48 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
Making Cell-Free Massive MIMO Competitive with MMSE Processing and Centralized Implementation
Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation
Cell-free Massive MIMO is considered as a promising technology for satisfying
the increasing number of users and high rate expectations in beyond-5G
networks. The key idea is to let many distributed access points (APs)
communicate with all users in the network, possibly by using joint coherent
signal processing. The aim of this paper is to provide the first comprehensive
analysis of this technology under different degrees of cooperation among the
APs. Particularly, the uplink spectral efficiencies of four different cell-free
implementations are analyzed, with spatially correlated fading and arbitrary
linear processing. It turns out that it is possible to outperform conventional
Cellular Massive MIMO and small cell networks by a wide margin, but only using
global or local minimum mean-square error (MMSE) combining. This is in sharp
contrast to the existing literature, which advocates for maximum-ratio
combining. Also, we show that a centralized implementation with optimal MMSE
processing not only maximizes the SE but largely reduces the fronthaul
signaling compared to the standard distributed approach. This makes it the
preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding
is also investigated and shown to bring negligible improvements.Comment: 14 pages, 6 figures, To appear in IEEE Transactions on Wireless
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
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the
user equipments (UEs) by geographically distributed access points (APs) by
means of joint transmission and reception. To limit the power consumption due
to fronthaul signaling and processing, each UE should only be served by a
subset of the APs, but it is hard to identify that subset. Previous works have
tackled this combinatorial problem heuristically. In this paper, we propose a
sparse distributed processing design for CF mMIMO, where the AP-UE association
and long-term signal processing coefficients are jointly optimized. We
formulate two sparsity-inducing mean-squared error (MSE) minimization problems
and solve them by using efficient proximal approaches with block-coordinate
descent. For the downlink, more specifically, we develop a virtually optimized
large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The
numerical results show that the proposed sparse processing schemes work well in
both uplink and downlink. In particular, they achieve almost the same spectral
efficiency as if all APs would serve all UEs, while the energy efficiency is
2-4 times higher thanks to the reduced processing and signaling.Comment: 37 pages, 9 figures, accepted for publication in the IEEE
Transactions on Wireless Communication
Foundations of User-Centric Cell-Free Massive MIMO
Imagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47