4,506 research outputs found
Spatial Resources Optimization in Distributed MIMO Networks with Limited Data Sharing
Wireless access through a large distributed network of low-complexity
infrastructure nodes empowered with cooperation and coordination capabilities,
is an emerging radio architecture, candidate to deal with the mobile data
capacity crunch. In the 3GPP evolutionary path, this is known as the Cloud-RAN
paradigm for future radio. In such a complex network, distributed MIMO
resources optimization is of paramount importance, in order to achieve capacity
scaling. In this paper, we investigate efficient strategies towards optimizing
the pairing of access nodes with users as well as linear precoding designs for
providing fair QoS experience across the whole network, when data sharing is
limited due to complexity and overhead constraints. We propose a method for
obtaining the exact optimal spatial resources allocation solution which can be
applied in networks of limited scale, as well as an approximation algorithm
with bounded polynomial complexity which can be used in larger networks. The
particular algorithm outperforms existing user-oriented clustering techniques
and achieves quite high quality-of-service levels with reasonable complexity.Comment: submitted to Globecom 2013 - Wireless Communications Symposiu
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Spatial Coordination Strategies in Future Ultra-Dense Wireless Networks
Ultra network densification is considered a major trend in the evolution of
cellular networks, due to its ability to bring the network closer to the user
side and reuse resources to the maximum extent. In this paper we explore
spatial resources coordination as a key empowering technology for next
generation (5G) ultra-dense networks. We propose an optimization framework for
flexibly associating system users with a densely deployed network of access
nodes, opting for the exploitation of densification and the control of overhead
signaling. Combined with spatial precoding processing strategies, we design
network resources management strategies reflecting various features, namely
local vs global channel state information knowledge exploitation, centralized
vs distributed implementation, and non-cooperative vs joint multi-node data
processing. We apply these strategies to future UDN setups, and explore the
impact of critical network parameters, that is, the densification levels of
users and access nodes as well as the power budget constraints, to users
performance. We demonstrate that spatial resources coordination is a key factor
for capitalizing on the gains of ultra dense network deployments.Comment: An extended version of a paper submitted to ISWCS'14, Special Session
on Empowering Technologies of 5G Wireless Communication
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
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
The deployment of underlay small base stations (SBSs) is expected to
significantly boost the spectrum efficiency and the coverage of next-generation
cellular networks. However, the coexistence of SBSs underlaid to an existing
macro-cellular network faces important challenges, notably in terms of spectrum
sharing and interference management. In this paper, we propose a novel
game-theoretic model that enables the SBSs to optimize their transmission rates
by making decisions on the resource occupation jointly in the frequency and
spatial domains. This procedure, known as interference draining, is performed
among cooperative SBSs and allows to drastically reduce the interference
experienced by both macro- and small cell users. At the macrocell side, we
consider a modified water-filling policy for the power allocation that allows
each macrocell user (MUE) to focus the transmissions on the degrees of freedom
over which the MUE experiences the best channel and interference conditions.
This approach not only represents an effective way to decrease the received
interference at the MUEs but also grants the SBSs tier additional transmission
opportunities and allows for a more agile interference management. Simulation
results show that the proposed approach yields significant gains at both
macrocell and small cell tiers, in terms of average achievable rate per user,
reaching up to 37%, relative to the non-cooperative case, for a network with
150 MUEs and 200 SBSs
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