1,594 research outputs found
User Partitioning for Less Overhead in MIMO Interference Channels
This paper presents a study on multiple-antenna interference channels,
accounting for general overhead as a function of the number of users and
antennas in the network. The model includes both perfect and imperfect channel
state information based on channel estimation in the presence of noise. Three
low complexity methods are proposed for reducing the impact of overhead in the
sum network throughput by partitioning users into orthogonal groups. The first
method allocates spectrum to the groups equally, creating an imbalance in the
sum rate of each group. The second proposed method allocates spectrum unequally
among the groups to provide rate fairness. Finally, geographic grouping is
proposed for cases where some receivers do not observe significant interference
from other transmitters. For each partitioning method, the optimal solution not
only requires a brute force search over all possible partitions, but also
requires full channel state information, thereby defeating the purpose of
partitioning. We therefore propose greedy methods to solve the problems,
requiring no instantaneous channel knowledge. Simulations show that the
proposed greedy methods switch from time-division to interference alignment as
the coherence time of the channel increases, and have a small loss relative to
optimal partitioning only at moderate coherence times.Comment: 34 pages, 11 figures, to appear in IEEE Trans. Wireless
Communication
Topological Interference Management with User Admission Control via Riemannian Optimization
Topological interference management (TIM) provides a promising way to manage
interference only based on the network connectivity information. Previous works
on the TIM problem mainly focus on using the index coding approach and graph
theory to establish conditions of network topologies to achieve the feasibility
of topological interference management. In this paper, we propose a novel user
admission control approach via sparse and low-rank optimization to maximize the
number of admitted users for achieving the feasibility of topological
interference management. To assist efficient algorithms design for the
formulated rank-constrained (i.e., degrees-of-freedom (DoF) allocation) l0-norm
maximization (i.e., user capacity maximization) problem, we propose a
regularized smoothed l1- norm minimization approach to induce sparsity pattern,
thereby guiding the user selection. We further develop a Riemannian
trust-region algorithm to solve the resulting rank-constrained smooth
optimization problem via exploiting the quotient manifold of fixed-rank
matrices. Simulation results demonstrate the effectiveness and near-optimal
performance of the proposed Riemannian algorithm to maximize the number of
admitted users for topological interference management.Comment: arXiv admin note: text overlap with arXiv:1604.0432
Signal Processing and Optimal Resource Allocation for the Interference Channel
In this article, we examine several design and complexity aspects of the
optimal physical layer resource allocation problem for a generic interference
channel (IC). The latter is a natural model for multi-user communication
networks. In particular, we characterize the computational complexity, the
convexity as well as the duality of the optimal resource allocation problem.
Moreover, we summarize various existing algorithms for resource allocation and
discuss their complexity and performance tradeoff. We also mention various open
research problems throughout the article.Comment: To appear in E-Reference Signal Processing, R. Chellapa and S.
Theodoridis, Eds., Elsevier, 201
Regularized Zero-Forcing Interference Alignment for the Two-Cell MIMO Interfering Broadcast Channel
In this paper, we propose transceiver design strategies for the two-cell
multiple-input multiple-output (MIMO) interfering broadcast channel where
inter-cell interference (ICI) exists in addition to interuser interference
(IUI). We first formulate the generalized zero-forcing interference alignment
(ZF-IA) method based on the alignment of IUI and ICI in multi-dimensional
subspace. We then devise a minimum weighted-mean-square-error (WMSE) method
based on regularizing the precoders and decoders of the generalized ZF-IA
scheme. In contrast to the existing weighted-sum-rate-maximizing transceiver,
our method does not require an iterative calculation of the optimal weights.
Because of this, the proposed scheme, while not designed specifically to
maximize the sum rate, is computationally efficient and achieves a faster
convergence compared to the known weighted-sum-rate maximizing scheme. Through
analysis and simulation, we show the effectiveness of the proposed regularized
ZF-IA scheme.Comment: 10 pages, 3 figure
Queueing Stability and CSI Probing of a TDD Wireless Network with Interference Alignment
This paper characterizes the performance of interference alignment (IA)
technique taking into account the dynamic traffic pattern and the
probing/feedback cost. We consider a time-division duplex (TDD) system where
transmitters acquire their channel state information (CSI) by decoding the
pilot sequences sent by the receivers. Since global CSI knowledge is required
for IA, the transmitters have also to exchange their estimated CSIs over a
backhaul of limited capacity (i.e. imperfect case). Under this setting, we
characterize in this paper the stability region of the system under both the
imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap
between these two resulting regions. Further, under each case, we provide a
centralized probing algorithm (policy) that achieves the max stability region.
