Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct-sequence (DS) spread spectrum techniques. In this study, a novel multiple channel estimator, which consists of several possible channel models and can be self-adjusted to fit different channel uncertain conditions, is proposed for MC-CDMA systems to effectively estimate channel coefficients in the frequency domain over time-varying multipath fading channels. Based on the proposed channel estimation, an enhanced minimum mean-square error (MMSE) equalizer is employed for symbol detection of MC-CDMA systems. Simulation results demonstrate that the proposed multiple channel estimator can track the channel more accurately than the conventional channel estimators. With the consideration of channel estimation error, the enhanced equalizer based on the proposed multiple channel estimator performs much better than those based on the conventional channel estimators in bit error rate (BER).

An adaptive sparse channel estimator based on compressive sensing for OFDM systems is proposed. Unlike conventional compressive channel estimator with a prior knowledge of the channel spaisity, the proposed channel estimator can extract information on sparsity of the channel adaptively by introducing the ASMP algorithm. Moreover, the proposed channel estimator has a superior estimate performance due to the backtracking refinement process in the ASMP algorithm. The estimation performance for the sparse channel is analysis in terms of mean square error (MSE). Simulation results demonstrate substantial performance gains of the proposed estimator over conventional compressive channel estimator.

This paper presents a novel iterative detection and channel estimation scheme that combines the effort of superimposed training (ST) and pilot-aided training (PAT) for multiple-input multiple-output (MIMO) flat fading channels. The proposed method, hereafter known as joint mean removal ST and PAT (MRST-PAT), implements an iterative detection and channel estimation that achieves the performance of data-dependent ST (DDST) algorithm, with the difference that the data arithmetic cyclic mean is estimated and removed from data at the receiver’s end. It is demonstrated that this iterative and cooperative detection and channel estimator algorithm surpasses the effects of data detection identifiability condition that DDST has shown when higher orders of modulation are used. Theoretical performance of the MRST-PAT scheme is provided and corroborated by numerical simulations. In addition, the performance comparison between the proposed method and different MIMO channel estimation techniques is analyzed. The joint effort between ST and PAT shows that MRST-PAT is a solid candidate in communications systems for multiamplitude constellations in Rayleigh fading channels, while achieving high-throughput data rates with manageable complexity and bit-error rate (BER) as a figure of merit.

Massive multi input multi output (MIMO) systems incorporate orthogonal frequency division multiplexing (OFDM) technology to render high data rate services for future wireless communication applications. The channel estimator (CE) employed by a reliable massive MIMO-OFDM system requires huge amount of overhead in the form of known and null data transmissions, hence limiting the system spectral efficiency (SE). Often, CE design is a tradeoff between SE and system reliability. In this paper, CE with three different overhead arrangements, namely time domain synchronous (TDS), comb type with cyclic prefix (CTCP), 2D grid type with cyclic prefix (GTCP) are investigated and a GTCP based CE is proposed which offers both high SE and improved system reliability. The proposed CE uses autocorrelation based denoising threshold for channel impulse response (CIR) estimation and does not require any knowledge of channel statistics (KCS). A 4 x 16 MIMO-OFDM system is simulated in a rayleigh fading channel environment with U-shaped doppler spectrum. From the bit error rate (BER) performance results inWiMax SUI-4, Advanced Television Technology Center (ATTC) and Brazil A channel environments, it is verified that the proposed CE with GTCP overhead and proposed denoising scheme, indeed improves both SE and system reliability. Hence it is suitable for application in all massive MIMO-OFDM systems.

Orthogonal time frequency and space (OTFS) modulation is a promising technology that satisfies high-Doppler requirements for future mobile systems. OTFS encodes information symbols and pilot symbols into the two-dimensional (2D) delay-Doppler (DD) domain. The received symbols suffer from inter-Doppler interference (IDI) in fading channels with fractional Doppler shifts sampled at noninteger indices in the DD domain. The IDI has been treated as an unavoidable effect because the fractional Doppler shifts cannot be obtained directly from the received pilot symbols. This paper provides a solution to channel estimation for fractional Doppler channels with lower computational complexity than a conventional channel estimation method using a pseudo sequence. The proposed estimation provides new insight into the OTFS input-output relation in the DD domain as a 2D circular convolution with a small approximation. According to the input-output relation, we also provide a low-complexity channel equalization method using the estimated channel information. We demonstrate the error performance of the proposed channel estimation and equalization in a high-Doppler channel by simulations. The simulation results show that the proposed channel estimation method outperforms the conventional channel estimation. The results also show that the proposed equalization method has a similar performance to the minimum mean square error equalizer using matrix inversion.

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.

