492 research outputs found
Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation
New techniques in cross-layer wireless networks are building demand for
ubiquitous channel sounding, that is, the capability to measure channel impulse
response (CIR) with any standard wireless network and node. Towards that goal,
we present a software-defined IEEE 802.11b receiver and CIR estimation system
with little additional computational complexity compared to 802.11b reception
alone. The system implementation, using the universal software radio peripheral
(USRP) and GNU Radio, is described and compared to previous work. By overcoming
computational limitations and performing direct-sequence spread-spectrum
(DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive
CIR estimation. We validate the channel sounder and present a drive test
campaign which measures hundreds of channels between WiFi access points and an
in-vehicle receiver in urban and suburban areas
A CM based equalizer for space-time spreading over channels with inter-Symbol interference
Space-time block coding (STBC) and a number of derivative techniques have been developed to maximize the diversity gain of a multi-input multi-output (MIMO) channel. This was later generalized for multi-user DS-CDMA systems through space-time spreading (STS), [3] and [4], which assumed flat fading as well as the availability of full channel state information (CSI) at the receiver. This paper focuses on the development of a blind chip-rate method for the equalization of STS over channels with inter-symbol interference (ISI). Simulation results are presented to demonstrate the convergence and noise resilience of the derived algorithm
Blind Demixing for Low-Latency Communication
In the next generation wireless networks, lowlatency communication is
critical to support emerging diversified applications, e.g., Tactile Internet
and Virtual Reality. In this paper, a novel blind demixing approach is
developed to reduce the channel signaling overhead, thereby supporting
low-latency communication. Specifically, we develop a low-rank approach to
recover the original information only based on a single observed vector without
any channel estimation. Unfortunately, this problem turns out to be a highly
intractable non-convex optimization problem due to the multiple non-convex
rankone constraints. To address the unique challenges, the quotient manifold
geometry of product of complex asymmetric rankone matrices is exploited by
equivalently reformulating original complex asymmetric matrices to the
Hermitian positive semidefinite matrices. We further generalize the geometric
concepts of the complex product manifolds via element-wise extension of the
geometric concepts of the individual manifolds. A scalable Riemannian
trust-region algorithm is then developed to solve the blind demixing problem
efficiently with fast convergence rates and low iteration cost. Numerical
results will demonstrate the algorithmic advantages and admirable performance
of the proposed algorithm compared with the state-of-art methods.Comment: 14 pages, accepted by IEEE Transaction on Wireless Communicatio
Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings
We tackle the multi-party speech recovery problem through modeling the
acoustic of the reverberant chambers. Our approach exploits structured sparsity
models to perform room modeling and speech recovery. We propose a scheme for
characterizing the room acoustic from the unknown competing speech sources
relying on localization of the early images of the speakers by sparse
approximation of the spatial spectra of the virtual sources in a free-space
model. The images are then clustered exploiting the low-rank structure of the
spectro-temporal components belonging to each source. This enables us to
identify the early support of the room impulse response function and its unique
map to the room geometry. To further tackle the ambiguity of the reflection
ratios, we propose a novel formulation of the reverberation model and estimate
the absorption coefficients through a convex optimization exploiting joint
sparsity model formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated for separating
individual speech signals through either structured sparse recovery or inverse
filtering the acoustic channels. The experiments conducted on real data
recordings demonstrate the effectiveness of the proposed approach for
multi-party speech recovery and recognition.Comment: 31 page
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