Multi-input Multi-output Channel Research Articles

MIMO Channel Modeling for Tunnel Based on Directive Scattering Model and Ray Tracing

In this paper, semideterministic multi‐input multi‐output (MIMO) channel modeling is proposed for a tunnel with two types of cross sections. To be more consistent with the MIMO propagation characteristics of the measured data, ray tracing (RT) is used to simulate the specular components (SCs) and a directed path channel modeling method based on directive scattering model (DSM) is used to simulate the diffuse components (DCs), which has an important contribution in MIMO channel capacity. The performance of the proposed model is compared in terms of delay domain, angle domain, and channel capacity. By analyzing the DSM and the propagation graph based on the Lambertian scattering model (LSM), it was demonstrated that the proposed method is more suitable in terms of comprehensive performance.

Design of equalizers for frequency selective MIMO systems with low redundancy

In this paper, we propose several designs of filter bank based decision feedback equalizers (DFE) for frequency selective multi-input multi-output (MIMO) systems. Transmission schemes over frequency selective channels are typically block based, and redundancy in form of guard intervals must be used to avoid inter-block interference (IBI). This paper will focus on the case where spectral efficiency demands low levels of redundancy. Then the remaining IBI will be eliminated by a separate feedback loop. The co-channel interference (CCI) inherited in MIMO channels will be eliminated by different DFE schemes. In another approach, a multi-tap MIMO DFE will be designed to deal with both IBI and CCI. The simulation results show that for quasi-static frequency selective MIMO channels, the multi-tap DFE provides the best performance while for Raleigh frequency selective MIMO channels, the dual loop DFE is a good solution for transmission with low redundancy.

A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm.

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the "Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)" of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.

Subcarrier Index Modulation for Spectral Efficient Frequency Division Multiplexing in Multi-Input Multi-Output Channels

This work presents spectral efficient frequency division multiplexing with subcarrier index modulation (SEFDM-SIM) for multi-input multi-output (MIMO) wireless networks. SEFDM employs non-orthogonal frequency division multiplexing to enhance the spectral efficiency (SE) at the expense of enhanced inter-carrier interference compared with traditional orthogonal frequency division multiplexing (OFDM). In SEFDM-SIM, the information is conveyed not only by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -ary signal constellations, as in SEFDM, but also by the indices of the subcarriers, which are activated according to the incoming bitstream. SE and bit error rate (BER) are used as the performance measures. Both the analytic and simulation results show that the proposed MIMO-SEFDM-SIM outperforms the conventional MIMO-OFDM and MIMO-OFDM-SIM in SE, while achieving an improved BER than the MIMO-OFDM and MIMO-SEFDM systems.

Channel Bi-Diagonalization for MIMO Communications

This letter proposes a multi-input multi-output (MIMO) transceiver design termed channel bi-diagonalization (CBD), whose precoder and equalizer bi-diagonalize the channel matrix. By representing the bidiagonal MIMO channel with a trellis diagram, we can apply the Viterbi algorithm for the maximum likelihood (ML) detection and decoding, or the BCJR algorithm for the maximum a posteriori (MAP) detection and decoding, which drastically simplifies the optimal MIMO communications. Simulation results verify the superior performance of the proposed CBD scheme.

Bias for the Trace of the Resolvent and Its Application on Non-Gaussian and Non-Centered MIMO Channels

The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow Gaussian distribution, where the ergodic mutual information (EMI) converges to a deterministic quantity. However, with non-Gaussian channels, there is a bias between the EMI and its deterministic equivalent (DE), whose evaluation is not available in the literature. This bias of the EMI is related to the bias for the trace of the resolvent in large RMT. In this paper, we first derive the bias for the trace of the resolvent, which is further extended to compute the bias for the linear spectral statistics (LSS). Then, we apply the above results on non-Gaussian MIMO channels to determine the bias for the EMI. It is also proved that the bias for the EMI is $-0.5$ times of that for the variance of the MI. Finally, the derived bias is utilized to modify the central limit theory (CLT) and calculate the outage probability. Numerical results show that the modified CLT significantly outperforms previous methods in approximating the distribution of the MI and improves the accuracy for the outage probability evaluation.

Performance Analysis of MIMO-HARQ Assisted V2V Communications With Keyhole Effect

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions.

