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A General Framework for Transmission with Transceiver Distortion and Some Applications

机译：收发器失真的通用传输框架及一些应用

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摘要

A general theoretical framework is presented for analyzing information transmission over Gaussian channels with memoryless transceiver distortion, which encompasses various nonlinear distortion models including transmit-side clipping, receive-side analog-to-digital conversion, and others. The framework is based on the so-called generalized mutual information (GMI), and the analysis in particular benefits from the setup of Gaussian codebook ensemble and nearest-neighbor decoding, for which it is established that the GMI takes a general form analogous to the channel capacity of undistorted Gaussian channels, with a reduced "effective" signal-to-noise ratio (SNR) that depends on the nominal SNR and the distortion model. When applied to specific distortion models, an array of results of engineering relevance is obtained. For channels with transmit-side distortion only, it is shown that a conventional approach, which treats the distorted signal as the sum of the original signal part and a uncorrelated distortion part, achieves the GMI. For channels with output quantization, closed-form expressions are obtained for the effective SNR and the GMI, and related optimization problems are formulated and solved for quantizer design. Finally, super-Nyquist sampling is analyzed within the general framework, and it is shown that sampling beyond the Nyquist rate increases the GMI for all SNR values. For example, with binary symmetric output quantization, information rates exceeding one bit per channel use are achievable by sampling the output at four times the Nyquist rate.
机译：提出了用于分析高斯信道上无记忆收发器失真的信息传输的通用理论框架，该框架包含各种非线性失真模型，包括发射侧限幅，接收侧模数转换等。该框架基于所谓的通用互信息（GMI），该分析特别受益于高斯码本整体和最近邻解码的设置，为此确定GMI采取了类似于GMI的通用形式。未失真的高斯信道的信道容量，其“有效”信噪比（SNR）降低，这取决于标称SNR和失真模型。当将其应用于特定的变形模型时，可以获得一系列与工程相关的结果。仅对于具有发送侧失真的信道，可以证明，将失真信号视为原始信号部分和不相关失真部分之和的传统方法可以实现GMI。对于具有输出量化的通道，获得有效SNR和GMI的闭式表达式，并为量化器设计制定和解决相关的优化问题。最后，在通用框架内对超级奈奎斯特采样进行了分析，结果表明，超出奈奎斯特速率的采样会提高所有SNR值的GMI。例如，对于二进制对称输出量化，通过以奈奎斯特速率的四倍对输出进行采样，可以获得每通道超过一位的信息速率。

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