MIMO Channel Research Articles

Performance Study of a Mimo-Shaped Cooling Plate in Pemfc Stack for Heat Transfer Enhancement

During the operation of high-power proton exchange membrane (PEM) fuel cell, huge heat is generated in the chemical reaction, which need be removed by efficient cooling design. To solve the problem of local overheating during PEMFC, a novel multi-input and output (MIMO) cooling flow field is proposed. The maximum temperature, temperature uniformity and pressure drop characteristics of the U-shaped and Z-shaped cooling flow fields with different MIMO channels configurations are researched by computational fluid dynamics (CFD) method. The results demonstrate that comparing with U-shaped cooling flow field, Z-shaped design can provide better temperature distribution with the same MIMO channel configuration. Moreover, the serpentine cooling flow field provides better cooling performance than parallel cooling flow field, but leads to higher pressure loss. Compared to other cases, Z-shaped serpentine MIMO channel presents the best cooling performance, reducing the maximum temperature value to 322.7 K.

Near-Optimal Performance With Low-Complexity ML-Based Detector for MIMO Spatial Multiplexing

In Spatial Multiplexing MIMO systems, many powerful non-linear detection techniques as sphere decoding have emerged to overcome the performance limitations of linear detection techniques. However, these non-linear techniques suffer from high complexity that increases dramatically with the number of antennas and the modulation order. Hence, they cannot be implemented on highly parallel hardware architecture and are thus not suitable for real-time high data rate transmission. In this letter, a new detection technique is proposed to approach the optimal performance obtained by Maximum Likelihood (ML) detector without increasing the complexity significantly. This detector is denoted by OSIC-ML since it combines two techniques: the Ordered Successive Interference Cancellation (OSIC) and the ML. The proposed OSIC-ML detector shows a near-optimal performance at very low complexity even with large scale MIMO and imperfect channel estimation, where this complexity can be efficiently controlled to achieve the desired complexity-performance tradeoff.

Incorporating Wireless Communication Parameters Into the E-Model Algorithm

Telecommunication service providers have to guarantee acceptable speech quality during a phone call to avoid a negative impact on the users' quality of experience. Currently, there are different speech quality assessment methods. ITU-T Recommendation G.107 describes the E-model algorithm, which is a computational model developed for network planning purposes focused on narrowband (NB) networks. Later, ITU-T Recommendations G.107.1 and G.107.2 were developed for wideband (WB) and fullband (FB) networks. These algorithms use different impairment factors, each one related to different speech communication steps. However, the NB, WB, and FB E-model algorithms do not consider wireless techniques used in these networks, such as Multiple-Input-Multiple-Output (MIMO) systems, which are used to improve the communication system robustness in the presence of different types of wireless channel degradation. In this context, the main objective of this study is to propose a general methodology to incorporate wireless network parameters into the NB and WB E-model algorithms. To accomplish this goal, MIMO and wireless channel parameters are incorporated into the E-model algorithms, specifically into the $I_{e,eff}$ and $I_{e,eff,WB}$ impairment factors. For performance validation, subjective tests were carried out, and the proposed methodology reached a Pearson correlation coefficient (PCC) and a root mean square error (RMSE) of $0.9732$ and $0.2351$, respectively. It is noteworthy that our proposed methodology does not affect the rest of the E-model input parameters, and it intends to be useful for wireless network planning in speech communication services.

Terahertz-Band MIMO-NOMA: Adaptive Superposition Coding and Subspace Detection

We consider the problem of efficient ultra-massive multiple-input multiple-output (UM-MIMO) data detection in terahertz (THz)-band non-orthogonal multiple access (NOMA) systems. We argue that the most common THz NOMA configuration is power-domain superposition coding over quasi-optical doubly-massive MIMO channels. We propose spatial tuning techniques that modify antenna subarray arrangements to enhance channel conditions. Towards recovering the superposed data at the receiver side, we propose a family of data detectors based on low-complexity channel matrix puncturing, in which higher-order detectors are dynamically formed from lower-order component detectors. We first detail the proposed solutions for the case of superposition coding of multiple streams in point-to-point THz MIMO links. We then extend the study to multi-user NOMA, in which randomly distributed users get grouped into narrow cell sectors and are allocated different power levels depending on their proximity to the base station. We show that successive interference cancellation is carried with minimal performance and complexity costs under spatial tuning. We derive approximate bit error rate (BER) equations, and we propose an architectural design to illustrate complexity reductions. Under typical THz conditions, channel puncturing introduces more than an order of magnitude reduction in BER at high signal-to-noise ratios while reducing complexity by approximately 90%.

Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

Fast Block LMS Based Estimation of Angularly Sparse Channels for Single-Carrier Wideband Millimeter Wave Hybrid MIMO Systems

Adaptive block-based least-mean squares (BLMS)-based techniques are conceived for channel estimation in single-carrier (SC) wideband millimeter wave (mmWave) hybrid MIMO systems. In this context, a frequency-domain channel estimation model is developed for SC wideband systems, followed by a novel fast BLMS (FBLMS) technique, which has a significantly lower computational complexity than the existing channel estimation schemes designed for mmWave hybrid MIMO systems. The proposed FBLMS technique is also robust, since it does not require any second-order statistical information, such as the cross-covariance vector and covariance matrix. Next a beamspace domain representation of the mmWave MIMO channel is obtained, followed by the development of the sparse-FBLMS (SFBLMS) scheme for the estimation of the wideband mmWave MIMO channel that additionally exploits the angular-sparsity for improved channel estimation performance. Analytical expressions are derived for the mean squared estimation error (MSEE) and mean squared observation error (MSOE) of both the proposed FBLMS and SFBLMS techniques. Furthermore, a systematic procedure is developed for determining the beneficial range of the values of the regularization parameter, which ensures a high channel estimation accuracy of the SFBLMS over FBLMS. A hybrid precoder and combiner design is also proposed for SC wideband systems by employing the channel estimates obtained using the above techniques. Simulation results are presented to illustrate the performance of the proposed BLMS-based schemes in comparison to the existing schemes, which closely match the theoretical results derived.

Non-Linear Base-Station Processing Within a 3GPP Compliant Framework

MIMO mobile systems, with a large number of antennas at the base-station side, enable the concurrent transmission of multiple, spatially separated information streams, and therefore, enable improved network throughput and connectivity both in uplink and downlink transmissions. Traditionally, such MIMO transmissions adopt linear base-station processing, that translates the MIMO channel into several single-antenna channels. While such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity and connectivity capabilities. Recently-proposed non-linear base-station processing methods claim this unexplored capacity and promise substantially increased network throughput and connectivity capabilities. Still, to the best of the authors’ knowledge, non-linear base-station processing methods not only have not yet been adopted by actual systems, but have not even been evaluated in a standard-compliant framework, involving all the necessary algorithmic modules required by a practical system. In this work, for the first time, we incorporate and evaluate non-linear base-station processing in a 3GPP standard environment. We outline the required research platform modifications and we verify that significant throughput gains can be achieved, both in indoor and outdoor settings, even when the number of base-station antennas is much larger than the number of transmitted information streams. Then, we identify missing algorithmic components that need to be developed to make non-linear base-station practical, and discuss future research directions towards potentially transformative next-generation mobile systems and base-stations (i.e., 6G) that explore currently unexploited non-linear processing gains.

Online Deep Neural Networks for MmWave Massive MIMO Channel Estimation With Arbitrary Array Geometry

In this paper, we propose an online training framework for mmWave Massive MIMO channel estimation (CE) with limited pilots, where the training is based on real-time received pilot samples from the base station without requiring knowledge of the true channel. To realize this, we propose four axioms for a legitimate online loss function, based on which we develop a model-free online training algorithm with convergence analysis. Simulation shows that the proposed online deep neural network (DNN) achieves comparable CE accuracy to model-based compressive sensing (CS) algorithms, while enjoying much faster computation. In addition, the proposed method is robust to various model mismatches and can adapt to the change of the underlying propagation environment.

