71 research outputs found
Fronthaul Quantization as Artificial Noise for Enhanced Secret Communication in C-RAN
This work considers the downlink of a cloud radio access network (C-RAN), in
which a control unit (CU) encodes confidential messages, each of which is
intended for a user equipment (UE) and is to be kept secret from all the other
UEs. As per the C-RAN architecture, the encoded baseband signals are quantized
and compressed prior to the transfer to distributed radio units (RUs) that are
connected to the CU via finite-capacity fronthaul links. This work argues that
the quantization noise introduced by fronthaul quantization can be leveraged to
act as "artificial" noise in order to enhance the rates achievable under
secrecy constraints. To this end, it is proposed to control the statistics of
the quantization noise by applying multivariate, or joint, fronthaul
quantization/compression at the CU across all outgoing fronthaul links.
Assuming wiretap coding, the problem of jointly optimizing the precoding and
multivariate compression strategies, along with the covariance matrices of
artificial noise signals generated by RUs, is formulated with the goal of
maximizing the weighted sum of achievable secrecy rates while satisfying per-RU
fronthaul capacity and power constraints. After showing that the artificial
noise covariance matrices can be set to zero without loss of optimaliy, an
iterative optimization algorithm is derived based on the concave convex
procedure (CCCP), and some numerical results are provided to highlight the
advantages of leveraging quantization noise as artificial noise.Comment: to appear in Proc. IEEE SPAWC 201
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Multi-Tenant C-RAN With Spectrum Pooling: Downlink Optimization Under Privacy Constraints
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.Comment: Submitted, 24 pages, 7 figure
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