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Pipelined adaptive DFE architectures using relaxed look-ahead

机译:使用轻松的前瞻性的流水线式自适应DFE架构

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Fine-grain pipelined adaptive decision-feedback equalizer (ADFE) architectures are developed using the relaxed look-ahead technique. This technique, which is an approximation to the conventional look-ahead computation, maintains functionality of the algorithm rather than the input-output behavior. Thus, it results in substantial hardware savings as compared to either parallel processing or look-ahead techniques. Pipelining of the decision feedback loop and the adaptation loop is achieved by the use of delay relaxation and sum relaxation. Both the conventional and the predictor form of ADFE have been pipelined. Results of the convergence analysis of the proposed algorithms are also provided. The performance of the pipelined algorithms for the equalization of a magnetic recording channel is studied. It is shown that the conventional ADFE results in an SNR loss of about 0.6 dB per unit increase in the speed-up factor. The predictor form of ADFE is much more robust and results in less than 0.1 dB SNR loss per unit increase in the speed-up factor. Speed-ups of up to 8 and 45 have been demonstrated for the conventional and predictor forms of ADFE.
机译:细粒度的流水线式自适应决策反馈均衡器(ADFE)体系结构是使用放松的超前技术开发的。这项技术是对传统超前计算的一种近似,它保持了算法的功能而不是输入输出行为。因此,与并行处理或超前技术相比,它可节省大量硬件。决策反馈回路和自适应回路的流水线通过使用延迟松弛和和松弛来实现。 ADFE的常规形式和预测形式都已进行了流水线处理。还提供了所提出算法的收敛性分析结果。研究了流水线算法对磁记录通道均衡的性能。结果表明,传统的ADFE会导致加速因子每增加SNR损失约0.6 dB。 ADFE的预测器形式更加健壮,并且加速因子每增加一个单位,SNR损失小于0.1 dB。对于ADFE的常规形式和预测形式,已经证明了高达8和45的加速。

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