6 research outputs found
RaPro: A Novel 5G Rapid Prototyping System Architecture
We propose a novel fifth-generation (5G) rapid prototyping (RaPro) system
architecture by combining FPGA-privileged modules from a software defined radio
(or FPGA-coprocessor) and high-level programming language for advanced
algorithms from multi-core general purpose processors. The proposed system
architecture exhibits excellent flexibility and scalability in the development
of a 5G prototyping system. As a proof of concept, a multi-user full-dimension
multiple-input and multiple-output system is established based on the proposed
architecture. Experimental results demonstrate the superiority of the proposed
architecture in large-scale antenna and wideband communication systems.Comment: accepted by IEEE Wireless Communication Letter
Implementation of Massive MIMO Uplink Receiver on RaPro Prototyping Platform
The updated physical layer standard of the fifth generation wireless
communication suggests the necessity of a rapid prototyping platform. To this
end, we develop RaPro, a multi-core general purpose processor-based massive
multiple-input-multiple-output (MIMO) prototyping platform. To enhance RaPro,
high performance detection and beamforming are needed, whereas both of them
request for accurate channel state information (CSI). In this paper, linear
minimum mean square error (LMMSE)-based channel estimator is adopted and
encapsulated inside RaPro to gain more accurate CSI. Considering the high
comlexity and unknown of channel statistics, we design low-complexity LMMSE
channel estimator to alleviate the rising complexity along with increasing
antenna number and set more computational resource aside for massive MIMO
uplink detection and downlink beamforming. Simulation results indicate the high
mean square error performance and robustness of designed low-complexity method.
Indoor and corridor scenario tests show prominent improvement in bit error rate
performance. Time cost analysis proves the practical use and real-time
transmission ability of the implemented uplink receiver on RaPro.Comment: 14 pages, 10 figure
A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations
In this work, a scalable and modular architecture for massive MIMO base
stations with distributed processing is proposed. New antennas can readily be
added by adding a new node as each node handles all the additional involved
processing. The architecture supports conjugate beamforming, zero-forcing, and
MMSE, where for the two latter cases a central matrix inversion is required.
The impact of the time required for this matrix inversion is carefully analyzed
along with a generic frame format. As part of the contribution, careful
computational, memory, and communication analyses are presented. It is shown
that all computations can be mapped to a single computational structure and
that a processing node consisting of a single such processing element can
handle a broad range of bandwidths and number of terminals
Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
On the time-varying channel estimation, the traditional downlink (DL) channel
restoration schemes usually require the reconstruction for the covariance of
downlink process noise vector, which is dependent on DL channel covariance
matrix (CCM). However, the acquisition of the CCM leads to unacceptable
overhead in massive MIMO systems. To tackle this problem, in this paper, we
propose a novel scheme for the DL channel tracking. First, with the help of
virtual channel representation (VCR), we build a dynamic uplink (UL) massive
MIMO channel model with the consideration of off-grid refinement. Then, a
coordinate-wise maximization based expectation maximization (EM) algorithm is
adopted for capturing the model parameters, including the spatial signatures,
the time-correlation factors, the off-grid bias, the channel power, and the
noise power. Thanks to the angle reciprocity, the spatial signatures,
timecorrelation factors and off-grid bias of the DL channel model can be
reconstructed with the knowledge of UL ones. However, the other two kinds of
model parameters are closely related with the carrier frequency, which cannot
be perfectly inferred from the UL ones. Instead of relearning the DL model
parameters with dedicated training, we resort to the optimal Bayesian Kalman
filter (OBKF) method to accurately track the DL channel with the partially
prior knowledge. At the same time, the model parameters will be gradually
restored. Specially, the factor-graph and the Metropolis Hastings MCMC are
utilized within the OBKF framework. Finally, numerical results are provided to
demonstrate the efficiency of our proposed scheme.Comment: 30 pages, 11 figure
Demonstration of multivariate photonics: blind dimensionality reduction with analog integrated photonics
Multi-antenna radio front-ends generate a multi-dimensional flood of
information, most of which is partially redundant. Redundancy is eliminated by
dimensionality reduction, but contemporary digital processing techniques face
harsh fundamental tradeoffs when implementing this class of functions. These
tradeoffs can be broken in the analog domain, in which the performance of
optical technologies greatly exceeds that of electronic counterparts. Here, we
present concepts, methods, and a first demonstration of multivariate photonics:
a combination of integrated photonic hardware, analog dimensionality reduction,
and blind algorithmic techniques. We experimentally demonstrate 2-channel, 1.0
GHz principal component analysis in a photonic weight bank using recently
proposed algorithms for synthesizing the multivariate properties of signals to
which the receiver is blind. Novel methods are introduced for controlling
blindness conditions in a laboratory context. This work provides a foundation
for further research in multivariate photonic information processing, which is
poised to play a role in future generations of wireless technology.Comment: 24 pages, 7 figure
Expectation Propagation Detector for Extra-Large Scale Massive MIMO
The order-of-magnitude increase in the dimension of antenna arrays, which
forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems,
enables substantial improvement in spectral efficiency, energy efficiency, and
spatial resolution. However, practical challenges, such as excessive
computational complexity and excess of baseband data to be transferred and
processed, prohibit the use of centralized processing. A promising solution is
to distribute baseband data from disjoint subsets of antennas into parallel
processing procedures coordinated by a central processing unit. This solution
is called subarray-based architecture. In this work, we extend the application
of expectation propagation (EP) principle, which effectively balances
performance and practical feasibility in conventional centralized MIMO detector
design, to fit the subarray-based architecture. Analytical results confirm the
convergence of the proposed iterative procedure and that the proposed detector
asymptotically approximates Bayesian optimal performance under certain
conditions. The proposed subarray-based EP detector is reduced to centralized
EP detector when only one subarray exists. In addition, we propose additional
strategies for further reducing the complexity and overhead of the information
exchange between parallel subarrays and the central processing unit to
facilitate the practical implementation of the proposed detector. Simulation
results demonstrate that the proposed detector achieves numerical stability
within few iterations and outperforms its counterparts.Comment: 15 pages, 16 figures, accepted by IEEE Transactions on Wireless
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