Multiple-input Multiple-output Systems Research Articles

Improvement of Internet of Things (IoT) Interference Based on Pre-Coding Techniques over 5G Networks

The advent of 5G technology has revolutionized wireless communication, offering unprecedented data rates, reduced latency, and enhanced connectivity. A critical component driving these advancements is Multiple-Input Multiple-Output (MIMO) technology. MIMO utilizes multiple antennas at both the transmitter and receiver ends to improve communication performance. In the context of the Internet of Things (IoT), MIMO plays a pivotal role in enhancing network efficiency, reliability, and capacity and can improve system capacity and reduce interference between different users. By leveraging MIMO, IoT devices can achieve higher data throughput and better signal quality, even in challenging environments. This is particularly important for IoT applications that require real-time data transmission and low latency, such as smart cities, autonomous vehicles, and industrial automation. Additionally, MIMO technology helps in mitigating interference and improving spectrum utilization, making it an essential enabler for the massive connectivity demands of IoT networks in the 5G era. However, due to the high channel dimension, complex channel estimation and precoding algorithms in the system, the system hardware and software overhead will increase. The precoding algorithms of massive MIMO systems are divided into three types: digital, analog and hybrid. The three types of precoding algorithms are summarized and compared, and the advantages and disadvantages of different precoding algorithms and applicable scenarios are summarized. The channel estimation schemes are divided into training estimation and blind estimation, and the advantages and disadvantages of the two types of schemes are summarized. It is pointed out that the reasonable use of the channel sparsity of massive MIMO can improve the quality of channel estimation and reduce the estimation overhead.

Optimal Reconfigurable Intelligent Surface Deployment for Secure Communication in Cell-Free Massive Multiple-Input Multiple-Output Systems with Coverage Area

Optimal Reconfigurable Intelligent Surface Deployment for Secure Communication in Cell-Free Massive Multiple-Input Multiple-Output Systems with Coverage Area

Adaptive Filtering for Channel Estimation in RIS-Assisted mmWave Systems

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA).

A Hybrid Genetic Algorithm with Tabu Search Using a Layered Process for High-Order QAM in MIMO Detection

In this paper, we propose a hybrid genetic algorithm (HGA) that embeds the tabu search mechanism into the genetic algorithm (GA) for multiple-input multiple-output (MIMO) detection. We modified the selection and crossover operation to maintain the diverse and wide exploration areas, which is an advantage of the GA, and the mutation operation to perform a local search for a specific region. In the mutation process, the ’tabu’ concept is also employed to prevent the repeated search of the same area. In addition, a layered detection process is applied simultaneously with the proposed algorithm, which not only improves the bit error rate performance but also reduces the computational complexity. We apply the layered HGA (LHGA) to the MIMO system with very high modulation order such as 64-quadrature amplitude modulation (QAM), 256-QAM, and 1024-QAM. Simulation results show that the LHGA outperforms conventional detection approaches. Especially, in the 1024-QAM MIMO system, the LHGA has less than 10% of computational complexity but a 6 dB signal-to-noise ratio (SNR) gain compared to the conventional GA-based MIMO detection scheme.

Linear Detection and Circuit Structure Based on ZF, MMSE Matrix Iteration and Operations

The Multiple-Input Multiple-Output (MIMO) technology substantially boosts the reliability and data transfer rates of communication links by enabling the parallel transmission of multiple data signals over the same frequency bandwidth, utilizing multiple transmit and receive antennas.Despite these enhancements, traditional MIMO systems are approaching their theoretical throughput limits, which underscores the need for developing new algorithms to tackle computational complexity.This paper categorizes the primary technical approaches into three types: iterative approximation methods, matrix operation-based methods, and the emerging Joint channel Estimation and Data detection (JED) algorithms.It delves into the Neumann series approximation for efficient matrix inversion in large-scale MIMO systems, the Gauss-Seidel iterative approach for soft-output detection, and LDLT decomposition-based algorithms for matrix inversion efficiency. Additionally, the paper emphasizes the JED algorithm, such as PROX, which presents a low-complexity solution for large Single-Input Multiple-Output (SIMO) wireless systems.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

