9,891 research outputs found
Dynamic Interference Mitigation for Generalized Partially Connected Quasi-static MIMO Interference Channel
Recent works on MIMO interference channels have shown that interference
alignment can significantly increase the achievable degrees of freedom (DoF) of
the network. However, most of these works have assumed a fully connected
interference graph. In this paper, we investigate how the partial connectivity
can be exploited to enhance system performance in MIMO interference networks.
We propose a novel interference mitigation scheme which introduces constraints
for the signal subspaces of the precoders and decorrelators to mitigate "many"
interference nulling constraints at a cost of "little" freedoms in precoder and
decorrelator design so as to extend the feasibility region of the interference
alignment scheme. Our analysis shows that the proposed algorithm can
significantly increase system DoF in symmetric partially connected MIMO
interference networks. We also compare the performance of the proposed scheme
with various baselines and show via simulations that the proposed algorithms
could achieve significant gain in the system performance of randomly connected
interference networks.Comment: 30 pages, 10 figures, accepted by IEEE Transaction on Signal
Processin
Decentralized Dynamic Hop Selection and Power Control in Cognitive Multi-hop Relay Systems
In this paper, we consider a cognitive multi-hop relay secondary user (SU)
system sharing the spectrum with some primary users (PU). The transmit power as
well as the hop selection of the cognitive relays can be dynamically adapted
according to the local (and causal) knowledge of the instantaneous channel
state information (CSI) in the multi-hop SU system. We shall determine a low
complexity, decentralized algorithm to maximize the average end-to-end
throughput of the SU system with dynamic spatial reuse. The problem is
challenging due to the decentralized requirement as well as the causality
constraint on the knowledge of CSI. Furthermore, the problem belongs to the
class of stochastic Network Utility Maximization (NUM) problems which is quite
challenging. We exploit the time-scale difference between the PU activity and
the CSI fluctuations and decompose the problem into a master problem and
subproblems. We derive an asymptotically optimal low complexity solution using
divide-and-conquer and illustrate that significant performance gain can be
obtained through dynamic hop selection and power control. The worst case
complexity and memory requirement of the proposed algorithm is O(M^2) and
O(M^3) respectively, where is the number of SUs
Limited Feedback Design for Interference Alignment on MIMO Interference Networks with Heterogeneous Path Loss and Spatial Correlations
Interference alignment is degree of freedom optimal in K -user MIMO
interference channels and many previous works have studied the transceiver
designs. However, these works predominantly focus on networks with perfect
channel state information at the transmitters and symmetrical interference
topology. In this paper, we consider a limited feedback system with
heterogeneous path loss and spatial correlations, and investigate how the
dynamics of the interference topology can be exploited to improve the feedback
efficiency. We propose a novel spatial codebook design, and perform dynamic
quantization via bit allocations to adapt to the asymmetry of the interference
topology. We bound the system throughput under the proposed dynamic scheme in
terms of the transmit SNR, feedback bits and the interference topology
parameters. It is shown that when the number of feedback bits scales with SNR
as C_{s}\cdot\log\textrm{SNR}, the sum degrees of freedom of the network are
preserved. Moreover, the value of scaling coefficient C_{s} can be
significantly reduced in networks with asymmetric interference topology.Comment: 30 pages, 6 figures, accepted by IEEE transactions on signal
processing in Feb. 201
CSI Feedback Reduction for MIMO Interference Alignment
Interference alignment (IA) is a linear precoding strategy that can achieve
optimal capacity scaling at high SNR in interference networks. Most of the
existing IA designs require full channel state information (CSI) at the
transmitters, which induces a huge CSI signaling cost. Hence it is desirable to
improve the feedback efficiency for IA and in this paper, we propose a novel IA
scheme with a significantly reduced CSI feedback. To quantify the CSI feedback
cost, we introduce a novel metric, namely the feedback dimension. This metric
serves as a first-order measurement of CSI feedback overhead. Due to the
partial CSI feedback constraint, conventional IA schemes can not be applied and
hence, we develop a novel IA precoder / decorrelator design and establish new
IA feasibility conditions. Via dynamic feedback profile design, the proposed IA
scheme can also achieve a flexible tradeoff between the degree of freedom (DoF)
requirements for data streams, the antenna resources and the CSI feedback cost.
We show by analysis and simulations that the proposed scheme achieves
substantial reductions of CSI feedback overhead under the same DoF requirement
in MIMO interference networks.Comment: 30 pages, 7 figures, accepted for publication by IEEE transactions on
signal processing in June, 201
Generalized Interference Alignment --- Part I: Theoretical Framework
Interference alignment (IA) has attracted enormous research interest as it
achieves optimal capacity scaling with respect to signal to noise ratio on
interference networks. IA has also recently emerged as an effective tool in
engineering interference for secrecy protection on wireless wiretap networks.
