Linear prediction and entropy coding are widely used in speech, audio, video, and other coding systems. Rate-distortion optimization (RDO) is another relevant coding technique, which is especially useful when the encoder performance depends on tunable parameters from distinct coding subsystems. This work presents a RDO strategy for variable-rate encoders that use linear prediction and entropy coding. The method is suitable when the signal to be compressed is short-term stationary and the prediction order varies over signal segments. The second contribution of this paper is to customize the method to the compression of time-domain OFDM-based cellular radio signals. The method enables a 4G / 5G packet-based fronthaul to flexibly trade-off rate and distortion depending on the amount of information to be transmitted and, consequently, benefit from multiplexing gain. Simulation results with LTE signals show that the proposed method achieves improved performance with a relatively low computational cost, while operating with time-domain signals under different traffic scenarios.

中文翻译：

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熵编码 LPC 的速率控制：在基于数据包的前传中的应用


线性预测和熵编码广泛应用于语音、音频、视频和其他编码系统。率失真优化 (RDO) 是另一种相关的编码技术，当编码器性能依赖于不同编码子系统的可调参数时，该技术特别有用。这项工作提出了一种使用线性预测和熵编码的可变速率编码器的 RDO 策略。当要压缩的信号是短期平稳的并且预测顺序随信号段变化时，该方法适用。本文的第二个贡献是定制了基于时域 OFDM 的蜂窝无线电信号压缩方法。该方法使基于 4G/5G 分组的前传能够根据要传输的信息量灵活地权衡速率和失真，从而受益于复用增益。 LTE 信号的仿真结果表明，所提出的方法在不同流量场景下使用时域信号运行时，以相对较低的计算成本实现了更高的性能。