52 research outputs found
Distributed Optimization of Multi-Cell Uplink Co-operation with Backhaul Constraints
We address the problem of uplink co-operative reception with constraints on
both backhaul bandwidth and the receiver aperture, or number of antenna signals
that can be processed. The problem is cast as a network utility (weighted sum
rate) maximization subject to computational complexity and architectural
bandwidth sharing constraints. We show that a relaxed version of the problem is
convex, and can be solved via a dual-decomposition. The proposed solution is
distributed in that each cell broadcasts a set of {\em demand prices} based on
the data sharing requests they receive. Given the demand prices, the algorithm
determines an antenna/cell ordering and antenna-selection for each scheduled
user in a cell. This algorithm, referred to as {\em LiquidMAAS}, iterates
between the preceding two steps. Simulations of realistic network scenarios
show that the algorithm exhibits fast convergence even for systems with large
number of cells.Comment: IEEE ICC Conference, 201
Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing
A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access
Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to
a central Virtual Base Station (VBS) pool. In particular, the key capabilities
of C-RANs in computing-resource sharing and real-time communication among the
VBSs are leveraged to design a joint dynamic radio clustering and cooperative
beamforming scheme that maximizes the downlink weighted sum-rate system utility
(WSRSU). Due to the combinatorial nature of the radio clustering process and
the non-convexity of the cooperative beamforming design, the underlying
optimization problem is NP-hard, and is extremely difficult to solve for a
large network. Our approach aims for a suboptimal solution by transforming the
original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP),
which can be solved efficiently using a proposed iterative algorithm. Numerical
simulation results show that our low-complexity algorithm provides
close-to-optimal performance in terms of WSRSU while significantly
outperforming conventional radio clustering and beamforming schemes.
Additionally, the results also demonstrate the significant improvement in
computing-resource utilization of C-RANs over traditional RANs with distributed
computing resources.Comment: 9 pages, 6 figures, accepted to IEEE MASS 201
Sparse Beamforming for Real-Time Resource Management and Energy Trading in Green C-RAN
This paper considers cloud radio access network with simultaneous wireless information and power transfer and finite capacity fronthaul, where the remote radio heads are equipped with renewable energy resources and can trade energy with the grid. Due to uneven distribution of mobile radio traffic and inherent intermittent nature of renewable energy resources, the remote radio heads may need real-time energy provisioning to meet the users' demands. Given the amount of available energy resources at remote radio heads, this paper introduces two provisioning strategies to strike an optimum balance among the total power consumption in the fronthaul, through adjusting the degree of partial cooperation among the remote radio heads, the total transmit power and the maximum or the overall real-time energy demand. More specifically, this paper formulates two sparse optimization problems and applies reweighted â„“ 1 -norm approximation for â„“ 0 -norm and semidefinite relaxation to develop two iterative algorithms for the proposed strategies. Simulation results confirm that both of the proposed strategies outperform two other recently proposed schemes in terms of improving energy efficiency and reducing overall energy cost of the network
A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks
The diverse service requirements coming with the
advent of sophisticated applications as well as a large number
of connected devices demand for revolutionary changes in the
traditional distributed radio access network (RAN). To this end,
Cloud-RAN (CRAN) is considered as an important paradigm
to enhance the performance of the upcoming fifth generation
(5G) and beyond wireless networks in terms of capacity, latency,
and connectivity to a large number of devices. Out of several
potential enablers, efficient resource allocation can mitigate various
challenges related to user assignment, power allocation, and
spectrum management in a CRAN, and is the focus of this paper.
Herein, we provide a comprehensive review of resource allocation
schemes in a CRAN along with a detailed optimization taxonomy
on various aspects of resource allocation. More importantly,
we identity and discuss the key elements for efficient resource
allocation and management in CRAN, namely: user assignment,
remote radio heads (RRH) selection, throughput maximization,
spectrum management, network utility, and power allocation.
Furthermore, we present emerging use-cases including heterogeneous
CRAN, millimeter-wave CRAN, virtualized CRAN, Non-
Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex
enabled CRAN to illustrate how their performance can
be enhanced by adopting CRAN technology. We then classify
and discuss objectives and constraints involved in CRAN-based
5G and beyond networks. Moreover, a detailed taxonomy of
optimization methods and solution approaches with different
objectives is presented and discussed. Finally, we conclude the
paper with several open research issues and future directions
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
- …