Channel Estimation Model Research Articles

Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems

Reconfigurable intelligent surface (RIS) consists of cost-effective passive elements which can be utilized in different scenarios in next-generation wireless communication. Deep learning (DL) algorithm plays a vital role in channel estimation (CE) due to the learning capability of DL tools to tackle the CE challenge. Bi-directional long-short term memory (Bi-LSTM) model collects data from both past (backward) and future (forward) simultaneously to improve prediction accuracy and provide an additional feature extraction capability. To take advantage of the Bi-LSTM model, in this paper, we proposed a Bi-LSTM model based CE and signal detection for RIS empowered multi-user multiple input single output downlink orthogonal frequency division multiplexing systems. To measure the performance of the proposed model, it is trained by two different deep neural network (DNN) optimization algorithms. Additionally, the proposed model is compared with four existing DNN models. The least square and minimum mean square error estimators are used to investigate CE and signal detection for performance comparison. The proposed Bi-LSTM model based RIS CE is capable of learning and generalizing rapidly and outperforms the comparable estimators when different pilots and cyclic prefix values are available. Simulation results confirm the effectiveness of the proposed model for CE and signal detection.

Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified Artificial Immune Algorithm

The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

Next Generation 5G Wireless Networks: A Comprehensive Survey

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Combining Time Reversal and Fast Marching Method in Wireless Indoor Positioning

Most of current wireless indoor positioning methods could not accurately obtain channel model, the mapping between spatial position and received signal features. The main factor for a precise channel model in an indoor environment is multipath effect. Time reversed (TR) wireless indoor positioning method has been validated to effectively reduce signals fading or time delay affected by multipath effect. However, these advantages are depended on a prior known channel model, without this condition, the accuracy of TR method will be seriously deteriorated. To solve the shortcoming of a general TR method in an unknown channel model application, we present a combining Time Reversal and Fast Marching Method (TR-FMM) positioning method. This method locates a target with two stages. In the stage one, the precise channel model of an indoor environment is estimated by FMM and simultaneous algebraic reconstruction technique (SART). In this stage, Time of Flight (TOF) information generated by some fixed spatial position anchors are used to fulfill the indoor channel model estimation, then the needed channel impulse response (CIR) for TR method will be obtained based on the estimated channel model. In the stage two, with the obtained CIR, any new joint mobile target will be accurately located by a general TR wireless indoor positioning method. Some numerical simulations have been presented to validate the proposed method. Simulative results depict the positioning deviation is less than 3cm for a newly joined mobile target with 1cm scale in a moderate complex indoor configure, and the accuracy of the positioning is improved 30 times comparing to a general TR method. The positioning time in the stage 2 is less than 3 minutes in a PC with 1.6 GHz dual CPUs and 2G Bytes memory. Obviously, the proposed method has great advantage in high accuracy and low complexity for wireless indoor positioning system.

Tail Extrapolation in MLSE Receivers Using Nonparametric Channel Model Estimation

This paper presents a method for determining the probability of rare events, in particular for probability density function (pdf) and bit error rate (BER) estimation. The derivation of the method is based on the presumption that the pdf is a member of a family of distributions very often named as the generalized exponential (GE) class of distributions. Based on high reliability estimations obtained in short simulation/measurement times, the low probably events are estimated accurately by extrapolation. The suggested method can be applied to some distributions that are different from GE distributions, such as noncentral chi-square distributions, to extrapolate to low probability events, with some extrapolation error. It can also be applied to BER estimation. The method is in particular helpful for estimating channels suffering from both severe signal distortion causing undesired intersymbol interference (ISI) of several symbols, and from severe noise. Such conditions prevail, for example, in metro and long haul high-speed optical fiber communication systems. So the method may be implemented in particular in maximum-likelihood sequence estimation (MLSE) optical receivers using nonparametric channel model estimation. A special use of the extrapolation method is explained for practical systems using trellis branch metrics derived from the estimated pdf to decode the transmitted sequence of symbols.