17 research outputs found
Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels
This paper introduces a vision transformer (ViT)-based deep joint source and
channel coding (DeepJSCC) scheme for wireless image transmission over
multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We
consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and
closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the
classical separation-based benchmarks with robustness to channel estimation
errors and showcases remarkable flexibility in adapting to diverse channel
conditions and antenna numbers without requiring retraining. Specifically, by
harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently
learns feature mapping and power allocation strategies tailored to the unique
characteristics of the source image and prevailing channel conditions.
Extensive numerical experiments validate the significant improvements in
transmission quality achieved by DeepJSCC-MIMO for both open-loop and
closed-loop MIMO systems across a wide range of scenarios. Moreover,
DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel
estimation errors, and different antenna numbers, making it an appealing
solution for emerging semantic communication systems.Comment: arXiv admin note: text overlap with arXiv:2210.1534
Combined source-channel coding for a power and bandwidth constrained noisy channel
This thesis proposes a framework for combined source-channel coding under power and bandwidth constrained noisy channel. The framework is then applied to progressive image coding transmission using constant envelope M-ary Phase Shift Key (MPSK) signaling over an Additive White Gaussian Channel (AWGN) channel. First the framework for uncoded MPSK signaling is developed. Then, its extended to include coded modulation using Trellis Coded Modulation (TCM) for MPSK signaling. Simulation results show that coded MPSK signaling performs 3.1 to 5.2 dB better than uncoded MPSK signaling depending on the constellation size. Finally, an adaptive TCM system is presented for practical implementation of the proposed scheme, which outperforms uncoded MPSK system over all signal to noise ratio (Es/No) ranges for various MPSK modulation formats.
In the second part of this thesis, the performance of the scheme is investigated from the channel capacity point of view. Using powerful channel codes like Turbo and Low Density Parity Check (LDPC) codes, the combined source-channel coding scheme is shown to be within 1 dB of the performance limit with MPSK channel signaling
Deep Learning Enabled Semantic Communication Systems
In the past decades, communications primarily focus on how to accurately and effectively transmit symbols (measured by bits) from the transmitter to the receiver. Recently, various new applications appear, such as autonomous transportation, consumer robotics, environmental monitoring, and tele-health. The interconnection of these applications will generate a staggering amount of data in the order of zetta-bytes and require massive connectivity over limited spectrum resources but with lower latency, which poses critical challenges to conventional communication systems. Semantic communication has been proposed to overcome the challenges by extracting the meanings of data and filtering out the useless, irrelevant, and unessential information, which is expected to be robust to terrible channel environments and reduce the size of transmitted data. While semantic communications have been proposed decades ago, their applications to the wireless communication scenario remain limited. Deep learning (DL) based neural networks can effectively extract semantic information and can be optimized in an end-to-end (E2E) manner. The inborn characteristics of DL are suitable for semantic communications, which motivates us to exploit DL-enabled semantic communication. Inspired by the above, this thesis focus on exploring the semantic communication theory and designing semantic communication systems. First, a basic DL based semantic communication system, named DeepSC, is proposed for text transmission. In addition, DL based multi-user semantic communication systems are investigated for transmitting single-modal data and multimodal data, respectively, in which intelligent tasks are performed at the receiver directly. Moreover, a semantic communication system with a memory module, named Mem-DeepSC, is designed to support both memoryless and memory intelligent tasks. Finally, a lite distributed semantic communication system based on DL, named L-DeepSC, is proposed with low complexity, where the data transmission from the Internet-of-Things (IoT) devices to the cloud/edge works at the semantic level to improve transmission efficiency. The proposed various DeepSC systems can achieve less data transmission to reduce the transmission latency, lower complexity to fit capacity-constrained devices, higher robustness to multi-user interference and channel noise, and better performance to perform various intelligent tasks compared to the conventional communication systems
Compression before Fusion: Broadcast Semantic Communication System for Heterogeneous Tasks
Semantic communication has emerged as new paradigm shifts in 6G from the
conventional syntax-oriented communications. Recently, the wireless broadcast
technology has been introduced to support semantic communication system toward
higher communication efficiency. Nevertheless, existing broadcast semantic
communication systems target on general representation within one stage and
fail to balance the inference accuracy among users. In this paper, the
broadcast encoding process is decomposed into compression and fusion to
improves communication efficiency with adaptation to tasks and
channels.Particularly, we propose multiple task-channel-aware sub-encoders
(TCE) and a channel-aware feature fusion sub-encoder (CFE) towards compression
and fusion, respectively. In TCEs, multiple local-channel-aware attention
blocks are employed to extract and compress task-relevant information for each
user. In GFE, we introduce a global-channel-aware fine-tuning block to merge
these compressed task-relevant signals into a compact broadcast signal.
