2,619 research outputs found
Design Guidelines for Training-based MIMO Systems with Feedback
In this paper, we study the optimal training and data transmission strategies
for block fading multiple-input multiple-output (MIMO) systems with feedback.
We consider both the channel gain feedback (CGF) system and the channel
covariance feedback (CCF) system. Using an accurate capacity lower bound as a
figure of merit, we investigate the optimization problems on the temporal power
allocation to training and data transmission as well as the training length.
For CGF systems without feedback delay, we prove that the optimal solutions
coincide with those for non-feedback systems. Moreover, we show that these
solutions stay nearly optimal even in the presence of feedback delay. This
finding is important for practical MIMO training design. For CCF systems, the
optimal training length can be less than the number of transmit antennas, which
is verified through numerical analysis. Taking this fact into account, we
propose a simple yet near optimal transmission strategy for CCF systems, and
derive the optimal temporal power allocation over pilot and data transmission.Comment: Submitted to IEEE Trans. Signal Processin
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
Channel Estimation for MIMO MC-CDMA Systems
The concepts of MIMO MC-CDMA are not new but the new technologies to improve
their functioning are an emerging area of research. In general, most mobile
communication systems transmit bits of information in the radio space to the
receiver. The radio channels in mobile radio systems are usually multipath
fading channels, which cause inter-symbol interference (ISI) in the received
signal. To remove ISI from the signal, there is a need of strong equalizer. In
this thesis we have focused on simulating the MIMO MC-CDMA systems in MATLAB
and designed the channel estimation for them
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
Pilot Decontamination Through Pilot Sequence Hopping in Massive MIMO Systems
This work concerns wireless cellular networks applying massive multiple-input
multiple-output (MIMO) technology. In such a system, the base station in a
given cell is equipped with a very large number (hundreds or even thousands) of
antennas and serves multiple users. Estimation of the channel from the base
station to each user is performed at the base station using an uplink pilot
sequence. Such a channel estimation procedure suffers from pilot contamination.
Orthogonal pilot sequences are used in a given cell but, due to the shortage of
orthogonal sequences, the same pilot sequences must be reused in neighboring
cells, causing pilot contamination. The solution presented in this paper
suppresses pilot contamination, without the need for coordination among cells.
Pilot sequence hopping is performed at each transmission slot, which provides a
randomization of the pilot contamination. Using a modified Kalman filter, it is
shown that such randomized contamination can be significantly suppressed.
Comparisons with conventional estimation methods show that the mean squared
error can be lowered as much as an order of magnitude at low mobility
- …