44 research outputs found
Degrees of Freedom and Achievable Rate of Wide-Band Multi-cell Multiple Access Channels With No CSIT
This paper considers a -cell multiple access channel with inter-symbol
interference. The primary finding of this paper is that, without instantaneous
channel state information at the transmitters (CSIT), the sum
degrees-of-freedom (DoF) of the considered channel is
with when the number of users per cell is sufficiently large,
where is the ratio of the maximum channel-impulse-response (CIR) length
of desired links to that of interfering links in each cell. Our finding implies
that even without instantaneous CSIT, \textit{interference-free DoF per cell}
is achievable as approaches infinity with a sufficiently large number
of users per cell. This achievability is shown by a blind interference
management method that exploits the relativity in delay spreads between desired
and interfering links. In this method, all inter-cell-interference signals are
aligned to the same direction by using a discrete-Fourier-transform-based
precoding with cyclic prefix that only depends on the number of CIR taps. Using
this method, we also characterize the achievable sum rate of the considered
channel, in a closed-form expression.Comment: Submitted to IEEE Transactions on Communication
MIMO Detection under Hardware Impairments: Learning with Noisy Labels
This paper considers a data detection problem in multiple-input
multiple-output (MIMO) communication systems with hardware impairments. To
address challenges posed by nonlinear and unknown distortion in received
signals, two learning-based detection methods, referred to as model-driven and
data-driven, are presented. The model-driven method employs a generalized
Gaussian distortion model to approximate the conditional distribution of the
distorted received signal. By using the outputs of coarse data detection as
noisy training data, the model-driven method avoids the need for additional
training overhead beyond traditional pilot overhead for channel estimation. An
expectation-maximization algorithm is devised to accurately learn the
parameters of the distortion model from noisy training data. To resolve a model
mismatch problem in the model-driven method, the data-driven method employs a
deep neural network (DNN) for approximating a-posteriori probabilities for each
received signal. This method uses the outputs of the model-driven method as
noisy labels and therefore does not require extra training overhead. To avoid
the overfitting problem caused by noisy labels, a robust DNN training algorithm
is devised, which involves a warm-up period, sample selection, and loss
correction. Simulation results demonstrate that the two proposed methods
outperform existing solutions with the same overhead under various hardware
impairment scenarios
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
In this paper, we propose a novel joint source-channel coding (JSCC) approach
for channel-adaptive digital semantic communications. In semantic communication
systems with digital modulation and demodulation, end-to-end training and
robust design of JSCC encoder and decoder becomes challenging due to the
nonlinearity of modulation and demodulation processes, as well as diverse
channel conditions and modulation orders. To address this challenge, we first
develop a new demodulation method which assesses the uncertainty of the
demodulation output to improve the robustness of the digital semantic
communication system. We then devise a robust training strategy that
facilitates end-to-end training of the JSCC encoder and decoder, while
enhancing their robustness and flexibility. To this end, we model the
relationship between the encoder's output and decoder's input using binary
symmetric erasure channels and then sample the parameters of these channels
from diverse distributions. We also develop a channel-adaptive modulation
technique for an inference phase, in order to reduce the communication latency
while maintaining task performance. In this technique, we adaptively determine
modulation orders for the latent variables based on channel conditions. Using
simulations, we demonstrate the superior performance of the proposed JSCC
approach for both image classification and reconstruction tasks compared to
existing JSCC approaches
FedVQCS: Federated Learning via Vector Quantized Compressed Sensing
In this paper, a new communication-efficient federated learning (FL)
framework is proposed, inspired by vector quantized compressed sensing. The
basic strategy of the proposed framework is to compress the local model update
at each device by applying dimensionality reduction followed by vector
quantization. Subsequently, the global model update is reconstructed at a
parameter server (PS) by applying a sparse signal recovery algorithm to the
aggregation of the compressed local model updates. By harnessing the benefits
of both dimensionality reduction and vector quantization, the proposed
