226 research outputs found
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines
In the era of sixth-generation (6G) wireless communications, integrated
sensing and communications (ISAC) is recognized as a promising solution to
upgrade the physical system by endowing wireless communications with sensing
capability. Existing ISAC is mainly oriented to static scenarios with
radio-frequency (RF) sensors being the primary participants, thus lacking a
comprehensive environment feature characterization and facing a severe
performance bottleneck in dynamic environments. To date, extensive surveys on
ISAC have been conducted but are limited to summarizing RF-based radar sensing.
Currently, some research efforts have been devoted to exploring multi-modal
sensing-communication integration but still lack a comprehensive review.
Therefore, we generalize the concept of ISAC inspired by human synesthesia to
establish a unified framework of intelligent multi-modal sensing-communication
integration and provide a comprehensive review under such a framework in this
paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition
of such intelligent integration and details its paradigm for the first time. We
commence by justifying the necessity of the new paradigm. Subsequently, we
offer a definition of SoM and zoom into the detailed paradigm, which is
summarized as three operation modes. To facilitate SoM research, we overview
the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then,
we introduce the mapping relationships between multi-modal sensing and
communications. Afterward, we cover the technological review on
SoM-enhance-based and SoM-concert-based applications. To corroborate the
superiority of SoM, we also present simulation results related to dual-function
waveform and predictive beamforming design. Finally, we propose some potential
directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys &
Tutorial
Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains
When base stations (BSs) are deployed with multiple antennas, they need to
have downlink (DL) channel state information (CSI) to optimize downlink
transmissions by beamforming. The DL CSI is usually measured at mobile stations
(MSs) through DL training and fed back to the BS in frequency division
duplexing (FDD). The DL training and uplink (UL) feedback might become
infeasible due to insufficient coherence time interval when the channel rapidly
changes due to high speed of MSs. Without the feedback from MSs, it may be
possible for the BS to directly obtain the DL CSI using the inherent relation
of UL and DL channels even in FDD, which is called DL extrapolation. Although
the exact relation would be highly nonlinear, previous studies have shown that
a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the
BS. Most of previous works on this line of research trained the NN using full
dimensional UL and DL channels; however, the NN training complexity becomes
severe as the number of antennas at the BS increases. To reduce the training
complexity and improve DL CSI estimation quality, this paper proposes a novel
DL extrapolation technique using simplified input and output of the NN. It is
shown through many measurement campaigns that the UL and DL channels still
share common components like path delays and angles in FDD. The proposed
technique first extracts these common coefficients from the UL and DL channels
and trains the NN only using the path gains, which depend on frequency bands,
with reduced dimension compared to the full UL and DL channels. Extensive
simulation results show that the proposed technique outperforms the
conventional approach, which relies on the full UL and DL channels to train the
NN, regardless of the speed of MSs.Comment: accepted for IEEE Acces
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