Multiple-input Multiple-output Wireless Communication Research Articles

Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally.

Supporting Immersive Video Streaming via V2X Communication

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method’s effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.

Array pattern synthesis with phase-only constraint based on scaled-ADMM algorithm

The utilization of constant modulus beamforming technology is prevalent in multiple-input multiple-output (MIMO) radar systems and MIMO wireless communication systems. This paper investigates the problem of phase-only pattern control and synthesis in linear antenna array systems. It imposes a fixed amplitude constraint on the excitation signal, which only involves a phase constraint. However, this constraint is NP-hard and non-convex. To effectively address this challenging problem, we propose an iterative solution based on the scaled form of ADMM. This solution provides a means to rapidly synthesize patterns and significantly contributes to mitigating interference in other directions. In addition, the convergence of the proposed method is proved theoretically, and it is further demonstrated that the algorithm converges to the KKT stationary point of the original problem. Finally, simulation results show that the proposed method is superior to the most advanced methods in terms of pattern synthesis and convergence speed.

Performance analysis of hybrid combining schemes under Middleton class‐A and SαS${\rm S}\alpha{\rm S}$ impulsive noise model

AbstractImpulsive noise poses a significant challenge to single input multiple output wireless communication systems. This paper assesses the effectiveness of hybrid combining techniques, namely selection combining‐equal gain combining and selection combining‐maximal ratio combining, in mitigating impulsive noise impact caused by both man‐made and natural phenomena. The evaluation considers three different noise models: Additive white Gaussian noise, Middleton class‐A impulsive noise, and symmetric alpha stable impulsive noise, across Rayleigh and Rician fading channels. Bit error rate is employed to gauge channel performance. Our research contribution lies in the comprehensive assessment of hybrid combining techniques under diverse noise models, addressing a critical research gap in wireless communication. Findings indicate that impulsive noise has a more pronounced impact on channel performance than additive white Gaussian noise, and hybrid combining techniques prove more effective in mitigating these effects. The results hold significant implications for single input multiple output wireless communication systems, guiding the development of more robust and reliable communication systems in impulsive noise scenarios.

A Hybrid of Encryption and Error Correction Method for Multiple-Input Multiple-Output Wireless Communication Systems

A Hybrid of Encryption and Error Correction Method for Multiple-Input Multiple-Output Wireless Communication Systems

A hybrid of encryption and error correction method for multiple-input multiple-output wireless communication systems

A hybrid of encryption and error correction method for multiple-input multiple-output wireless communication systems

Linear digital precoding technology in massive multiple input multiple output wireless communication system

Linear digital precoding technology in massive multiple input multiple output wireless communication system

A Novel THz Massive MIMO Beam Domain Channel Model for 6G Wireless Communication Systems

In this paper, a three dimensional (3D) geometry based stochastic model (GBSM) considering planar antenna array is firstly proposed for the sixth generation (6 G) terahertz (THz) massive multiple-input multiple-output (MIMO) wireless communication systems. Then, a novel beam domain channel model (BDCM) is derived from the proposed GBSM based on the Fourier transform matrix from the spatial domain to the angle domain in horizontal and vertical directions simultaneously. The THz propagation characteristics, including non-negligible diffuse scattering and limited order of reflection, are considered in these two channel models. In addition, the proposed GBSM and BDCM can capture the spherical wavefront and spatial non-stationarity characteristics in massive MIMO channels by deriving steering vectors of near-field clusters and partly visible clusters, respectively. The angle-domain sparsity property of the BDCM can be observed, which helps reduce the complexity of the GBSM and improve the mathematical tractability. Typical statistical properties of the proposed GBSM and BDCM are derived and compared. The effects of the spherical wavefront on space-time-frequency correlation functions (STF-CFs) and channel capacity are also studied for the proposed GBSM and BDCM. The power leakage of the BDCM caused by the spherical wavefront and visible region (VR) is thoroughly analyzed. It is found that the statistical properties of the GBSM and BDCM fit well and are considerably influenced by the spherical wavefront and VRs.

Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0

Secure and reliable wireless communication systems are essential for the successful integration of smart grid-enabled wireless communications and Industry 5.0 applications. This work proposes a hybrid precoding design for secure smart-grid multiple-input multiple-output (MIMO) wireless communications in Industry 5.0 to maximize the average secrecy capacity of the system while minimizing user outages crucial for maintaining the security and privacy of sensitive data in critical infrastructure. To achieve this, we develop a mathematical model that considers the impact of various system parameters, such as eavesdropping and interference caused by the active eavesdropper and noise, on the secrecy capacity. To find an efficient solution to the optimization problem, we propose an algorithm that decouples the original optimization problem into a series of subproblems and uses iterative techniques to find the optimal values, thereby improving computational efficiency. The hybrid precoding scheme is an effective technique for optimizing the design parameters of MIMO-based secure wireless communication systems. Our proposed approach provides a practical solution for achieving this optimization. Our numerical results demonstrate that our proposed scheme outperforms traditional benchmark schemes, maximizing the average secrecy capacity while minimizing user outages. Our work highlights the importance of secure wireless communication systems in Industry 5.0 and smart grid applications. The proposed approach provides an efficient method for designing secure wireless communication systems that can effectively address the unique challenges posed by these critical infrastructure systems.

A Novel Beam Domain Channel Model for B5G Massive MIMO Wireless Communication Systems

In this paper, a novel beam domain channel model (BDCM) is proposed for beyond fifth generation (B5G) massive multiple-input multiple-output (MIMO) wireless communication systems. Different from conventional massive MIMO BDCMs which assumed the far-field plane wavefront effect, the proposed BDCM considers more realistic spherical wavefront caused by near-field effect. We transform a massive MIMO geometry-based stochastic model (GBSM) from the antenna domain to the beam domain through specific algorithms to obtain the novel BDCM. The space-time-frequency correlations of both the GBSM and BDCM are studied, and the correlations for both models at the cluster level are similar. We also compare the quasi-stationary distance (QSD), computational complexity, and channel capacity for both models. Results show that in comparison to the GBSM, the novel BDCM has lower complexity and similar accuracy if the number of beams is sufficiently large. Furthermore, we compare the singular value spreads (SVSs) of both channel models with channel measurements under the same conditions. Both the novel BDCM and GBSM are close to the measurement. Through the above analysis, the novel BDCM is proved to be more convenient for information theory and signal processing researches than the conventional GBSMs.

A Self-Decoupling Technique to Realize Dense Packing of Antenna Elements in MIMO Arrays for Wideband Sub-6 GHz Communication Systems.

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other. Current from the adjacent ports cancels their impact which results in low mutual coupling. The additional benefit of this configuration is realizing a densely packed array. The ground plane of each radiating element on the array board itself are isolated to mitigate surface wave propagations to suppress mutual coupling between the antenna elements. The radiating elements are based on a modified edge-fed circular patch antenna that includes a curved slot line and open-circuited stub to widen the array's impedance bandwidth with no impact on the antenna's footprint size. The proposed technique was verified with the design of an antenna array of matrix size 4 × 4 centered at 3.5 GHz. The array had a measured impedance bandwidth of 4 GHz from 1.5 GHz to 5.5 GHz, which corresponds to a fractional bandwidth of 114%, peak gain of 3 dBi and radiation efficiency of 84%. Its average diversity gain and envelope correlation coefficient (ECC) over its operating band are 9.6 dB and <0.016, respectively. The minimum isolation achieved between the radiating elements is better than 15 dB. The dimensions of the array are 0.4 × 0.4 × 0.039λ_g^3. The proposed array has characteristics suitable for sub-6 GHz wireless communication systems.

Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.

Zero Forcing Uplink Detection Through Large-Scale RIS: System Performance and Phase Shift Design

A multiple-input multiple-output wireless communication system is analytically studied, which operates with the aid of a large-scale reconfigurable intelligent surface (LRIS). LRIS is equipped with multiple passive elements with discrete phase adjustment capabilities, and independent Rician fading conditions are assumed for both the transmitter-to-LRIS and LRIS-to-receiver links. A direct transceiver link is also considered which is modeled by Rayleigh fading distribution. The system performance is analytically studied when the linear yet efficient zero-forcing detection is implemented at the receiver. In particular, the outage performance is derived in closed-form expression for different system configuration setups with regards to the available channel state information (CSI) at the receiver. In fact, the case of both perfect and imperfect CSI is analyzed. Also, an efficient phase shift design approach at LRIS is introduced, which is linear on the number of passive elements and receive antennas. The proposed phase shift design can be applied on two different modes of operation; namely, when the system strives to adapt either on the instantaneous or statistical CSI. Finally, some impactful engineering insights are provided, such as how the channel fading conditions, CSI, discrete phase shift resolution, and volume of antenna/LRIS element arrays impact on the overall system performance.