Interference Mitigation in B5G Network Architecture for MIMO and CDMA: State of the Art, Issues, and Future Research Directions

The emergence of Beyond 5G (B5G) networks introduces novel challenges related to interference management, particularly within the context of Multiple-Input, Multiple-Output (MIMO) and Code Division Multiple Access (CDMA) technologies. In this comprehensive review paper, we delve into the intricacies of interference mitigation techniques within the B5G framework, with a specific focus on MIMO and CDMA systems. Firstly, we provide a brief overview of MIMO and CDMA principles, emphasizing their significance in B5G networks. MIMO leverages spatial diversity by employing multiple antennas in both the transmitter and the receiver, thereby enhancing capacity and reliability. CDMA, on the other hand, enables multiple users to share the same frequency band by assigning unique codes to each user. Next, we categorize the various types of interference encountered in MIMO and CDMA systems. These include co-channel interference, adjacent-channel interference, and multiuser interference. Understanding these interference sources is crucial for designing effective mitigation strategies. Our exploration of interference mitigation techniques covers state-of-the-art approaches tailored for MIMO and CDMA scenarios. Lastly, we discuss future research directions in interference mitigation for B5G networks. This review paper provides valuable insights for researchers, practitioners, and network designers seeking to enhance the robustness and efficiency of B5G communication systems by effectively mitigating interference in MIMO and CDMA contexts.

Present View and Perspectives in the Optimization of Electrical Parameters of Wireless Communication System

Abstract The paper aims to analyze the performance of wireless communication systems by focusing on key electrical parameters such as signal power, bandwidth, signal-to-noise ratio (SNR), attenuation, and modulation techniques. The study utilizes advanced concepts including MIMO (Multiple Input Multiple Output) systems, dynamic power control, and Shannon-Hartley theorem, all analyzed using MATLAB/Octave for simulation and optimization purposes. The investigation is conducted to optimize the efficiency and reliability of wireless networks, ensuring better resource management and improved quality of service (QoS).

A dual-port MIMO antenna system for 5G and Wi-Fi 6E communications using a parasitic element

The increasing demand for high-speed, reliable wireless communication necessitates innovative antenna designs that can efficiently support emerging technologies such as 5G and Wi-Fi 6E. Traditional antenna solutions often struggle to meet the compactness, isolation, and efficiency requirements posed by these advanced systems. This research addresses these challenges by introducing a compact dual-hook multiple input multiple output (MIMO) antenna specifically tailored for 5G and Wi-Fi 6E applications. The proposed antenna offers dual-band operation in a small size with high isolation at both resonance frequencies (3.6 GHz and 7.1 GHz). The novel design has a simple and low-profile structure and can be easily integrated into practical applications. It reduces the cost and complexity of the system. The MIMO technology has a good reflection coefficient, a good efficiency and a maximum gain of 3.85 dB. To enhance the effeciency of this structure, parasitic element and defective ground structure (DGS) are combined. The twin-hook radiators are printed symmetrically on the top layer of low-cost FR-4 substrate with a small size of 19 × 36 mm2 (0.23 λ0 × 0.45 λ0, with λ0 is the guided wavelength at the lowest operating frequency). In addition, the DGS is formed by cutting two symmetrical slots into the semi-ground plane to provide better impedance matching at both resonant frequencies. Based on the simulation findings, the -10dB return loss bandwidth of the proposed structure reaches approximately 14,08% and 10,21% for 3.6GHz and 7.1GHz, respectfully. On the other hand, high isolation is obtained, reaching over -21 dB in the low frequency band and -29 dB in the high frequency band. Also, for verification of MIMO system characteristics, diversity gain (DG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC) and channel capacity loss (CCL) are analysed and used to investigate the proposed MIMO antenna diversity performance.

Fixed-time resource allocation algorithm for high-order MIMO nonlinear multi-agent systems

This article develops a distributed nonsingular fixed-time algorithm for the resource allocation problem of high-order multiple-input multiple-output (MIMO) nonlinear multi-agent systems (MASs). Different from well-known resource allocation issues, our research considers the inequality constraint and high-order dynamics of systems. In order to make the MASs fixed-time stable while the optimization problem converge to the optimal solution within fixed time, we design the optimization protocol leveraging the integral type Lyapunov function and fractional power nonlinear filter. What is more, we construct the distributed estimators to estimate the gradient information of other agents instead of directly using it. This protocol ensures that all the signals remain semi-global practical fixed-time stable (SGPFTS), and concurrently, all the agents’ outputs converge to the neighborhood of optimal solutions of the global objective function. Finally, the validity of the theoretical results is verified by a simulation.

Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer

To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power allocation of users. To solve this CLSMOP, a constrained large-scale multi-objective evolutionary algorithm (CLSMOEA), considering the dimensionality reduction as well as the balance of objectives and constraints, is explored. The Lagrange multiplier is first used to construct a two-scale optimization model, bridging original large-scale decision space of variables and small-scale decision space of coefficients of Lagrange multiplier. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces emphasizing objectives and constraints, respectively. In summary, the optimization in large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in small-scale space helps the population cross the infeasible areas to approach constrained PF, and the GD-based reproduction of offspring further guarantees the solution convergence. Ten representative and state-of-the-art constrained multi-objective evolutionary algorithms (MOEAs) and unconstrained MOEA have been compared to the proposed CLSMOEA to demonstrate its effectiveness through comparative experiments on some well-known benchmark problems (with 1000 variables), and MaMIMO-LU (with 1024 antennas and 256, 512, and 1024 users). Experimental results show that the proposed CLSMOEA can obtain the best SE-EE tradeoff.

A wideband circularly-polarized MIMO filtering array antenna using dual-layer circular patch

ABSTRACT A novel wideband circularly-polarized (CP) MIMO filtering array antenna with dual-layer circular cross-slotted patch is introduced. The presented element consists of a two-way feeding structure designed with equal amplitude and 90° phase difference, and a dual-layer circular cross-slotted patch structure. An aperture-couple-driven patch is positioned between a cross-slotted patch and a dual-feed structure. It facilitates magnetic coupling to achieve a high-frequency resonant mode and radiation null. Meanwhile, four arc-shaped patches are electrically coupled to the driver patch by surrounding it with four shorting vias to improve the bandwidth. In multiple-input multiple-output (MIMO) wireless systems, a two-port MIMO dual-CP filtering array antenna for the Sub-6 G band is proposed. The design utilizes four proposed CP antenna elements and a dual-port transmission structure. The dual-port transmission structure consists of two sets of T-junction power dividers that are 180° phase shifted with opposite polarization. The dual-CP MIMO filtering array antenna of the impedance bandwidth is 21.8% (3.1 ~ 3.9 GHz). The AR bandwidth is 13.1% (3.3 ~ 3.7 GHz), while the gain bandwidth is 20.1%. Additionally, the isolation between the antenna elements is less than −15.0 dB. The envelope correlation coefficient between its two ports is less than 0.02.

Search for optimal parameters of active structural suspension elements by determining their integral stiffness and damping characteristics

The paper presents an approach to the modelling of the “Rotor in active magnetic bearings” system and the subsequent search for optimal support parameters to ensure non-resonant operation of the machine in the entire range of operating rotational speeds. The object of the study is active magnetic bearings for the rotor of an industrial fan for local ventilation of the mine, which was previously installed in the rolling element bearings and due to the lack of the necessary load on them, have large frictional efficiency losses. To solve the problems with the loss of efficiency, it is proposed to use active magnetic bearings with a control system based on a proportional-integral-derivative controller with control principles of single input-single output, which is a cheaper and simpler system to implement compared to multiple input-multiple output systems, however, can be an effective tool for solving the described problems in the operation of the machine. The subject of the study is the dynamics of the rotor of an industrial fan in active magnetic bearings, which are modeled using spring-damper elements with stiffness and damping coefficients which are determined using the approach developed in the work. It is based on the representation of active magnetic bearings as an electromechanical system with components the dynamics of which are described by transfer functions in the frequency domain. The formation of the transfer function of the active magnetic bearing as a whole and its subsequent inclusion in the system of rotor dynamics equations allows obtaining relatively simple dependences of the stiffness and damping coefficients of the bearings on the rotor rotational speed. These dependencies contain the physical parameters of active magnetic bearing components and the parameters of their control systems which allows not only simulating the dynamics of the rotor in such bearings, but also searching for their optimal parameters, in particular, the value of the bias current in the electromagnets, which is necessary to ensure the absence of resonances of the rotor with synchronous excitation loads in the entire range of operating rotational speeds, which is confirmed by plotting the dependences of the rotor critical speeds on the bias current, as well as the Campbell diagram for the optimal value of the bias current.

