2D regression channel estimation for equalizing OFDM signals

Chang, Ming-Xian; Su, Yu T.

doi:10.1109/vetecs.2000.851454

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…The data rate is chosen as 110 Mbps and the channel models (CM) considered are CM1, CM2, CM3, and CM4 [13]. The block size L b and the length of a spreading sequence L sc are (L b , L sc ) = (128, 12) or (64, 24). The maximum channel delays L p 's of CM1-CM4 are 149, 160, 288, and 472 chip intervals, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…3 compares the performance of the proposed system, the RAKE receiver with maximum ratio combining, and the BPSK modulation in the AWGN channel. We consider (L b , L sc ) = (64, 24) and (128, 12). Noiseless channel estimation is assumed.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For time-varying channels, a model-based algorithm, borrowed from OFDM systems, can be applied [12].…”

Section: Channel Estimation 1) Pilot-based Estimationmentioning

confidence: 99%

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A Novel Architecture of Single-Carrier Block Transmission DS-CDMA

Chang

Yang

2006

IEEE Vehicular Technology Conference

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Single-carrier (SC) block transmission [1][2]is an efficient scheme in multipath channels. With frequency-domain equalization, SC block transmission avoids complex time-domain equalization. There are also several systems proposed that combine SC-FDE with code-division multiple access (CDMA). However, unlike conventional direct-sequence (DS) CDMA, the present CDMA systems with FDE may be not suitable for asynchronous users, for lack of a signal model that could suitably express the despread signal in frequency domain.In this paper, we propose a novel architecture of SC block transmission DS-CDMA that can be applied for asynchronous users. A new signal model is derived that represents spreading, channel effect, and despreading as a series of circular convolutions. This signal model helps in deriving a new frequency-domain minimum mean-square error (MMSE) equalization algorithm for SC block transmission CDMA. Based on the proposed signal model, we also propose a simple channel estimation algorithm. Our architecture and signal model can be applied for further development of multi-user detection algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

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A Novel Architecture of Single-Carrier Block Transmission DS-CDMA

Chang

Yang

2006

IEEE Vehicular Technology Conference

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“…The main concept is to find a function that best fits the training data behavior to perform predictions. For example, linear [175][176][177], polynomial [178], 2D nonlinear [99,179,180], and support vector [172,173,[181][182][183][184][185][186][187] regressions have been employed in channel estimation for multicarrier systems. Regression algorithms go under the supervised learning paradigm.…”

Section: Regressionmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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“…The advantage of pilot based estimators are its reliability and accuracy [2] [5].In a blind estimation technique the estimator makes use of Maximum Likelihood(ML) technique in predicting Cyclic Prefix(CP) [6] or by frequency synchronization scheme using OFDM symbols with identical halves [7]. In order to mitigate the effect of ISI predictors either use statistical approach like Least Square (LS) or minimum mean square Error (MMSE) [8] [9] or by neural network approach like Radial Basis Function (RBF) network or Self Organizing Map(SOM) network [10] [11].…”

Section: Related Workmentioning

confidence: 99%

Neural network based BER prediction for 802.16e channel

Gowrishankar¹,

Babu²,

Satyanarayana³

2007

2007 15th International Conference on Software, Telecommunications and Computer Networks

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The prediction of Bit Error Rate (BER) in IEEE 802.16e Mobile WirlessMAN network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, BER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as Recurrent Radial Basis Function (RRBF) Network and Echo State Network (ESN). The Predicted BER will match very well with the simulation results.

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2D regression channel estimation for equalizing OFDM signals

Cited by 9 publications

References 4 publications

A Novel Architecture of Single-Carrier Block Transmission DS-CDMA

A Novel Architecture of Single-Carrier Block Transmission DS-CDMA

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Neural network based BER prediction for 802.16e channel

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