1,155 research outputs found
Underlay Cognitive Radio with Full or Partial Channel Quality Information
Underlay cognitive radios (UCRs) allow a secondary user to enter a primary
user's spectrum through intelligent utilization of multiuser channel quality
information (CQI) and sharing of codebook. The aim of this work is to study
two-user Gaussian UCR systems by assuming the full or partial knowledge of
multiuser CQI. Key contribution of this work is motivated by the fact that the
full knowledge of multiuser CQI is not always available. We first establish a
location-aided UCR model where the secondary user is assumed to have partial
CQI about the secondary-transmitter to primary-receiver link as well as full
CQI about the other links. Then, new UCR approaches are proposed and carefully
analyzed in terms of the secondary user's achievable rate, denoted by ,
the capacity penalty to primary user, denoted by , and capacity
outage probability. Numerical examples are provided to visually compare the
performance of UCRs with full knowledge of multiuser CQI and the proposed
approaches with partial knowledge of multiuser CQI.Comment: 29 Pages, 8 figure
Exploiting hidden pilots for carrier frequency offset estimation for generalized MC-CDMA systems
This paper proposes a novel carrier frequency offset (CFO)
estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi-
lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator
An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
In this paper, an orthogonal stochastic gradient descent (O-SGD) based
learning approach is proposed to tackle the wireless channel over-training
problem inherent in artificial neural network (ANN)-assisted MIMO signal
detection. Our basic idea lies in the discovery and exploitation of the
training-sample orthogonality between the current training epoch and past
training epochs. Unlike the conventional SGD that updates the neural network
simply based upon current training samples, O-SGD discovers the correlation
between current training samples and historical training data, and then updates
the neural network with those uncorrelated components. The network updating
occurs only in those identified null subspaces. By such means, the neural
network can understand and memorize uncorrelated components between different
wireless channels, and thus is more robust to wireless channel variations. This
hypothesis is confirmed through our extensive computer simulations as well as
performance comparison with the conventional SGD approach.Comment: 6 pages, 4 figures, conferenc
Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection
Signal detection in large multiple-input multiple-output (large-MIMO) systems
presents greater challenges compared to conventional massive-MIMO for two
primary reasons. First, large-MIMO systems lack favorable propagation
conditions as they do not require a substantially greater number of service
antennas relative to user antennas. Second, the wireless channel may exhibit
spatial non-stationarity when an extremely large aperture array (ELAA) is
deployed in a large-MIMO system. In this paper, we propose a scalable iterative
large-MIMO detector named ANPID, which simultaneously delivers 1) close to
maximum-likelihood detection performance, 2) low computational-complexity
(i.e., square-order of transmit antennas), 3) fast convergence, and 4)
robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates
a damping demodulation step into stationary iterative (SI) methods and
alternates between two distinct demodulated SI methods. Simulation results
demonstrate that ANPID fulfills all the four features concurrently and
outperforms existing low-complexity MIMO detectors, especially in highly-loaded
large MIMO systems.Comment: Accepted by IEEE GLOBECOM 202
On Chernoff Lower-Bound of Outage Threshold for Non-Central -Distributed MIMO Beamforming Gain
The cumulative distribution function (CDF) of a non-central
-distributed random variable (RV) is often used when measuring the
outage probability of communication systems. For adaptive transmitters, it is
important but mathematically challenging to determine the outage threshold for
an extreme target outage probability (e.g., or less). This motivates
us to investigate lower bounds of the outage threshold, and it is found that
the one derived from the Chernoff inequality (named Cher-LB) is the most
{effective} lower bound. The Cher-LB is then employed to predict the
multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency
communication, concerning the first-order Markov time-varying channel. It is
exhibited that, with the proposed Cher-LB, pessimistic prediction of the
beamforming gain is made sufficiently accurate for guaranteed reliability as
well as the transmit-energy efficiency.Comment: 6 pages, 4 figures, published on GLOBECOM 202
Sherman-Morrison Regularization for ELAA Iterative Linear Precoding
The design of iterative linear precoding is recently challenged by extremely
large aperture array (ELAA) systems, where conventional preconditioning
techniques could hardly improve the channel condition. In this paper, it is
proposed to regularize the extreme singular values to improve the channel
condition by deducting a rank-one matrix from the Wishart matrix of the
channel. Our analysis proves the feasibility to reduce the largest singular
value or to increase multiple small singular values with a rank-one matrix when
the singular value decomposition of the channel is available. Knowing the
feasibility, we propose a low-complexity approach where an approximation of the
regularization matrix can be obtained based on the statistical property of the
channel. It is demonstrated, through simulation results, that the proposed
low-complexity approach significantly outperforms current preconditioning
techniques in terms of reduced iteration number for more than in both
ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the
channel is i.i.d. Rayleigh fading.Comment: 7 pages, 5 figures, IEEE ICC 202
Power Allocation for FDMA-URLLC Downlink with Random Channel Assignment
Concerning ultra-reliable low-latency communication (URLLC) for the downlink
operating in the frequency-division multiple-access with random channel
assignment, a lightweight power allocation approach is proposed to maximize the
number of URLLC users subject to transmit-power and individual user-reliability
constraints. Provided perfect channel-state-information at the transmitter
(CSIT), the proposed approach is proven to ensure maximized URLLC users.
Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC
users without compromising the individual user reliability by using a
pessimistic evaluation of the channel gain. It is demonstrated, through
numerical results, that the proposed approach can significantly improve the
user capacity and the transmit-power efficiency in Rayleigh fading channels.
With imperfect CSIT, the proposed approach can still provide remarkable user
capacity at limited cost of transmit-power efficiency.Comment: 6 pages, 6 figures, published on the conference of PIMRC 202
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