Multi-Intelligent Reflecting Surfaces and Artificial Noise-Assisted Cell-Free Massive MIMO Against Simultaneous Jamming and Eavesdropping.

In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users. Then, a joint optimization framework based on the block coordinate descent (BCD) algorithm is constructed, and a non-convex optimization solution based on the univariate function optimization and semi-definite relaxation (SDR) is proposed with the aim of maximising the minimum achievable secrecy rate for users. By solving the univariate function maximisation problem, the multi-variable coupled non-convex problem is transformed into a solvable convex problem, obtaining the optimal AP beamforming, AN matrix and IRS phase shift matrix. Specifically, in a single-user scenario, the scheme of multiple intelligent reflecting surfaces combined with artificial noise can improve the user's achievable secrecy rate by about 11% compared to the existing method (single intelligent reflective surface combined with artificial noise) and about 2% compared to the scheme assisted by multiple intelligent reflecting surfaces without artificial noise assistance.

Flexible dual-band antenna for ISM/5G enabled MIMO systems with pattern diversity for wireless body area networks

Abstract This paper outlines the design of a six-element multiple-input–multiple-output (MIMO) antenna with pattern diversity for industrial scientific medical (ISM)/5G-enabled wireless body area networks (WBANs). Within the MIMO configuration, each element has a quasi-yagi antenna configuration implemented on an ultrathin microwave laminate. The proposed quasi-yagi antenna has a small form factor of 25 × 25 mm, featuring a dipole-like radiator excited through a microstrip-line to tapered slot-line transition. The antenna’s radiators are patterned to ensure a dual-narrow impedance bandwidth. The conventional strip-line director in the planar yagi is replaced with a semicircular loop-like director, enhancing directional radiation patterns. This proposed flexible antenna offers versatile functionality by operating at both ISM standards of 2.45 GHz and the 5G wireless local area network standard at 3.5 GHz. The quasi-yagi elements are strategically distributed in a hexagonal formation to construct the six-element MIMO scheme with pattern diversity, resulting in a tangential radiation pattern suitable for on-body communication. Following fabrication, the prototype MIMO antenna’s simulation results are validated through real-time measurements. The proposed antenna exhibits an average gain exceeding 3.5 dBi across both operating bands. Furthermore, the proposed MIMO antenna exhibits promising performance metrics suitable for densely cluttered WBAN environments.

Observer-Based Prescribed Performance Adaptive Neural Network Tracking Control for Fractional-Order Nonlinear Multiple-Input Multiple-Output Systems Under Asymmetric Full-State Constraints

In this work, the practical prescribed performance tracking issue for a class of fractional-order nonlinear multiple-input multiple-output (MIMO) systems with asymmetric full-state constraints and unmeasurable system states is investigated. A neural network (NN) nonlinear state observer is developed to estimate the unmeasurable states. Furthermore, the barrier Lyapunov functions with the settling time regulator are employed to deal with the asymmetric full-state constraint from the fractional-order MIMO system. On this ground, the prescribed performance adaptive tracking control approach is designed, assuring that all system states do not exceed the prescribed boundaries, and the tracking errors converge to the predetermined compact sets within a predefined time. Finally, two simulation examples are presented to show the effectiveness and practicability of the proposed control scheme.