Maximum a Posteriori Probability (MAP) Joint Fine Frequency Offset and Channel Estimation for MIMO Systems With Channels of Arbitrary Correlation

Carrier frequency offset (CFO) and channel estimation is a classic topic with a large body of prior work using the maximum likelihood (ML) approach together with the Cramer-Rao lower bound (CRLB) analysis. We give the maximum a posteriori probability (MAP) estimation solution, which is particularly useful for tracking. Unlike the ML cases, the corresponding Bayesian CRLB (BCRLB) shows a clear relation with parameters and low complexity algorithms are provided to achieve the BCRLB in almost all SNR range. Among them, the universal algorithm takes a new approach and avoids error floors of the traditional approach. We assume that the time invariant MIMO channel within a packet can have spatial correlation and nonzero mean. The estimation is based on pilot signals. An unexpected result is that the joint MAP estimation is equivalent to an individual MAP estimation of the frequency offset first, again different from the ML results. We provide insight on the pilot/training signal design based on the BCRLB. Unlike past algorithms that trade performance and/or complexity for the accommodation of time varying channels, the MAP solution provides a different route for dealing with time variation. Within a short enough (segment of) packet where the channel and CFO are approximately time invariant, the low complexity algorithm can be employed. Similar to the belief propagation, the estimation of the previous (segment of) packet can serve as the prior knowledge for the next (segment of) packet.

Study on Propagation Characteristics of Indoor 28 GHz MIMO Channel Based on the SBR/Image Method

The combination of millimeter-wave and MIMO channel is a hotspot in the research of fifth-generation (5G) communication in recent years. In this paper, the SBR method is used to simulate a typical indoor office environment in the line-ofsight (LoS) scenario and the near-field scenario. The frequency is 28 GHz, and the receiver is an 8*8 vertical planar array. Firstly, the effectiveness of the SBR method is verified by analyzing the power angle profiles (PAPs) of the azimuth angle of arrival (AAoA). Secondly, the spatial non-stationary characteristics of the millimeter-wave MIMO channel are analyzed through the received power and power delay distribution of elements at the antenna’s array. Some characteristics of the millimeter-wave MIMO channel are revealed from these analyses.

Improving the Performance of MIMO Fading Channel Simulators Using New Parameterization Method

The research aims to modeling and simulation of fading channels for providing necessary tool to develop communication systems. The modeling and simulation of MIMO fading channels is studied for both isotropic- and non-isotropic scattering cases for one-ring model, so in this context, three methods for MIMO simulation channel models are studied: Extended Method of Exact Doppler Spread (EMEDS), Modified Method of Equal Areas (MMEA) and Method Lp-Norm (LPNM). Three new methods are also suggested to design the simulation model of MIMO channel: Modified Extended Method of Exact Doppler Spread (MEMEDS), New Modified Method of Equal Areas (NMMEA) and Modified Lp-Norm method (MLPNM). The performance of each proposed method is compared with the original method by comparing the statistical properties ACF, 2D-CCF and CF of both reference and simulation models.

Построение аналитической модели радиоканала MIMO на основе аппроксимации полной корреляционной матрицы

This paper addresses the experimental wireless MIMO channel modeling and validation based on channel sounding data using the approximation of the full channel correlation matrix. Measurement were carried out in indoor laboratory environment at central frequency 2.3 GHz. An analytical MIMO channel model is presented based on optimal approximation of channel covariance matrix. Approximation of a full channel covariance matrix is based on the optimal Kronecker product series expansion of the sample covariance matrix. The channel correlation matrices calculated from the measured channel coefficients were decomposed using Van Loan and Pitsanis approximation algorithm. Experimental validation of such model is presented. The accuracy of the MIMO channel modeling was evaluated by the correlation matrix distance and by calculating of CDF of channel capacity. The results show that these models have good agreement with the MIMO channel measured data. Also two popular analytical MIMO channel models – Kronecker and Weichselberger models are evaluated and compared with the presented channel model.

Efficient Channel Estimation in Massive MIMO Partially Centralized Cloud-Radio Access Network Systems

This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.

