It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we consider a different approach by taking an orthogonal FFT base, subdivide its image into appropriate sub-channels and let each subchannel take only a fraction of the load. To show that this approach can actually achieve the full capacity we provide i) new concentration inequalities, and ii) devise a sparsity capture effect, i.e where the sub-division can be driven such that the activity in each each sub-channel is sparse by design. We show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase.
翻译:众所周知, CS 能够推动大规模随机访问协议。 通常, 协议会在某些超负荷的系统中运作, 从而可以开发宽度。 在本文中, 我们考虑一种不同的方法, 采用一个正正方形FFFT基, 将其图像分入适当的子通道, 并让每个子通道只吸收其中的一小部分。 要显示这个方法实际上可以达到我们提供的全部容量 (i) 新的浓度不平等, 并且 (ii) 设计一个宽度捕捉效果, 也就是分层可以驱动每个子通道的活动因设计而稀疏。 我们通过模拟来显示, 系统可以伸缩, 导致30倍的容量增长 。