PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems

Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.     

Modified leakage-based transmit filter designs for multi-user MIMO systems

This paper proposes two modified definitions of signal-to-leakage-plus-noise ratio (mSLNR) as criteria for linear transmit filter design in Multi-User MIMO systems. The proposed criteria incorporate receiver structures and priority weight matrices into the precoder design, which can effectively exploit unused receive signal spaces, in the case that available eigenmodes are not fully transmitted, and can potentially prioritise users’ data streams. Iterative SLNR (iSLNR) precoding algorithms based on the proposed mSLNR definitions are also presented. Further, the variations of the iSLNR algorithms are thoroughly studied. In particular, the impact of the mSLNR definitions, choices of receive filters and iteration types on the convergence property and sum-rate performance is discussed. Moreover, the effect of weight matrices on users’ prioritisation is elaborated. For extreme weight values, the proposed algorithms are shown to converge to either eigenbeamforming or null-space decomposition techniques. Further, a robust design of the proposed schemes for the case of imperfect channel state information (CSI) is presented. It is shown that adequate knowledge of effective receive subspaces can be attained by simply assuming matched filters (MF) in the iterative precoding process, while further improvement can be obtained by an actual implementation of interference-mitigation-capable receivers.     

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.     

Design of bitstream-based adaptive-connected array architecture for mmWave Multiuser MIMO systems

We investigate a bitstream-based adaptive-connected massive multiple-input multiple-output (MIMO) architecture that trades off between high-power full-connected and low-performance sub-connected hybrid precoding architectures. The proposed adaptive-connected architecture which enables each data stream to be computed independently and in parallel, consists of fewer phase shifters (PS) and switches than the other adaptive-connected architectures. With smaller array groups, the proposed architecture uses fewer PS and switches, so that its power consumption gradually decreases in millimeter wave (mmWave) Multiuser MIMO (MU-MIMO) system. To fully demonstrate the performance of the proposed architecture in mmWave MU-MIMO system with practical constraints, we combine the connection-state matrix with the hybrid precoders and combiners to maximize energy efficiency (EE) of the system equipped with the proposed architecture. We then propose the hybrid precoding and combining (HPC) scheme suitable for multi-user and multi-data streams which utilizes the SCF algorithm to obtain the constant modulus of the analog precoder at convergence. In the digital precoding and combining stage, the digital precoder and combiner are designed to reduce the amount of computation by utilizing the singular value decomposition (SVD) of corresponding equivalent channel. In the mmWave MU-MIMO-OFDM system equipped with the proposed architecture, with the increase of the total number of data streams, simulation results demonstrate that we can exploit the proposed HPC scheme to achieve better EE than the traditional hybrid full-connected architecture exploiting some existing schemes.     

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.     

Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems

This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.     

AN-aided beamforming for IRS-assisted SWIPT systems

This paper studies artificial noise (AN)-aided beamforming design in an intelligent reflecting surface (IRS)-assisted system empowered by simultaneous wireless information and power transfer (SWIPT) technique. Multiple power splitting (PS) single-antenna receivers simultaneously receive information and energy from a multi-antenna base station (BS). Although all users are legitimate, in each transmission interval only one receiver is authorized to receive information and the others are only allowed to harvest power which are considered as unauthorized receivers (URs). To prevent information decoding by URs, AN signal is transmitted from the BS. We adopt a non-linear model for energy harvesting. In the optimization problem, we minimize the total transmit power, and for this purpose, we utilize an alternating optimization (AO) algorithm. For the non-convex rank-one constraint for IRS phase shifts, we utilize a sequential rank-one constraint relaxation (SROCR) algorithm. In addition to single antenna URs scenario, we investigate multi-antenna URs scenario and evaluate their performance. Simulation results validate the effectiveness of using IRS.     

“Orthogonalized” sparsity enhanced mismatch models for wireless heterogeneous networks

Performance of MIMO precoder for heterogeneous networks can be hindered by a lack of accurate channel state information. The sparsity enhanced mismatch model (SEMM) has been proposed recently to account for the channel estimate mismatch problem by exploiting the inherent sparse characteristics of MIMO interference channels. When (single user-MIMO) SU-MIMO precoder design takes into account the SEMM, it was shown to have better transmission performance compared to the conventional norm ball mismatch model (NBMM) in a single-user multi-victims scenario. However, when communicating and interference channels are highly correlated, which can happen frequently in ultra-dense heterogeneous networks, performance of the SEMM precoder degrades and in some cases, underperforms the NBMM precoder. An “orthogonalized” SEMM (OSEMM) is proposed herein to modify the SEMM such that it is better suited for correlated channels. The concept of orthogonalization of channels is not new but this work uses it to enhance the SEMM, which creates synergy between transmission performance and robustness toward channel mismatch error. Two variants of the OSEMM are proposed, namely the OSEMM-LQ and OSEMM-SVD, to modify the basis expansion model that is an integral part of the SEMM. The resulting mismatch model influences the design of the SU-MIMO precoder that aims to maximize certain transmission criterion. Even though SU-MIMO precoding is considered, a new channel correlation definition, which acts as a metric for the OSEMM, is given that allows for user selection in a multiuser scenario such that optimal performance can be attained for the targeted user. Analytical and simulation results are given that highlight the difference in performance between the two variants of the OSEMM.     

