Multi-Tenant C-RAN With Spectrum Pooling: Downlink Optimization Under Privacy Constraints

TL;DR

This paper investigates downlink spectrum pooling in multi-tenant C-RAN systems, optimizing bandwidth, precoding, and compression strategies while balancing inter-operator privacy and capacity constraints.

Contribution

It introduces a novel quantization noise correlation method to control privacy-distortion trade-offs in spectrum pooling for multi-tenant C-RANs.

Findings

Correlated quantization noise improves privacy control.

Optimized strategies enhance spectrum efficiency under privacy constraints.

Numerical results demonstrate the effectiveness of the proposed approach.

Abstract

Spectrum pooling allows multiple operators, or tenants, to share the same frequency bands. This work studies the optimization of spectrum pooling for the downlink of a multi-tenant Cloud Radio Access Network (C-RAN) system in the presence of inter-tenant privacy constraints. The spectrum available for downlink transmission is partitioned into private and shared subbands, and the participating operators cooperate to serve the user equipments (UEs) on the shared subband. The network of each operator consists of a cloud processor (CP) that is connected to proprietary radio units (RUs) by means of finite-capacity fronthaul links. In order to enable interoperator cooperation, the CPs of the participating operators are also connected by finite-capacity backhaul links. Inter-operator cooperation may hence result in loss of privacy. Fronthaul and backhaul links are used to transfer quantized baseband signals. Standard quantization is considered first. Then, a novel approach based on the idea of correlating quantization noise signals across RUs of different operators is proposed to control the trade-off between distortion at UEs and inter-operator privacy. The problem of optimizing the bandwidth allocation, precoding, and fronthaul/backhaul compression strategies is tackled under constraints on backhaul and fronthaul capacity, as well as on per-RU transmit power and inter-operator privacy. For both cases, the optimization problems are tackled using the concave convex procedure (CCCP), and extensive numerical results are provided.