System Model and Channel Sparsification

Abstract

In C-RAN, only a small fraction of the entries in the channel matrix have reasonably large amplitudes, because a user is only close to a small number of RRHs in its neighborhood, and vice versa. Thus, ignoring the small entries in the channel matrix would significantly sparsify the matrix, which can potentially lead to significant reduction in the computational complexity and channel estimation overhead. The question is to what extent can the channel matrix be sparsified without substantially compromising the system performance. In this chapter, we attempt to address this question. In particular, we propose a threshold-based channel matrix sparsification method, where the matrix entries are ignored according to the distance between the users and RRHs. We derive a closed-form expression describing the relationship between the threshold and the SINR loss due to channel spasification. The analysis serves as a convenient guideline to set the threshold subject to a tolerable SINR loss.