In 2017, Polyanskiy showed that the trade-off between power and bandwidth efficiency for massive Gaussian random access is governed by two fundamentally different regimes: low power and high power. For both regimes, tight performance bounds were found by Zadik et al., in 2019. This work utilizes recent results on the exact block error probability of Gaussian random codes in additive white Gaussian noise to propose practical methods based on iterative soft decoding to closely approach these bounds. In the low power regime, this work finds that orthogonal random codes can be applied directly. In the high power regime, a more sophisticated effort is needed. This work shows that power-profile optimization by means of linear programming, as pioneered by Caire et al. in 2001, is a promising strategy to apply. The proposed combination of orthogonal random coding and iterative soft decoding even outperforms the existence bounds of Zadik et al. in the low power regime and is very close to the non-existence bounds for message lengths around 100 and above. Finally, the approach of power optimization by linear programming proposed for the high power regime is found to benefit from power imbalances due to fading which makes it even more attractive for typical mobile radio channels.

中文翻译：

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大规模高斯多址系统中随机编码的软干扰消除

2017年，Polyanskiy表明，大规模高斯随机访问的功率和带宽效率之间的权衡受两个根本不同的机制支配：低功率和高功率。对于这两种方式，Zadik等人在2019年发现了严格的性能界限。这项工作利用了最新的高斯随机码在加性高斯白噪声中的精确块错误概率的结果，提出了基于迭代软解码的逼近实用方法这些界限。在低功率状态下，这项工作发现可以直接应用正交随机码。在大功率体制下，需要做出更复杂的努力。这项工作表明，由Caire等人率先提出的通过线性编程进行功率分布优化。在2001年，这是一个很有前途的策略。提出的正交随机编码和迭代软解码的组合甚至优于Zadik等人的存在边界。在低功率状态下，对于消息长度在100以上的消息，非常接近不存在的界限。最后，发现针对高功率方案提出的通过线性规划进行功率优化的方法因衰落而受益于功率不平衡，这使其对典型的移动无线电信道更具吸引力。