Applying Nearest Convex Hull Classification (NCHC) to blind user identification in a massive Multiple Input Multiple Output (MIMO) communications system is proposed. The method is blind in the way that the Base Station (BS) only requires a training sequence containing unknown data symbols obtained from the user without further knowledge on the channel, modulation, coding or even noise power. We evaluate the algorithm under the assumption of gaussian transmit signals using the non-rigorous replica method. To facilitate the computations the existence of an Operator Valued Free Fourier Transform is postulated, which is verified by Monte Carlo simulation. The replica computations are conducted in the large but finite system by applying saddle-point integration with inverse temperature $β$ as the large parameter. The classifier accuracy is estimated by gaussian approximation through moment-matching.

翻译：本文提出将最近凸包分类法应用于大规模多输入多输出通信系统中的盲用户识别。该方法的"盲"体现在基站仅需用户提供的包含未知数据符号的训练序列，无需掌握信道、调制、编码乃至噪声功率等先验信息。我们基于高斯发射信号假设，采用非严格的复本方法对该算法进行评估。为简化计算，假设存在算子值自由傅里叶变换，并通过蒙特卡洛仿真验证其有效性。在大型有限系统中，以逆温度$β$作为大参数进行鞍点积分来完成复本计算。分类器精度通过矩匹配的高斯近似方法进行估计。