Lossy Compression for Compute-and-Forward in Limited Backhaul Uplink Multicell Processing

We study the transmission over a cloud radio access network in which multiple base stations (BS) are connected to a central processor (CP) via finite-capacity backhaul links. We propose two lattice-based coding schemes. In the first scheme, the base stations decode linear combinations of the transmitted messages, in the spirit of compute-and-forward (CoF), but differs from it essentially in that the decoded equations are remapped to linear combinations of the channel input symbols, sent compressed in a lossy manner to the central processor, and are not required to be linearly independent. Also, by opposition to the standard CoF, an appropriate multi-user decoder is utilized to recover the sent messages. The second coding scheme generalizes the first one by also allowing, at each relay node, a joint compression of the decoded equation and the received signal. Both schemes apply in general, but are more suited for situations in which there are more users than base stations. We show that both schemes can outperform standard CoF and successive Wyner-Ziv schemes in certain regimes, and illustrate the gains through some numerical examples.Comment: Submitted to IEEE Transactions on Communication

Uplink multi-cell processing: Approximate sum capacity under a sum backhaul constraint

Abstract—This paper investigates an uplink multi-cell processing (MCP) model where the cell sites are linked to a central processor (CP) via noiseless backhaul links with limited capacity. A simple compress-and-forward scheme is employed, where the base-stations (BSs) quantize the received signals and send the quantized signals to the CP using distributed Wyner-Ziv compression. The CP decodes the quantization codewords first, then decodes the user messages as if the users and the CP form a virtual multiple-access channel. This paper formulates the problem of maximizing the overall sum rate under a sum backhaul constraint for such a setting. It is shown that setting the quantization noise levels to be uniform across the BSs maximizes the achievable sum rate under high signal-to-noise ratio (SNR). Further, for general SNR a low-complexity fixed-point iteration algorithm is proposed to optimize the quantization noise levels. This paper further shows that with uniform quantization noise levels, the compress-and-forward scheme with Wyner-Ziv compression already achieves a sum rate that is within a constant gap to the sum capacity of the uplink MCP model. The gap depends linearly on the number of BSs in the network but is independent of the SNR and the channel matrix. I

Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis

Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities