Time-varying Multipath Channels Research Articles

Improvement in Time Varying Multipath Channel Estimation

In this work, we propose a new algorithm that modifies the LS channel estimation technique by using a feedback LS channel estimator to improve upon its estimation capabilities and still retaining its simplicity. To examine its usefulness especially for wireless mobile applications, performance in terms of the symbol error rate is analyzed with varying Doppler frequency and delay parameters, which is found to be quite optimistic in comparison to LMMSE and LRMMSE algorithms.

Multichannel equalization method based on vector time-reversal and decision feedback equalizer technology

A kind of underwater communication receiver combined with Vector Time-Reversal algorithm and multichannel decision feedback equalizer (VTR-MDFE) is studied, which can address the problem of inter-symbol interference and waveform distortion, resulting from time-varying multipath channel. Impacts of environmental noise can be suppressed by use of vector sensor’s combined directivity and Corresponding vector signal processing technology. Meanwhile, error rate is reduced while system capacity is enhanced by time-reversal algorithm’s anti-multipath ability. The performance of VTR-MDFE are proved by simulated results in the communication channel provided by a ray-based acoustic model, for different ocean conditions and source-receiver geometries.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio (SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine (ELM) to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR (0 dB) environment in the time-varying multipath Rayleigh fading channel.

Proposals of multipath time-variant channel and additive coloured noise modelling for underwater acoustic OFDM-based systems

Proposals of multipath time-variant channel and additive coloured noise modelling for underwater acoustic OFDM-based systems

Proposals of multipath time-variant channel and additive coloured noise modelling for underwater acoustic OFDM-based systems

This paper presents the results of Underwater Acoustic (UWA) channel measurements, including the power delay profile and the Doppler power spectrum, and further uses them to analyse the Orthogonal Frequency Division Multiplexing (OFDM) system performance. The UWA channel simulation model is derived from the measurement data by applying a widely optimisation algorithm called the Lp-norm method. The close match of correlation functions of the measured and channel simulation model demonstrates correctness of our proposed channel modelling. Moreover, we also propose to use the Autoregressive (AR) generation method for characterising ambient noise in UWA systems. From the UWA channel simulation and coloured noise models, performance of OFDM system using different channel estimation techniques is investigated to analyse the impact of acoustic medium as multipath, Doppler and coloured noise effects. The numerical results manifest superior improvements of the so-called sparse channel estimation over other traditional ones such as least squares or minimum mean square estimation.

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in $$2\times 1$$2?1 space---time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.

Spectrum Sensing for Self-Organizing Network in the Presence of Time-Variant Multipath Flat Fading Channels and Unknown Noise Variance

Cognitive radio, as an important means of implementing spectrum-awareness and dynamic sharing, is of great significance to the future deployment of self-organizing networks. Given its practical application, cognitive radio technology may operate in various adverse self-organizing networks environments. For example, in wireless mobile communication, the multipath propagation with time-varying fading coefficients and unknown noise variance becomes inevitable. Unfortunately, most existing spectrum sensing methods could hardly acquire good performance in this adverse situation. To overcome this difficulty, in this paper we present a novel spectrum sensing algorithm in realistic cognitive radio applications. Firstly, we establish a dynamic state-space model which gives full consideration to the evolution characteristics of primary user's state and time-variant multipath flat fading channel, while the received signal processed by matched filtering is viewed as the observation. On this basis, spectrum sensing is realized by estimating the primary user's state, multipath channel impulse response and noise variance jointly and iteratively. This formulated problem is solved based on maximum a posteriori probability criterion and marginal particle filtering technology. Simulations demonstrate that the sensing performance achieved by proposed algorithm is satisfactory and at the same time, the channel response and noise variance are estimated accurately.

Experimental results of adaptive multichannel decision feedback equalization in shallow channel

A kind of underwater communication receiver combined with mean-squared error adaptive error adaptive algorithm and multichannel decision feedback equalization (AMDFE) is studied, which can address the problem of underwater communications over a time-varying multipath channel in the presence of worse intersymbol interference and waveform distortion. Its performances in different range and sound profile have been analyzed by simulated shallow water channels in the earlier paper. In this paper, the performances of AMDFE in different underwater channels with different parameters are mainly demonstrated by the experiments on the lake and sea. The anti-multipath ability and parameters selection regulation of AMDFE in both near and long range are proved by real data in shallow water.

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

In this paper, we propose a space-alternating generalized expectation maximization (SAGE) based joint channel estimation and data detection algorithm in compressive sensing (CS) framework for orthogonal frequency-division multiplexing (OFDM) systems in rapidly time-varying sparse multipath channels. Using dynamic parametric channel model, the sparse multipath channel is parameterized by a small number of distinct paths, each represented by the path delays and path gains. In our model, we assume that the path gains rapidly vary within the OFDM symbol duration while the number of paths and path delays vary symbol by symbol. Since the convergency of the SAGE algorithm needs statistically independent parameter set of interest to be estimated, we specifically choose the discrete orthonormal Karhunen---Loeve basis expansion model (DKL-BEM) to provide statistically independent BEM coefficients within one OFDM symbol duration and use just a few significant BEM coefficients to represent the rapidly time-varying path gains. The resulting SAGE algorithm that also incorporates inter-channel interference cancellation updates the data sequences and the channel parameters serially. The computer simulations show that our proposed algorithm has better channel estimation and symbol error rate performance than that of the orthogonal matching pursuit algorithm that is commonly proposed in the CS literature.

Simulation Model of Wireless Channel

The change of channel is caused by Doppler effect, and the degree of change is related to relative velocity in the condition of the certain carrier frequency. The multipath fading channel is independent of each other, whose amplitude obeys Rayleigh distribution and the phase obeys uniform distribution. The model of multipath time-varying transmission channel is built. Through the pro-cess of channel model building, the simulation results produced by the channel model verify the effect of the proposed model in the aspect of reducing test data. In a communication system, signal passing through the channel is involved with the process of digital modulation and demodulation. Binary sequence signal is modulated into a complex sequence in the process of modulation before the transmission in the channel, in order to accommodate the wireless channel transmission. With the increase of SNR, BER is overall slightly drops. However there exists violent fluctuation and it presents the random variation of details in the multipath channel. I employ the mathematical model of multipath time-varying channel, i.e. Jakes model to be simulated so as to compare with the AGWN channel in the same situation. Jakes model has the characteristics of the reference chan-nel compared with the AGWN. BER does not change with the increase of SNR significantly and coincides with multipath time-varying channel. The BER considerably decreases with the increase of SNR in the AGWN channel.

Energy Detection Based Spectrum Sensing for Cognitive Radios Over Time-Frequency Doubly Selective Fading Channels

Cognitive radios may operate in practice under various adverse environments. For typical mobile and short-range scenarios, wireless links may tend to be time and frequency selective, i.e., the multipath propagations with time-varying fading coefficients will be inevitable. To cope with the encountered doubly-selective channels, in this paper we present a new spectrum sensing algorithm for distributed applications. First, a dynamic discrete state-space model is established to characterize sensing process, where the occupancy state of primary band and the time-varying multipath channel are treated as two hidden states, while the summed energy is adopted as the observed output. With this new paradigm, spectrum sensing is realized by acquiring primary states and time-dependent multipath channel jointly. For the formulated problem, unfortunately, Bayesian statistical inference may be impractical due to the absence of likelihoods and involved non-stationary distributions. To remedy this problem, an iterative algorithm is further designed by resorting to sequential importance sampling techniques; thus, the dynamic non-Gaussian multipath channel and primary states are estimated recursively. Another critical challenge, e.g., the noise uncertainty, is also considered, which may be incorporated conveniently into this sensing diagram and, furthermore, addressed effectively by the designed algorithm. Simulations validate the proposed algorithm. While classical schemes fail to deal with doubly selective channels, the new sensing scheme can exploit the underlying channel memory and operate well, which provides a great promise to realistic applications.

Study on interleave-division multiple-access technology over complicated time-variant channels

In this paper,the IDMA technology is studied in complicated time-variant multipath channels. A theoretical derivation of iterative process in the receiver which is suitable for WBAN transmission property will be given based on central-limit theorem.By the methodology combing theoretical synthesis and computational simulation,a comprehensive research focusing the necessity,feasibility and system performance analysis of the IDMA integrated WBAN is conducted in this paper.

차세대 지상파 4K UHD & HD 융합방송을 위한 전송 시스템 개발에 관한 연구

세계적으로 아날로그 방송에서 디지털 방송으로의 전환이 마무리되고 있는 가운데, 앞으로 다가올 UHDTV 등 실감의 대용량 방송 및 방송 통신 융합 환경에 대비하기 위한 새로운 차기 방송 표준에 관한 연구의 필요성이 날로 높아져 가고 있다. 특히, 저해상도 서비스에 머물러 있던 모바일 방송에서는 기존 HD급의 서비스 제공이 가능한 모바일 실감방송으로 진화하기 때문에 고정/이동 방송 서비스가 동시에 가능한 고효율 전송 원천 기술 개발이 이루어질 필요가 있다. 이에 최근 지상파 단일 채널을 통해 4K UHD (거치형) & HD (모바일) 방송 서비스를 동시에 제공하는 융합방송 시스템 개발에 대해서 연구가 진행되고 있다. 본 논문에서는 최신의 디지털 방송 전송 기법 및 영상 압축 기술에 대해서 소개하고, 지상파 단일 채널을 통해 4K & HD 융합방송 서비스를 제공하기 위한 서비스 모델 및 전송 요구 사항들에 대해서 분석한다. 이 후 최신의 방송 전송 기법 및 영상 압축 기술을 적용하였을 때, 6MHz & 8MHz 대역폭에서 지상파 4K UHD & HD 융합방송을 전송하기 위해 필요한 최적의 전송 파라메터를 도출하고 이를 이용해 AWGN 및 TU-6 채널에서의 융합방송 수신 성능을 검증하였다. 이를 통해 6MHz 대역폭에서의 원활한 HD 모바일 데이터의 수신이 140 Km/h 수신기 속도까지는 가능하나, 그 이상에서는 대역폭의 한계로 인해 원활한 수신이 이루어지지 않음을 확인하였다. 그리고 대역폭을 8 MHz로 확장할 경우, 매우 빠른 다중경로 페이딩 채널에서도 4K UHD 및 HD 계층 데이터의 수신이 원활히 이루어지는 것을 보였다. The worldwide transition from analog to digital broadcasting has now been completed and the need to study next generation standards for Ultra High Definition TV (UHDTV) broadcasting, and broadcasting & communication convergence systems is rapidly growing. In particular, high resolution mobile broadcasting services are needed to satisfy recent consumers. Therefore, the development of highly efficient convergence broadcasting systems that provide fixed/mobile broadcasting through a single channel is needed. In this paper, a service scenario and requirements for providing 4K UHD & HD convergence broadcasting services through a terrestrial single channel are analyzed by employing the latest transmission and A/V codec technologies. Optimized transmission parameters for 6 MHz & 8 MHz terrestrial bandwidths are drawn, and receiving performances are measured under Additive White Gaussian Noise (AWGN) and time-varying multipath channels. From the results, in a 6 MHz bandwidth, the reliable receiving of HD layer data can be achieved when the receiver velocity is maximum 140 Km/h and is not achieved when the velocity is over 140 Km/h due to the limit of bandwidth. When the bandwidth is extended to 8 MHz, the reliable receiving of both 4K UHD and HD layer data is achieved under a very fast fading multipath channel.

A precoding and detection scheme for OFDM based wireless communication system in high-speed environment

A loss of subchannel orthogonality due to time variant multipath channels in orthogonal frequency-division multiplexing (OFDM) systems leads to interchannel interference (ICI) which increases the error floor in proportion to the Doppler frequency. This paper proposes a simple precoding and detection scheme which can compensate for the effect of ICI in multipath fading channel. In this scheme, the data symbols modulated in frequency domain are divided into several groups, and the precoding is performed by inserting several redundancy carriers at both sides of each data group. Then the simplified ICI cancelation can be achieved at the receiver through inverse fast Fourier transform (IFFT) of grouped data symbols followed by traditional detection method, such as minimum mean-square error (MMSE) and zero forcing (ZF). The proposed scheme has the robust performance and low complexity. More important it does not require any channel state information. The simulation results show that the proposed scheme keeps good performance in high speed scenario even when the vehicle velocity is over 500km/h.

A Novel Channel Estimation Method Based on Power-Delay Profile

Considering the physical layer specification of the IEEE802.11 protocol as background, a novel LSE (least square estimation) algorithm based on power-delay profile of wireless channel is proposed. In multi-path time-variant channel, receiver carries out a primary MMSE estimation using training symbols and pilots of OFDM symbol firstly. Then the channel impulse response is obtained from frequency response estimated by MMSE. Finally, LSE algorithm is applied to revise the channel impulse response according to the power-delay profile. The LSE algorithm can reduce the noise level of MMSE estimation in time-domain which is equivalent to increasing the SNR to the output of 2-D wiener filter. Simulation results confirm that the proposed LSE algorithm gains a better performance in contrast to MMSE algorithm. As the Doppler frequency shift increases, LSE outperforms more significantly than MMSE algorithm.

Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels

Abstract Cognitive radio has more extensive application in recent years, and it may operate in complex wireless environmental condition such as communication systems with time-variant multipath flat-fading channel. As an essential technology for cognitive radio, most existing spectrum sensing methods are designed for time-invariant propagation channel; thus, it could be extremely difficult to achieve acceptable sensing performance when we apply them to deal with time-variant multipath fading channel. In order to overcome this obstacle, we design a novel spectrum sensing method in this investigation. Firstly, a dynamic state-space model is proposed in which two different hidden Markov models are employed to abstract the evolution of primary user state and time-variant multipath flat-fading channel gain. Based on the dynamic state-space model, the spectrum sensing problem is formulated as blind estimation problem. Relying on maximum a posteriori probability criterion and particle filtering technology, a joint estimation algorithm of the time-variant channel gain and primary user state is presented. Experimental simulations demonstrate the superior performance of our presented sensing scheme, which could be used potentially in realistic cognitive radio systems.

Leveraging spatial diversity to mitigate interference in underwater acoustic communication networks

Many acoustic channels suffer from interference which is neither narrowband nor impulsive. This relatively long duration partial band interference can be particularly detrimental to system performance. We survey recent work in interference mitigation and orthogonal frequency division multiplexing (OFDM) as background motivation to develop a spatial diversity receiver for use in underwater networks. The network consists of multiple distributed cabled hydrophones that receive data transmitted over a time-varying multipath channel in the presence of partial band interference produced by interfering active sonar signals as well as marine mammal vocalizations. In operational networks, many dropped messages are lost due to partial band interference which corrupts different portions of the received signal depending on the relative position of the interferers, information source and receivers due to the slow speed of propagation.

Mitigation of Inter-Symbol Interference in Underwater Acoustic Communication Using Spatial Filter

The underwater acoustic communication (UAC) is characterized by doubly spread channel. It is included in the time-variant doppler shift and delay-time spreads due to multiple paths. To compensate such distorted signals, various techniques including time-reversal processing, spatial diversity, phase estimator, and equalizer are being applied. In this paper, a spatial filter based on the beamforming is proposed as a method to mitigate such inter-symbol interferences that are generated in time-varying multipath channels. The proposed technique realizes coherent communications by steering the direction of the desired signals and improves the performance of UAC by increasing the signal-to-interference plus noise ratio using the array gain.

Channel interpolation for LTE uplink systems with high mobility using Slepian sequences

Single Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink communication since it combines the low peak to average power ratio feature of single-carrier transmission systems with the multipath resistance and flexible frequency allocation of orthogonal frequency-division multiple access (OFDMA). Unfortunately, SC-FDMA transmission over time-varying multipath fading channels for very high speed environments the subcarrier orthogonality is destroyed giving rise to intercarrier interference (ICI) due to channel variation within an SC-FDMA symbol. This paper is, therefore, focused on the use of time-domain channel interpolator for LTE uplink systems in large Doppler spread scenarios to improve channel tracking performance. The resulting interpolation algorithm is based on the discrete prolate spheroidal wave functions (DPSWF) which is particularly well suited to represent the rapidly time-varying fading channel due to optimum finite time and frequency support feature. The variations of the channel taps are tracked first by a Kalman filter in time domain during training symbols. Then, a DPSWF based channel interpolator is applied to recover the time variation of channel taps between training symbols within two consecutive slots in a single subframe. The performance of the proposed interpolator is compared with a polynomial interpolator whose order is adjusted adaptively to achieve the best fit under particular user mobilities. Moreover, the analytical mean square error (MSE) performance of DPSWF is derived and compared with the simulations. The results show that the DPSWF represents the time-varying fading channel very effectively and the proposed algorithm has excellent channel estimation performance in high SNR even with a very small number of channel interpolation parameters employed in the algorithm.

Sparse Underwater Acoustic Channel Parameter Estimation Using a Wideband Receiver Array

This paper considers a channel parameter estimation problem using a wideband multichannel receiver array. We estimate the sparse underwater acoustic communication channel parameters, such as time-delay, incidence angle, Doppler frequency, and complex amplitude of impinging multipath components. Multichannel signals from a receiver array are modeled to obtain additional sparsity in the incidence angle and the angle-delay-Doppler-spread function is defined to parameterize the channel. To estimate the principal entries of the angle-delay-Doppler-spread function, we propose a modified version of the orthogonal matching pursuit (OMP) algorithm to utilize a redundant dictionary. We introduce a space-alternating scheme which divides the entire parameter search space into smaller subsets to avoid handling large parameter search space. The performance of the proposed method is evaluated using two experimental data: one from large-scale water tank with the capability of generating surface gravity wave and the other from shallow sea water which shows sparse channel structure. Our results demonstrate that the proposed method accurately estimates the time-varying multipath channel parameters with lower residual error than the OMP.