36,582 research outputs found
Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
Algorithms for Massive MIMO uplink detection and downlink precoding typically
rely on a centralized approach, by which baseband data from all antenna modules
are routed to a central node in order to be processed. In the case of Massive
MIMO, where hundreds or thousands of antennas are expected in the base-station,
said routing becomes a bottleneck since interconnection throughput is limited.
This paper presents a fully decentralized architecture and an algorithm for
Massive MIMO uplink detection and downlink precoding based on the Stochastic
Gradient Descent (SGD) method, which does not require a central node for these
tasks. Through a recursive approach and very low complexity operations, the
proposed algorithm provides a good trade-off between performance,
interconnection throughput and latency. Further, our proposed solution achieves
significantly lower interconnection data-rate than other architectures,
enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal
Processin
Performance Analysis of the Decentralized Eigendecomposition and ESPRIT Algorithm
In this paper, we consider performance analysis of the decentralized power
method for the eigendecomposition of the sample covariance matrix based on the
averaging consensus protocol. An analytical expression of the second order
statistics of the eigenvectors obtained from the decentralized power method
which is required for computing the mean square error (MSE) of subspace-based
estimators is presented. We show that the decentralized power method is not an
asymptotically consistent estimator of the eigenvectors of the true measurement
covariance matrix unless the averaging consensus protocol is carried out over
an infinitely large number of iterations. Moreover, we introduce the
decentralized ESPRIT algorithm which yields fully decentralized
direction-of-arrival (DOA) estimates. Based on the performance analysis of the
decentralized power method, we derive an analytical expression of the MSE of
DOA estimators using the decentralized ESPRIT algorithm. The validity of our
asymptotic results is demonstrated by simulations.Comment: 18 pages, 5 figures, submitted for publication in IEEE Transactions
on 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
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