper and with the imperfect channel state information (CSI) of the eavesdropping channel. To enhance the secrecy performance, we employ multiple antennas at all legitimate nodes and utilize two linear beamforming schemes, i.e., maximal ratio transmission (MRT) and transmission zero-forcing (TZF), at the transmitter and maximal ratio combining (MRC) scheme at the receiver. The probability density function (PDF) of the output SNR for different numbers of antennas at the transmitter and receiver is plotted and discussed. After that, we apply this PDF to derive the closed-form expressions of the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) of the considered MIMO-DF relay system over Rayleigh fading channels for MRT/MRC and TZF/MRC schemes. In addition, based on the obtained analysis results, we provide the location of the relay that minimizes the system SOPs. All the analysis results are validated by Monte-Carlo simulations. The results show that the TZF/MRC scheme provides better SOP and ESC than the MRT/MRC scheme. Moreover, the number of antennas at the legitimate nodes and the location of the eavesdropper significantly impact the secrecy performance.

Security-Oriented Pilot and Data Transmission for URLLC in Mission-Critical IoT Scenarios

In this paper, we focus on the joint design of channel training and data transmission for secure ultra-reliable and low-latency communications (URLLC) in mission-critical Internet-of-Things (IoT) scenarios, e.g., intelligent transportation and remote control. Specifically, we consider a multiple-input multiple-output multi-antenna eavesdropper (MIMOME) system, and the role of artificial noise (AN) for securing URLLC in this system is studied. In the channel training phase, we use the two-way discriminatory channel estimation (DCE) protocol with AN injection to suppress the channel estimation accuracy at eavesdropper. Meanwhile, the AN-assisted secrecy beamforming scheme is adopted to mask the confidential data signals. Following by the security enhancements above, we provide a quantitative definition of achievable effective secrecy rate (AESR) to measure the performance of our URLLC system with imperfect channel state information (CSI) and short-packet feature. Then, a non-asymptotic closed-form lower bound of AESR is provided to make the numerical calculation tractable, and the asymptotic system performance in the high-SNR regime is also studied to gain a comprehensive insight. Based on the cyclic coordinated search method, we propose an iterative resource allo-cation algorithm to maximize the AESR of our system, where the blocklength and transmit power assigned to the reverse/forward pilots, confidential data signals and AN are jointly optimized. In addition, numerical results reveal the AESR performance of our URLLC system for different system parameters, and demonstrate the convergence and superiority of our proposed algorithm.

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO Networks With Global and Local CSI

This paper investigates beamforming techniques for in-band full-duplex multi-cell multi-user (IBFD-MCMU) wireless networks considering hardware impairments (HWIs), channel uncertainty, and limited channel state information (CSI). For the global CSI scenario, we first enhance zero-forcing (ZF), maximum ratio transmission and combining (MRTC), and minimum mean-squared error (MMSE) beamforming to be compatible with multi-antenna users and IBFD base stations. Then, we investigate beamforming schemes for the local CSI assumption where only intra-cell channel knowledge is fully available at the base stations, and inter-cell channels are known statistically due to the limited training resources. With these limitations, two MMSE-based methods are proposed, which regard unknown CSI as random instances (i.e., eMMSE-RI) and noise (i.e., eMMSE-N). We explore the self-interference cancellation (SIC) capability of beamformers and evaluate the performance of these methods in 3GPP scenarios. Numerical results reveal that the enhanced MMSE beamforming can achieve the desired IBFD with low analog SIC depth, inspiring a low-cost IBFD transceiver design. The practical imperfections (e.g., transceiver HWIs, channel uncertainty, limited CSI) decrease the achievable sum rate but do not reduce the IBFD gain. With CSI limitations, eMMSE-RI outperforms eMMSE-N for microcells, where the probability of the presence of LOS is high, whereas eMMSE-N performs better when cell size is enlarged.

Joint Node Association and Beamforming for Millimeter Wave Networks With Multiple Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surface (RIS) is a promising technology to increase the spatial degrees-of-freedom in sparse millimeter wave (mmWave) channels. In RIS-assisted mmWave networks with multiple nodes, however, optimization of node-association and beamforming (BF) may require a large channel training overhead. This paper proposes a joint node-association and cooperative BF scheme to maximize the weighted sum rate with a low overhead based on a two-step design. In the first step, only slowly varying channel information are used to determine the node-association, analog active BF, and passive BF. In the second step, the baseband BF and power allocation are designed only using the effective channel information with reduced dimension. It is shown that the proposed scheme outperforms benchmark schemes and is scalable to the numbers of nodes.