Sum rates for multi-user MIMO vector perturbation precoding with regularization

In this paper, we analyze the sum rate performance of multiuser multi-antenna downlink channel. We consider Rayleigh fading environment when regularized vector perturbation precoding (R-VPP) method is used at the transmitter. We derive expressions for the sum rate in terms of the variance of the received signal. We also provide a closed form approximation for the mean squared error (MSE) which is shown to work well for the whole range of SNR. Further, we also propose a simpler expression for R-VPP sum rate based on MSE. The simulation results show that the proposed expressions for R-VPP sum rate closely match the sum rate found by the entropy estimation. Our results show that when compared with other linear and non-linear precoding methods (like zero-forcing precoder, linear minimum mean square error (MMSE) precoder and VPP), R-VPP sum rate performance is very close to DPC for all SNR values. It is also noted that the sum rate performance of the linear MMSE precoder is very close to the R-VPP at low to medium SNR range. Finally we also compared the merits of performing regularization for VPP as compared to the greedy rate maximizing user scheduling. It turns out that the R-VPP with or without user selection performs better than the VPP systems with user selection.     

Deep learning-driven MIMO: Data encoding and processing mechanism

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.     

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.     

Scaled and nonlinear multi-objective model for Downlink and Uplink exposure in massive MIMO

When designing wireless networks with a large number of wireless broadband devices, it is important to take into account the induced Uplink (UL) and Downlink (DL) exposure from base station broadcasting. This work focuses on the reduction of power consumption, low downlink and uplink electromagnetic exposure, and the optimization of locations and power levels of large Multiple Input–Multiple Output-Long Term Evolution base stations (MIMO-LTE). Additionally, it anticipates achieving maximum user coverage. In order to handle the power consumption and Electro Magnetic Field (EMF) exposure, a Nonlinear and Scaled Weight objective model (NLSW) is defined (DL and UL exposure). An enhanced Particle Swarm Optimization (PSO) technique is employed to resolve this optimization issue. Finally, DL exposure, UL exposure, and power performance of a NLSW are validated compared to the traditional linear model. Increased antenna leads to a 25% reduction in DL dosage. The minimum BS deployed in the area with the number of antenna elements until it reaches the ideal value is related to the UL exposure maximization.     

Orbit response matrix analysis and lattice periodicity restoration of the SSRF storage ring

In this paper, we present numerically and experimentally the linear beam-optics distortion in the SSRF storage ring and the correction of optics by using a number of quadrupole magnets installed in the storage ring. The measured orbit-response matrices were fitted to the model-response matrices to obtain the β and the dispersion functions in the storage ring. By readjusting the currents of quadrupole-magnet power supplies, we were able to successfully restore the optics parameters to values very close to the design ones, with rms deviations around 1%. This periodicity restoration is verified with the β function measurement,     

Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture

In this paper, we introduce a mixed- analog-to-digital converter (ADC) architecture for massive multiple-input multiple-output (MIMO) systems and study the system’s performance mainly includes the achievable spectral efficiency and energy efficiency. In principle, the mixed-ADC architecture permits the one part of antennas at the base station (BS) are connected to speed and expensive full-resolution ADCs and the remaining part of the antennas are connected to the cheap low-resolution ADCs. By applying the general maximum-ratio combining detector, a tractable approximate expression for the achievable SE is obtained. Leveraging on the derived results, the effects of the number of BS antennas and the percent of the full-resolution ADCs on the achievable SE are investigated. Results show that the achievable SE increases with the percent of the full-resolution ADCs and the number of BS antennas. Based on a realistic power consumption model, we evaluate the energy efficiency for the considered mixed-ADC architecture. Moreover, under the certain achievable SE constraint, we maximize the energy efficiency by adjusting the number of low-resolution ADCs and the resolution bits of the corresponding ADC device. Numerical results showcase that the energy efficiency can be improved by enhancing the average transmitted power, and there exists an optimal number of resolution bits and the number of antennas to maximize the energy efficiency, which indicates that the application of mixed-ADC architecture has a great potential in future mobile communication system.     

