Multiple-input Multiple-output Communication Systems Research Articles

Development model of a high-performance multiple input multiple output microstrip antenna based on a planar series array with 8×2 elements for 5G communication systems

MIMO (Multiple Input Multiple Output) makes a major contribution to 5G communication systems by increasing network capacity and spectrum efficiency. In 5G, MIMO enables the use of multiple antennas at base stations and user devices, allowing simultaneous sending and receiving of data over multiple paths. This significantly increases data throughput and connection reliability, especially in environments with high user density. In addition, MIMO technology supports the implementation of beamforming, which focuses signals on a specific direction, reduces interference, and improves signal coverage and quality, making it one of the keys to achieving faster and more responsive 5G performance. Therefore, antennas with wide bandwidth, high gain and MIMO performance are crucial for supporting 5G communication systems. This paper proposes a high-performance MIMO microstrip antenna based on a series planar array with 8×2 elements operating at a resonant frequency of 3.5 GHz for 5G communication systems. A spiral stub and a feed inset are proposed to control the reflection coefficient and bandwidth of the antenna while the series planar array is proposed to increase the gain. To support the MIMO communication system, the proposed antenna is separated into two different ports with a certain distance. From the measurement results, the proposed antenna has high performance indicated by a wide bandwidth of 680 MHz (3–3.68 GHz) and a high gain of 17.8 dB at a resonant frequency of 3.5 GHz. In addition, the proposed antenna has high mutual coupling and diversity indicated by ECC and DG of 0.001 and 9.99 dB, respectively. This work provides a solution to design a high-performance microstrip antenna and can be implemented as a receiving antenna for 5G communication systems

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Controlled alignment imaging optical MIMO communication system based on light spot detection of arrayed light sources

Visible light communication (VLC) technology has become one of the potential key technologies for 6 G due to its unique features, such as abundant license-free spectrum, high confidentiality, and low electromagnetic interference. In this paper, an optical multiple-input multiple-output (MIMO) communication system based on imaging reception is proposed, which can improve the data transmission rate and reduce interference between different sub-channels. To overcome the problem of optical communication link alignment, in this paper, we established an imaging optical MIMO communication system with a controlled light source array (CLSA) and proposed an optical alignment strategy to address the alignment issue. The scheme decouples the constraint that the light spot cannot be aligned with the detector in the communication system under the influence of optical parameters. Based on the light spot detection CLSA module, the communication system can ensure the stability of the link alignment when the communication distance changes. Experimental results show that when the communication distance is 20-80 m, the bit error rate (BER) of the communication system remains below 10-6. The single-channel communication rate of the system is 5 Mbps, and the total communication rate is 20 Mbps through the four-channel rate multiplexing. Moreover, if the communication distance increases to the range of 80-100 m, the BER increases, but it is always lower than the forward error correction (FEC) threshold.

Diversity-multiplexing tradeoff for cooperative MIMO radar and communications system

In this paper, multiple-input multiple-output (MIMO) integrated radar and communications (IRC) system is studied, where the radar and communications parts work cooperatively, leading to a hybrid active-passive (HAP) MIMO radar and a radar-aided (RA) MIMO communications subsystems. To unify the performance metric for both subsystems, the diversity gain and multiplexing gain are derived for the RA MIMO communications and the HAP MIMO radar subsystems, based on which the tradeoff between the diversity and multiplexing gains is analyzed theoretically for each of the subsystems. The obtained diversity gain and multiplexing gain can facilitate the design and optimization of the IRC system. Employing these two unified performance metrics, the IRC system antenna number assignment is formulated as a multi-objective optimization problem, and the Pareto solutions are provided. The correctness of theoretical analysis is verified by simulations. Our results can provide useful guidance for system designers.

