We propose a numerically efficient method for evaluating the random-coding union bound with parameter $s$ on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian codebooks and operating over a memoryless block-fading channel. Our method relies on the saddlepoint approximation, which, differently from previous results reported for similar scenarios, is performed with respect to the number of fading blocks (a.k.a. diversity branches) spanned by each codeword, instead of the number of channel uses per block. This different approach avoids a costly numerical averaging of the error probability over the realizations of the fading process and of its pilot-based estimate at the receiver and results in a significant reduction of the number of channel realizations required to estimate the error probability accurately. Our numerical experiments for both single-antenna communication links and massive multiple-input multiple-output (MIMO) networks show that, when two or more diversity branches are available, the error probability can be estimated accurately with the saddlepoint approximation with respect to the number of fading blocks using a numerical method that requires about two orders of magnitude fewer Monte-Carlo samples than with the saddlepoint approximation with respect to the number of channel uses per block.
翻译:我们建议一种数字高效的方法来评价随机编码结合,以参数$s$为单位,根据一个试点辅助传输计划,使用高西亚编码簿,在无内存区块淡化通道上操作,在有限区块制度下可以实现的误差概率来评估随机编码结合,我们的方法依靠的是马鞍点近似值,这与以前为类似情况报告的结果不同,是针对每个编码词的淡化区块数(a.k.a.多样性分支),而不是每个区块的频道使用次数。这一不同的方法避免了在接收器实现淡化过程及其试点估计的误差概率的昂贵数字平均率,结果大大减少了准确估计误差概率所需的频道实现次数。我们对单安纳通信链接和大型多投影多输出(MIMO)网络的数字实验显示,在具备两个或两个以上区块的分支时,误差概率可以用马鞍点的近似值来准确估计,因为使用数字方法需要大约两个波级的淡化区块的平面,与每区块的平面样品相比,最短的Mont-Car样品。