2,649 research outputs found
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
Integrated Sensing and Communication: Joint Pilot and Transmission Design
This paper studies a communication-centric integrated sensing and
communication (ISAC) system, where a multi-antenna base station (BS)
simultaneously performs downlink communication and target detection. A novel
target detection and information transmission protocol is proposed, where the
BS executes the channel estimation and beamforming successively and meanwhile
jointly exploits the pilot sequences in the channel estimation stage and user
information in the transmission stage to assist target detection. We
investigate the joint design of pilot matrix, training duration, and transmit
beamforming to maximize the probability of target detection, subject to the
minimum achievable rate required by the user. However, designing the optimal
pilot matrix is rather challenging since there is no closed-form expression of
the detection probability with respect to the pilot matrix. To tackle this
difficulty, we resort to designing the pilot matrix based on the
information-theoretic criterion to maximize the mutual information (MI) between
the received observations and BS-target channel coefficients for target
detection. We first derive the optimal pilot matrix for both channel estimation
and target detection, and then propose an unified pilot matrix structure to
balance minimizing the channel estimation error (MSE) and maximizing MI. Based
on the proposed structure, a low-complexity successive refinement algorithm is
proposed. Simulation results demonstrate that the proposed pilot matrix
structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the
significant region improvement of our proposed design as compared to other
benchmark schemes. Furthermore, it is unveiled that as the communication
channel is more correlated, the Rate-MI region can be further enlarged.Comment: This papar answers the optimal space code-time design for supporting
ISA
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.Comment: Accepted at ICC 201
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