Cooperative systems based signal processing techniques with applications to three-dimensional video transmission

Three-dimensional (3-D) video has recently emerged to offer an immersive multimedia experience that can not be offered by two-dimensional (2-D) video applications. Currently, both industry and academia are focused on delivering 3-D video services to wireless communication systems. Modern video communication systems currently adopt cooperative communication and orthogonal frequency division multiplexing (OFDM) as they are an attractive solution to combat fading in wireless communication systems and achieve high data-rates. However, this strong motivation to transmit the video signals over wireless systems faces many challenges. These are mainly channel bandwidth limitations, variations of signal-to-noise ratio (SNR) in wireless channels, and the impairments in the physical layer such as time varying phase noise (PHN), and carrier frequency offset (CFO). In response to these challenges, this thesis seeks to develop efficient 3-D video transmission methods and signal processing algorithms that can overcome the effects of error-prone wireless channels and impairments in the physical layer. In the first part of the thesis, an efficient unequal error protection (UEP) scheme, called video packet partitioning, and a new 3-D video transceiver structure are proposed. The proposed video transceiver uses switching operations between various UEP schemes based on the packet partitioning to achieve a trade- off between system complexity and performance. Experimental results show that the proposed system achieves significantly high video quality at different SNRs with the lowest possible bandwidth and system complexity compared to direct transmission schemes. The second part of the thesis proposes a new approach to joint source-channel coding (JSCC) that simultaneously assigns source code rates, the number of high and low priority packets, and channel code rates for the application, network, and physical layers, respectively. The proposed JSCC algorithm takes into account the rate budget constraint and the available instantaneous SNR of the best relay selection in cooperative systems. Experimental results show that the proposed JSCC algorithm outperforms existing algorithms in terms of peak signal-to-noise ratio (PSNR). In the third part of the thesis, a computationally efficient training based approach for joint channel, CFO, and PHN estimation in OFDM systems is pro- posed. The proposed estimator is based on an expectation conditional maximization (ECM) algorithm. To compare the estimation accuracy of the proposed estimator, the hybrid Cram´er-Rao lower bound (HCRB) of hybrid parameters of interest is derived. Next, to detect the signal in the presence of PHN, an iterative receiver based on the extended Kalman filter (EKF) for joint data detection and PHN mitigation is proposed. It is demonstrated by numerical simulations that, compared to existing algorithms, the performance of the proposed ECM-based estimator in terms of the mean square error (MSE) is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high SNRs. Finally, this study extends the research on joint channel, PHN, and CFO estimation one step forward from OFDM systems to cooperative OFDM systems. An iterative algorithm based on the ECM in cooperative OFDM networks in the presence of unknown channel gains, PHNs and CFOs is applied. Moreover, the HCRB for the joint estimation problem in both decode-and-forward (DF) and amplify-and-forward (AF) relay systems is presented. An iterative algorithm based on the EKF for data detection and tracking the unknown time-varying PHN throughout the OFDM data packet is also used. For more efficient 3-D video transmission, the estimation algorithms and UEP schemes based packet portioning were combined to achieve a more robust video bit stream in the presence of PHNs. Applying this combination, simulation results demonstrate that promising bit-error-rate (BER) and PSNR performance can be achieved at the destination at different SNRs and PHN variance. The proposed schemes and algorithms offer solutions for existing problems in the techniques for applications to 3-D video transmission

Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission

Multi-node communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet-of-Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this paper, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Federated learning enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g. about 10 peak signal-to-noise ratio gain in the 3 dB channel condition.Comment: This paper has been accepted by IEEE Internet of Things Journa

Feature Allocation for Semantic Communication with Space-Time Importance Awareness

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration

Deep Joint Source-Channel Coding for Wireless Image Transmission

We propose a novel joint source and channel coding (JSCC) scheme for wireless image transmission that departs from the conventional use of explicit source and channel codes for compression and error correction, and directly maps the image pixel values to the complex-valued channel input signal. Our encoder-decoder pair form an autoencoder with a nontrainable layer in the middle, which represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms separation-based digital transmission at low signal-to-noise ratio (SNR) and low channel bandwidth regimes in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect” as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC can learn to communicate without explicit pilot signals or channel estimation, and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values

Efficient mobile video transmission based on a joint coding scheme

In this paper, we propose a joint coding design which uses the Symbol Forward Error Correction (S-FEC) at the application layer. The purpose of this work is on one hand to minimize the Packet Loss Rate (PLR) and, on the other hand to maximize the visual quality of video transmitted over a wireless network (WN).The scheme proposed is founded on a FEC adaptable with the semantics of the H.264/AVC video encoding.This mechanism relies upon a rate distortion algorithm, controlling the channel code rates under the global rate constraints given by the WN.Based on a data partitioning (DP) tool, both packet type and packet length are taken into account by the proposed optimization mechanism which leads to unequal error protection (UEP). The performance of the proposed JSCC unequal error control is illustrated over wireless network by performing simulations under different channel conditions. The simulation results are then compared with an equal error protection (EEP) scheme