Adaptive Non-Uniform Hybrid Beamforming for mmWave Train-to-Ground Communications in High-Speed Railway Scenarios

Higher frequency bands, especially millimeter wave (mmWave), are a key enabler for future high speed railway (HSR) wireless communication systems. Hybrid analog and digital beamforming (HBF) schemes operating at mmWave frequency bands can exploit the sparse nature of the channel by using large scale antenna arrays (LSAs) to shape multiple beams that overcome the severe path loss. However, due to the long and straight geometric characteristic of the rail track, there is a significant variance in the train-to-ground communication rate when the train is near or far from the BS. To address this issue, a novel hybrid beamforming scheme is developed based on a non-uniform beam design. We first model and investigate different beam design schemes in terms of spectral efficiency. Then, an efficient straight route based adaptively analog beamforming scheme with the non-uniform beam is proposed for HSR, which is designed to resist path loss. Finally, an adaptive non-uniform HBF algorithm based on the orthogonal matching pursuit method (OMP) is developed to minimize the rate gap between different locations by selecting the appropriate predefined beams. Numerical results show that the proposed hybrid beamforming scheme can efficiently ensure signal coverage, and reduce the variance of the train-to-ground communication rate along a straight track route.

Cooperative Localization in Hybrid Active and Passive Wireless Sensor Networks With Unknown Tx Power

Hybrid active and passive wireless sensor networks (HWSNs) gain advantages in extending the network lifetime and reducing the overall cost. Because the passive sensors without battery harvest the energy from distributed active sensor signal beam, and only a few active sensors can maintain a large-scale network. Thus, how to track the passive sensor’s location is essential for network management. Since the active sensors are sparsely deployed, cooperative localization which employs passive sensors to locate themselves together is a promising solution. In this paper, we analyze the energy beam generated by the active sensors on the cooperative localization accuracy of the passive sensors. We consider the spatial-temporal cooperative localization based on the received signal strength (RSS) model with unknown Tx power information of each sensor due to the limited processing capabilities, circuit complexity and energy constraints. We formulate the Fisher information matrix (FIM) and the corresponding Cramér-Rao lower bound (CRLB) for the static full connected network and dynamic spatial-temporal recursive network. Accordingly, energy beamforming schemes are proposed to optimize localization accuracy and energy efficiency problems. For the optimal localization problem, we derive the closed-form solution of the optimal energy beamforming wave. For the optimal energy efficiency problem, we propose a semi-definite programming (SDP) solution to achieve optimal energy consumption with a self-calibration method, which can address the over-relax problem. Extensive simulation results indicate that our proposed beamforming schemes have high localization accuracy and lower power consumption compared with the existing power allocation-based schemes.

Hybrid Beamforming in mmWave Massive MIMO for IoV With Dual-Functional Radar Communication

The dual-functional radar communication (DFRC) has been regarded as one of the most attractive solutions for facilitating vehicle applications in internet of vehicles (IoV) by utilizing millimeter Wave (mmWave) and massive multiple-in-multiple-out (MIMO) technologies. In this paper, we propose a hybrid beamforming (HBF) scheme with low power consumption and fully directional beamforming gains. The proposed HBF scheme integrates digital beamforming (DBF) with analog beamforming (ABF) to adopt a fully connected (F-C) structure to realize high spectral efficiency (SE) and accurate target sensing. Specifically, for HBF in a single-user MIMO (SU-MIMO) system, we design a fast Riemannian manifold optimization (FRMO) method by minimizing the weighted summation of the communication beamforming error and radar beamforming error. The proposed method can achieve optimal DBF for downlink communication and guarantee the desired beampattern for radar sensing. For HBF in a multiuser MIMO (MU-MIMO) system, we formulate the weighted optimization problem to mitigate multiuser interference (MUI). We design a low-complexity method, namely, the adaptive particle swarm optimization (APSO) algorithm, to obtain a near-optimal solution for HBF based on the criteria of minimizing MUI and the radar beamforming error. We conduct excessive simulations to evaluate the proposed algorithms for the HBF design in both the SU-MIMO system and MU-MIMO system. The numerical results demonstrate that the proposed algorithms achieve near-optimal performance compared to existing HFB methods with high computational efficiency in terms of beamforming gains and SE.

Secure Transmission Design for Virtual Antenna Array-Aided Device-to-Device Multicast Communications

This paper investigates the physical-layer security in a Virtual Antenna Array (VAA)-aided Device-to-Device Multicast (D2DM) communication system, where the User Equipments (UEs) who cache the common content will form a VAA system and cooperatively multicast the content to UEs who desire it. Specifically, with the target of securing the VAA-aided D2DM communication under the threat of multiple eavesdroppers, we propose a secure beamforming scheme by jointly considering the formed VAA and the Base Station (BS). For obtaining the optimal beamforing vectors, a nonsmooth and nonconvex Weight Sum Rate Maximization Problem (WRMP) is formulated and solved using Successive Convex Approximation (SCA) approach. Furthermore, we consider the worst case that eavesdroppers cooperatively form a eavesdrop VAA to enhance the overhearing capacity. In the worst case, we modify the securing beamforming scheme, formulate the corresponding WRMP and solve it using a two-level optimization. Simulation results validate the improvements of the VAA-aided D2DM scheme in terms of communication security compared with conventional D2DM schemes.