A Dynamic 3-D Wideband GBSM for Cooperative Massive MIMO Channels in Intelligent High-Speed Railway Communication Systems

Coordinated multipoint (CoMP) and massive multiple-input multiple-output (mMIMO) are two of promising technologies in future intelligent high-speed railway (HSR) communication systems, whose performance is fundamentally determined by the characteristics of cooperative mMIMO channels. This article proposes a dynamic three-dimensional (3-D) wideband geometry-based stochastic model (GBSM) for HSR cooperative mMIMO channels. The proposed GBSM employs a sphere model and two elliptic-cylinder models to describe common and uncommon clusters for different links, and integrates the dynamic cluster evolution in both time and array domains. Statistical properties of the cooperative mMIMO model are investigated, such as multi-link spatial cross-correlation function and sum rate capacity. A corresponding dynamic 3-D wideband simulation model for the HSR cooperative mMIMO channel is also proposed. Finally, numerical results of the statistical properties are analyzed, and the proposed model is validated by realistic HSR channel measurement data, in terms of dual-link spatial cross-correlation and channel capacity. This model is more practical and will be helpful for facilitating the design and performance evaluation of future intelligent HSR communication systems.

Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimzation for MIMO system incorporated with smart antenna

With the radio channels physical limits, achieving higher data rate in the multi-channel systems is been a biggest concern. Hence, various spatial domain techniques have been introduced by incorporating array of antenna elements (i.e., smart antenna) in recent past for the channel limit expansion in mobile communication antennas. These smart antennas help to yield the improved array gain or bearm forming gain and hence by power efficiency enhanmaent in the channel and antenna range expansion. The use of smart antenna leads to spatial diversity and minimizes the fading effect and improves link reliability. However, in the process of antenna design, the proper channel modelling is is biggest concern which affect the wireless system performance. The recent works of MIMO design systems have discussed the issues in number of antenna selection which suggests that optimization of MIMO channel capacity is required. Hence, a Particle Swarm Optimization based channel capacity optimzation for MIMO system incorporated with smart antenna is introduced in this paper. From the outcomes it is been found that the proposed PSO based MIMO system achieves better convergenece speed which results in better channel capacity.

Parallel Peak Cancellation Signal-Based PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Transmission

Parallel Peak Cancellation Signal-Based PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Transmission

MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes

Relay Nodes (RNs) have received special attention as a radio access technology which can overcome channel fading and improve the channel capacity in high-speed and dense vehicle environments. RNs have been the object of standardization by the 3GPP; however, this process has not been accompanied by the development of hardware that allows for evaluation of the advantages of RNs. Software Defined Radio (SDR) has emerged as a promising technology to implement the concept of RNs at low cost. In this paper, a detailed study of the MIMO wireless channel between an evolved Node-B (eNB) and an RN is carried out. The developed algorithms are implemented in an SDR platform for Decode-and-Forward (D&F) relay node evaluation, resulting in significant improvements of its capabilities on the MIMO channel. A pilot symbol-assisted channel estimation algorithm based on combinations of the Least-Squares (LS) technique and Bi-Cubic (BCI), Bi-Linear (BLI), and Bi-Nearest Neighbors (BNNI) interpolation methods is considered. Furthermore, the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) equalization schemes are studied. In the tests conducted, Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios are considered, demonstrating the capabilities of the developed platform. The performance measurements using different modulation schemes are compared under the same conditions. The simulation results show that the LS technique together with the BCI and MMSE methods performed the best among all evaluated channel estimation and equalization algorithms, in terms of the Error Vector Magnitude (EVM) performance of the received Resource Grid (RG). Furthermore, we show that the Bit Error Rate (BER) and throughput of the core network increase when using the 2 × 2 MIMO technique.

Terahertz Massive MIMO With Holographic Reconfigurable Intelligent Surfaces

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

The Hermitian Jacobi Process: A Simplified Formula for the Moments and Application to Optical Fiber MIMO Channels

The Hermitian Jacobi Process: A Simplified Formula for the Moments and Application to Optical Fiber MIMO Channels