A Spinorization of a Constrained Vector System and a Spinor Reconstruction Theorem

An elementary method of conctructing a spinor from vectors satisfying constraint conditions is proposed. We consider orthonormal triad and tetrad as an orientable physical object and introduce parameter representations of them, in terms of the Euler angles and the pseudo-Euler angles. Having determined the transformation property of the parameters, we set up the spinor determining equation. This equation is solved. The solution (spinor) contains four arbitrary complex constants, in 3 + 1 dimensional space. Using the proposed method, we prove the spinor reconstruction theorem, i.e. the original Dirac spinor can be reconstructed from seven of the sixteen hermitian bilinear forms, except the overall phase factor (the gauge freedom of the 1st kind). The energy density of the spinor field is written in terms of currents and their space derivatives.     

An energy efficient scheme by exploiting multi-hop D2D communications for 5G networks

The multi-hop Device-to-Device (M-D2D) communication has a potential to serve as a promising technology for upcoming 5G networks. The prominent reason is that the M-D2D communication has the potential to improve coverage, enhanced spectrum efficiency, better link quality, and energy-efficient communication. One of the major challenges for M-D2D communication is the mitigation of interference between the cellular user (CUs) and M-D2D users. Considering this mutual interference constraint, this work investigates the problem of optimal matching of M-D2D links and CUs to form spectrum-sharing partners to maximize overall sum rates of the cell under QoS and energy efficiency (EE) constraints. In this paper, we investigate the interference management for multi-hop (more than one-hop) D2D communication scenarios where we propose a channel assignment scheme along with a power allocation scheme. The proposed channel assignment scheme is based on the Hungarian method in which the channel assignment for M-D2D pairs is done by minimum interference value. The power allocation scheme is based on Binary Particle swarm optimization (BPSO). This scheme calculates the specific power values for all the individual M-D2D links. We have done a comprehensive simulation and the result portrays that our proposed scheme performs better compared to the previous work mentioned in the literature. The results clearly indicate that the proposed scheme enhances the EE of up to 13% by producing the optimal assignment of channels and power for the CUs and M-D2D users.     

灰度人脸识别形态学相关的一般理论研究

提出一般形态学相关概念,并提出一种小型联合变换相关器的硬件设计以实现一般形态学相关.提出两种改进的一般形态学相关算法,灰度图像按某种分解方法分解成一系列二值图像片.在第一种算法中,每片二值联合图像片的边缘被检测,其功率谱求和.在第二种算法中,一种情况是每片的联合变换功率谱被二值化或细化再求和;另一种情况是这些片的联合变换功率谱的总和被二值化或细化.计算机模拟结果表明,改进后的算法能改善高相似度灰度人脸图像识别的鉴别率.     

Dual-Vivaldi wideband nanoantenna with high radiation efficiency over the infrared frequency band

A dual-Vivaldi nanoantenna is proposed to demonstrate the possibility of wideband operation at IR frequencies. The antenna geometry design is guided by the material properties of metals at IR frequencies. According to our numerical results, this nanoantenna has both high radiation efficiency and good impedance-matching properties over a wide frequency band (more than 122%) in the IR frequency band. The design is based on the well-known Vivaldi antenna placed on quartz substrate but operating as a pair instead of a single element. Such a pair of Vivaldi antennas oriented in opposite directions produces the main lobe in the broadside direction (normal to the axes of the antennas) rather than the usual peak gain along the axis (end fire) of a single Vivaldi antenna. The dual-Vivaldi nanoantenna is easy to fabricate in a conventional electron-beam lithography process, and it provides a large number of degrees of freedom, facilitating design for ultra-wideband operation.     

Negative photoconductivity in low-dimensional materials

In recent years,low-dimensional materials have received extensive attention in the field of electronics and optoelectronics.Among them,photoelectric devices based on photoconductive effect in low-dimensional materials have a broad development space.In contrast to positive photoconductivity,negative photoconductivity(NPC)refers to a phenomenon that the conductivity decreases under illumination.It has novel application prospects in the field of optoelectronics,memory,and gas detection,etc.In this paper,we review reports about the NPC effect in low-dimensional materials and systematically summarize the mechanisms to form the NPC effect in existing low-dimensional materials.     

Modified complex method for constrained design and optimization of optical multilayer thin-film devices

A new computer design program based on the modified-complex method has been developed for constrained optimization and refinement of optical thin-film multilayer devices. This program is having a provision to include both limiting constraints as well as constraint equations. Constraints are suitably accommodated to generate designs which are practicable and withstand high laser power. Various rapid convergent processes like dynamic contraction and expansion of feasible vertices are incorporated for efficient scanning of the constraint parametric space. A broad band IR antireflection coating has been designed to test its relative efficiency with respect to other available methods. A wide varieties of highly useful multilayer devices have been successfully developed using this method.