16,146 research outputs found
Predictive and core-network efficient RRC signalling for active state handover in RANs with control/data separation
Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic
increase in signalling overhead. This suggests a paradigm shift towards a signalling conscious cellular
architecture with intelligent mobility management. In this direction, a futuristic radio access network
with a logical separation between control and data planes has been proposed in research community. It
aims to overcome limitations of the conventional architecture by providing high data rate services under
the umbrella of a coverage layer in a dual connection mode. This approach enables signalling efficient
HO procedures, since the control plane remains unchanged when the users move within the footprint of
the same umbrella. Considering this configuration, we propose a core-network efficient radio resource
control (RRC) signalling scheme for active state HO and develop an analytical framework to evaluate its
signalling load as a function of network density, user mobility and session characteristics. In addition,
we propose an intelligent HO prediction scheme with advance resource preparation in order to minimise
the HO signalling latency. Numerical and simulation results show promising gains in terms of reduction
in HO latency and signalling load as compared with conventional approaches
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Load-Aware Modeling and Analysis of Heterogeneous Cellular Networks
Random spatial models are attractive for modeling heterogeneous cellular
networks (HCNs) due to their realism, tractability, and scalability. A major
limitation of such models to date in the context of HCNs is the neglect of
network traffic and load: all base stations (BSs) have typically been assumed
to always be transmitting. Small cells in particular will have a lighter load
than macrocells, and so their contribution to the network interference may be
significantly overstated in a fully loaded model. This paper incorporates a
flexible notion of BS load by introducing a new idea of conditionally thinning
the interference field. For a K-tier HCN where BSs across tiers differ in terms
of transmit power, supported data rate, deployment density, and now load, we
derive the coverage probability for a typical mobile, which connects to the
strongest BS signal. Conditioned on this connection, the interfering BSs of the
tier are assumed to transmit independently with probability ,
which models the load. Assuming - reasonably - that smaller cells are more
lightly loaded than macrocells, the analysis shows that adding such access
points to the network always increases the coverage probability. We also
observe that fully loaded models are quite pessimistic in terms of coverage.Comment: to appear, IEEE Transactions on Wireless Communication
SINR-based k-coverage probability in cellular networks with arbitrary shadowing
We give numerically tractable, explicit integral expressions for the
distribution of the signal-to-interference-and-noise-ratio (SINR) experienced
by a typical user in the down-link channel from the k-th strongest base
stations of a cellular network modelled by Poisson point process on the plane.
Our signal propagation-loss model comprises of a power-law path-loss function
with arbitrarily distributed shadowing, independent across all base stations,
with and without Rayleigh fading. Our results are valid in the whole domain of
SINR, in particular for SINR<1, where one observes multiple coverage. In this
latter aspect our paper complements previous studies reported in [Dhillon et
al. JSAC 2012]
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