Wavelet based image compression integrating error protection via arithmetic coding with forbidden symbol and map metric sequential decoding with ARQ retransmission

The phenomenal growth of digital multimedia applications has forced the communication

Evaluation of Interference-Cancellation Based MAC Protocols for Vehicular Communications

Vehicular communications form an important part of future intelligent transport systems. Wireless connectivity between vehicles can enhance safety in vehicular networks and enable new services such as adaptive traffic control, collision detection and avoidance. As several new algorithms are being developed for enhancing vehicle to vehicle wireless connectivity, it is important to validate the performance of these algorithms using reasonably accurate wireless channel models. Specifically, some recent developments in the medium access control (MAC) layer algorithms appear to have the potential to improve the performance of vehicle to vehicle communications; however, these algorithms have not been validated with realistic channel models encountered in vehicular communications. The aforementioned issues are addressed in this thesis and correspondingly, there are two main contributions - (i) A complete IEEE 802.11p based transceiver model has been simulated in MATLAB and its performance & reliability are tested using existing empirically-developed wireless channel models. (ii) A new MAC layer algorithm based on slotted ALOHA with successive interference cancellation(SIC) has been evaluated and tested by taking into consideration the performance of underlying physical layer. The performance of slotted ALOHA-SIC and the already existing carrier sense multiple access with collision avoidance (CSMA/CA) scheme with respect to channel access delay and average packet loss ratio is also studied

Iterative receivers and multichannel equalisation for time division multiple access systems

The thesis introduces receiver algorithms improving the performance of TDMA mobile radio systems. Particularly, we consider receivers utilising side information, which can be obtained from the error control coding or by having a priori knowledge of interference sources. Iterative methods can be applied in the former case and interference suppression techniques in the latter. Convolutional coding adds redundant information into the signal and thereby protects messages transmitted over a radio channel. In the coded systems the receiver is usually comprised of separate channel estimation, detection and channel decoding tasks due to complexity restrictions. This suboptimal solution suffers from performance degradation compared to the optimal solution achieved by optimising the joint probability of information bits, transmitted symbols and channel impulse response. Conventional receiver utilises estimated channel state information in the detection and detected symbols in the channel decoding to finally obtain information bits. However, the channel decoder provides also extrinsic information on the bit probabilities, which is independent of the received information at the equaliser input. Therefore it is beneficial to re-perform channel estimation and detection using this new extrinsic information together with the original input signal. We apply iterative receiver techniques mainly to Enhanced General Packet Radio System (EGPRS) using GMSK modulation for iterative channel estimation and 8-PSK modulation for iterative detection scheme. Typical gain for iterative detection is around 2 dB and for iterative channel estimation around 1 dB. Furthermore, we suggest two iteration rounds as a reasonable complexity/performance trade-off. To obtain further complexity reduction we introduce the soft trellis decoding technique that reduces the decoder complexity significantly in the iterative schemes. Cochannel interference (CCI) originates from the nearby cells that are reusing the same transmission frequency. In this thesis we consider CCI suppression by joint detection (JD) technique, which detects simultaneously desired and interfering signals. Because of the complexity limitations we only consider JD for two binary modulated signals. Therefore it is important to find the dominant interfering signal (DI) to achieve the best performance. In the presence of one strong DI, the JD provides major improvement in the receiver performance. The JD requires joint channel estimation (JCE) for the two signals. However, the JCE makes the implementation of the JD more difficult, since it requires synchronised network and unique training sequences with low cross-correlation for the two signals.reviewe

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution 512 x 1024, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pre-trained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048. We show that the temporal layers trained in this way generalize to different fine-tuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://research.nvidia.com/labs/toronto-ai/VideoLDM/Comment: Conference on Computer Vision and Pattern Recognition (CVPR) 2023. Project page: https://research.nvidia.com/labs/toronto-ai/VideoLDM

The Telecommunications and Data Acquisition Report

Developments in programs managed by the Jet Propulsion Laboratory's Office of Telecommunications and Data acquisition are discussed. Space communications, radio antennas, the Deep Space Network, antenna design, Project SETI, seismology, coding, very large scale integration, downlinking, and demodulation are among the topics covered

Single antenna interference cancellation in asynchronous GSM/GPRS networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 73-74).In this project, we have proposed a decorrelator-based single antenna interference cancellation algorithm for the asynchronous GSM/GPRS network. The algorithm is tested according to the current SAIC/DARP performance requirement in the computer simulation, and is shown to give various gains in different test scenarios.by Chung Chan.M.Eng