78 research outputs found
Circular Convolution Filter Bank Multicarrier (FBMC) System with Index Modulation
Orthogonal frequency division multiplexing with
index modulation (OFDM-IM), which uses the subcarrier indices
as a source of information, has attracted considerable interest
recently. Motivated by the index modulation (IM) concept, we
build a circular convolution filter bank multicarrier with index
modulation (C-FBMC-IM) system in this paper. The advantages
of the C-FBMC-IM system are investigated by comparing the
interference power with the conventional C-FBMC system. As
some subcarriers carry nothing but zeros, the minimum mean
square error (MMSE) equalization bias power will be smaller
comparing to the conventional C-FBMC system. As a result,
our C-FBMC-IM system outperforms the conventional C-FBMC
system. The simulation results demonstrate that both BER and
spectral efficiency improvement can be achieved when we apply
IM into the C-FBMC system
Robust MMSE Precoding Strategy for Multiuser MIMO Relay Systems with Switched Relaying and Side Information
In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201
Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation
For many practical applications in wireless communications, we need to
recover a structured sparse signal from a linear observation model with dynamic
grid parameters in the sensing matrix. Conventional expectation maximization
(EM)-based compressed sensing (CS) methods, such as turbo compressed sensing
(Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have
double-loop iterations, where the inner loop (E-step) obtains a Bayesian
estimation of sparse signals and the outer loop (M-step) obtains a point
estimation of dynamic grid parameters. This leads to a slow convergence rate.
Furthermore, each iteration of the E-step involves a complicated matrix inverse
in general. To overcome these drawbacks, we first propose a successive linear
approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of
both sparse signals and dynamic grid parameters. Besides, we simplify the
matrix inverse operation based on the majorization-minimization (MM)
algorithmic framework. In addition, we extend our proposed algorithm from an
independent sparse prior to more complicated structured sparse priors, which
can exploit structured sparsity in specific applications to further enhance the
performance. Finally, we apply our proposed algorithm to solve two practical
application problems in wireless communications and verify that the proposed
algorithm can achieve faster convergence, lower complexity, and better
performance compared to the state-of-the-art EM-based methods.Comment: 13 pages, 17 figures, submitted to IEEE Transactions on Wireless
Communication
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field
This paper considers an integrated sensing and communication system, where
some radar targets also serve as communication scatterers. A location domain
channel modeling method is proposed based on the position of targets and
scatterers in the scattering environment, and the resulting radar and
communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We
propose a joint scattering environment sensing and channel estimation scheme to
enhance the target/scatterer localization and channel estimation performance
simultaneously, where a spatially non-stationary Markov random field (MRF)
model is proposed to capture the 2-D joint burst sparsity. An expectation
maximization (EM) based method is designed to solve the joint estimation
problem, where the E-step obtains the Bayesian estimation of the radar and
communication channels and the M-step automatically learns the dynamic position
grid and prior parameters in the MRF. However, the existing sparse Bayesian
inference methods used in the E-step involve a high-complexity matrix inverse
per iteration. Moreover, due to the complicated non-stationary MRF prior, the
complexity of M-step is exponentially large. To address these difficulties, we
propose an inverse-free variational Bayesian inference algorithm for the E-step
and a low-complexity method based on pseudo-likelihood approximation for the
M-step. In the simulations, the proposed scheme can achieve a better
performance than the state-of-the-art method while reducing the computational
overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless
Communication
A Two-stage Multiband Radar Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference
Multiband fusion is an important technique for radar sensing, which jointly
utilizes measurements from multiple non-contiguous frequency bands to improve
the sensing performance. In the multi-band radar sensing signal model, there
are many local optimums in the associated likelihood function due to the
existence of high frequency component, which makes it difficult to obtain
high-accuracy parameter estimation. To cope with this challenge, we divide the
radar target parameter estimation into two stages equipped with different but
equivalent signal models, where the first-stage coarse estimation is used to
narrow down the search range for the next stage, and the second-stage refined
estimation is based on the Bayesian approach to avoid the convergence to a bad
local optimum of the likelihood function. Specifically, in the coarse
estimation stage, we employ a weighted root MUSIC algorithm to achieve initial
estimation. Then, we apply the block stochastic successive convex approximation
(SSCA) approach to derive a novel stochastic particle-based variational
Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar
target parameters in the refined stage. Unlike the conventional particle-based
VBI (PVBI) in which only the probability of each particle is optimized and the
per-iteration computational complexity increases exponentially with the number
of particles, the proposed SPVBI optimizes both the position and probability of
each particle, and it adopts the block SSCA to significantly improve the
sampling efficiency by averaging over iterations. As such, it is shown that the
proposed SPVBI can achieve a better performance than the conventional PVBI with
a much smaller number of particles and per-iteration complexity. Finally,
extensive simulations verify the advantage of the proposed algorithm over
various baseline algorithms
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