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

Recent research has shown that it is possible to achieve near capacity performance with multiple antennas using iterative techniques with an outer channel code. For the iterative multi-input multi-output (MIMO) system to operate at extremely high data rates, the design and development of practically feasible inner MIMO detector is an important issue. A new soft-input soft-output MIMO detector is presented in this paper. A tree-like algorithm is developed by arranging the maximum a posteriori equations in a specific form, The algorithm exploits the concepts of ordering, symbol cancellation, interference nulling and multiple quantization, which have been very successful in Vertical Bell laboratories layered space time (V-Blast) system. Monte-Carlo simulations are presented analyzing different aspects of the soft detector. The performance is also compared with other well-known iterative methods including the modified sphere decoder used with outer convolutional and turbo codes. Results presented show that the detector not only has very good performance, but also has many practical advantages, compared to other methods, like an ability to easily control performance-complexity tradeoff

In previous works, density evolution schemes have been used to design an irregular low-density parity-check (LDPC) code for the multi-input multi-output (MIMO) systems. However the code design based on the density evolution can be used only for simple code structure like LDPC codes and repeat-accumulate (RA) codes. Recently simple and accurate irregular LDPC code design method based on the extrinsic information transfer (EXIT) chart was introduced for an AWGN channel. This code design scheme is much flexible, therefore it can be used for any kind of code structure like turbo codes. In this paper we design an irregular LDPC code for a MIMO system using the EXIT chart based scheme. The EXIT charts are used to obtain the optimal edge degree distribution of the irregular LDPC code combined with the MMSE-SIC MIMO detector. This scheme can be generalized for designing the optimal iterative MIMO detector combined with any kind of channel codes including turbo codes.

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.

최근 대용량, 초고속 통신 서비스의 비중이 높아짐에 따라 배열 안테나를 사용하는 MIMO(Multi-Input Multi-Output) 시스템의 활용이 중요하게 평가되고 있다. 하지만 기존의 MIMO 시스템은 다수의 안테나를 사용하기 때문에 안테나의 개수만큼 RF(Radio Frequency) 체인이 요구되며, 이로 인해 복잡도와 전력 소모가 크다는 단점을 가진다. 이러한 기존 MIMO 시스템의 단점을 해결하기 위해서 단일 능동 소자와 다수의 기생 소자를 이용하는 방식의 ESPAR(Electronically Steerable Parasitic Array Radiator) 안테나를 사용하는 빔 공간 MIMO 시스템에 대한 연구가 활발히 진행되고 있다. ESPAR 안테나를 사용하는 빔 공간 MIMO 시스템은 단일 RF 체인으로 구성되어 있기 때문에 기존 MIMO 시스템의 단점을 해결할 수 있다. 본 논문에서는 시스템의 보안성을 향상시키기 위해 QPSK(Quadrature Phase Shift Keying) 변조 방식의 ESPAR 안테나를 사용하는 빔 공간 MIMO 시스템에 비예측성, 비주기성, 구현의 용이성, 민감한 초기조건 등의 특징을 가지고 있는 카오스 통신 알고리즘을 적용하여 QPSK 변조 방식의 ESPAR 안테나를 사용하는 카오스 빔 공간 MIMO 시스템을 구성하고 성능을 평가한다. Recently, utilization of MIMO(Multi-Input Multi-Output) system using array antennas is evaluated significantly according to the extension of high-capacity and high-speed communication services. However, MIMO system has disadvantages such as high-complexity and high-power-consumption, because RF(Radio Frequency) chain is required as antenna number, and several array antenna is used in conventional MIMO system. In order to solve these problems, research about beamspace MIMO system using ESPAR(Electronically Steerable Parasitic Array Radiator) antenna that has single RF chain by using one active antenna and several parasitic elements has been studied actively. Beamspace MIMO system using ESPAR antenna is possible to solve the problems of conventional MIMO system, because this system is composed by single RF chain. In this paper, in order to improve the system security, chaos communication algorithm that has characteristics such as non-periodic, non-predictability, easy implementation and initial condition is applied to QPSK (Quadrature Phase Shift Keying) modulated beamspace MIMO system. We design the chaos QPSK modulated beamspace MIMO system, and evaluate SER performance of this system.

Transmit diversity schemes have been studied for high spectral-efficiency and high bit-rate transmission, such as multi-input multi-output (MIMO) systems. In this paper, a novel dual polarized ceiling-mounted antenna is designed and simulated. In the MIMO systems, forward error correction coding is essential for high quality communications. At the two typical scenes, the single-input single-output system compared with two single polarized antennas and an dual polarized antenna MIMO system is tested. It is shown that the MIMO system have improved throughput level obviously. The dual polarized ceiling-Mounted dual streams MIMO antenna will save much construction cost in the long-term evaluation systems in the future, which can be called the “green” MIMO antenna.

Multi input multi output (MIMO) system with the multiple antennas at both the transmitter and receiver is the emerging technology in the modern wireless system. MIMO systems are well adopted due to its high spectral efficiency and high data rates. For the detection of MIMO systems, a powerful technique called Lattice Reduction (LR) algorithm are used to obtain the better orthogonality in the channel matrices and is aided with the linear and low complexity detectors such as Zero forcing(ZF) and Minimum mean square error (MMSE) for the reduction in complexity. In this paper we propose a new algorithm called Enhanced lattice reduction algorithm to improve the error performance for the linear detectors with the lesser complexity and also it is aided with the non linear precoding called Tomlinson-Harashima precoding (THP) in the presence of channel state information (CSI). Lattice reduction aided with THP improves the performance linearly. BER performance of the proposed algorithm is determined and it is compared with the other LR aided algorithms in the literature.

