46,035 research outputs found
Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels
Massive multiple-input--multiple-output (MIMO) systems can suffer from
coherent intercell interference due to the phenomenon of pilot contamination.
This paper investigates a two-layer decoding method that mitigates both
coherent and non-coherent interference in multi-cell Massive MIMO. To this end,
each base station (BS) first estimates the channels to intra-cell users using
either minimum mean-squared error (MMSE) or element-wise MMSE (EW-MMSE)
estimation based on uplink pilots. The estimates are used for local decoding on
each BS followed by a second decoding layer where the BSs cooperate to mitigate
inter-cell interference. An uplink achievable spectral efficiency (SE)
expression is computed for arbitrary two-layer decoding schemes. A closed-form
expression is then obtained for correlated Rayleigh fading, maximum-ratio
combining, and the proposed large-scale fading decoding (LSFD) in the second
layer. We also formulate a sum SE maximization problem with both the data power
and LSFD vectors as optimization variables. Since this is an NP-hard problem,
we develop a low-complexity algorithm based on the weighted MMSE approach to
obtain a local optimum. The numerical results show that both data power control
and LSFD improves the sum SE performance over single-layer decoding multi-cell
Massive MIMO systems.Comment: 17 pages; 10 figures; Accepted for publication in IEEE Transactions
on Communication
Two-Layer Decoding in Cellular Massive MIMO Systems with Spatial Channel Correlation
This paper studies a two-layer decoding method that mitigates inter-cell
interference in multi-cell Massive MIMO systems. In layer one, each base
station (BS) estimates the channels to intra-cell users and uses the estimates
for local decoding on each BS, followed by a second decoding layer where the
BSs cooperate to mitigate inter-cell interference. An uplink achievable
spectral efficiency (SE) expression is computed for arbitrary two-layer
decoding schemes, while a closed-form expression is obtained for correlated
Rayleigh fading channels, maximum-ratio combining (MRC), and large-scale fading
decoding (LSFD) in the second layer. We formulate a non-convex sum SE
maximization problem with both the data power and LSFD vectors as optimization
variables and develop an algorithm based on the weighted MMSE (minimum mean
square error) approach to obtain a stationary point with low computational
complexity.Comment: 4 figures. Accepted by ICC 2019. arXiv admin note: substantial text
overlap with arXiv:1807.0807
Two-Layer Decoding in Cellular Massive MIMO Systems with Spatial Channel Correlation
This paper studies a two-layer decoding method that mitigates inter-cell
interference in multi-cell Massive MIMO systems. In layer one, each base
station (BS) estimates the channels to intra-cell users and uses the estimates
for local decoding on each BS, followed by a second decoding layer where the
BSs cooperate to mitigate inter-cell interference. An uplink achievable
spectral efficiency (SE) expression is computed for arbitrary two-layer
decoding schemes, while a closed-form expression is obtained for correlated
Rayleigh fading channels, maximum-ratio combining (MRC), and large-scale fading
decoding (LSFD) in the second layer. We formulate a non-convex sum SE
maximization problem with both the data power and LSFD vectors as optimization
variables and develop an algorithm based on the weighted MMSE (minimum mean
square error) approach to obtain a stationary point with low computational
complexity.Comment: 4 figures. Accepted by ICC 2019. arXiv admin note: substantial text
overlap with arXiv:1807.0807
Delay Reduction in Multi-Hop Device-to-Device Communication using Network Coding
This paper considers the problem of reducing the broadcast decoding delay of
wireless networks using instantly decodable network coding (IDNC) based
device-to-device (D2D) communications. In a D2D configuration, devices in the
network can help hasten the recovery of the lost packets of other devices in
their transmission range by sending network coded packets. Unlike previous
works that assumed fully connected network, this paper proposes a partially
connected configuration in which the decision should be made not only on the
packet combinations but also on the set of transmitting devices. First, the
different events occurring at each device are identified so as to derive an
expression for the probability distribution of the decoding delay. The joint
optimization problem over the set of transmitting devices and the packet
combinations of each is, then, formulated. The optimal solution of the joint
optimization problem is derived using a graph theory approach by introducing
the cooperation graph and reformulating the problem as a maximum weight clique
problem in which the weight of each vertex is the contribution of the device
identified by the vertex. Through extensive simulations, the decoding delay
experienced by all devices in the Point to Multi-Point (PMP) configuration, the
fully connected D2D (FC-D2D) configuration and the more practical partially
connected D2D (PC-D2D) configuration are compared. Numerical results suggest
that the PC-D2D outperforms the FC-D2D and provides appreciable gain especially
for poorly connected networks
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
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