Fronthaul-Constrained Uplink OFDM in C-Ran with Hybrid Decoding

Abstract

Cloud radio access network (CRAN) is a promising candidate for the next-generation wireless communication systems. In CRAN, remote radio heads (RRHs) are deployed to serve users in a target area, which are connected to a central processor (CP) via limited-capacity links termed the fronthaul. In practice, the large amount of information transferred between each RRH and the CP for centralized processing can easily exceed its fronthaul capacity. This motivates our study in this paper on a new hybrid decoding scheme here the RRHs can either locally decode-and-forward the user messages to save the fronthaul capacity, or quantize and forward their signals to the CP for joint decoding as in the conventional CRAN (i.e., forward-and-decode). We consider the uplink transmission in an orthogonal frequency division multiplexing (OFDM)-based CRAN with multiple RRHs to serve a single user, where the proposed hybrid decoding is performed on each OFDM sub-channel (SC). We consider a joint optimization of the processing mode selections (decode-and-forward or forward-and-decode) along with the user's power allocation over all SCs to maximize the sum-rate for the user subject to the RRHs' individual fronthaul capacity constraints and the user's total power constraint. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We also compare the performance of the proposed hybrid decoding with existing schemes that perform either decode-and-forward or forward-and-decode processing at all SCs, which shows promising throughput gains.