These stability regions and scheduling policies are given for the symmetric
system where all the path loss coefficients are equal to each other, as well as
for the general system. For the symmetric system, we compare the stability
region of IA with the one achieved by a time division multiple access (TDMA)
system where each transmitter applies a simple singular value decomposition
technique (SVD). We then propose a scheduling policy that consists in switching
between these two techniques, leading the system, under some conditions, to
achieve a bigger stability region. Under the general system, the adopted
scheduling policy is of a high computational complexity for moderate number of
pairs, consequently we propose an approximate policy that has a reduced
complexity but that achieves only a fraction of the system stability region. A
characterization of this fraction is provided.Comment: 66 pages, 13 figures, 1 table, submitted to IEEE Transactions on
Information Theor
On the Degrees of Freedom for Opportunistic Interference Alignment with 1-Bit Feedback: The 3 Cell Case
Opportunistic interference alignment (OIA) exploits channel randomness and
multiuser diversity by user selection. For OIA the transmitter needs channel
state information (CSI), which is usually measured on the receiver side and
sent to the transmitter side via a feedback channel. Lee and Choi show that
degrees of freedom (DoF) per transmitter are achievable in a 3-cell MIMO
interference channel assuming perfect real-valued feedback. However, the
feedback of a real-valued variable still requires infinite rate. In this paper,
we investigate 1-bit quantization for opportunistic interference alignment
(OIA) in 3-cell interference channels. We prove that 1-bit feedback is
sufficient to achieve the optimal DoF in 3-cell MIMO interference channels
if the number of users per cell is scaled as . Importantly,
the required number of users for OIA with 1-bit feedback remains the same as
with real-valued feedback. For a given system configuration, we provide an
optimal choice of the 1-bit quantizer, which captures most of the capacity
provided by a system with real-valued feedback. Using our new 1-bit feedback
scheme for OIA, we compare OIA with IA and show that OIA has a much lower
complexity and provides a better rate in the practical operation region of a
cellular communication system
Low Complexity Opportunistic Interference Alignment in -Transmitter MIMO Interference Channels
In this paper, we propose low complexity opportunistic methods for
interference alignment in -transmitter MIMO interference channels by
exploiting multiuser diversity. We do not assume availability of channel state
information (CSI) at the transmitters. Receivers are required to feed back
analog values indicating the extent to which the received interference
subspaces are aligned. The proposed opportunistic interference alignment (OIA)
achieves sum-rate comparable to conventional OIA schemes but with a
significantly reduced computational complexity.Comment: 8 pages, 8 figures, typos corrected, some clarifications added in
'Performance Comparison
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
The deviation of continuous and discrete complex random variables from the
traditional proper and symmetric assumption to a generalized improper and
asymmetric characterization (accounting correlation between a random entity and
its complex conjugate), respectively, introduces new design freedom and various
potential merits. As such, the theory of impropriety has vast applications in
medicine, geology, acoustics, optics, image and pattern recognition, computer
vision, and other numerous research fields with our main focus on the
communication systems. The journey begins from the design of improper Gaussian
signaling in the interference-limited communications and leads to a more
elaborate and practically feasible asymmetric discrete modulation design. Such
asymmetric shaping bridges the gap between theoretically and practically
achievable limits with sophisticated transceiver and detection schemes in both
coded/uncoded wireless/optical communication systems. Interestingly,
introducing asymmetry and adjusting the transmission parameters according to
some design criterion render optimal performance without affecting the
bandwidth or power requirements of the systems. This dual-flavored article
initially presents the tutorial base content covering the interplay of
reality/complexity, propriety/impropriety and circularity/noncircularity and
then surveys majority of the contributions in this enormous journey.Comment: IEEE COMST (Early Access
Interference Alignment Schemes Using Latin Square for Kx3 MIMO X Channel
In this paper, we study an interference alignment (IA) scheme with finite
time extension and beamformer selection method with low computational
complexity for X channel. An IA scheme with a chain structure by the Latin
square is proposed for Kx3 multiple-input multiple-output (MIMO) X channel.
Since the proposed scheme can have a larger set of possible beamformers than
the conventional schemes, its performance is improved by the efficient
beamformer selection for a given channel. Also, we propose a condition number
(CN) based beamformer selection method with low computational complexity and
its performance improvement is numerically verified
Generalized Low-Rank Optimization for Topological Cooperation in Ultra-Dense Networks
Network densification is a natural way to support dense mobile applications
under stringent requirements, such as ultra-low latency, ultra-high data rate,
and massive connecting devices. Severe interference in ultra-dense networks
poses a key bottleneck. Sharing channel state information (CSI) and messages
across transmitters can potentially alleviate interferences and improve system
performance. Most existing works on interference coordination require
significant CSI signaling overhead and are impractical in ultra-dense networks.
This paper investigate topological cooperation to manage interferences in
message sharing based only on network connectivity information. In particular,
we propose a generalized low-rank optimization approach to maximize achievable
degrees-of-freedom (DoFs). To tackle the challenges of poor structure and
non-convex rank function, we develop Riemannian optimization algorithms to
solve a sequence of complex fixed rank subproblems through a rank growth
strategy. By exploiting the non-compact Stiefel manifold formed by the set of
complex full column rank matrices, we develop Riemannian optimization
algorithms to solve the complex fixed-rank optimization problem by applying the
semidefinite lifting technique and Burer-Monteiro factorization approach.
Numerical results demonstrate the computational efficiency and higher DoFs
achieved by the proposed algorithms
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