430 research outputs found
Interference Suppression in WCDMA with Adaptive Thresholding based Decision Feedback Equaliser
WCDMA is considered as one of the 3G wireless standards by 3GPP. Capacity calculation shows that WCDMA systems have more capacity compared to any other multiple access technique such as time division multiple access (TDMA) or frequency division multiple access (FDMA). So it is widely used. Rake receivers are used for the detection of transmitted data in case of WCDMA communication systems due to its resistance to multipath fading. But rake receiver treat multiuser interference (MUI) as AWGN and have limitation in overcoming the effect of multiple access interference (MAI) when the SNR is high. A de-correlating matched filter has been used in this thesis, which eliminates and improves system performance. But the given receiver works well only in the noise free environment. A DFE, compared to linear equaliser, gives better performance at severe ISI condition. The only problem in this equalisation technique is to select the number of symbols that are to be fed back. This thesis gives an idea on multiple symbol selection, based on sparity where an adaptive thresholding algorithm is used that computes the number of symbols to feedback. Simulated results show a significant performance improvement for Regularised Rake receiver along with thresholding in terms of BER compared to a rake receiver, de-correlating rake receiver and regularised rake receiver. The performance of the receiver in different channels is also analysed
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING
In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited
High mobility in OFDM based wireless communication systems
Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Übertragungsschema in die meisten drahtlosen Systemen, die wir täglich verwenden, übernommen. Es bringt mehrere inhärente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der Bitübertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher Mobilität stark beeinträchtigt. In dieser Arbeit untersuchen wir die Auswirkungen der Mobilität auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschätzen und seine Auswirkungen am Empfänger zu kompensieren. Auf Compressed Sensing (CS) basierende Kanalschätzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal präzise, robust und rechnerisch effizient abzuschätzen. Darüber hinaus wird ein Cognitive Framework entwickelt und getestet, das die Mobilität im Kanal erkennt und ein optimales Schätzungsschema konfiguriert. Das Cognitive Framework gewährleistet ein rechnerisch optimales Kanalschätzungsschema für alle möglichen Kanalbedingungen. Wir zeigen außerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung für drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgeführt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher Mobilität in OFDM-basierten drahtlosen Kommunikationssystemen
OFDM demodulation in underwater time-reversed shortned channels
This work addresses the problem of OFDM transmission
in dispersive underwater channels where impulse responses
lasting tens of miliseconds cannot be reliably handled
by recently proposed methods due to limitations of channel
estimation algorithms. The proposed approach relies on passive
time reversal for multichannel combining of observed waveforms
at an array of sensors prior to OFDM processing, which produces
an equivalent channel with a shorter impulse response that
can be handled much more easily. A method for tracking
the narrowband residual phase variations of the channel after
Doppler preprocessing is proposed. This is a variation of an
existing technique that can improve the spectral efficiency of
OFDM by reducing the need for pilot symbols. This work also
examines techniques to handle sparse impulse responses and
proposes a channel estimation method where an l1 norm is
added to the standard least-squares cost function to transparently
induce sparseness in the vector of channel coefficients. Algorithms
are assessed using data collected during the UAB’07 experiment,
which was conducted in Trondheim fjord, Norway, in September
2007. Data were transmitted with bandwidths of 1.5 and 4.5 kHz,
and recorded at a range of about 800 m in a 16-hydrophone array.
Significant multipath was observed over a period of at least 30
ms.FC
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
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