47,716 research outputs found
Self-Interference Cancellation Using Time-Domain Phase Noise Estimation in OFDM Full-Duplex Systems
In full-duplex systems, oscillator phase noise (PN) problem is considered the
bottleneck challenge that may face the self-interference cancellation (SIC)
stage especially when orthogonal frequency division multiplexing (OFDM)
transmission scheme is deployed. Phase noise degrades the SIC performance
significantly, if not mitigated before or during the SIC technique. The
presence of the oscillator phase noise has different impacts on the transmitted
data symbol like common phase error (CPE) and inter-carrier interference (ICI).
However, phase noise can be estimated and mitigated digitally in either time or
frequency domain. Through this work, we propose a novel and simple time domain
self-interference (SI) phase noise estimation and mitigation technique. The
proposed algorithm is inspired from Wiener filtering in time domain. Simulation
results show that the proposed algorithm has a superior performance than the
already-existing time-domain or frequency domain PN mitigation solutions with a
noticeable reduction in the computational complexity
Adaptive Nonlinear RF Cancellation for Improved Isolation in Simultaneous Transmit-Receive Systems
This paper proposes an active radio frequency (RF) cancellation solution to
suppress the transmitter (TX) passband leakage signal in radio transceivers
supporting simultaneous transmission and reception. The proposed technique is
based on creating an opposite-phase baseband equivalent replica of the TX
leakage signal in the transceiver digital front-end through adaptive nonlinear
filtering of the known transmit data, to facilitate highly accurate
cancellation under a nonlinear TX power amplifier (PA). The active RF
cancellation is then accomplished by employing an auxiliary transmitter chain,
to generate the actual RF cancellation signal, and combining it with the
received signal at the receiver (RX) low noise amplifier (LNA) input. A
closed-loop parameter learning approach, based on the decorrelation principle,
is also developed to efficiently estimate the coefficients of the nonlinear
cancellation filter in the presence of a nonlinear TX PA with memory, finite
passive isolation, and a nonlinear RX LNA. The performance of the proposed
cancellation technique is evaluated through comprehensive RF measurements
adopting commercial LTE-Advanced transceiver hardware components. The results
show that the proposed technique can provide an additional suppression of up to
54 dB for the TX passband leakage signal at the RX LNA input, even at
considerably high transmit power levels and with wide transmission bandwidths.
Such novel cancellation solution can therefore substantially improve the TX-RX
isolation, hence reducing the requirements on passive isolation and RF
component linearity, as well as increasing the efficiency and flexibility of
the RF spectrum use in the emerging 5G radio networks.Comment: accepted to IEE
Knowledge-Aided STAP Using Low Rank and Geometry Properties
This paper presents knowledge-aided space-time adaptive processing (KA-STAP)
algorithms that exploit the low-rank dominant clutter and the array geometry
properties (LRGP) for airborne radar applications. The core idea is to exploit
the fact that the clutter subspace is only determined by the space-time
steering vectors,
{red}{where the Gram-Schmidt orthogonalization approach is employed to
compute the clutter subspace. Specifically, for a side-looking uniformly spaced
linear array, the} algorithm firstly selects a group of linearly independent
space-time steering vectors using LRGP that can represent the clutter subspace.
By performing the Gram-Schmidt orthogonalization procedure, the orthogonal
bases of the clutter subspace are obtained, followed by two approaches to
compute the STAP filter weights. To overcome the performance degradation caused
by the non-ideal effects, a KA-STAP algorithm that combines the covariance
matrix taper (CMT) is proposed. For practical applications, a reduced-dimension
version of the proposed KA-STAP algorithm is also developed. The simulation
results illustrate the effectiveness of our proposed algorithms, and show that
the proposed algorithms converge rapidly and provide a SINR improvement over
existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic
Systems, 201
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
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