However, despite the numerous works dedicated to IA, two of its fundamental
issues, i.e., feasibility conditions and transceiver design, are not completely
addressed in the literature. In this two part paper, a generalised interference
alignment (GIA) technique is proposed to enhance the IA's capability in secrecy
protection. A theoretical framework is established to analyze the two
fundamental issues of GIA in Part I and then the performance of GIA in
large-scale stochastic networks is characterized to illustrate how GIA benefits
secrecy protection in Part II. The theoretical framework for GIA adopts
methodologies from algebraic geometry, determines the necessary and sufficient
feasibility conditions of GIA, and generates a set of algorithms that can solve
the GIA problem. This framework sets up a foundation for the development and
implementation of GIA.Comment: Minor Revision at IEEE Transactions on Signal Processin
Identifying network communities with a high resolution
Community structure is an important property of complex networks. An
automatic discovery of such structure is a fundamental task in many
disciplines, including sociology, biology, engineering, and computer science.
Recently, several community discovery algorithms have been proposed based on
the optimization of a quantity called modularity (Q). However, the problem of
modularity optimization is NP-hard, and the existing approaches often suffer
from prohibitively long running time or poor quality. Furthermore, it has been
recently pointed out that algorithms based on optimizing Q will have a
resolution limit, i.e., communities below a certain scale may not be detected.
In this research, we first propose an efficient heuristic algorithm, Qcut,
which combines spectral graph partitioning and local search to optimize Q.
Using both synthetic and real networks, we show that Qcut can find higher
modularities and is more scalable than the existing algorithms. Furthermore,
using Qcut as an essential component, we propose a recursive algorithm, HQcut,
to solve the resolution limit problem. We show that HQcut can successfully
detect communities at a much finer scale and with a higher accuracy than the
existing algorithms. Finally, we apply Qcut and HQcut to study a
protein-protein interaction network, and show that the combination of the two
algorithms can reveal interesting biological results that may be otherwise
undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at
http://cic.cs.wustl.edu/qcut/supplemental.pd
Hierarchical Radio Resource Optimization for Heterogeneous Networks with Enhanced Inter-cell Interference Coordination (eICIC)
Interference is a major performance bottleneck in Heterogeneous Network
(HetNet) due to its multi-tier topological structure. We propose almost blank
resource block (ABRB) for interference control in HetNet. When an ABRB is
scheduled in a macro BS, a resource block (RB) with blank payload is
transmitted and this eliminates the interference from this macro BS to the pico
BSs. We study a two timescale hierarchical radio resource management (RRM)
scheme for HetNet with dynamic ABRB control. The long term controls, such as
dynamic ABRB, are adaptive to the large scale fading at a RRM server for
co-Tier and cross-Tier interference control. The short term control (user
scheduling) is adaptive to the local channel state information within each BS
to exploit the multi-user diversity. The two timescale optimization problem is
challenging due to the exponentially large solution space. We exploit the
sparsity in the interference graph of the HetNet topology and derive structural
properties for the optimal ABRB control. Based on that, we propose a two
timescale alternative optimization solution for the user scheduling and ABRB
control. The solution has low complexity and is asymptotically optimal at high
SNR. Simulations show that the proposed solution has significant gain over
various baselines.Comment: 14 pages, 8 figure
Global solutions to fractional programming problem with ratio of nonconvex functions
This paper presents a canonical dual approach for minimizing a sum of quadratic function and a ratio of nonconvex functions in Rⁿ. By introducing a parameter, the problem is first equivalently reformed as a nonconvex polynomial minimization with elliptic constraint. It is proved that under certain conditions, the canonical dual is a concave maximization problem in R² that exhibits no duality gap. Therefore, the global optimal solution of the primal problem can be obtained by solving the canonical dual problem.This paper was partially supported by a grant (AFOSR FA9550–10-1–0487) from the US Air Force Office of Scientific
Research. Dr. Ning Ruan was supported by a funding from the Australian Government under the Collaborative Research Networks
(CRN) program
Tuning the thermal conductivity of graphene nanoribbons by edge passivation and isotope engineering: a molecular dynamics study
Using classical molecular dynamics simulation, we have studied the effect of
edge-passivation by hydrogen (H-passivation) and isotope mixture (with random
or supperlattice distributions) on the thermal conductivity of rectangular
graphene nanoribbons (GNRs) (of several nanometers in size). We found that the
thermal conductivity is considerably reduced by the edge H-passivation. We also
find that the isotope mixing can reduce the thermal conductivities, with the
supperlattice distribution giving rise to more reduction than the random
distribution. These results can be useful in nanoscale engineering of thermal
transport and heat management using GNRs.Comment: 4 pages, 4 figure
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