136 research outputs found
Gaussian 1-2-1 Networks: Capacity Results for mmWave Communications
This paper proposes a new model for wireless relay networks referred to as
"1-2-1 network", where two nodes can communicate only if they point "beams" at
each other, while if they do not point beams at each other, no signal can be
exchanged or interference can be generated. This model is motivated by
millimeter wave communications where, due to the high path loss, a link between
two nodes can exist only if beamforming gain at both sides is established,
while in the absence of beamforming gain the signal is received well below the
thermal noise floor. The main result in this paper is that the 1-2-1 network
capacity can be approximated by routing information along at most paths,
where is the number of relays connecting a source and a destination through
an arbitrary topology
Efficiently Finding Simple Schedules in Gaussian Half-Duplex Relay Line Networks
The problem of operating a Gaussian Half-Duplex (HD) relay network optimally
is challenging due to the exponential number of listen/transmit network states
that need to be considered. Recent results have shown that, for the class of
Gaussian HD networks with N relays, there always exists a simple schedule,
i.e., with at most N +1 active states, that is sufficient for approximate
(i.e., up to a constant gap) capacity characterization. This paper investigates
how to efficiently find such a simple schedule over line networks. Towards this
end, a polynomial-time algorithm is designed and proved to output a simple
schedule that achieves the approximate capacity. The key ingredient of the
algorithm is to leverage similarities between network states in HD and edge
coloring in a graph. It is also shown that the algorithm allows to derive a
closed-form expression for the approximate capacity of the Gaussian line
network that can be evaluated distributively and in linear time. Additionally,
it is shown using this closed-form that the problem of Half-Duplex routing is
NP-Hard.Comment: A short version of this paper was submitted to ISIT 201
Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks
In this paper, we investigate a spectrum sensing algorithm for detecting
spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO)
transmissions for OFDM systems using Compressive Sensing (CS) tools. This
extends the energy detector to allow for detecting transmission opportunities
even if the band is already energy filled. We show that the task described
above is not performed efficiently by regular MIMO decoders (such as MMSE
decoder) due to possible sparsity in the transmit signal. Since CS
reconstruction tools take into account the sparsity order of the signal, they
are more efficient in detecting the activity of the users. Building on
successful activity detection by the CS detector, we show that the use of a
CS-aided MMSE decoders yields better performance rather than using either
CS-based or MMSE decoders separately. Simulations are conducted to verify the
gains from using CS detector for Primary user activity detection and the
performance gain in using CS-aided MMSE decoders for decoding the PU
information for future relaying.Comment: accepted for PIMRC 201
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