论文标题
降低的复杂性最大样本检测，次优基本要求
A Reduced-Complexity Maximum-Likelihood Detection with a sub-optimal BER Requirement
论文作者
论文摘要
最大似然（ML）检测是一种最佳信号检测方案，由于其较高的计算复杂性，通常很难实现，尤其是在多输入多输出（MIMO）方案中。在具有$ N_T $传输天线的系统中，使用$ M $ - ARY调制，ML-MIMO检测器需要$ M^{n_t} $成本函数（CF）评估，然后进行搜索操作，用于检测具有最小CF值的符号。但是，实用的系统需要比特误差比（BER）是应用依赖性的，这可能是最佳的。这意味着可能无需始终拥有最小的CF解决方案。相反，需要搜索符合所需的亚最佳BER的解决方案。在这项工作中，我们通过获得BER和CF之间的关系，为SISO/MIMO系统提出了一种新的检测器设计，这也提高了ML检测器对亚最佳BER的计算复杂性。
Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often difficult to implement due to its high computational complexity, especially in a multiple-input multiple-output (MIMO) scenario. In a system with $N_t$ transmit antennas employing $M$-ary modulation, the ML-MIMO detector requires $M^{N_t}$ cost function (CF) evaluations followed by a search operation for detecting the symbol with the minimum CF value. However, a practical system needs the bit-error ratio (BER) to be application-dependent which could be sub-optimal. This implies that it may not be necessary to have the minimal CF solution all the time. Rather it is desirable to search for a solution that meets the required sub-optimal BER. In this work, we propose a new detector design for a SISO/MIMO system by obtaining the relation between BER and CF which also improves the computational complexity of the ML detector for a sub-optimal BER.
