291 research outputs found
Designing Precoding and Receive Matrices for Interference Alignment in MIMO Interference Channels
Interference is a key bottleneck in wireless communication
systems. Interference alignment is a management
technique that align interference from other transmitters in
the least possibly dimension subspace at each receiver and
provides the remaining dimensions for free interference signal.
An uncoordinated interference is an example of interference
which cannot be aligned coordinately with interference from
coordinated part; consequently, the performance of interference
alignment approaches are degraded. In this paper, we propose a
rank minimization method to enhance the performance of interference
alignment in the presence of uncoordinated interference
sources. Firstly, to obtain higher multiplexing gain, a new rank
minimization based optimization problem is proposed; then, a
new class of convex relaxation is introduced which can reduce
the optimal value of the problem and obtain lower rank solutions
by expanding the feasibility set. Simulation results show that our
proposed method can obtain considerably higher multiplexing
gain and sum rate than other approaches in the interference
alignment framework
Interference alignment for one-hop and two-hops MIMO systems with uncoordinated interference
Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference, i.e., interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the l2 and the Schatten-p norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
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