Performance Analysis of Power Allocation and User-Pairing Techniques for MIMO-NOMA in VLC Systems

In this paper, we evaluate the performance of multiple-input multiple-output (MIMO) communication systems applied with a non-orthogonal multiple access (NOMA)-based indoor visible light communication (VLC). We present two efficient user-pairing algorithms for NOMA in VLC, aiming to enhance achievable data rates effectively. Our investigation involves the application of three low-complexity power allocation techniques. Comparative analysis reveals performance enhancements when employing the proposed schemes, especially when contrasted with NOMA without user pairing and orthogonal frequency division multiple access (OFDMA). Additionally, we explore the performance of both algorithms in scenarios with both even and odd numbers of users. Simulation results demonstrate the superiority of NOMA in comparison to OFDMA.

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.

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

Deep Learning–Based Channel Extrapolation and Multiuser Beamforming for RIS-aided Terahertz Massive MIMO Systems over Hybrid-Field Channels

The reconfigurable intelligent surface (RIS) is a promising technology for terahertz (THz) massive multiple-input multiple-output (MIMO) communication systems. However, acquiring high-dimensional channel state information (CSI) and realizing efficient active/passive beamforming for RIS are challenging owing to its cascaded channel structure and lack of signal processing units. To overcome these challenges, this study proposes a deep learning (DL)-based physical signal processing scheme for RIS-aided THz massive MIMO systems over hybrid far-near field channels wherein channel estimation with low pilot overhead and robust beamforming are implemented. Specifically, first, an end-to-end DL-based channel estimation framework that consists of pilot design, CSI feedback, subchannel estimation, and channel extrapolation is introduced. In this framework, only some RIS elements are first activated, a subsampling RIS channel is then estimated, and a DL-based extrapolation network is finally used to reconstruct the full-dimensional CSI. Next, to maximize the sum rate under imperfect CSI, a DL-based scheme is developed to simultaneously design hybrid active beamforming at the base station and passive beamforming at the RIS. Simulation results show that the proposed channel extrapolation scheme achieves better CSI reconstruction performance than conventional schemes while greatly reducing pilot overhead. Moreover, the proposed beamforming scheme outperforms conventional schemes in terms of robustness to imperfect CSI.

Помехоустойчивость многопользовательских систем MIMO при неидеальной оценке канала и других искажениях

The technology of multi-antenna MIMO (Multiple Input Multiple Output) systems is actively used in modern wireless communication systems. MIMO can enhance the performance of wireless data transmission systems, but their effectiveness depends on the transmission conditions. Ideal conditions are represented as a channel with a large number of possible signal propagation paths and an error-free estimation of its parameters. The estimation error significantly affects the performance of the beamforming algorithms used to mitigate inter-user interference in multi-user MIMO (MU-MIMO) systems. Spatial correlation of the channel results in a decrease in the number of independent information transmission streams. These factors can significantly reduce the performance of multi- antenna systems. For this reason, operating MIMO communication systems under non-ideal conditions is a topical issue. The paper examines the performance of various beamforming algorithms in such conditions. It describes the communication systems with MIMO, beamforming algorithms, and distorting effects. The paper includes a developed simulation model of the communication channel with MU-MIMO accounting for a variety of distorting factors. The results demonstrate the bit-error probability dependences for different simulation scenarios.

An Efficient Mapping Scheme on Neural Networks for Linear Massive MIMO Detection

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.

Optical MIMO Communication Based on Joint Control of Base Station and OPA-Type OIRS

As a new type of optical communication device, the optical intelligent reflecting surface (OIRS) can reflect, shape and split optical signals in real time, and is expected to expand the application scenarios of optical wireless communication. However, in the existing research, a single OIRS cannot support optical multiple input multiple output (MIMO) communication since OIRS performs the same modulation on all incident light. In this paper, we propose an optical MIMO communication system based on joint control of base station and optical phased array (OPA)-type OIRS. The communication system utilizes the OPA -type OIRS's characteristic of superimposing the input optical phase and the OIRS's phase to implement a joint beam control scheme of the base station and the OIRS, thereby realizing optical MIMO communication with a single OIRS. Considering the OIRS's physical model, pointing error, atmospheric turbulence and attenuation, our work establishes the mathematical model of the optical MIMO communication system, and derives the closed-form expression of the probability density function (PDF) of the channel fading, asymptotic outage probability and bit error rate (BER) for the first time. Experiments and simulations are built to verify the feasibility and accuracy of our system model and theoretical derivation, which proves that our results are consistent with the actual system.

