514 research outputs found
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
Signal Processing in Arrayed MIMO Systems
Multiple-Input Multiple-Output (MIMO) systems, using antenna arrays at both
receiver and transmitter, have shown great potential to provide high bandwidth
utilization efficiency. Unlike other reported research on MIMO systems which
often assumes independent antennas, in this thesis an arrayed MIMO system
framework is proposed, which provides a richer description of the channel charac-
teristics and additional degrees of freedom in designing communication systems.
Firstly, the spatial correlated MIMO system is studied as an array-to-array
system with each array (Tx or Rx) having predefined constrained aperture. The
MIMO system is completely characterized by its transmit and receive array man-
ifolds and a new spatial correlation model other than Kronecker-based model is
proposed. As this model is based on array manifolds, it enables the study of the
effect of array geometry on the capacity of correlated MIMO channels.
Secondly, to generalize the proposed arrayed MIMO model to a frequency
selective fading scenario, the framework of uplink MIMO DS-CDMA (Direct-
Sequence Code Division Multiple Access) systems is developed. DOD estimation
is developed based on transmit beamrotation. A subspace-based joint DOA/TOA
estimation scheme as well as various spatial temporal reception algorithms is also
proposed.
Finally, the downlink MIMO-CDMA systems in multiple-access multipath fading channels are investigated. Linear precoder and decoder optimization problems
are studied under different criterions. Optimization approaches with different
power allocation schemes are investigated. Sub-optimization approaches with
close-form solution and thus less computation complexity are also proposed
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS)
transmission for enabling ultra-reliable low-latency communications (URLLC). To
guarantee excellent reliability performance, pragmatic precoder design is an
effective and indispensable solution. However, the design requires accurate
instantaneous channel state information at the transmitter (ICSIT) which is not
always available in practice. Motivated by this, we adopt a deep learning (DL)
approach to exploit implicit features from estimated historical delay-Doppler
domain channels (DDCs) to directly predict the precoder to be adopted in the
next time frame for minimizing the frame error rate (FER), that can further
improve the system reliability without the acquisition of ICSIT. To this end,
we first establish a predictive transmission protocol and formulate a general
problem for the precoder design where a closed-form theoretical FER expression
is derived serving as the objective function to characterize the system
reliability. Then, we propose a DL-based predictive precoder design framework
which exploits an unsupervised learning mechanism to improve the practicability
of the proposed scheme. As a realization of the proposed framework, we design a
DDCs-aware convolutional long short-term memory (CLSTM) network for the
precoder design, where both the convolutional neural network and LSTM modules
are adopted to facilitate the spatial-temporal feature extraction from the
estimated historical DDCs to further enhance the precoder performance.
Simulation results demonstrate that the proposed scheme facilitates a flexible
reliability-latency tradeoff and achieves an excellent FER performance that
approaches the lower bound obtained by a genie-aided benchmark requiring
perfect ICSI at both the transmitter and receiver.Comment: 31 pages, 12 figure
Interference suppression and parameter estimation in wireless communication systems over time-varing multipath fading channels
This dissertation focuses on providing solutions to two of the most important problems in wireless communication systems design, namely, 1) the interference suppression, and 2) the channel parameter estimation in wireless communication systems over time-varying multipath fading channels. We first study the interference suppression problem in various communication systems under a unified multirate transmultiplexer model. A state-space approach that achieves the optimal realizable equalization (suppression of inter-symbol interference) is proposed, where the Kalman filter is applied to obtain the minimum mean squared error estimate of the transmitted symbols. The properties of the optimal realizable equalizer are analyzed. Its relations with the conventional equalization methods are studied. We show that, although in general a Kalman filter has an infinite impulse response, the Kalman filter based decision-feedback equalizer (Kalman DFE) is a finite length filter. We also propose a novel successive interference cancellation (SIC) scheme to suppress the inter-channel interference encountered in multi-input multi-output systems. Based on spatial filtering theory, the SIC scheme is again converted to a Kalman filtering problem. Combining the Kalman DFE and the SIC scheme in series, the resultant two-stage receiver achieves optimal realizable interference suppression. Our results are the most general ever obtained, and can be applied to any linear channels that have a state-space realization, including time-invariant, time-varying, finite impulse response, and infinite impulse response channels. The second half of the dissertation devotes to the parameter estimation and tracking of single-input single-output time-varying multipath channels. We propose a novel method that can blindly estimate the channel second order statistics (SOS). We establish the channel SOS identifiability condition and propose novel precoder structures that guarantee the blind estimation of the channel SOS and achieve diversities. The estimated channel SOS can then be fit into a low order autoregressive (AR) model characterizing the time evolution of the channel impulse response. Based on this AR model, a new approach to time-varying multipath channel tracking is proposed
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