138 research outputs found
Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks
n source and destination pairs randomly located in an area want to
communicate with each other. Signals transmitted from one user to another at
distance r apart are subject to a power loss of r^{-alpha}, as well as a random
phase. We identify the scaling laws of the information theoretic capacity of
the network. In the case of dense networks, where the area is fixed and the
density of nodes increasing, we show that the total capacity of the network
scales linearly with n. This improves on the best known achievability result of
n^{2/3} of Aeron and Saligrama, 2006. In the case of extended networks, where
the density of nodes is fixed and the area increasing linearly with n, we show
that this capacity scales as n^{2-alpha/2} for 2<alpha<3 and sqrt{n} for
alpha>3. The best known earlier result (Xie and Kumar 2006) identified the
scaling law for alpha > 4. Thus, much better scaling than multihop can be
achieved in dense networks, as well as in extended networks with low
attenuation. The performance gain is achieved by intelligent node cooperation
and distributed MIMO communication. The key ingredient is a hierarchical and
digital architecture for nodal exchange of information for realizing the
cooperation.Comment: 56 pages, 16 figures, submitted to IEEE Transactions on Information
Theor
One-Hop Throughput of Wireless Networks with Random Connections
We consider one-hop communication in wireless networks with random
connections. In the random connection model, the channel powers between
different nodes are drawn from a common distribution in an i.i.d. manner. An
scheme achieving the throughput scaling of order , for any
, is proposed, where is the number of nodes. Such achievable
throughput, along with the order upper bound derived by Cui et al.,
characterizes the throughput capacity of one-hop schemes for the class of
connection models with finite mean and variance.Comment: Submitted to IEEE Communications Letter
Double-Directional Information Azimuth Spectrum and Relay Network Tomography for a Decentralized Wireless Relay Network
A novel channel representation for a two-hop decentralized wireless relay
network (DWRN) is proposed, where the relays operate in a completely
distributive fashion. The modeling paradigm applies an analogous approach to
the description method for a double-directional multipath propagation channel,
and takes into account the finite system spatial resolution and the extended
relay listening/transmitting time. Specifically, the double-directional
information azimuth spectrum (IAS) is formulated to provide a compact
representation of information flows in a DWRN. The proposed channel
representation is then analyzed from a geometrically-based statistical modeling
perspective. Finally, we look into the problem of relay network tomography
(RNT), which solves an inverse problem to infer the internal structure of a
DWRN by using the instantaneous doubledirectional IAS recorded at multiple
measuring nodes exterior to the relay region
Spatial Interference Cancelation for Mobile Ad Hoc Networks: Perfect CSI
Interference between nodes directly limits the capacity of mobile ad hoc
networks. This paper focuses on spatial interference cancelation with perfect
channel state information (CSI), and analyzes the corresponding network
capacity. Specifically, by using multiple antennas, zero-forcing beamforming is
applied at each receiver for canceling the strongest interferers. Given spatial
interference cancelation, the network transmission capacity is analyzed in this
paper, which is defined as the maximum transmitting node density under
constraints on outage and the signal-to-interference-noise ratio. Assuming the
Poisson distribution for the locations of network nodes and spatially i.i.d.
Rayleigh fading channels, mathematical tools from stochastic geometry are
applied for deriving scaling laws for transmission capacity. Specifically, for
small target outage probability, transmission capacity is proved to increase
following a power law, where the exponent is the inverse of the size of antenna
array or larger depending on the pass loss exponent. As shown by simulations,
spatial interference cancelation increases transmission capacity by an order of
magnitude or more even if only one extra antenna is added to each node.Comment: 6 pages; submitted to IEEE Globecom 200
Demystifying the Scaling Laws of Dense Wireless Networks: No Linear Scaling in Practice
We optimize the hierarchical cooperation protocol of Ozgur, Leveque and Tse,
which is supposed to yield almost linear scaling of the capacity of a dense
wireless network with the number of users . Exploiting recent results on the
optimality of "treating interference as noise" in Gaussian interference
channels, we are able to optimize the achievable average per-link rate and not
just its scaling law. Our optimized hierarchical cooperation protocol
significantly outperforms the originally proposed scheme. On the negative side,
we show that even for very large , the rate scaling is far from linear, and
the optimal number of stages is less than 4, instead of as required for almost linear scaling. Combining our results and the
fact that, beyond a certain user density, the network capacity is fundamentally
limited by Maxwell laws, as shown by Francheschetti, Migliore and Minero, we
argue that there is indeed no intermediate regime of linear scaling for dense
networks in practice.Comment: 5 pages, 6 figures, ISIT 2014. arXiv admin note: substantial text
overlap with arXiv:1402.181
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