Design Of MIMO Systems Research Articles

Retraction Note: Design and analysis of MIMO system for THz communication using terahertz patch antenna array based on photonic crystals with graphene

Retraction Note: Design and analysis of MIMO system for THz communication using terahertz patch antenna array based on photonic crystals with graphene

Anti-Jamming Hybrid Beamforming Design for Millimeter-Wave Massive MIMO Systems

Anti-Jamming Hybrid Beamforming Design for Millimeter-Wave Massive MIMO Systems

Joint AP Selection and Grey Wolf Optimization Based Pilot Design for Cell-Free Massive MIMO Systems

Joint AP Selection and Grey Wolf Optimization Based Pilot Design for Cell-Free Massive MIMO Systems

Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems

Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems

Signal Model and Linear Combining Design for Multi-Numerology Massive MIMO Systems

Signal Model and Linear Combining Design for Multi-Numerology Massive MIMO Systems

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Novel Beamforming Design for IRS-Assisted Cell-Free Massive MIMO Systems

Utilizing Partial Reciprocity to Efficient Codebook Design for FDD Massive MIMO Systems

Utilizing Partial Reciprocity to Efficient Codebook Design for FDD Massive MIMO Systems

Deep Learning Based Joint Hybrid Precoding and Combining Design for mmWave MIMO Systems

Deep Learning Based Joint Hybrid Precoding and Combining Design for mmWave MIMO Systems

Two-Timescale Design for RIS-Aided Multicell MIMO Systems with Transceiver Hardware Impairments

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of the RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at the base stations (BS). The expression of the uplink achievable rate is derived and analyzed. Moreover, the genetic algorithm (GA) is used to optimize the phase shifts of the RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs and reduce the demand for BS antennas, which can significantly reduce the hardware costs at the BS.

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

Spectral efficiency of hybrid precoding and combining design for mm-Wave multi-user massive MIMO systems

DNN Based Hybrid Precoding Design for IRS-Aided mmWave MIMO Systems With Phase Modulation Array

DNN Based Hybrid Precoding Design for IRS-Aided mmWave MIMO Systems With Phase Modulation Array

Robust precoder design with transmit antenna selection for overlay cognitive radio massive MIMO system

Massive MIMO systems were utilized in overlay Cognitive Radio (CR) networks to allow simultaneous primary and secondary data transmission while enhancing the spectral efficiency. However, the precoder design of a massive MIMO system especially in the overlay CR paradigm usually has a trade-off between achieved data rate and complexity. In this paper, the precoder design for overlay Cognitive Radio (CR) massive MIMO system is introduced, where a two-stage precoder that accommodates Transmit Antenna Selection (TAS) is proposed for the Secondary Base Station (SBS). The unselected antennas in the cognitive base station are proposed to be utilized to relay the data of the Primary Users (PUs) to maximize the network sum rate. The proposed precoder design for primary data aims to cancel the interference inflicted on Secondary Users (SUs) as a result of relaying the primary data. Moreover, a fast algorithm is proposed which aims to select and distribute the SBS antennas for primary and secondary data transmission to achieve better data and convergence rates, this algorithm is called Transmit Antenna Selection and Distribution (TASD). Simulation results show that the proposed precoders' design and the TASD algorithm enhance the total system sum rate (primary and secondary rates) compared to the conventional TAS (where the unselected antennas were not used to relay PUs' data). This enhancement was achieved by relaying primary data without affecting the rate of SUs. As a result, the computational time of the TASD algorithm is reduced by 52% compared to the full array case while the total sum rate of the algorithm is only 3% less than the full array scenario. Furthermore, the results in case of using a practical correlated channel proved the robustness of the proposed TASD algorithm, as it achieved nearly the same results compared to uncorrelated channels.

Achievable Rate Maximization Pattern Design for Reconfigurable MIMO Antenna Array

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions.

Passive Beamforming Design of IRS-Assisted MIMO Systems Based on Deep Learning.

In the intelligent reflecting surface (IRS)-assisted MIMO systems, optimizing the passive beamforming of the IRS to maximize spectral efficiency is crucial. However, due to the unit-modulus constraint of the IRS, the design of an optimal passive beamforming solution becomes a challenging task. The feature input of existing schemes often neglects to exploit channel state information (CSI), and all input data are treated equally in the network, which cannot effectively pay attention to the key information and features in the input. Also, these schemes usually have high complexity and computational cost. To address these issues, an effective three-channel data input structure is utilized, and an attention mechanism-assisted unsupervised learning scheme is proposed on this basis, which can better exploit CSI. It can also better exploit CSI by increasing the weight of key information in the input data to enhance the expression and generalization ability of the network. The simulation results show that compared with the existing schemes, the proposed scheme can effectively improve the spectrum efficiency, reduce the computational complexity, and converge quickly.