Deep Convolutional and Recurrent Neural-Network-Based Optimal Decoding for RIS-Assisted MIMO Communication

The reconfigurable intelligent surface (RIS) is one of the most innovative and revolutionary technologies for increasing the effectiveness of wireless systems. Deep learning (DL) is a promising method that can enhance system efficacy using powerful tools in RIS-based environments. However, the lack of extensive training of the DL model results in the reduced prediction of feature information and performance failure. Hence, to address the issues, in this paper, a combined DL-based optimal decoding model is proposed to improve the transmission error rate and enhance the overall efficiency of the RIS-assisted multiple-input multiple-output communication system. The proposed DL model is comprised of a 1-dimensional convolutional neural network (1-D CNN) and a gated recurrent unit (GRU) module where the 1-D CNN model is employed for the extraction of features from the received signal with further process over the configuration of different layers. Thereafter, the processed data are used by the GRU module for successively retrieving the transmission signal with a minimal error rate and accelerating the convergence rate. It is initially trained offline using created OFDM data sets, after which it is used online to track the channel and extract the transmitted data. The simulation results show that the proposed network performs better than the other technique that was previously used in terms of bit error rate and symbol error rate. The outcomes of the model demonstrate the suitability of the proposed model for use with the next-generation wireless communication system.

Measurements and Characteristics Analysis of 6G Ultra-Massive MIMO Wireless Channels With Different Antenna Configurations and Scenarios

In this paper, ultra-massive multiple-input multiple-output (MIMO) channel measurements at 5.3 GHz are conducted, including different antenna array configurations and different measurement scenarios. The antenna array configurations include uniform linear array (ULA) and distributed uniform linear array (DULA). The measurement scenarios include single-user fixed-to-fixed (F2F), multi-user F2F, and single-user fixed-to-mobile (F2M) measurement scenarios. The statistical properties of ultra-massive MIMO channels are studied, including delay power spectral density (DPSD), space cross-correlation function (SCCF), angle cross-correlation function (ACCF), frequency correlation function (FCF), root mean square (RMS) delay spread (DS), RMS angle spread (AS), singular value spread (SVS), scalar product (SP), level crossing rate (LCR), average fading duration (AFD), Doppler power spectral density (PSD), etc. The specific ultra-massive MIMO channel characteristics are investigated and validated by channel measurements, including spatial non-stationarity, spherical wavefront behavior, channel hardening, and sparse properties in the angle domain. In addition, channel capacities are investigated. The channel measurement results will be a great importance for ultra-massive MIMO communication system deployments.

Fast gradient algorithm for complex ICA and its application to the MIMO systems

This paper proposes a new gradient-descent algorithm for complex independent component analysis and presents its application to the Multiple-Input Multiple-Output communication systems. Algorithm uses the Lie structure of optimization landscape and toral decomposition of gradient matrix. The theoretical results are validated by computer simulation and compared to several classes of algorithms, gradient descent, quasi-Newton as well as complex JADE. The simulations performed showed excellent results of the algorithm in terms of speed, stability of operation and the quality of separation. A characteristic feature of gradient methods is their quick response to changes in the input signal. The good results of the proposed algorithm indicate potential use in on-line applications.

A low-complexity algorithm based on variational Bayesian inference for MIMO channel estimation

With an increase in the number of transmitters in multiple-input multiple-output (MIMO) communication systems, there is a cubic rise in the computational complexity of the traditional sparse Bayesian learning (SBL) channel estimation algorithm. While various algorithms are effective for single-input single-output (SISO) systems, they are not suitable for the MIMO scenario. This paper introduces a MIMO channel estimation algorithm based on variational Bayesian inference (VBI) by assuming the independence of the variational distribution among different channels. The high-dimensional channel vectors estimated in the conventional MIMO-SBL algorithm are decomposed into multiple parallel low-dimensional channel vectors with different sparsity using VBI. Consequently, the complexity exhibits a linear relationship with the number of transmitters, as demonstrated through numerical analysis. Simulations confirm the improved estimation accuracy of the MIMO-VBI algorithm. Experimental results reveal that MIMO systems can achieve lower bit error rates using the MIMO-VBI algorithm, with reduced runtime for channel lengths exceeding 100 symbols.

Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning

Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate. In this paper, we propose a semi-data-aided channel estimator for multiple-input multiple-output communication systems. Our strategy is to leverage reinforcement learning (RL) for selecting reliable detected symbols, then update the channel estimate by utilizing only the selected symbols as additional pilot signals. Towards this end, we first define a Markov decision process (MDP) which sequentially decides whether to use each detected symbol as an additional pilot signal. We then develop an RL algorithm to find an effective policy of the MDP based on a Monte Carlo tree search approach. In this algorithm, we exploit the a-posteriori probability for approximating both the optimal future actions and the corresponding state transitions of the MDP and derive a closed-form expression for the optimal policy under the approximations. A key advantage of the proposed channel estimator is that it requires less computational complexity than conventional iterative data-aided channel estimators. Simulation results demonstrate that the proposed channel estimator effectively mitigates both channel estimation error and detection performance loss caused by insufficient pilot signals.

A Study of Mutual Coupling Suppression between Two Closely Spaced Planar Monopole Antenna Elements for 5G New Radio Massive MIMO System Applications

5G NR (new radio) introduces the concept of massive MIMO (multiple-input multiple-output) technology, in which a larger number of antenna arrays are installed on the transceiver. Due to the increased number of antenna elements allocated close to each other (approximately at a distance of half a wavelength), mutual coupling becomes a serious problem leading to performance degradation of the MIMO communication system. In this communication, two different configurations of closely spaced antenna array elements are studied. In order to reduce the mutual coupling, a combination of a metamaterial-based frequency-selective surface (FSS), a metallic strip, and a slot element in the ground plane is examined. It is found that the proposed technique significantly suppresses mutual coupling from −12 dB to −25 dB. Both designs are fabricated and experimentally validated. The simulation results are in good agreement with the measurements. The proposed mutual coupling reduction technique may be suitable for massive MIMO systems in fifth-generation (5G) new radio applications.

Complex field manipulation based on OAM spectrum analysis with an arbitrarily distributed antenna array

In the next generation multiple-input multiple-output (MIMO) communication system, a higher effective degree of freedom (EDoF) is desired to increase the channel capacity. Hence, complex field manipulation, which enriches the diversity of the amplitude and phase distribution, is necessary for an antenna array. In this paper, the complex field manipulation (CFM) scheme based on an arbitrarily distributed antenna array is proposed, which takes the orbital angular momentum (OAM) spectrum analysis as the bridge. Whether the antenna element transformation states are fixed or flexible, the CFM can be achieved with different schemes. This work may promote the application of OAM spectrum analysis and the development of complex field manipulation in the high-EDoF MIMO communication system.

Optimization of LPI-FDA-MIMO Radar and MIMO Communication for Spectrum Coexistence

This paper studies the spectral efficiency of a multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) and a MIMO communication system in the same frequency band. To suppress mutual interference for both systems, and enhance low probability of intercept (LPI) capability of radar system, we propose a joint design of the transmit waveform and receive filter of radar, and the code-book employed by the communication system to form its space-time code matrix. Our problem formulationis to maximize the signal-to-interference-plus-noise ratio (SINR) and simultaneously minimize the probability of target detection of the radar signal under the peak-to-average ratio and communication rate constraints. Since the design problem is non-convex, we first transform it into three sub-optimization problems. An iterative algorithm is then developed to solve these subproblems to obtain the solution of the original problem. Simulation results show that the devised approach can effectively reduce the mutual interference as well as increase the SINR and LPI for radar system.