6,468 research outputs found
Low-complexity soft ML detection for generalized spatial modulation
[EN] Generalized Spatial Modulation (GSM) is a recent Multiple-Input Multiple-Output (MIMO) scheme, which achieves high spectral and energy efficiencies. Specifically, soft-output detectors have a key role in achiev-ing the highest coding gain when an error-correcting code (ECC) is used. Nowadays, soft-output Maxi-mum Likelihood (ML) detection in MIMO-GSM systems leads to a computational complexity that is un-feasible for real applications; however, it is important to develop low-complexity decoding algorithms that provide a reasonable computational simulation time in order to make a performance benchmark available in MIMO-GSM systems. This paper presents three algorithms that achieve ML performance. In the first algorithm, different strategies are implemented, such as a preprocessing sorting step in order to avoid an exhaustive search. In addition, clipping of the extrinsic log-likelihood ratios (LLRs) can be incor-porating to this algorithm to give a lower cost version. The other two proposed algorithms can only be used with clipping and the results show a significant saving in computational cost. Furthermore clipping allows a wide-trade-off between performance and complexity by only adjusting the clipping parameter.Acknowledgements This work has been partially supported by Spanish Ministry of Science, Innovation and Universities and by European Union through grant RTI2018-098085-BC41 (MCUI/AEI/FEDER) , by GVASimarro, MA.; GarcÃa Mollá, VM.; MartÃnez ZaldÃvar, FJ.; Gonzalez, A. (2022). Low-complexity soft ML detection for generalized spatial modulation. Signal Processing. 196:1-12. https://doi.org/10.1016/j.sigpro.2022.10850911219
Generalized Spatial Modulation in Large-Scale Multiuser MIMO Systems
Generalized spatial modulation (GSM) uses transmit antenna elements but
fewer transmit radio frequency (RF) chains, . Spatial modulation (SM)
and spatial multiplexing are special cases of GSM with and
, respectively. In GSM, in addition to conveying information bits
through conventional modulation symbols (for example, QAM), the
indices of the active transmit antennas also convey information bits.
In this paper, we investigate {\em GSM for large-scale multiuser MIMO
communications on the uplink}. Our contributions in this paper include: ()
an average bit error probability (ABEP) analysis for maximum-likelihood
detection in multiuser GSM-MIMO on the uplink, where we derive an upper bound
on the ABEP, and () low-complexity algorithms for GSM-MIMO signal detection
and channel estimation at the base station receiver based on message passing.
The analytical upper bounds on the ABEP are found to be tight at moderate to
high signal-to-noise ratios (SNR). The proposed receiver algorithms are found
to scale very well in complexity while achieving near-optimal performance in
large dimensions. Simulation results show that, for the same spectral
efficiency, multiuser GSM-MIMO can outperform multiuser SM-MIMO as well as
conventional multiuser MIMO, by about 2 to 9 dB at a bit error rate of
. Such SNR gains in GSM-MIMO compared to SM-MIMO and conventional MIMO
can be attributed to the fact that, because of a larger number of spatial index
bits, GSM-MIMO can use a lower-order QAM alphabet which is more power
efficient.Comment: IEEE Trans. on Wireless Communications, accepte
Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays
This work analyzes the performance of the implementable detectors for
multiple-input-multiple-output (MIMO) orthogonal frequency division
multiplexing (OFDM) technique under specific and realistic operation system
condi- tions, including antenna correlation and array configuration.
Time-domain channel model has been used to evaluate the system performance
under realistic communication channel and system scenarios, including different
channel correlation, modulation order and antenna arrays configurations. A
bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high
performance combined with high capacity systems and manageable computational
complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel
selectivity effect, while the impact of the number of antennas, adoption of
linear against heuristic-based detection schemes, and the spatial correlation
effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM
context.Comment: 26 pgs, 16 figures and 5 table
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
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
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