framework effectively reduces the communication overhead of local update
transmissions. Both the design of the vector quantizer and the key parameters
for the compression are optimized so as to minimize the reconstruction error of
the global model update under the constraint of wireless link capacity. By
considering the reconstruction error, the convergence rate of the proposed
framework is also analyzed for a smooth loss function. Simulation results on
the MNIST and CIFAR-10 datasets demonstrate that the proposed framework
provides more than a 2.5% increase in classification accuracy compared to
state-of-art FL frameworks when the communication overhead of the local model
update transmission is less than 0.1 bit per local model entry
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks
In this paper, a communication-efficient federated learning (FL) framework is
proposed for improving the convergence rate of FL under a limited uplink
capacity. The central idea of the proposed framework is to transmit the values
and positions of the top- entries of a local model update for uplink
transmission. A lossless encoding technique is considered for transmitting the
positions of these entries, while a linear transformation followed by the
Lloyd-Max scalar quantization is considered for transmitting their values. For
an accurate reconstruction of the top- values, a linear minimum mean squared
error method is developed based on the Bussgang decomposition. Moreover, an
error feedback strategy is introduced to compensate for both compression and
reconstruction errors. The convergence rate of the proposed framework is
analyzed for a non-convex loss function with consideration of the compression
and reconstruction errors. From the analytical result, the key parameters of
the proposed framework are optimized for maximizing the convergence rate for
the given capacity. Simulation results on the MNIST and CIFAR-10 datasets
demonstrate that the proposed framework outperforms state-of-the-art FL
frameworks in terms of classification accuracy under the limited uplink
capacity
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
This paper proposes a novel communication-efficient split learning (SL)
framework, named SplitFC, which reduces the communication overhead required for
transmitting intermediate feature and gradient vectors during the SL training
process. The key idea of SplitFC is to leverage different dispersion degrees
exhibited in the columns of the matrices. SplitFC incorporates two compression
strategies: (i) adaptive feature-wise dropout and (ii) adaptive feature-wise
quantization. In the first strategy, the intermediate feature vectors are
dropped with adaptive dropout probabilities determined based on the standard
deviation of these vectors. Then, by the chain rule, the intermediate gradient
vectors associated with the dropped feature vectors are also dropped. In the
second strategy, the non-dropped intermediate feature and gradient vectors are
quantized using adaptive quantization levels determined based on the ranges of
the vectors. To minimize the quantization error, the optimal quantization
levels of this strategy are derived in a closed-form expression. Simulation
results on the MNIST, CIFAR-10, and CelebA datasets demonstrate that SplitFC
provides more than a 5.6% increase in classification accuracy compared to
state-of-the-art SL frameworks, while they require 320 times less communication
overhead compared to the vanilla SL framework without compression
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Data-aided channel estimation is a promising solution to improve channel
estimation accuracy by exploiting data symbols as pilot signals for updating an
initial channel estimate. In this paper, we propose a semi-data-aided channel
estimator for multiple-input multiple-output communication systems. Our
strategy is to leverage reinforcement learning (RL) for selecting reliable
detected symbols among the symbols in the first part of transmitted data block.
This strategy facilitates an update of the channel estimate before the end of
data block transmission and therefore achieves a significant reduction in
communication latency compared to conventional data-aided channel estimation
approaches. Towards this end, we first define a Markov decision process (MDP)
which sequentially decides whether to use each detected symbol as an additional
pilot signal. We then develop an RL algorithm to efficiently find the best
policy of the MDP based on a Monte Carlo tree search approach. In this
algorithm, we exploit the a-posteriori probability for approximating both the
optimal future actions and the corresponding state transitions of the MDP and
derive a closed-form expression for the best policy. Simulation results
demonstrate that the proposed channel estimator effectively mitigates both
channel estimation error and detection performance loss caused by insufficient
pilot signals