This paper addresses the problem of frequency offsets and channel gains estimation for a multi-input multi-output (MIMO) system in flat-fading channels. The general case where the frequency offsets are possibly different for each transmit antenna is considered. The maximum-likelihood (ML) estimation of the carrier frequency offset for each transmit antenna in a MIMO system is investigated in this paper, assuming that a training sequence is available. The exact solution to this estimation problem turns out to be too complex as it involves a search over a multi-dimensional domain. To solve this complex estimation problem, a novel joint estimation algorithm of frequency offsets and channel gains is proposed. Frequency offsets are first estimated by the particle swarm optimization theory. Then channel gains are estimated by the ML estimator. Simulation results show that the proposed algorithm has better performance as compared with the correlation-based estimation algorithm and asymptotically achieves the Cramer-Rao lower bound (CRLB), which provides a new idea to solve the problem of joint frequency offsets and channel gains estimation for MIMO systems.

A unified approach is developed for the study of multi-input, multi-output (MIMO) systems under fast fading with quantized covariance feedback. In such systems, the receiver computes, using perfect channel state information (CSI), the covariance matrices to be adopted by the transmitter corresponding to the current channel realization, and feeds back a quantized version of this information to the transmitter using finite, say Nf, bits per channel realization. Our analysis is based on a geometric framework we developed in a companion paper for manifolds of positive semi-definite (covariance) matrices with various trace and rank constraints. That analysis applies to MIMO systems in a unified manner to optimal as well as various suboptimal precoding methods for obtaining input covariances. These include, for example, the capacity achieving spatio-temporal water-filling strategy, the incorporation of just spatial water-filling and reduced-rank beamforming with power allocation in both cases, as well as MIMO systems with antenna selection. For a given system strategy, including the one that achieves capacity, the gap between the communication rates in the perfect and quantized covariance cases is shown to be O(2-Nf/N), where N is the dimension of the particular Pn manifold used for quantization. This dimension depends on the precoding strategy adopted and strongly determines how quickly the communication rate with quantized covariance feedback approaches that with perfect CSI at the transmitter (CSIT). Our results show how the choice of covariance quantization manifold can be tailored to the available feedback rate in MIMO system design.

This paper deals with a approximating method for large time-delays of multi-input multi-output (MIMO) dynamical systems. The methodology is an improved rational approximation approach. Time delay terms of the state space equations can be described by delay matrix in the complex domain and approximated by rational matrix Pade-type functions. The minimum variance estimation error is minimized to obtain rational approximation of MIMO systems to guarantee the poles located in the left half plane. Matrix Pade-type approximation can largely reduce the instability and the overshoot, so the fast-response property is improved. Simulation results of the proposed method are presented to illustrate the correctness and effectively.

This paper analyzes the effectiveness of the reaching law approach for sliding mode control of discrete time systems. The method is extended for sliding mode control for multi-input multi-output (MIMO) systems. Modified reaching law for reducing the chattering is proposed. The performance of the proposed controller is analyzed by carrying out simulation studies on MIMO systems. The performance of the controller is also compared with that of the equivalent control.

In this paper, we study the achievable rate of the training-based multi-input multi-output (MIMO) systems over time-varying flat fading channels modeled with the $L$-th order autoregressive, AR($L$), process. Using the Bayesian Cram\'{e}r-Rao lower bound (BCRB) to characterize the mean squared error of channel estimation, the achievable rate of the MIMO systems is investigated from the information-theoretical perspective. The optimum lengths for the training and the data blocks are determined to maximize the achievable rate. Besides, by modeling the time-varying wireless fading channel with a proper AR($L$) model for the specific normalized Doppler frequency, $f_dT_s$, the aforementioned results can be extended to characterize the achievable rate in a more realistic time-varying wireless fading channel.

This paper develops a multiple fixed model blending based adaptive parameter identification scheme for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification scheme is proven to be asymptotically stable for uncertain linear time-invariant MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification scheme, a linear-quadratic (LQ) optimal multiple model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability and effectiveness of the proposed MMAC scheme.

In this article, an attempt is made to design the fractional order proportional integral derivative (FO-PID) controller for multi-input multi-output (MIMO) system using teaching learning-based optimization (TLBO) algorithm. The control of MIMO system always offers a complicated control paradigm due to its structure. It is realized that dynamic systems can be effectively represented by fractional order dynamics which is based on fractional calculus. The PID controller is still the most reliable controller due to its uncomplicated structure, and the FO-PID controller is predicted to have better performance as compared to conventional PID controller due to increment of two more parameters. The performance of non-integer PID controller depends upon the five constants; hence, the tuning of these five constants is most important for the optimum performance. In the presented work, TLBO technique is adopted for the tuning of the non-integer PID controller. The designed controller is implemented on the four tank MIMO system in MATLAB. The performance of the designed controller is also compared from the previous results available in literature.KeywordsFour tank MIMO systemFO-PIDTLBO

<p class="Default">The consistent demand for higher data rates and need to send giant volumes of data while not compromising the quality of communication has led the development of a new generations of wireless systems. But range and data rate limitations are there in wireless devices. In an attempt to beat these limitations, Multi Input Multi Output (MIMO) systems will be used which also increase diversity and improve the bit error rate (BER) performance of wireless systems. They additionally increase the channel capacity, increase the transmitted data rate through spatial multiplexing, and/or reduce interference from other users. MIMO systems therefore create a promising communication system because of their high transmission rates without additional bandwidth or transmit power and robustness against multipath fading. This paper provides the overview of Multiuser MIMO system. A detailed review on how to increase performance of system and reduce the bit error rate (BER) in different fading environment e.g. Rayleigh fading, Rician fading, Nakagami fading, composite fading.</p>

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli–Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.