2,603 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
On the Total Energy Efficiency of Cell-Free Massive MIMO
We consider the cell-free massive multiple-input multiple-output (MIMO)
downlink, where a very large number of distributed multiple-antenna access
points (APs) serve many single-antenna users in the same time-frequency
resource. A simple (distributed) conjugate beamforming scheme is applied at
each AP via the use of local channel state information (CSI). This CSI is
acquired through time-division duplex operation and the reception of uplink
training signals transmitted by the users. We derive a closed-form expression
for the spectral efficiency taking into account the effects of channel
estimation errors and power control. This closed-form result enables us to
analyze the effects of backhaul power consumption, the number of APs, and the
number of antennas per AP on the total energy efficiency, as well as, to design
an optimal power allocation algorithm. The optimal power allocation algorithm
aims at maximizing the total energy efficiency, subject to a per-user spectral
efficiency constraint and a per-AP power constraint. Compared with the equal
power control, our proposed power allocation scheme can double the total energy
efficiency. Furthermore, we propose AP selections schemes, in which each user
chooses a subset of APs, to reduce the power consumption caused by the backhaul
links. With our proposed AP selection schemes, the total energy efficiency
increases significantly, especially for large numbers of APs. Moreover, under a
requirement of good quality-of-service for all users, cell-free massive MIMO
outperforms the colocated counterpart in terms of energy efficiency
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
The characterization of the global maximum of energy efficiency (EE) problems
in wireless networks is a challenging problem due to the non-convex nature of
investigated problems in interference channels. The aim of this work is to
develop a new and general framework to achieve globally optimal solutions.
First, the hidden monotonic structure of the most common EE maximization
problems is exploited jointly with fractional programming theory to obtain
globally optimal solutions with exponential complexity in the number of network
links. To overcome this issue, we also propose a framework to compute
suboptimal power control strategies characterized by affordable complexity.
This is achieved by merging fractional programming and sequential optimization.
The proposed monotonic framework is used to shed light on the ultimate
performance of wireless networks in terms of EE and also to benchmark the
performance of the lower-complexity framework based on sequential programming.
Numerical evidence is provided to show that the sequential fractional
programming framework achieves global optimality in several practical
communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal
Processin
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