Binary flower pollination algorithm based user scheduling for multiuser MIMO systems

In this article, a multiuser (MU) multiinput multioutput (MIMO) system is considered, which is essential to support a huge number of subscribers without consuming extra bandwidth or power. Dirty paper coding (DPC) for MU MIMO channel achieves the peak sum-rate for the MU multiple antenna system at the cost of high computational complexity. Both user and antenna scheduling with a population based meta-heuristic algorithm, i.e. binary flower pollination algorithm (binary FPA) has been demonstrated in this article to achieve system sum-rate comparable to DPC with very less computational complexity and time complexity. Moreover, binary FPA shows a significant improvement in system throughput/sum-rate performance as compared to other population based meta-heuristic algorithms like binary bat algorithm (binary BA) and binary genetic algorithm (binary GA). Furthermore, the proposed binary FPA algorithm successfully achieves higher system sum-rate as compared to random search scheme and different existing suboptimal scheduling algorithms from literature as well. The binary FPA has also better convergence rate and searching ability than both binary BA and binary GA techniques. The percentage deviation achieved by the proposed binary FPA algorithm is quite less than that of binary BA, binary GA, random search method, and existing suboptimal scheduling algorithms from the literature. The efficiency of binary FPA in all these fronts is verified using exhaustive simulation studies.

Performance Evaluation of VBLAST and Linear Receivers for mmWave MIMO Systems with Ray-Tracing Channel Models

In this paper, the performance of a millimeter wave (mmWave) Multi-Input Multi-Output (MIMO) system is investigated using low complexity receivers. The receivers include linear Maximum Ratio Combiner (MRC), linear Zero Forcing (ZF) receiver, or linear Minimum Mean-Square Error (MMSE) receiver, in addition to ZF Vertical Bell Laboratories Layered Space-Time (ZF-VBLAST). A MIMO channel model based on a ray-tracing approach and the uniform theory of diffraction has been used. The effect of increasing the number of rays used in the channel model is investigated on the performance of all receivers. For all the scenarios, it has been found out that the ZF-VBLAST structure provides the best symbol-error-rate performance followed by the linear MMSE, then the ZF receiver, and finally the MRC receiver. The trade-off paid for achieving better performance is the increased receiver complexity. The system’s performance is compared under a conventional statistical channel model and a ray-based channel model for a fifth-generation (5G) mobile radio system. It is noted that these models do not accurately characterize the specific environment, hence resulting in an optimistic estimate of the system performance. Finally, the ray-tracing channel model is modified to account for the impact of the presence of trees within the environment. The results show that the performance degradation due to the presence of trees is between 2 to 5 dB for ZF and MMSE receivers, respectively.

Efficient MIMO Signal Predistortion for Secrecy-Enhancing

Massive machine type communications or internet of things (IoT) over wireless systems have invoked attention in terms of security problems, especially for multi-input multi-output (MIMO) schemes. In this paper, we propose efficient physical layer security (PLS) enhancing methods for space-time block coding (STBC), as well as spatial multiplexing (SM) schemes. The proposed methods pre-distort the transmit signals by using the phase information of the legitimate MIMO channel, and, as a result, the illegal eavesdropper cannot extract any information. The proposed predistortion for the STBC schemes is done so that the channel matrix at the receiver is a real-valued one, which results in full-rate and full-diversity gain for more than two transmit antennae. Therefore, compared to the conventional schemes, the proposed scheme eventually leads to the higher performance gain and lower detection complexity in addition to the high security protection. By extending the principle of the proposed method for STBC, a predistortion scheme is also proposed for SM by using the phase information of the column space of the channel matrix. The simulation results investigated in this paper reveal that the proposed methods can achieve enhanced error rate performances, as well as high security protection.

Outage Analysis of Reconfigurable Intelligent Surface Aided MIMO Communications With Statistical CSI

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection

The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers.

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

CSI intelligent feedback for massive MIMO systems in V2I scenarios

With the rapid development of the Internet of vehicles (IoV), vehicle to everything (V2X) has strict requirements for ultra-reliable and low latency communications (URLLC), and massive multi-input multi-output (MIMO) channel state information (CSI) feedback can effectively support URLLC communication in 5G vehicle to infrastructure (V2I) scenarios. Existing research applies deep learning (DL) to CSI feedback, but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect. However, the actual channel still has the influence of additive noise and nonlinear effects, especially in the high-speed V2I scene, the channel characteristics are more complex and time-varying. In response to the above problems, this paper proposes a CSI intelligent feedback network model for V2I scenarios, named residual mixnet (RM-Net). The network learns the channel characteristics in the V2I scenario at the vehicle user (User Equipment, UE), compresses the CSI and sends it to the channel; the roadside base station (Base Station, BS) receives the data and learns the compressed data characteristics, and then restore the original CSI. The system simulation results show that the RM-Net training speed is fast, requires fewer training samples, and its performance is significantly better than the existing DL-based CSI feedback algorithm. It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise. At the same time, the network still has good performance under high compression ratio and low signal-to-noise ratio (SNR).

