883 research outputs found
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
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
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