814 research outputs found
Joint Resource Allocation for eICIC in Heterogeneous Networks
Interference coordination between high-power macros and low-power picos
deeply impacts the performance of heterogeneous networks (HetNets). It should
deal with three challenges: user association with macros and picos, the amount
of almost blank subframe (ABS) that macros should reserve for picos, and
resource block (RB) allocation strategy in each eNB. We formulate the three
issues jointly for sum weighted logarithmic utility maximization while
maintaining proportional fairness of users. A class of distributed algorithms
are developed to solve the joint optimization problem. Our framework can be
deployed for enhanced inter-cell interference coordination (eICIC) in existing
LTE-A protocols. Extensive evaluation are performed to verify the effectiveness
of our algorithms.Comment: Accepted by Globecom 201
On/Off Macrocells and Load Balancing in Heterogeneous Cellular Networks
The rate distribution in heterogeneous networks (HetNets) greatly benefits
from load balancing, by which mobile users are pushed onto lightly-loaded small
cells despite the resulting loss in SINR. This offloading can be made more
aggressive and robust if the macrocells leave a fraction of time/frequency
resource blank, which reduces the interference to the offloaded users. We
investigate the joint optimization of this technique - referred to in 3GPP as
enhanced intercell interference coordination (eICIC) via almost blank subframes
(ABSs) - with offloading in this paper. Although the joint cell association and
blank resource (BR) problem is nominally combinatorial, by allowing users to
associate with multiple base stations (BSs), the problem becomes convex, and
upper bounds the performance versus a binary association. We show both
theoretically and through simulation that the optimal solution of the relaxed
problem still results in an association that is mostly binary. The optimal
association differs significantly when the macrocell is on or off; in
particular the offloading can be much more aggressive when the resource is left
blank by macro BSs. Further, we observe that jointly optimizing the offloading
with BR is important. The rate gain for cell edge users (the worst 3-10%) is
very large - on the order of 5-10x - versus a naive association strategy
without macrocell blanking
Matching Theory for Future Wireless Networks: Fundamentals and Applications
The emergence of novel wireless networking paradigms such as small cell and
cognitive radio networks has forever transformed the way in which wireless
systems are operated. In particular, the need for self-organizing solutions to
manage the scarce spectral resources has become a prevalent theme in many
emerging wireless systems. In this paper, the first comprehensive tutorial on
the use of matching theory, a Nobelprize winning framework, for resource
management in wireless networks is developed. To cater for the unique features
of emerging wireless networks, a novel, wireless-oriented classification of
matching theory is proposed. Then, the key solution concepts and algorithmic
implementations of this framework are exposed. Then, the developed concepts are
applied in three important wireless networking areas in order to demonstrate
the usefulness of this analytical tool. Results show how matching theory can
effectively improve the performance of resource allocation in all three
applications discussed
Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks
This paper addresses the cell association problem in the downlink of a
multi-tier heterogeneous network (HetNet), where base stations (BSs) have
finite number of resource blocks (RBs) available to distribute among their
associated users. Two problems are defined and treated in this paper: sum
utility of long term rate maximization with long term rate quality of service
(QoS) constraints, and global outage probability minimization with outage QoS
constraints. The first problem is well-suited for low mobility environments,
while the second problem provides a framework to deal with environments with
fast fading. The defined optimization problems in this paper are solved in two
phases: cell association phase followed by the optional RB distribution phase.
We show that the cell association phase of both problems have the same
structure. Based on this similarity, we propose a unified distributed algorithm
with low levels of message passing to for the cell association phase. This
distributed algorithm is derived by relaxing the association constraints and
using Lagrange dual decomposition method. In the RB distribution phase, the
remaining RBs after the cell association phase are distributed among the users.
Simulation results show the superiority of our distributed cell association
scheme compared to schemes that are based on maximum signal to interference
plus noise ratio (SINR)
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