1,738 research outputs found
Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection
Signal detection in large multiple-input multiple-output (large-MIMO) systems
presents greater challenges compared to conventional massive-MIMO for two
primary reasons. First, large-MIMO systems lack favorable propagation
conditions as they do not require a substantially greater number of service
antennas relative to user antennas. Second, the wireless channel may exhibit
spatial non-stationarity when an extremely large aperture array (ELAA) is
deployed in a large-MIMO system. In this paper, we propose a scalable iterative
large-MIMO detector named ANPID, which simultaneously delivers 1) close to
maximum-likelihood detection performance, 2) low computational-complexity
(i.e., square-order of transmit antennas), 3) fast convergence, and 4)
robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates
a damping demodulation step into stationary iterative (SI) methods and
alternates between two distinct demodulated SI methods. Simulation results
demonstrate that ANPID fulfills all the four features concurrently and
outperforms existing low-complexity MIMO detectors, especially in highly-loaded
large MIMO systems.Comment: Accepted by IEEE GLOBECOM 202
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
A Scalable VLSI Architecture for Soft-Input Soft-Output Depth-First Sphere Decoding
Multiple-input multiple-output (MIMO) wireless transmission imposes huge
challenges on the design of efficient hardware architectures for iterative
receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping,
often approached by sphere decoding (SD). In this paper, we introduce the - to
our best knowledge - first VLSI architecture for SISO SD applying a single
tree-search approach. Compared with a soft-output-only base architecture
similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the
architectural modifications for soft input still allow a one-node-per-cycle
execution. For a 4x4 16-QAM system, the area increases by 57% and the operating
frequency degrades by 34% only.Comment: Accepted for IEEE Transactions on Circuits and Systems II Express
Briefs, May 2010. This draft from April 2010 will not be updated any more.
Please refer to IEEE Xplore for the final version. *) The final publication
will appear with the modified title "A Scalable VLSI Architecture for
Soft-Input Soft-Output Single Tree-Search Sphere Decoding
Low complexity scalable MIMO sphere detection through antenna detection reordering
This paper describes a novel low complexity scalable multiple-input multiple-output (MIMO) detector that does not require preprocessing and the optimal squared l2-norm computations to achieve good bit error (BER)
performance. Unlike existing detectors such as Flexsphere that use preprocessing before MIMO detection to improve performance, the proposed detector instead performs multiple search passes, where each search pass detects the transmit stream with a different permuted detection order.
In addition, to reduce the number of multipliers required in
the design, we use l1-norm in place of the optimal squared
l2-norm. To ameliorate the BER performance loss due to l1-
norm, we propose squaring then scaling the l1-norm. By changing the number of parallel search passes and using norm scaling, we show that this design achieves comparable performance to Flexsphere with reduced resource
requirement or achieves BER performance close to exhaustive search with increased resource requirement.National Science Foundatio
Message Passing in C-RAN: Joint User Activity and Signal Detection
In cloud radio access network (C-RAN), remote radio heads (RRHs) and users
are uniformly distributed in a large area such that the channel matrix can be
considered as sparse. Based on this phenomenon, RRHs only need to detect the
relatively strong signals from nearby users and ignore the weak signals from
far users, which is helpful to develop low-complexity detection algorithms
without causing much performance loss. However, before detection, RRHs require
to obtain the realtime user activity information by the dynamic grant
procedure, which causes the enormous latency. To address this issue, in this
paper, we consider a grant-free C-RAN system and propose a low-complexity
Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified
channel, which jointly detects the user activity and signal. Since active users
are assumed to transmit Gaussian signals at any time, the user activity can be
regarded as a Bernoulli variable and the signals from all users obey a
Bernoulli-Gaussian distribution. In the BGMP, the detection functions for
signals are designed with respect to the Bernoulli-Gaussian variable. Numerical
results demonstrate the robustness and effectivity of the BGMP. That is, for
different sparsified channels, the BGMP can approach the mean-square error
(MSE) of the genie-aided sparse minimum mean-square error (GA-SMMSE) which
exactly knows the user activity information. Meanwhile, the fast convergence
and strong recovery capability for user activity of the BGMP are also verified.Comment: Conference, 6 pages, 7 figures, accepted by IEEE Globecom 201
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