Error governor for active fault tolerance in PID control of MIMO systems

Input saturation and actuator faults are common issues in control system engineering. This paper proposes an Error Governor (EG) policy that dynamically manages the feedback error to enhance the tracking error in Multiple Input-Multiple Output (MIMO) closed-loop system controlled by Proportional-Integral-Derivative (PID) regulators. By integrating an Adaptive Kalman Filter (AKF) for optimal fault estimation with the EG scheme, the solution enables fault-tolerant control without requiring modifications to the baseline controller, which can be impractical or unsuitable in certain applications. Furthermore, the proposed control scheme completely avoids windup, thereby replacing conventional Anti-Windup (AW) schemes for PIDs, and is computationally cheap and easy to implement, needing only the same inputs as conventional AW algorithms and the actuator fault estimation. Simulation results on the MIMO model of the Zagi flying-wing aerial vehicle show that the EG reduces the tracking error in presence of actuator faults by − 49.92 % in terms of Integral Absolute Error (IAE), outperforming conventional AW methods.

Leveraging Massive MIMO for Enhanced Energy Efficiency in High-Density IoT Networks

Maximizing energy efficiency (EE) in massive multiple-input multiple-output (MIMO) systems, while supporting the rapid expansion of Internet of Things (IoT) devices, is a critical challenge. In this paper, we delve into the intricate operations geared toward enhancing EE in such complex environments. To effectively support a multitude of IoT devices, we adopt a strategy of heavy reference signal (RS) reuse, and in this circumstance, we formulate the EE metrics and their corresponding inverses to determine pivotal operational parameters. These EE-centric parameters encompass factors such as the number of service antennas in the base station (BS), the number of IoT devices, and permissible coverage extents. Our objective is to calibrate these parameters to meet a predefined EE threshold, ensuring optimal system performance. Additionally, we recognize the indispensable role of Peak-to-Average Power Ratio (PAPR) reduction techniques, particularly in multicarrier systems, to further enhance EE. As such, we employ clipping-based PAPR reduction methods to mitigate signal distortions and bolster overall efficiency. Theoretical EE metrics are derived based on formulated signal-to-interference-plus-noise ratios (SINRs), yielding insightful closed-form expressions for the operational parameters. Leveraging two distinct EE metric models, we undertake parameter determinations, accounting for the levels of approximation. Intriguingly, our analysis reveals that even simplified models exhibit remarkable applicability in real-world scenarios, with a minimal margin of error. The results not only underscore the practical applicability of our theoretical constructs but also highlight the potential for significant EE enhancements in massive MIMO systems, thereby contributing to sustainable evolution in the IoT era.

Algorithm for image transmission in the MIMO-OFDM system in the presence of active interference and structural distortions

Problem Statement. With the advent and ever-growing development of wireless communications, the demand for high-speed, reliable and energy-efficient wireless data transmission methods has become an urgent need in our increasingly interconnected world. One of the most promising technologies to meet this need is a combination of multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) systems. However, as with any technology, these systems face challenges. One of the significant obstacles to their effective implementation is the presence of active interference and structural distortions in image transmission. This paper is devoted to solving this problem, focusing on improving the adaptive image transmission algorithm in the MIMO-OFDM system under such adverse conditions. Objective. Improving the efficiency of a wireless data transmission system in an environment with multiple reflections and active interference by reducing the probability of a bit error based on the development and research of spatio-temporal methods of signal processing and the development of an adaptive algorithm. Results. The main features of image transmission via MIMO-OFDM communication systems are considered. The main characteristics of such communication systems are given, the structural diagram of the transceiver part, models of interference that occur in the communication system, the structure of pilot signals for channel estimation and the algorithm of operation of such a communication system are considered. The adaptive algorithm for processing spatio-temporal signals in the MIMO-OFDM communication system is considered and improved. The MIMO-OFDM system is simulated, and an in-house model of a simulator of such a communication system is developed, experiments are conducted to study the influence of spatial correlations between antennas on the operation of such a system, the gain from using the adaptive algorithm for processing spatio-temporal signals is shown and substantiated, which demonstrates its applied value. Practical significance. The proposed modification of the adaptive algorithm showed a stable improvement in terms of the quality of transmitted images under conditions of active interference and structural distortions. This makes the practical application of the adaptive algorithm useful, for example, in the situation of transmitting images from a UAV under conditions of a complex interference-signal environment and is capable of significantly improving the quality of transmitted images.

A Sub-Channel Spatial Homogeneity-Based Channel Estimation Method for Underwater Optical Densely Arrayed MIMO Systems

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing Nt sub-channels of K-sparse to 2Nt irrespective of CIR residual compensation.