Performance and Capacity Optimization for High Speed Railway Communications Using UAV-IRS Assisted Massive MIMO System

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario.

Performance Evaluation of Hybrid Precoder Design for Multi-User Massive MIMO Systems with Low-Resolution ADCs/DACs

This paper presents a comprehensive analysis and design of a hybrid precoding system tailored for mmWave multi-user massive MIMO systems in both downlink and uplink scenarios. The proposed system employs a two-stage precoding approach, incorporating UQ and NUQ techniques, along with low-resolution DACs in downlink and ADCs in uplink to address hardware limitations. The system considers Zero Forcing and Minimum Mean Square Error algorithms as digital precoding methods for the uplink scenario, while exploring the impact of different DAC resolutions on system performance. Extensive simulations reveal that the proposed system surpasses conventional analog beamforming methods, particularly in multi-user scenarios involving inter-user interference. In downlink, the system demonstrates a trade-off between SE and EE, achieving higher Energy Efficiency with NUQ. In uplink, NUQ and UQ converters exhibit similar performance trends regardless of the chosen combiner algorithm. The proposed system attains enhanced Spectral and Energy Efficiency while maintaining reduced complexity and overhead. The study significantly contributes to the advancement of efficient and effective mmWave multi-user massive MIMO systems by providing a thorough analysis of various quantization schemes and precoding techniques. The findings of this research are expected to aid in the optimization of 5G and beyond technologies, particularly in high-density deployment scenarios.

Design and Simulation of MIMO Systems over Rayleigh Fading Channel and Using Near-Maximum-Likelihood Detection

While coding is the main element for the success of the MIMO system implementation, the communication channel is the main component that ascertains this. This paper describes the design and simulation of a 2x2, 4x4, and 8x8 MIMO system which is used exclusively in 5G mobile systems. The detection performed in such a system is near-maximum-likelihood detection. The noise added at the front end of the receiver is additive white Gaussian noise with zero mean and variance which depends upon the signal-to-noise ratio (SNR) while the signal is transmitted over a frequency-selective Rayleigh fading channel. The MIMO system operates at a much higher level of complexity to exploit the channel space-time resources. Orthogonal space-time block codes are used at the transmitter. An Image is transmitted and the received image is compared with the transmitted image and BER is calculated. This is done for varying SNRs. The whole project is performed over a front-end application where the various parameters are populated and run. This makes the simulation extremely easy for the 2x2 or 4x4 or 8x8 MIMO application with varying channel parameters. The modulation scheme used in this work is BPSK.

Joint Transceiver and Intelligent Reflecting Surface Design for mmWave Massive MIMO Systems

This article aims to maximize the spectral efficiency of an intelligent reflecting surface (IRS)-assisted hybrid millimeter-wave massive multiple-input–multiple-output system by jointly optimizing the hybrid precoding at the base station, hybrid combining at the user equipment, and reflecting beamforming at the IRS, where the reflecting beamforming and analog precoding/combining are implemented with finite-resolution phase shifters. This joint design problem can be decoupled into reflecting beamforming and hybrid precoding/combining design problems. However, such a decoupled approach experiences difficulties in solving the reflecting beamforming design problem. A known solution further reformulates the reflecting beamforming design problem into a more tractable problem addressed by a Riemannian manifold optimization (RMO)-based algorithm. However, the application of the RMO-based algorithm to solve the reformulated problem may lead to performance degradation given that the reformulated problem differs from the original one. Moreover, the RMO-based algorithm is tailored for the IRS with infinite-resolution phase shifters rather than practical finite-resolution phase shifters. Furthermore, the complexity of the RMO-based algorithm remains high. Therefore, we propose two coordinate descent method-based algorithms to tackle these issues. Simulation results demonstrate the effectiveness of the proposed algorithms compared with the state-of-the-art RMO-based algorithm.

Structure algorithms, normal forms and their role in the problem of feedback design for input-affine nonlinear MIMO systems

Structure algorithms, normal forms and their role in the problem of feedback design for input-affine nonlinear MIMO systems

Capacity Analysis and Optimal Spacing Design for Compact Array Massive MIMO Systems With Finite Aperture

To achieve large-scale antenna deployment in limited space, compact arrays should be widely used. In this letter, we discuss the mathematical relationship between system capacity and the aperture of compact uniform linear arrays (ULAs). In particular, the theoretical analysis benefits from the Marcenko-Pastur law and Toeplitz feature of the mutual coupling and correlation matrices. The element spacing optimization problem is then formulated to maximize the system capacity under a finite aperture. After illustrating its convexity, the optimal spacing can be obtained by applying one-dimensional search methods. Further, the discussions can be readily extended to compact uniform planar arrays (UPAs) with finite apertures. Numerical results show the superiority of optimal spacing in system capacity for both ULAs and UPAs to half-wavelength spacing.