222 research outputs found
On the Matrix Inversion Approximation Based on Neumann Series in Massive MIMO Systems
Zero-Forcing (ZF) has been considered as one of the potential practical
precoding and detection method for massive MIMO systems. One of the most
important advantages of massive MIMO is the capability of supporting a large
number of users in the same time-frequency resource, which requires much larger
dimensions of matrix inversion for ZF than conventional multi-user MIMO
systems. In this case, Neumann Series (NS) has been considered for the Matrix
Inversion Approximation (MIA), because of its suitability for massive MIMO
systems and its advantages in hardware implementation. The
performance-complexity trade-off and the hardware implementation of NS-based
MIA in massive MIMO systems have been discussed. In this paper, we analyze the
effects of the ratio of the number of massive MIMO antennas to the number of
users on the performance of NS-based MIA. In addition, we derive the
approximation error estimation formulas for different practical numbers of
terms of NS-based MIA. These results could offer useful guidelines for
practical massive MIMO systems.Comment: accepted to conference; Proc. IEEE ICC 201
Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations
Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to
be one of the key technologies in next-generation multi-user cellular systems,
based on the upcoming 3GPP LTE Release 12 standard, for example. In this work,
we propose - to the best of our knowledge - the first VLSI design enabling
high-throughput data detection in single-carrier frequency-division multiple
access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate
matrix inversion algorithm relying on a Neumann series expansion, which
substantially reduces the complexity of linear data detection. We analyze the
associated error, and we compare its performance and complexity to those of an
exact linear detector. We present corresponding VLSI architectures, which
perform exact and approximate soft-output detection for large-scale MIMO
systems with various antenna/user configurations. Reference implementation
results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to
achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale
MIMO system. We finally provide a performance/complexity trade-off comparison
using the presented FPGA designs, which reveals that the detector circuit of
choice is determined by the ratio between BS antennas and users, as well as the
desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
Fast matrix inversion updates for massive MIMO detection and precoding
In this letter, methods and corresponding complexities for fast matrix inversion updates in the context of massive multiple-input multiple-output (MIMO) are studied. In particular, we propose an on-the-fly method to recompute the zero forcing (ZF) filter when a user is added or removed from the system. Additionally, we evaluate the recalculation of the inverse matrix after a new channel estimation is obtained for a given user. Results are evaluated numerically in terms of bit error rate (BER) using the Neumann series approximation as the initial inverse matrix. It is concluded that, with fewer operations, the performance after an update remains close to the initial one.info:eu-repo/semantics/acceptedVersio
A Low Complexity Space-Time Block Codes Detection for Cell-Free Massive MIMO Systems
The new generation of telecommunication systems must provide acceptable data
rates and spectral efficiency for new applications. Recently massive MIMO has
been introduced as a key technique for the new generation of telecommunication
systems. Cell-free massive MIMO system is not segmented into cells. Each BS
antennas are distributed throughout the environment and each user is served by
all BSs, simultaneously.
In this paper, the performance of the multiuser cell-free massive MIMO-system
exploying space-time block codes in the uplink, and with linear decoders is
studied. An Inverse matrix approximation using Neumann series is proposed to
reduce the computational and hardware complexity of the decoding in the
receiver.
For this purpose, each user has two antennas, and also for improving the
diversity gain performance, space-time block codes are used in the uplink.
Then, Neumann series is used to approximate the inverse matrix in ZF and MMSE
decoders, and its performance is evaluated in terms of BER and spectral
efficiency.
In addition, we derive lower bound for throughput of ZF decoder.
The simulation results show that performance of the system , in terms of BER
and spectral efficiency, is better than the single-antenna users at the same
system. Also, the BER performance in a given system with the proposed method
will be close to the exact method.Comment: 5 pages, 4 figures, Accepted for ICEE202
- …