2,969 research outputs found
Reliable Prediction of Channel Assignment Performance in Wireless Mesh Networks
The advancements in wireless mesh networks (WMN), and the surge in
multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of
network performance issues. These issues are intricately linked to the adverse
impact of endemic interference. Thus, interference mitigation is a primary
design objective in WMNs. Interference alleviation is often effected through
efficient channel allocation (CA) schemes which fully utilize the potential of
MRMC environment and also restrain the detrimental impact of interference.
However, numerous CA schemes have been proposed in research literature and
there is a lack of CA performance prediction techniques which could assist in
choosing a suitable CA for a given WMN. In this work, we propose a reliable
interference estimation and CA performance prediction approach. We demonstrate
its efficacy by substantiating the CA performance predictions for a given WMN
with experimental data obtained through rigorous simulations on an ns-3 802.11g
environment.Comment: Accepted in ICACCI-201
Near Optimal Channel Assignment for Interference Mitigation in Wireless Mesh Networks
In multi-radio multi-channel (MRMC) WMNs, interference alleviation is
affected through several network design techniques e.g., channel assignment
(CA), link scheduling, routing etc., intelligent CA schemes being the most
effective tool for interference mitigation. CA in WMNs is an NP-Hard problem,
and makes optimality a desired yet elusive goal in real-time deployments which
are characterized by fast transmission and switching times and minimal
end-to-end latency. The trade-off between optimal performance and minimal
response times is often achieved through CA schemes that employ heuristics to
propose efficient solutions. WMN configuration and physical layout are also
crucial factors which decide network performance, and it has been demonstrated
in numerous research works that rectangular/square grid WMNs outperform random
or unplanned WMN deployments in terms of network capacity, latency, and network
resilience. In this work, we propose a smart heuristic approach to devise a
near-optimal CA algorithm for grid WMNs (NOCAG). We demonstrate the efficacy of
NOCAG by evaluating its performance against the minimal-interference CA
generated through a rudimentary brute-force technique (BFCA), for the same WMN
configuration. We assess its ability to mitigate interference both,
theoretically (through interference estimation metrics) and experimentally (by
running rigorous simulations in NS-3). We demonstrate that the performance of
NOCAG is almost as good as the BFCA, at a minimal computational overhead of
O(n) compared to the exponential of BFCA
Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks
Designing high performance channel assignment schemes to harness the
potential of multi-radio multi-channel deployments in wireless mesh networks
(WMNs) is an active research domain. A pragmatic channel assignment approach
strives to maximize network capacity by restraining the endemic interference
and mitigating its adverse impact on network performance. Interference
prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being
a crucial aspect that is seldom addressed in research endeavors. In this
effort, we propose a set of intelligent channel assignment algorithms, which
focus primarily on alleviating the RCI. These graph theoretic schemes are
structurally inspired by the spatio-statistical characteristics of
interference. We present the theoretical design foundations for each of the
proposed algorithms, and demonstrate their potential to significantly enhance
network capacity in comparison to some well-known existing schemes. We also
demonstrate the adverse impact of radio co- location interference on the
network, and the efficacy of the proposed schemes in successfully mitigating
it. The experimental results to validate the proposed theoretical notions were
obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n
environments.Comment: Accepted @ ICACCI-201
Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation
Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on
wired infrastructure especially with the advent of the multi-radio
multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer
viz., augmented network capacity, uninterrupted connectivity and reduced
latency, are depreciated by the detrimental effect of prevalent interference.
Interference mitigation is thus a prime objective in WMN deployments. It is
often accomplished through prudent channel allocation (CA) schemes which
minimize the adverse impact of interference and enhance the network
performance. However, a multitude of CA schemes have been proposed in research
literature and absence of a CA performance prediction metric, which could aid
in the selection of an efficient CA scheme for a given WMN, is often felt. In
this work, we offer a fresh characterization of the interference endemic in
wireless networks. We then propose a reliable CA performance prediction metric,
which employs a statistical interference estimation approach. We carry out a
rigorous quantitative assessment of the proposed metric by validating its CA
performance predictions with experimental results, recorded from extensive
simulations run on an ns-3 802.11g environment
- …