End-to-End Rate Enhancement in C-RAN Using Multi-Pair Two-Way Computation

Cloud radio-access networks (C-RAN) have been proposed as an enabling technology for keeping up with the requirements of next-generation wireless networks. Most existing works on C-RAN consider the uplink or the downlink separately. However, designing the uplink and the downlink jointly may bring additional advantage, especially if message source-destination information is taken into account. In this paper, this idea is demonstrated by considering pairwise message exchange between users in a C-RAN. A multi-pair two-way transmission scheme is proposed which targets maximizing the end-to-end user data rates. In the proposed scheme, a lattice-based computation strategy is used, where the baseband processing unit (BBU) pool decodes integer linear combinations of paired users' codewords instead of decoding linear combinations of individual codewords. The BBU pool then compresses the computed signals and forwards them to the remote radio heads (RRHs), which decompress the signals and send them to the users. Finally, each user decodes its desired message using its own message as side information. The achievable rate of this scheme is derived, optimized, and evaluated numerically. Results reveal that significant end-to-end rate improvement can be achieved using the proposed scheme compared to existing schemes

On the Capacity Regions of Cloud Radio Access Networks with Limited Orthogonal Fronthaul

Uplink and downlink cloud radio access networks are modeled as two-hop K-user L-relay networks, whereby small base-stations act as relays for end-to-end communications and are connected to a central processor via orthogonal fronthaul links of finite capacities. Simplified versions of network compress-forward (or noisy network coding) and distributed decode-forward are presented to establish inner bounds on the capacity region for uplink and downlink communications, that match the respective cutset bounds to within a finite gap independent of the channel gains and signal to noise ratios. These approximate capacity regions are then compared with the capacity regions for networks with no capacity limit on the fronthaul. Although it takes infinite fronthaul link capacities to achieve these "fronthaul-unlimited" capacity regions exactly, these capacity regions can be approached approximately with finite-capacity fronthaul. The total fronthaul link capacities required to approach the fronthaul-unlimited sum-rates (for uplink and downlink) are characterized. Based on these results, the capacity scaling law in the large network size limit is established under certain uplink and downlink network models, both theoretically and via simulations