Look-Ahead Sphere Decoding: Algorithm and performance evaluation

M. Gamba, G. Masera
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Abstract

Multiple Input Multiple Output (MIMO) systems are recognized as a key enabling technology in high performance wireless communications. Among known MIMO detectors, the Sphere Decoding Algorithm (SDA) is capable of optimal performance with a reduced computational complexity, compared to a Maximum Likelihood (ML) detector. However the hardware implementation of the SDA still is a major design challenge, especially with high throughput constraints. This paper proposed a modified version of the SDA, which achieves a significant throughput increase and guarantees ML performance, at the price of limited additional complexity. This new solution, called LASDA (Look-Ahead SDA) is mainly based on formal algorithm transformations, namely look-ahead, pipelining and retiming, applied at signal processing level. In addition, a modified tree search strategy, called test&restart, allows a limited increase of the iteration number. The analysis of the proposed LASDA shows that it is almost twice as fast as SDA, while simulation results exhibit ML performance.
前瞻球体解码:算法与性能评估
多输入多输出(MIMO)系统被认为是高性能无线通信的关键使能技术。在已知的MIMO检测器中,与最大似然(ML)检测器相比,Sphere解码算法(SDA)具有降低计算复杂度的最佳性能。然而,SDA的硬件实现仍然是一个主要的设计挑战,特别是在高吞吐量限制下。本文提出了一种改进版本的SDA,以有限的额外复杂性为代价,实现了显著的吞吐量提高并保证了ML性能。这种新的解决方案被称为LASDA (Look-Ahead SDA),主要基于在信号处理层面应用的形式算法转换,即前瞻性、流水线化和重定时。此外,一种修改过的树搜索策略,称为test&restart,允许有限地增加迭代次数。对LASDA算法的分析表明,它的速度几乎是SDA算法的两倍,而仿真结果显示了机器学习的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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