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

Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This work studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network (C-RAN) architecture. Digital processing is carried out at a baseband processing unit (BBU) in the "cloud" and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU 2) imperfect effective CSI. Optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio frequency (RF) beamforming at the RRHs is a coupled problem, since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. Numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors

Joint Design of Wireless Fronthaul and Access Links in Massive MIMO CRANs

Cloud radio access network (CRAN) has emerged as a promising mobile network architecture for the current 5th generation (5G) and beyond networks. This thesis focuses on novel architectures and optimization approaches for CRAN systems with massive multiple-input multiple-output (MIMO) enabled in the wireless fronthaul link. In particular, we propose a joint design of wireless fronthaul and access links for CRANs and aim to maximize the network spectral efficiency (SE) and energy efficiency (EE). Regarding downlink transmission in massive MIMO CRANs, the precoding designs of the access link are optimized by accounting for both perfect instantaneous channel state information (CSI) and stochastic CSI of the access link separately. The system design adopts a decompress-and-forward (DCF) scheme at the remote radio heads (RRHs), with optimization of the multivariate compression covariance noise. Constrained by the maximum power budgets set for the central unit (CU) and RRHs, we aim to maximize the network sum-rate and minimize the total transmit power for all user equipments (UEs). Moreover, we present a separate optimization design and compare its performance, feasibility, and computational efficiency with the proposed joint design. Considering the uplink transmission, we utilize a compress-and-forward (CF) scheme at the RRHs. Assuming that perfect CSI is available at the CU, our objective is to optimize the precoding matrix of the access link while adopting conventional precoding methods for the fronthaul link. This thesis also proposes an unmanned aerial vehicle (UAV)-enabled CRAN architecture with a massive MIMO CU as a supplement system to the terrestrial communication networks. The locations of UAVs are optimized along with compression noise, precoding matrices, and transmit power. To tackle the non-convex optimization problems described above, we employ efficient iterative algorithms and conduct a thorough exploration of practical simulations, yielding promising results that outperform benchmark schemes. In summary, this thesis explores future wireless CRAN architectures, leveraging promising technologies including massive MIMO and UAV-enabled communications. Furthermore, this work presents comprehensive optimization designs aimed at further enhancing the network efficiency

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions