An Optimum Linear Receiver for Multiple Channel Digital Transmission Systems

Etten, Wim van

doi:10.1109/tcom.1975.1092893

Cited by 73 publications

(41 citation statements)

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“…The noise power at higher SNR values can be negligible as we seen in eq. (22), and the MMSE detector become ZF detector.…”

Section: Simulation Outputs and Discussionmentioning

confidence: 99%

“…The Zero Forcing (ZF) detector is another kind of linear transformation based detector developed by Van Etten [22] for a multiple channel multiplexing communication system exposed to both inferences namely, Inter Symbol Interference and interchannel interference. The detector is first analysed in the space division multiplexing based VBLAST systems by Foschini, Golden, Wolniansky and Valenzuela [23][24][25] after the arrival of multipleantenna technologies during mid-1990's.…”

Section: Linear Mimo Signal Detectorsmentioning

confidence: 99%

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Efficient Channel Estimation and Signal Detection Schemes for Multiple Input and Multiple Output System under WiMax Environment

Kavitha¹,

Bagadi²

2017

IJIES

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Abstract:The multiple input Multiple Output (MIMO) is a wireless technology that can offer high spectral efficiency, capacity gain and/or diversity gain. On the other hand, the Worldwide Interoperability for Microwave Access (WiMAX) is a promising wireless technology which delivers data at high rates covering larger area. To retrieve desired signals in such technologies, we need to incorporate appropriate Channel estimation and signal detection techniques at the receiver. Thus, in this paper we propose Scaled Least Square-Maximum Likelihood (SLS-ML) and Reduced Minimum Mean Square Error-ML (RMMSE-ML) receivers for MIMO system under WiMAX environment. The proposed techniques are consistently outperforming the exiting Least Square-Maximum Likelihood (LS-ML) and Minimum Mean Square Error-ML (MMSE-ML) receivers. The RMMSE and SLS techniques minimize channel estimation error and the Maximum Likelihood (ML) detector cancels residual interference from other transmitting antennas. In the simulation study, the Bit Error Rate (BER) performance evaluation of 2×2 and 4×4 antenna structures with different detector and channel estimator techniques over various Erceg's channel models has been discussed. The investigations show that the ML detection applied to the mobile WiMAX systems provide better performance than other detector systems, irrespective of the number of antenna array size and the fading effect.

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“…The noise power at higher SNR values can be negligible as we seen in eq. (22), and the MMSE detector become ZF detector.…”

Section: Simulation Outputs and Discussionmentioning

confidence: 99%

Section: Linear Mimo Signal Detectorsmentioning

confidence: 99%

Efficient Channel Estimation and Signal Detection Schemes for Multiple Input and Multiple Output System under WiMax Environment

Kavitha¹,

Bagadi²

2017

IJIES

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“…The two linear networks were trained using the µ-LMS algorithm, the single layer non-linear network was trained with the Delta Rule and the MLP was trained with the backpropagation (BP) algorithm (also called Generalised Delta Rule) respectively. The Delta Rule simply involves multiplying the previous weight change equation (2) by F'(z i ) = z i (1-z i ). The well known BP…”

Section: Binary Datamentioning

confidence: 99%

“…This overlapping inevitably introduces adjacent channel (or cross-channel) interference which reduces the SNR in the receiver unless steps are taken to remove it. A multiple channel communication model is described by Van Etten [2] which uses an MxM interference matrix so that received signals are linear combinations of the wanted and interfering signals. In this paper the interference is considered to be resulting entirely from nearest adjacent sub-channels and the amount of interference is a fixed fraction ε of each of the adjacent signals, which is called the fractional crosstalk (or fractional overlap) parameter.…”

Section: Introductionmentioning

confidence: 99%

Multiple channel crosstalk removal using limited connectivity neural networks

Craven

Curtis

Hayes‐Gill

Proceedings of Third International Conference on Electronics, Circuits, and Systems

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Limited connectivity neural network architectures are investigated for the removal of crosstalk in systems using mutually overlapping sub-channels for the communication of multiple signals, either analogue or digital. The crosstalk error is modelled such that a fixed proportion of the signals in adjacent channels is added to the main signal. Different types of neural networks, trained using gradient descent algorithms, are tested as to their suitability for reducing the errors caused by a combination of crosstalk and additional gaussian noise. In particular we propose a single layer limited connectivity neural network since it promises to be the most easily implemented in hardware. A variable gain neuron structure is described which can be used for both analogue and digital data.

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“…Optimum receive structures for MIMO systems were developed by Van Etten [66], [67] and Kaye and George [37] in the 1970's. More recent research by Foschini [24] and Telatar [60] in the mid 1990's has…”

Section: Multiple Input Multiple Output Systemsmentioning

confidence: 99%