Diversity Order and Measure of MIMO Antennas in Single-User, Multiuser, and Massive MIMO Wireless Communications

In this letter, the diversity order (DO) and the diversity measure (DM) of multiple-input multiple-output (MIMO) antennas in generic single-user, multiuser, and massive MIMO wireless communications are calculated for several practical case studies. These performance metrics are calculated from the antenna correlation coefficient (ACC) matrix. The DO is calculated from the rank of the ACC matrix, and the DM is obtained from the trace and Frobenius norm of the ACC matrix. Numerical results are provided. In particular, it is shown that though the DM is always less than the DO, it incorporates the effect of antenna correlation on the diversity gain in a better way.

NON-ORTHOGONAL MULTIPLE ACCESS WITH V-BLAST UNDER OPTIMUM ORDERING

Non-orthogonal multiple access (NOMA) is expected to be one of the fundamental technologies of the upcoming 6G standard. This is mainly due to NOMA's potential in enabling all the users to utilize the same frequency band and the same time slot. In this paper, NOMA is implemented in a multiple-input multiple-output wireless communication system. By implementing zero-forcing V-BLAST algorithm into NOMA, spectral efficiency is improved, and successive interference cancellation is carried out. To perform optimum ordering, receive antennas’ signatures are compared and which signal to be decoded first is determined. Outage probability results of the system with optimum ordering and the same system without ordering are compared. In this way, a 3 dB gain is shown to be attained in terms of signal-to-noise ratio.

Proper Power Azimuth Spectrum Estimation for MIMO Channels via Gans Mapping Inversion

The use of antenna arrays is indispensable in radar and multiple-input multiple-output (MIMO) wireless communications areas, as it permits the treatment of wavefronts in the spatial domain, enabling important applications such as beamforming and target detection. Spatial information is commonly given in terms of spatial correlation functions (SCFs), but its representation in terms of angular domain, such as the power azimuth spectrums (PASs), is sometimes more convenient. Therefore, transforming from SCF to PAS, and vice versa, is often required. This is commonly done by employing so-called beamformers, where the Bartlett beamformer (BB) is the most commonly utilized. However, the use of beamformers alone is not enough for retrieving PAS information from a given SCF. Particularly, this paper shows that a non-linear operation is missing in relating SCFs to PASs as provided by current beamformers. The paper also shows the mathematical expressions that correctly relate SCFs to PASs and wavenumber spectrums (WNSs) for the general case of systems with antenna arrays. The developed theory results in two new beamformers, which, corroborated by simulation results, solve any previous inconsistency in estimating PASs from SCFs.

A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

A General 3D Non-Stationary 6G Channel Model With Time-Space Consistency

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement.

Ultra-High Mobility Analysis of MIMO Wireless System in Tunnel Scenarios

This letter studies the effect of ultra-high mobility on the performance of multiple-input multiple-output (MIMO) wireless communication system in tunnel scenarios. It is found that the Doppler frequency shift generated by the ultra-high mobility reduces the correlation coefficient. Importantly, it is observed that there exists a key inflection point distance in the ultra-high speed tunnel scene. When this distance is exceeded, the channel correlation of MIMO can be significantly reduced. Moreover, it is perceived that the inflection point distance is relatively stable and is less affected by the communication frequency. On this basis, we propose a high-quality communication area with low MIMO channel correlation. For the ultra-high speed of 720km/h, the inflection distance is about 95m, and the reduction of MIMO channel correlation at 3.5GHz and 28GHz can reach up to 52.7% and 41.2%, respectively. The findings are extremely useful for the applications of the mobile MIMO communication system in tunnel and railway scenarios.

STBC Identification for Multi-User Uplink SC-FDMA Asynchronous Transmissions Exploiting Iterative Soft Information Feedback of Error Correcting Codes

With the advancement and widespread implementation of multiple-input multiple-output (MIMO) wireless communication systems over the last decade, space-time block coding (STBC) identification has become a critical task for intelligent radios. Previous examinations of STBC identification were focused on single-user transmissions over single-carrier and multi-carrier systems in combination with uncoded broadcasts. Practical systems, on the other hand, contain many users and employ error-correcting codes. For the first time in literature, this work explores the problem of STBC identification for multi-user uplink transmissions in single-carrier frequency division multiple access (SC-FDMA) systems. We take another step closer to real systems by addressing asynchronous transmissions and by conducting multi-user channel estimation. We also exploit the outputs of the channel decoder, which is usually used in many practical systems, to improve the identification and estimation processes. The mathematical analysis demonstrates that the maximum-likelihood (ML) solution of STBC identification, channel estimation, and synchronization can be executed by an iterative approach. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to separate the overlaid signals arriving at the base-station (BS). The parameters under consideration for each user are then updated using an expectation-maximization (EM) processor. Simulation results show that the proposed architecture outperforms other identification methods published in the literature while maintaining a reasonable level of processing time.