48 research outputs found
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
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
Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures
The fifth generation (5G) of cellular systems is introducing Ultra-Reliable
Low-Latency Communications (URLLC) services alongside more conventional
enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular
architecture is evolving from a base station-centric deployment to a fog-like
set-up that accommodates a flexible functional split between cloud and edge. In
this paper, a novel solution is proposed that enables the non-orthogonal
coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge
Nodes (ENs), while eMBB communications are handled centrally at a cloud
processor as in a Cloud-Radio Access Network (C-RAN) system. This solution
guarantees the low-latency requirements of the URLLC service by means of edge
processing, e.g., for vehicle-to-cellular use cases, as well as the high
spectral efficiency for eMBB traffic via centralized baseband processing. Both
uplink and downlink are analyzed by accounting for the heterogeneous
performance requirements of eMBB and URLLC traffic and by considering practical
aspects such as fading, lack of channel state information for URLLC
transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity,
and different coexistence strategies, such as puncturing.Comment: Submitted as Journal Pape
Compression and Recovery in Cell-free Cloud Radio Access Network
Cloud radio access network (C-RAN) is an evolved network architecture for future mobile communication systems. It aims to provide higher spectral efficiency, lower energy consumption and reduced cost of operations and maintenance for the network, which will enable the operators to not only satisfy growing user demands, but provide new services and applications. However, the huge load on the fronthaul network which connects the baseband unit (BBU) and a large number of remote radio heads (RRHs) is a significant challenge. To improve the fronthaul performance, a data compression and recovery scheme based on compressive sensing is proposed in this thesis. First, the theory of compressive sensing is studied, including the essential principles, standard compressive sensing model, potential measurement matrices, etc. Several popular recovery algorithms in compressive sensing are demonstrated in detail. Secondly, a compression and recovery scheme is proposed for the uplink of a cell-free C-RAN system. In the proposed scheme, compressive sensing is applied by exploiting the sparsity of user data. In particular, the multi-access fading in this system is incorporated into the formulation of the compressive sensing model. The aggregated measurement matrix which contains both the channel matrix and the fronthaul compression matrix is shown to satisfy the restricted isometry property (RIP) condition. Furthermore, two different recovery algorithms, basis pursuit denoising (BPDN) and sparsity adaptive matching pursuit (SAMP), are used respectively for estimating the sparse signals. The major advantage is that they do not require the sparsity of user data as a prior information during the process of signal recovery. It allows easy applications in many practical scenarios where the number of non-zero elements of the signals is not available. The simulation results show that the proposed scheme can efficiently alleviate the heavy burden on the fronthaul network, and meanwhile provide stable signal recovery for this system