601 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
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
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
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones
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
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
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