1,212 research outputs found
Multipath Multiplexing for Capacity Enhancement in SIMO Wireless Systems
This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN)
data transmission and detection approach for a single input multiple output
(SIMO) system. It is assumed that the signal having a bandwidth is
transmitted through a wireless channel with multipath components. Under
this assumption, the current paper provides a novel and simple OFTN
transmission and symbol-by-symbol detection approach that exploits the
multiplexing gain obtained by the multipath characteristic of wideband wireless
channels. It is shown that the proposed design can achieve a higher
transmission rate than the existing one (i.e., orthogonal frequency division
multiplexing (OFDM)). Furthermore, the achievable rate gap between the proposed
approach and that of the OFDM increases as the number of receiver antennas
increases for a fixed value of . This implies that the performance gain of
the proposed approach can be very significant for a large-scale multi-antenna
wireless system. The superiority of the proposed approach is shown
theoretically and confirmed via numerical simulations. {Specifically, we have
found {upper-bound average} rates of 15 bps/Hz and 28 bps/Hz with the OFDM and
proposed approaches, respectively, in a Rayleigh fading channel with 32 receive
antennas and signal to noise ratio (SNR) of 15.3 dB. The extension of the
proposed approach for different system setups and associated research problems
is also discussed.Comment: IEEE Transactions on Wireless Communication
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
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
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
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