Notably, we retrieve the bottleneck in DeepBroadcast and leverage information
bottleneck theory to further optimize the parameter tuning of TCEs and CFE.We
substantiate our approach through experiments on a range of heterogeneous tasks
across various channels with additive white Gaussian noise (AWGN) channel,
Rayleigh fading channel, and Rician fading channel. Simulation results evidence
that the proposed DeepBroadcast outperforms the state-of-the-art methods
Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference
5G has expanded the traditional focus of wireless systems to embrace two new
connectivity types: ultra-reliable low latency and massive communication. The
technology context at the dawn of 6G is different from the past one for 5G,
primarily due to the growing intelligence at the communicating nodes. This has
driven the set of relevant communication problems beyond reliable transmission
towards semantic and pragmatic communication. This paper puts the evolution of
low-latency and massive communication towards 6G in the perspective of these
new developments. At first, semantic/pragmatic communication problems are
presented by drawing parallels to linguistics. We elaborate upon the relation
of semantic communication to the information-theoretic problems of
source/channel coding, while generalized real-time communication is put in the
context of cyber-physical systems and real-time inference. The evolution of
massive access towards massive closed-loop communication is elaborated upon,
enabling interactive communication, learning, and cooperation among wireless
sensors and actuators.Comment: Submitted for publication to IEEE BITS (revised version preprint
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure
Progressive transmission of medical images
A novel adaptive source-channel coding scheme for progressive transmission of medical images with a feedback system is therefore proposed in this dissertation. The overall design includes Discrete Wavelet Transform (DWT), Embedded Zerotree Wavelet (EZW) coding, Joint Source-Channel Coding (JSCC), prioritization of region of interest (RoI), variability of parity length based on feedback, and the corresponding hardware design utilising Simulink. The JSCC can achieve an efficient transmission by incorporating unequal error projection (UEP) and rate allocation. An algorithm is also developed to estimate the number of erroneous data in the receiver. The algorithm detects the address in which the number of symbols for each subblock is indicated, and reassigns an estimated correct data according to a decision making criterion, if error data is detected. The proposed system has been designed based on Simulink which can be used to generate netlist for portable devices. A new compression method called Compressive Sensing (CS) is also revisited in this work. CS exhibits many advantages in comparison with EZW based on our experimental results. DICOM JPEG2000 is an efficient coding standard for lossy or lossless multi-component image coding. However, it does not provide any mechanism for automatic RoI definition, and is more complex compared to our proposed scheme. The proposed system significantly reduces the transmission time, lowers computation cost, and maintains an error-free state in the RoI with regards to the above provided features. A MATLAB-based TCP/IP connection is established to demonstrate the efficacy of the proposed interactive and adaptive progressive transmission system. The proposed system is simulated for both binary and symmetric channel (BSC) and Rayleigh channel. The experimental results confirm the effectiveness of the design.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Efficient Distributed Detection for Wireless Sensor Networks
Negli ultimi anni si è assistito ad una crescita esponenziale delle tecnologie per la fabbricazione di micro dispositivi ed, in particolare, di sensori. Il costo di tali sensori si è ridotto, portando ad un crescente interesse in reti di sensori, ad esempio, per il monitoraggio ambientale. D'altro canto, l'utilizzo di reti di sensori nel campo militare ha una lunga storia. In tutti i casi, l'obiettivo di una rete di sensori è quello di identificare lo stato di un fenomeno di interesse attraverso l'azione collaborativo di più sensori. Un esempio di tale azione è la rivelazione distribuita. In questa tesi, viene studiato come incorporare le caratteristiche intrisìnseche del fenomeno sotto osservazione nella progettazione di algoritmi di rivelazione distribuita in reti di sensori.Recent years have witnessed an exponential growth of micro device manufacturing techniques and, in particular, of powerful sensor devices. The costs of these sensors have dropped, leading to an increasing interest on sensor networks for civilian applications, e.g., environmental monitoring. The use of sensor networks in the military field has, on the other hand, a long history. In all cases, the goal of a sensor network is to identify the status of a phenomenon of interest through a collaborative action of the sensors. An instance of this collaborative action is given by distributed detection. The increasing interest for sensor networks has, therefore, spurred a significant activity on the design of efficient distributed detection techniques. In this thesis, we investigate how the structural properties of the physical phenomenon under observation can be taken into account in designing distributed detection algorithms for sensor networks