8 research outputs found
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
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 complexit
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