4 research outputs found
Over-the-air computation for cooperative wideband spectrum sensing and performance analysis
For sensor network aided cognitive radio, cooperative wideband spectrum sensing can distribute the sampling and computing pressure of spectrum sensing to multiple sensor nodes (SNs) in an efficient way. However, this may incur high latency due to distributed data aggregation, especially when the number of SNs is large. In this paper, we propose a novel cooperative wideband spectrum sensing scheme using over-the-air computation. Its key idea is to utilize the superposition property of wireless channel to implement the summation of Fourier transform. This avoids distributed data aggregation by computing the target function directly. The performance of the proposed scheme is analyzed with imperfect synchronization between different SNs. Furthermore, a synchronization phase offset (SPO) estimation and equalization method is proposed. The corresponding performance after equalization is also derived. A working prototype based on universal software radio periphera (USRP) and Monte Carlo simulation is built to verify the performance of the proposed scheme
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS)
transmission for enabling ultra-reliable low-latency communications (URLLC). To
guarantee excellent reliability performance, pragmatic precoder design is an
effective and indispensable solution. However, the design requires accurate
instantaneous channel state information at the transmitter (ICSIT) which is not
always available in practice. Motivated by this, we adopt a deep learning (DL)
approach to exploit implicit features from estimated historical delay-Doppler
domain channels (DDCs) to directly predict the precoder to be adopted in the
next time frame for minimizing the frame error rate (FER), that can further
improve the system reliability without the acquisition of ICSIT. To this end,
we first establish a predictive transmission protocol and formulate a general
problem for the precoder design where a closed-form theoretical FER expression
is derived serving as the objective function to characterize the system
reliability. Then, we propose a DL-based predictive precoder design framework
which exploits an unsupervised learning mechanism to improve the practicability
of the proposed scheme. As a realization of the proposed framework, we design a
DDCs-aware convolutional long short-term memory (CLSTM) network for the
precoder design, where both the convolutional neural network and LSTM modules
are adopted to facilitate the spatial-temporal feature extraction from the
estimated historical DDCs to further enhance the precoder performance.
Simulation results demonstrate that the proposed scheme facilitates a flexible
reliability-latency tradeoff and achieves an excellent FER performance that
approaches the lower bound obtained by a genie-aided benchmark requiring
perfect ICSI at both the transmitter and receiver.Comment: 31 pages, 12 figure
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
Beamforming design for intelligent reflecting surface (IRS)-assisted
multi-user communication (IRS-MUC) systems critically depends on the
acquisition of accurate channel state information (CSI). However, channel
estimation (CE) in IRS-MUC systems causes a large signaling overhead for
training due to the large number of IRS elements. In this paper, taking into
account user mobility, we adopt a deep learning (DL) approach to implicitly
learn the historical line-of-sight (LoS) channel features and predict the IRS
phase shifts to be adopted for the next time slot for maximization of the
weighted sum-rate (WSR) of the IRS-MUC system. With the proposed predictive
approach, we can avoid full-scale CSI estimation and facilitate low-dimensional
CE for transmit beamforming design such that the signaling overhead is reduced
by a scale of , where is the number of IRS elements. To this
end, we first develop a universal DL-based predictive beamforming (DLPB)
framework featuring a two-stage predictive-instantaneous beamforming mechanism.
As a realization of the developed framework, a location-aware convolutional
long short-term memory (CLSTM) graph neural network (GNN) is developed to
facilitate effective predictive beamforming at the IRS, where a CLSTM module is
first adopted to exploit the spatial and temporal features of the considered
channels and a GNN is then applied to empower the designed neural network with
high scalability and generalizability. Furthermore, in the second stage, based
on the predicted IRS phase shifts, an instantaneous CSI-aware fully-connected
neural network is designed to optimize the transmit beamforming at the access
point. Simulation results demonstrate that the proposed framework not only
achieves a better WSR performance and requires a lower CE overhead compared
with state-of-the-art benchmarks, but also is highly scalable in the numbers of
users.Comment: 30 pages, 14 figures, journal pape