1,263 research outputs found
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
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
Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems
This paper considers pilot design to mitigate pilot contamination and provide
good service for everyone in multi-cell Massive multiple input multiple output
(MIMO) systems. Instead of modeling the pilot design as a combinatorial
assignment problem, as in prior works, we express the pilot signals using a
pilot basis and treat the associated power coefficients as continuous
optimization variables. We compute a lower bound on the uplink capacity for
Rayleigh fading channels with maximum ratio detection that applies with
arbitrary pilot signals. We further formulate the max-min fairness problem
under power budget constraints, with the pilot signals and data powers as
optimization variables. Because this optimization problem is non-deterministic
polynomial-time hard due to signomial constraints, we then propose an algorithm
to obtain a local optimum with polynomial complexity. Our framework serves as a
benchmark for pilot design in scenarios with either ideal or non-ideal
hardware. Numerical results manifest that the proposed optimization algorithms
are close to the optimal solution obtained by exhaustive search for different
pilot assignments and the new pilot structure and optimization bring large
gains over the state-of-the-art suboptimal pilot design.Comment: 16 pages, 8 figures. Accepted to publish at IEEE Transactions on
Wireless Communication
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
Beamspace Aware Adaptive Channel Estimation for Single-Carrier Time-varying Massive MIMO Channels
In this paper, the problem of sequential beam construction and adaptive
channel estimation based on reduced rank (RR) Kalman filtering for
frequency-selective massive multiple-input multiple-output (MIMO) systems
employing single-carrier (SC) in time division duplex (TDD) mode are
considered. In two-stage beamforming, a new algorithm for statistical
pre-beamformer design is proposed for spatially correlated time-varying
wideband MIMO channels under the assumption that the channel is a stationary
Gauss-Markov random process. The proposed algorithm yields a nearly optimal
pre-beamformer whose beam pattern is designed sequentially with low complexity
by taking the user-grouping into account, and exploiting the properties of
Kalman filtering and associated prediction error covariance matrices. The
resulting design, based on the second order statistical properties of the
channel, generates beamspace on which the RR Kalman estimator can be realized
as accurately as possible. It is observed that the adaptive channel estimation
technique together with the proposed sequential beamspace construction shows
remarkable robustness to the pilot interference. This comes with significant
reduction in both pilot overhead and dimension of the pre-beamformer lowering
both hardware complexity and power consumption.Comment: 7 pages, 3 figures, accepted by IEEE ICC 2017 Wireless Communications
Symposiu
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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