7,500 research outputs found
Capacity Analysis of LTE-Advanced HetNets with Reduced Power Subframes and Range Expansion
The time domain inter-cell interference coordination techniques specified in
LTE Rel. 10 standard improves the throughput of picocell-edge users by
protecting them from macrocell interference. On the other hand, it also
degrades the aggregate capacity in macrocell because the macro base station
(MBS) does not transmit data during certain subframes known as almost blank
subframes. The MBS data transmission using reduced power subframes was
standardized in LTE Rel. 11, which can improve the capacity in macrocell while
not causing high interference to the nearby picocells. In order to get maximum
benefit from the reduced power subframes, setting the key system parameters,
such as the amount of power reduction, carries critical importance. Using
stochastic geometry, this paper lays down a theoretical foundation for the
performance evaluation of heterogeneous networks with reduced power subframes
and range expansion bias. The analytic expressions for average capacity and 5th
percentile throughput are derived as a function of transmit powers, node
densities, and interference coordination parameters in a heterogeneous network
scenario, and are validated through Monte Carlo simulations. Joint optimization
of range expansion bias, power reduction factor, scheduling thresholds, and
duty cycle of reduced power subframes are performed to study the trade-offs
between aggregate capacity of a cell and fairness among the users. To validate
our analysis, we also compare the stochastic geometry based theoretical results
with the real MBS deployment (in the city of London) and the hexagonal-grid
model. Our analysis shows that with optimum parameter settings, the LTE Rel. 11
with reduced power subframes can provide substantially better performance than
the LTE Rel. 10 with almost blank subframes, in terms of both aggregate
capacity and fairness.Comment: Submitted to EURASIP Journal on Wireless Communications and
Networking (JWCN
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks
In heterogeneous cellular networks (HCNs), it is desirable to offload mobile
users to small cells, which are typically significantly less congested than the
macrocells. To achieve sufficient load balancing, the offloaded users often
have much lower SINR than they would on the macrocell. This SINR degradation
can be partially alleviated through interference avoidance, for example time or
frequency resource partitioning, whereby the macrocell turns off in some
fraction of such resources. Naturally, the optimal offloading strategy is
tightly coupled with resource partitioning; the optimal amount of which in turn
depends on how many users have been offloaded. In this paper, we propose a
general and tractable framework for modeling and analyzing joint resource
partitioning and offloading in a two-tier cellular network. With it, we are
able to derive the downlink rate distribution over the entire network, and an
optimal strategy for joint resource partitioning and offloading. We show that
load balancing, by itself, is insufficient, and resource partitioning is
required in conjunction with offloading to improve the rate of cell edge users
in co-channel heterogeneous networks
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
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