Joumana Farah

A novel approach to achieve unequal error protection for video transmission over 3G wireless networks

Abstract In this paper, we present a novel unequal error protection technique that enhances the video transmission quality over wireless networks. The case of application considered is a UMTS/TDD transmission system for H263 compressed and turbo-coded video sequences. The overall redundancy added to the compressed stream is non-uniformly distributed between the succeeding video frames in order to minimize the mean distortion over the transmitted sequence. The repartition of the redundancy on the video stream is optimized using an analytical approach which aims to alleviate the error propagation along the sequence. Different puncturing patterns of the rate 1 3 turbo-coder were considered in our simulations. The results obtained here are compared to those with a classical equal error protection scheme. We demonstrate that the gain in the system performances can reach 1.5 dB (in terms of the mean peak signal-to-noise ratio) without any significant increase in the transmission rate or the receiver complexity.

Resource Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) for Future Radio Access

This paper investigates a new strategy for radio resource allocation applying a non-orthogonal multiple access (NOMA) scheme. It calls for the cohabitation of users in the power domain at the transmitter side and for successive interference canceller (SIC) at the receiver side. Taking into account multi-user scheduling, subband assignment and transmit power allocation, a hybrid NOMA scheme is introduced. Adaptive switching to orthogonal signaling (OS) is performed whenever the non-orthogonal cohabitation in the power domain does not improve the achieved data rate per subband. In addition, a new power allocation technique based on waterfilling is introduced to improve the total achieved system throughput. We show that the proposed strategy for resource allocation improves both the spectral efficiency and the cell-edge user throughput. It also proves to be robust in the case of communications in crowded areas.

Feedback Channel Suppression in Distributed Video Coding with Adaptive Rate Allocation and Quantization for Multiuser Applications

We present a novel rate allocation technique for distributed multiuser video coding systems without the need for a permanent feedback channel. Based on analytical calculations, the system unequally distributes the available bandwidth among the different users, taking into account the actual amount of movement in the transmitted video as well as the transmission conditions of each user. On one hand, the quantization parameters are dynamically tuned in order to optimize the decoding quality. On the other hand, a frame dropping mechanism allows the system to avoid unnecessary channel use, when the analytical estimations show that the successful decoding of a given frame is not possible because of very high motion or bad channel conditions. A significant gain in the system performance is noticed compared with the case of equal allocation of channel resources and constant quantization parameters.

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The targets accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process. The Kalman filter is used afterward to combine predictions of the targets positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison with well-known algorithms is also provided.

Fusion of Global and Local Motion Estimation for Distributed Video Coding

The quality of side information plays a key role in distributed video coding. In this paper, we propose a new approach that consists of combining global and local motion compensation at the decoder side. The parameters of the global motion are estimated at the encoder using scale invariant feature transform features. Those estimated parameters are sent to the decoder in order to generate a globally motion compensated side information. Conversely, a locally motion compensated side information is generated at the decoder based on motion-compensated temporal interpolation of neighboring reference frames. Moreover, an improved fusion of global and local side information during the decoding process is achieved using the partially decoded Wyner-Ziv frame and decoded reference frames. The proposed technique improves significantly the quality of the side information, especially for sequences containing high global motion. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.9 dB for a Group of Pictures (GOP) size of 2, and up to 4.65 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

New Adaptive Algorithms for GOP Size Control with Return Channel Suppression in Wyner-Ziv Video Coding

We present novel algorithms for adaptive GOP size control in distributed Wyner-Ziv video coding, where an H.264 video codec is used for intracoding of key frames. The proposed algorithms rely on theoretical calculations to estimate the bit rate necessary for the successful decoding of Wyner-Ziv frames without the need for a feedback channel, which makes the system suitable for broadcasting applications. Additionally, in regions where H.264 intracoding outperforms Wyner-Ziv coding, the system automatically switches to intracoding mode in order to improve the overall performance. Simulations results show a significant gain in the average PSNR that can reach 3 dB compared to pure H.264 intracoding, and 0.8 dB compared to fixed-GOP Wyner-Ziv coding.

Kernel-based localization using fingerprinting in wireless sensor networks

Indoor localization is an important issue in wireless sensor networks for a very large number of applications. Recently, localization techniques based on the received signal strength indicator (RSSI) have been widely used due to their simple and low cost implementation. In this paper, we propose an algorithm for localization in wireless sensor networks based on radio-location fingerprinting and kernel methods. The proposed method is compared to another well-known localization algorithm in the case of real data collected in an indoor environment where RSSI measures are affected by noise and other interferences.

Content adaptive gop size control with feedback channel suppression in distributed video coding

This paper presents a novel algorithm for content adaptive GOP size control in distributed video coding. The GOP size is dynamically varied along the sequence, depending on motion activity. Automatic mode selection allows the system to switch between H.264 intra-coding and Wyner-Ziv coding modes to optimize the overall performance. Furthermore, the encoder determines a suitable compression ratio for the Wyner-Ziv frames without the need for a feedback channel. Simulation results show significant improvement in the average system performance, compared to fixed GOP Wyner-Ziv and H.264 intra-coding.

Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks

This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) exchanged by these sensors in an indoor WSN. In this context, two models are determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated RSSI/distance models. The targets position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully comprehensive study of the choice of parameters of the proposed distance models and their performances is provided, as well as a study of the performance of the two proposed tracking methods. Comparisons with recently proposed methods are also provided.

New Optimal and Suboptimal Resource Allocation Techniques for Downlink Non-orthogonal Multiple Access

Abstract This paper investigates several new strategies for the allocation of radio resources (bandwidth and transmission power) using a non-orthogonal multiple access (NOMA) scheme with successive interference cancellation (SIC) in a cellular downlink system. In non-orthogonal access with SIC, the same subband is allocated to multiple users, which requires elaborate multiuser scheduling and subband assignment techniques, compared to orthogonal multiplexing. While taking into account various design issues, we propose and compare several optimum and suboptimum power allocation schemes. These are jointly implemented with multiple user scheduling strategies. Besides, a minimization of the total amount of used bandwidth is targeted. Also, to increase the total achieved system throughput, a hybrid orthogonal-non orthogonal scheme is introduced. This hybrid scheme enables a dynamic switching to orthogonal signaling whenever the non-orthogonal cohabitation in the power domain does not improve the achieved data rate per subband. Extensive simulation results show that the proposed strategies for resource allocation can improve both the spectral efficiency and the cell-edge user throughput, especially when compared to previous schemes employing either orthogonal signaling or NOMA with static inter-subband power allocation. They also prove to be robust in the context of crowded areas.