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

In this paper, we propose a robust channel estimation method in OFDM systems over fast fading channels. In consideration of the effect of null subcarriers, the proposed channel estimation is first performed in the frequency domain. By making efficient use of channel correlation and the property of ICI to reduce the number of needed parameters for channel estimation, robust channel estimation for ICI is accomplished in parallel, pilot cluster by pilot cluster. It is then transformed into the time domain to obtain a gain in noise reduction and to reduce interpolation error as well. Simulation results show that the proposed channel estimation has good performance compared to previous works.

Narrowband Internet of Things (NB-IoT) was proposed by 3GPP in Release 13 to provide low power wide area connections to a high-volume of devices. With the millions of devices expected to connect to 5G through its massive machine-type communication use case, the strict orthogonality and synchronization requirement of cyclic-prefix orthogonal division multiplexing (CP-OFDM) will demand a very large amount of control information between devices and the core network. To overcome this challenge, we study a new air interface for NB-IoT, which utilizes a non-orthogonal multicarrier transmission scheme. The waveform considered in this work is filter-bank muilticarrier with quadrature amplitude modulation (FBMC-QAM). The loss of orthogonality in FBMC-QAM results in severe levels of intrinsic interference. Iterative detection and decoding (IDD) using low density parity check (LDPC) encoding and decoding is implemented together with iterative interference cancellation (IIC) to remove the inherent interference and improve the BER rate performance. Simulation results indicate that the proposed IDD receiver can effectively improve BER performance under time-varying channels.

Block synchronization is a prerequisite to data-dependent superimposed training (DDST) based channel estimation. Mainly designed for single input single output (SISO) system, the existing block synchronization algorithms can hardly work for multi-input multi-output (MIMO) system. A new joint block synchronization, channel and dc-offset estimation algorithm based on balanced zero correlation zone (ZCZ) sequence for MIMO system is proposed in this paper. Analysis and simulation results show that the new algorithm has the same performance for SISO systems as the existing algorithms when their block and cyclic prefix lengths are the same. While for MIMO systems, the performance of the proposed algorithm is much better than that of the existing algorithms.

This paper proposes an iterative detection and decoding (IDD) scheme with a blind channel estimator in the physical layer of long range widearea network (LoRaWAN). The proposed scheme exchanges log-likelihood ratios (LLRs) as soft decision values between the LoRa demodulator and the Hamming decoder to improve the signal detection capability while obtaining the iterative gain. In the typical LoRa demodulation using hard decision without IDD, the received signal is detected by the binary values, i.e. "0" or "1"; and thus, the demodulation and decoding processes are simplified. However, the resulting rounding error occurs the mutual information loss, leading to non-negligible performance degradation. In order to cope with this issue, it is vital to capture the stochastic behavior of the input and output values of the demodulator and decoder. The LLR based on the stochastic signal model enables to improve the error correction capability of the decoder and enhance the iterative gain. Finally, computer simulation results explicitly demonstrate the efficacy of the proposed method in terms of bit error rate (BER) performance.

Latest communications standards have incorporated wireless multi-input multi-output (MIMO) systems combining the use of space-time coding (STC) and spatial multiplexing (SM) techniques. One of the challenges of such space-time coded multiplexing (STCM) systems is the design of high performance and low complexity detectors at large MIMO dimensions, where existing group-based and direct detection detectors suffer from either performance degradation or high computational cost. In this paper, we present pairwise element-based lattice reduction (ELR)-aided detectors for the STCM systems. By utilizing the symmetric structure of the equivalent channel matrix, the proposed pairwise ELR (PELR)-aided detectors approximately halve the complexity of the original ELR-aided detectors while keeping similar bit-error-rate (BER) performance. They also have a lower BER and similar complexity compared to other state-of-the-art detectors. We demonstrate the better tradeoff between the BER and complexity of our PELR-aided detectors under large MIMO systems with simulations.

We investigate a channel estimation technique based on a comb-type pilot arrangement for a MIMO OFDM system. Especially, when the channel varies with time, an efficient channel estimation technique with channel correlation is proposed for accurate estimation. Simulation results show that the proposed channel estimator is accurate and effective for tracking variations of channels between multiple transmit antennas and receive antennas in time-varying radio channels.

In OFDM systems, channel estimation is a crucial consideration for improving system performance. The channel fading effects are eliminated by the equalization of received signal, and the equalizer coefficients are determined according to the results of channel estimation. In this paper, the channel estimation of OFDM systems in time domain for fast fading channel is investigated and developed. The time-domain channel estimation scheme is achieved by inserting pseudo-noise (PN) sequences in the prefix of each OFDM symbol, and therefore the channel impulse response can be estimated. These proposed algorithms include both least-squares and maximum-likelihood estimation techniques. The proposed techniques can perform better than the conventional channel estimation with correlators. Some comparative simulations are given to illustrate the superior performance of the proposed channel estimation techniques.