Source-channel coding for robust image transmission and for dirty-paper coding

In this dissertation, we studied two seemingly uncorrelated, but conceptually related problems in terms of source-channel coding: 1) wireless image transmission and 2) Costa ("dirty-paper") code design. In the first part of the dissertation, we consider progressive image transmission over a wireless system employing space-time coded OFDM. The space-time coded OFDM system based on a newly built broadband MIMO fading model is theoretically evaluated by assuming perfect channel state information (CSI) at the receiver for coherent detection. Then an adaptive modulation scheme is proposed to pick the constellation size that offers the best reconstructed image quality for each average signal-to-noise ratio (SNR). A more practical scenario is also considered without the assumption of perfect CSI. We employ low-complexity decision-feedback decoding for differentially space- time coded OFDM systems to exploit transmitter diversity. For JSCC, we adopt a product channel code structure that is proven to provide powerful error protection and bursty error correction. To further improve the system performance, we also apply the powerful iterative (turbo) coding techniques and propose the iterative decoding of differentially space-time coded multiple descriptions of images. The second part of the dissertation deals with practical dirty-paper code designs. We first invoke an information-theoretical interpretation of algebraic binning and motivate the code design guidelines in terms of source-channel coding. Then two dirty-paper code designs are proposed. The first is a nested turbo construction based on soft-output trellis-coded quantization (SOTCQ) for source coding and turbo trellis- coded modulation (TTCM) for channel coding. A novel procedure is devised to balance the dimensionalities of the equivalent lattice codes corresponding to SOTCQ and TTCM. The second dirty-paper code design employs TCQ and IRA codes for near-capacity performance. This is done by synergistically combining TCQ with IRA codes so that they work together as well as they do individually. Our TCQ/IRA design approaches the dirty-paper capacity limit at the low rate regime (e.g., < 1:0 bit/sample), while our nested SOTCQ/TTCM scheme provides the best performs so far at medium-to-high rates (e.g., >= 1:0 bit/sample). Thus the two proposed practical code designs are complementary to each other

Semantic and effective communications

Shannon and Weaver categorized communications into three levels of problems: the technical problem, which tries to answer the question "how accurately can the symbols of communication be transmitted?"; the semantic problem, which asks the question "how precisely do the transmitted symbols convey the desired meaning?"; the effectiveness problem, which strives to answer the question "how effectively does the received meaning affect conduct in the desired way?". Traditionally, communication technologies mainly addressed the technical problem, ignoring the semantics or the effectiveness problems. Recently, there has been increasing interest to address the higher level semantic and effectiveness problems, with proposals ranging from semantic to goal oriented communications. In this thesis, we propose to formulate the semantic problem as a joint source-channel coding (JSCC) problem and the effectiveness problem as a multi-agent partially observable Markov decision process (MA-POMDP). As such, for the semantic problem, we propose DeepWiVe, the first-ever end-to-end JSCC video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. We also further show that it is possible to use predefined constellation designs as well as secure the physical layer communication against eavesdroppers for deep learning (DL) driven JSCC schemes, making such schemes much more viable for deployment in the real world. For the effectiveness problem, we propose a novel formulation by considering multiple agents communicating over a noisy channel in order to achieve better coordination and cooperation in a multi-agent reinforcement learning (MARL) framework. Specifically, we consider a MA-POMDP, in which the agents, in addition to interacting with the environment, can also communicate with each other over a noisy communication channel. The noisy communication channel is considered explicitly as part of the dynamics of the environment, and the message each agent sends is part of the action that the agent can take. As a result, the agents learn not only to collaborate with each other but also to communicate "effectively'' over a noisy channel. Moreover, we show that this framework generalizes both the semantic and technical problems. In both instances, we show that the resultant communication scheme is superior to one where the communication is considered separately from the underlying semantic or goal of the problem.Open Acces

Optimized Scalable Image and Video Transmission for MIMO Wireless Channels

In this chapter, we focus on proposing new strategies to efficiently transfer a compressed image/video content through wireless links using a multiple antenna technology. The proposed solutions can be considered as application layer physical layer (APP-PHY) cross layer design methods as they involve optimizing both application and physical layers. After a wide state-of-the-art study, we present two main solutions. The first focuses on using a new precoding algorithm that takes into account the image/video content structure when assigning transmission powers. We showed that its results are better than the existing conventional precoders. Second, a link adaptation process is integrated to efficiently assign coding parameters as a function of the channel state. Simulations over a realistic channel environment show that the link adaptation activates a dynamic process that results in a good image/video reconstruction quality even if the channel is varying. Finally, we incorporated soft decoding algorithms at the receiver side, and we showed that they could induce further improvements. In fact, almost 5 dB peak signal-to-noise ratio (PSNR) improvements are demonstrated in the case of transmission over a Rayleigh channel