Nondiffracting beam length adjustment by using 3-bit coding metasurfaces

In this paper, a nondiffracting beamforming scheme employing 3-bit coding metasurfaces to adjust the beam lengths is proposed. The 3-bit coding is implemented with a four-layer phase control unit whose phase can be adjusted by tuning the side length of the metal square. Simulation and measured results show that the electric field distributions generated by the metasurface is independent of the polarization of the incident wave due to the central symmetry of the structure. Experimental results are provided which verifies the feasibility of the proposed scheme in adjusting the nondiffracting beam lengths. A relative error as small as 6.0% in the beam lengths is observed, and all results prove the proposed 3-bit coding metasurfaces can be applied to generate different nondiffracting beam lengths.

Fingerprint Based Codebook for RIS Passive Beamforming Training

In this article, we propose a new RIS passive beamforming scheme in two main stages. First, a fingerprint-based codebook (FP-CB) design phase occurs, where the area of interest is divided into a number of points and the optimal reflection patterns (RPs) corresponding to these points are determined and stored alongside the coordinates of these points in the codebook database (DB). Second, there is the searching and learning online stage, in which, based on the receiver (RX) and FP points’ locations, the system determines a group of candidate RPs. Then, it just searches through them instead of examining the entire CB RPs to select the best RP that can be used for configuring RIS during the data transmission period. The proposed mechanism proves that designing a positioning information-based CB can highly reduce the system overhead computational complexity and enhance performance comparable to the conventional CB-based scheme and the full channel estimation (CE)-based scheme. For example, selecting only 10 candidate RPs from the FP-CB can obtain a better effective achievable rate than a CE-based scheme in a rapidly changing channel.

UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface: Modeling, Analysis, and Optimization

Reconfigurable intelligent surface (RIS) offers tremendous spectrum-and-energy efficiency in wireless networks. With the agility and mobility of an unmanned aerial vehicle (UAV), RIS can be mounted on a UAV to enable three-dimensional (3D) signal reflections, reliable air-ground connections, and higher configuration flexibility. However, the scalability of the aperture gain and the spatial multiplexing could not be guaranteed in a single UAV-enabled aerial RIS due to UAV’s limited payload and line-of-sight-dominated air-ground connection. In this paper, we study a UAV swarm-enabled aerial RIS (SARIS)-assisted downlink communication system. The objective of the considered SARIS system is to maximize the weighted sum-rate of ground users by designing the transmit beamforming at the base station (BS), the phase shifts of SARIS reflecting elements, and SARIS 3D placement. For joint BS and SARIS beamforming design, we introduce two beamforming schemes with low computational complexity. For SARIS placement design, the optimal SARIS 3D position is obtained by leveraging the tools from stochastic geometry and considering the distributions of ground users. Simulation results confirm the validity of the analytical derivations. In particular, the SARIS placement plays a vital role in the system performance when the distances between users and the BS increase.

Uplink Multiple Access With Semi-Grant-Free Transmission in Integrated Satellite-Aerial-Terrestrial Networks

This paper investigates a semi-grant-free (SGF) based transmission strategy to provide a flexible connectivity for various kinds of users in an integrated satellite-aerial-terrestrial network (ISATN). Herein, a high-altitude platform (HAP) termed as a grant-based user (GBU), which serves multiple mobile terminals (MTs) through space division multiple access (SDMA), wants to access a satellite network with multiple earth stations (ESs) termed as grant-free users (GFUs) simultaneously via non-orthogonal multiple access (NOMA) assisted SGF. To this end, we first propose two SGF-based uplink transmission schemes for both perfect channel state information (CSI) and imperfect CSI cases. When perfect CSI is available, a zero-forcing based beamforming (BF) scheme is used in HAP network while an adaptive transmit power allocation (ATPA) approach is adopted for SGF transmission. When only imperfect CSI is available, BF scheme employing the derived channel correlation matrix of HAP-MT link is proposed to achieve SDMA, and a novel ATPA strategy with rate probability constraint is proposed to guarantee quality-of-service of the GBU. Next, we derive the closed-form throughput expressions to evaluate the performance of the considered ISATN with the proposed two SGF-based schemes. Finally, computer simulations are conducted to validate the theoretical performance analysis and show the superiority of the proposed schemes over the related works. Moreover, our numerical results not only demonstrate a satisfactory performance of the proposed SGF-based scheme using imperfect CSI, but also reveal the impact of CSI errors on the system performance.