861 research outputs found
Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks
Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be
promising in the fifth generation (5G) wireless networks. H-CRANs enable users
to enjoy diverse services with high energy efficiency, high spectral
efficiency, and low-cost operation, which are achieved by using cloud computing
and virtualization techniques. However, H-CRANs face many technical challenges
due to massive user connectivity, increasingly severe spectrum scarcity and
energy-constrained devices. These challenges may significantly decrease the
quality of service of users if not properly tackled. Non-orthogonal multiple
access (NOMA) schemes exploit non-orthogonal resources to provide services for
multiple users and are receiving increasing attention for their potential of
improving spectral and energy efficiency in 5G networks. In this article a
framework for energy-efficient NOMA H-CRANs is presented. The enabling
technologies for NOMA H-CRANs are surveyed. Challenges to implement these
technologies and open issues are discussed. This article also presents the
performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure
Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network
The limited fronthaul capacity imposes a challenge on the uplink of
centralized radio access network (C-RAN). We propose to boost the fronthaul
capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by
globally optimizing the power sharing between channel estimation and data
transmission both for the user devices (UDs) and the remote radio units (RRUs).
Intuitively, allocating more power to the channel estimation will result in
more accurate channel estimates, which increases the achievable throughput.
However, increasing the power allocated to the pilot training will reduce the
power assigned to data transmission, which reduces the achievable throughput.
In order to optimize the powers allocated to the pilot training and to the data
transmission of both the UDs and the RRUs, we assign an individual power
sharing factor to each of them and derive an asymptotic closed-form expression
of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN
consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU)
links. We then exploit the C-RAN architecture's central computing and control
capability for jointly optimizing the UDs' power sharing factors and the RRUs'
power sharing factors aiming for maximizing the fronthaul capacity. Our
simulation results show that the fronthaul capacity is significantly boosted by
the proposed global optimization of the power allocation between channel
estimation and data transmission both for the UDs and for their host RRUs. As a
specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs
deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing
factors improves 33\% compared with the one attained without optimizing power
sharing factors
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
On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems
Massive multi-user (MU) multiple-input multiple-output (MIMO) promises
significant gains in spectral efficiency compared to traditional, small-scale
MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or
minimum mean-square error (MMSE)-based methods, typically rely on centralized
processing at the base station (BS), which results in (i) excessively high
interconnect and chip input/output data rates, and (ii) high computational
complexity. In this paper, we investigate the achievable rates of decentralized
equalization that mitigates both of these issues. We consider two distinct BS
architectures that partition the antenna array into clusters, each associated
with independent radio-frequency chains and signal processing hardware, and the
results of each cluster are fused in a feedforward network. For both
architectures, we consider ZF, MMSE, and a novel, non-linear equalization
algorithm that builds upon approximate message passing (AMP), and we
theoretically analyze the achievable rates of these methods. Our results
demonstrate that decentralized equalization with our AMP-based methods incurs
no or only a negligible loss in terms of achievable rates compared to that of
centralized solutions.Comment: Will be presented at the 2017 IEEE International Symposium on
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