Channel Reciprocity Analysis and Feedback Mechanism Design for Mobile Beamforming Systems

Accurate channel state information (CSI) is essential to increasing throughput in multi-input, multi-output (MIMO) systems with digital beamforming. CSI can be acquired by channel estimation and reported via feedback mechanisms. While the training and feedback overhead are typically proportional to the number of antennas, uplink measurements can be utilized to predict downlink CSI assuming perfect channel reciprocity in a time division duplex (TDD) mode. However, many works make the assumption that channels are perfectly reciprocal, which is often not the case in practice due to poor channel estimation and physical channel asymmetry. In this work, we investigate the key challenges in channel feedback performance, including TX-RX imbalance, channel coherence, and interference for mobile systems. We evaluate IEEE 802.11ac-based implicit and explicit feedback schemes with both emulated and in-field MIMO channels, particularly in regards to drone-based transmissions. Our analysis with channel emulation shows that implicit feedback is susceptible to channel reciprocity errors, while explicit feedback is more sensitive to Doppler effects. We propose a hybrid feedback mechanism that increases the throughput by 32% over conventional feedback methods. We additionally evaluate the impact of frequency offset asymmetry on the performance of distributed mobile systems. Our in-field experiments demonstrate that explicit feedback can provide better throughput improvement than implicit feedback in highly-mobile air-to-ground channels. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding the impacts of asymmetric factors of channel reciprocity for drone-based beamforming systems.

Performance Analysis of LTE system for 2x2 Rayleigh and Rician Fading Channel

Development of LTE is a milestone in the field of communication engineering. Compared to the performance of 2G the network involving LTE provides much higher data rates and support much more services. The evolution to LTE has provided the users to utilize many commercial activities to bring forward to the public and provide them the improved services compared to older versions of communication technologies. As the growth of LTE has provided much faster network still there are improvements going on the new technology. Various performance measures have been taken into considerations has the networks has performed very well on such test. One of the performance analyses has been presented in this paper comparing the performance of the LTE system over the Rayleigh and Rician fading channel. In the paper the performance of the MIMO LTE has been analyzed for the networks with various modulation schemes for Rayleigh and Rician fading channel with a 2x2 MIMO channel for EPA 0Hz, EVA 5Hz, EVA 70Hz. By varying Signal to Noise Ratio (SNR), Bit Error Rate (BER) of LTE is calculated for different Multi-Input Multi-Output (MIMO) schemes. The analysis is concluded in terms of speed of the data rate in Mbps.

WITHDRAWN: A Simulation on lattice reduction techniques for wireless communication MIMO QAM 64x64 and above

Abstract In wireless communication system, signal those pass through in transmission and receiving antenna in MIMO (Multi-input multi- output) channel to lattice reduce over a QAM (quadrature-amplitude-modulation) system, which is simulated maximum likelihood (ML) estimation and minimum mean square error (MMSE) . In this paper “A Simulation on Lattice Reduction Techniques for wireless Communication MIMO with QAM 64x64 and above”, here the minimum likelihood (ML) estimation and MMSE for multi-input multi-output (MIMO) channels. Generally, in OFDM (orthogonally frequency division multiplexing) system of a MMSE system completely eliminates interference and the results in suboptimal performance due to noise enhancement in symbol. The main aim simulated MMSE detector that the transmitter must spend within M QAM quadrature- amplitude-modulation () alphabet and the ML, MMSE allow to transmitted signal and remove the interference which comes from in receiver and pass through it. Which is QAM 64x64 above simulated BER (bit error rate) with SNR.

A MIMO Channel Prediction Scheme Based on Multi-Task Learning

This paper proposes a multi-input multi-output (MIMO) channel prediction scheme using multi-task learning algorithm. Based on the spatially correlated MIMO channel Channel State Information (CSI) observations, a multi-task least square support vector machine (MTLS-SVM) is trained, where the CSI prediction for each antenna pair can be modeled as one task and jointly learning between these tasks are implemented. Then the future CSI is predicted by this MTLS-SVM. By using the relatedness of the multiple tasks, the spatial correlations between different antenna pairs can fully be exploited and hence better channel prediction performance can be achieved compared with the single task prediction scheme.

A Novel 3D UAV Channel Model for A2G Communication Environments Using AoD and AoA Estimation Algorithms

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Semantically Secure Lattice Codes for Compound MIMO Channels

We consider compound multi-input multi-output (MIMO) wiretap channels where minimal channel state information at the transmitter (CSIT) is assumed. Code construction is given for the special case of isotropic mutual information, which serves as a conservative strategy for general cases. Using the flatness factor for MIMO channels, we propose lattice codes universally achieving the secrecy capacity of compound MIMO wiretap channels up to a constant gap (measured in nats) that is equal to the number of transmit antennas. The proposed approach improves upon existing works on secrecy coding for MIMO wiretap channels from an error probability perspective, and establishes information theoretic security (in fact semantic security). We also give an algebraic construction to reduce the code design complexity, as well as the decoding complexity of the legitimate receiver. Thanks to the algebraic structures of number fields and division algebras, our code construction for compound MIMO wiretap channels can be reduced to that for Gaussian wiretap channels, up to some additional gap to secrecy capacity.