Adaptive wireless multimedia communication systems

Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DPJSCC) for image transmission, which aims at protecting private information and achieving superior image transmission performance. In particular, we propose a disentangled information bottleneck objective to compress the private information in public subcodewords and improve the reconstruction quality simultaneously. To optimize JSCC neural networks using the proposed objective, we derive a differentiable estimation based on variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective algorithm that encrypts the private subcodewords, achieving joint optimization with JSCC neural networks. The proposed algorithm involves an encryptor for encrypting private information and a decryptor for recovering it at the legitimate receiver. A loss function is derived based on the maximum entropy principle for jointly training the encryptor, decryptor, and JSCC decoder to maximize eavesdropping uncertainty and improve reconstruction quality. Experimental results show that DPJSCC reduces eavesdropping accuracy on private information by up to 18% and decreases inference time by 10%.

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.

Due to their computational complexity, iterative decoder components such as source and channel decoders are usually implemented using specialised dedicated hardware. This leads to the scenario where each different iterative decoder component of the receiver requires its own hardware. Due to their relatively high complexity, many capacity‐approaching techniques proposed in the literature have not yet been invoked in wireless sensor network applications, despite their potential benefits of facilitating a reduced transmission power or extended communication range. Against this background, the authors propose an energy‐efficient architecture comprised of multiple computation unit (CU), which is sufficiently flexible for accommodating different iterative decoder components using the same hardware. In this study, the flexible architecture is applied to Joint Source and Channel Coding (JSCC), comprising the Unary Error Correction (UEC) code, a turbo code and an iterative demodulator. The authors conceive a flexible technique for controlling the hardware, which supports a high hardware‐exploitation ratio for the CU, reaching a utilisation of 88%, compared with 68% achieved in similar solutions reported in the open literature.

Joint source and channel coding (JSCC) using combined trellis coded quantization (TCQ) and trellis coded continuous phase modulation (CPM) is studied. Optimal soft decoding based on the a posteriori probability (APP) algorithm for trellis coded CPM is developed for the considered systems. It has been demonstrated that the combined TCQ/CPM systems are both power and bandwidth efficient compared with the combined TCQ/TCM/8PSK systems. Furthermore, a TCQ design algorithm for the noisy channel is developed. The novelties of this work is the use of a soft decoder and the APP algorithm for combined TCQ/CPM systems.

An iterative decoding approach to joint source and channel coding (JSCC) using combined trellis-coded quantization (TCQ) and continuous phase modulation (CPM) is proposed. The channel is assumed to be the additive white Gaussian noise channel. This iterative procedure exploits the structure of the TCQ encoder and the continuous phase modulator. The performance in terms of the signal-to-distortion ratio (SDR) is compared with that of a combined TCQ/trellis-coded modulation (TCM) system. It is shown that the combined TCQ/CPM systems are both power- and bandwidth-efficient, compared with the combined TCQ/TCM system. For source encoding rate R=2 b/sample, it is observed that the combined TCQ/CPM systems with iterative decoding working at symbol level converge faster than the systems working at bit level. The novelty of this work is the use of a soft decoder and an iterative decoding algorithm for TCQ-based JSCC systems. The combined TCQ/CPM with iterative decoding is considered for the first time.

$3.5m US funding for eight new wireless projects.

Adaptive M-QAM systems with maximal-ratio diversity (MRD) over composite arbitrarily correlated Nakagami-m fading and log-normal shadowing channels are analyzed. The slow adaptive modulation (SAM) technique is considered which adapts the constellation signaling to slow channel variations and achieves good performance in terms of spectral efficiency (SE) and bit error outage (BEO) despite its low feedback rate. Non-ideal channel estimation (CE) based on pilot symbols is assumed. Analytical expressions and tight bounds for the bit error probability are obtained which are used for deriving analytical expressions and tight bounds for the BEO and achieved SE (ASE). Performance evaluation results have shown that fading correlation and CE errors degrade the performance of SAM systems. The partition of resources for CE and information data requires a careful definition of the ASE and results have shown the tradeoff between CE quality and ASE.

In semantic communication (SemCom), the gain of deep learning-based joint source and channel coding (JSCC) has been proved to partly come from the analog transmission of semantic features extracted directly from the source signal. While, the current mainstream digital communication system design results in the gains almost vanishing when bit-oriented modulation (e.g., QPSK) is employed. To tackle this problem, we propose a Semantic-Oriented Modulation (SOM) method that enables direct mapping from an analog value of a semantic feature to a sequence of discrete values without bit conversion. SOM employs a hierarchical design to mitigate discretization loss and optimizes resource allocation by exploiting the varying significance of semantic features, reducing the data volume by 29.17%. We provide an analysis that reveals the impact of modulation orders and hierarchical layers, guiding the SOM’s design. Simulations in four tasks show that SOM outperforms JSCC with bit-oriented modulation, and the implementation of the Software Defined Radio (SDR) platform confirms its compatibility with existing systems and its potential for latency reduction.

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 non-trainable 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.

This letter presents a novel generative semantic communications framework for wireless image transmission, designed to excel in dynamic channel conditions and at low signal-to-noise ratios (SNRs). The proposed framework, dubbed DiffMoECom (Diffusion Mixture-of-Experts Communications), leverages a Mixture-of-Experts (MoE) architecture integrated with prompt learning to dynamically select optimal channel adaptation strategies by activating the most relevant prompt experts. A Joint Source and Channel Coding (JSCC) backbone is employed to transmit the low-frequency components of the source image. Furthermore, we introduce a range-space inversion mechanism, which projects the coarse JSCC output into a noisy latent space. This facilitates faithful semantic reconstruction of high-frequency details through conditional diffusion-based null space compensation. Extensive experiments across various fading channels and SNR conditions demonstrate that our method consistently surpasses state-of-the-art DeepJSCC and generative approaches, achieving superior performance in distortion metrics with competitive inference efficiency.

In this letter, we present an efficient cross-entropy (CE)-based algorithm for the design of variable-length error-correcting (VLEC) codes under the joint source and channel coding (JSCC) framework. The algorithm enables us to construct the near-optimal VLEC codes that have the minimum average codeword length (ACL) with low search complexity. The efficiency of the proposed CE-based algorithm makes it possible to construct the VLEC codes that have small ACL values under various free-distance constraints, especially for large-sized signal alphabets.

In order to improve the error-resilient capability and transmission efficiency for image transmission over noisy channels, we contrive a joint source and channel coding (JSCC) scheme, in which adaptive segmentation and rate compatible punctured convolutional code (RCPC) are used. Specifically, adaptive segmentation segments an image into different sizes of blocks whilst determining different levels of source significance; RCPC code is then used to provide unequal error protection for individual blocks. Various simulations show that this scheme can support robust image transmission in a very effective way while yielding high visual quality.

We propose an adaptive multicarrier modulation technique, where the transmit rate and power are adapted in both time and frequency. We derive the optimal adaptation strategy and determine its performance. We also compare this performance with that of multicarrier systems that adapt only in the temporal or spectral domains. Our results indicate that as long as these domains have enough degrees of freedom, adaptive modulation across one domain performs almost the same as the more complex two dimensional adaptive policy. If however rate or power is restricted to be constant over the time and/or frequency domains, two dimensional adaption policy shows significantly better performances than adaptive modulation across one domain.

In this work, Automatic-Repeat-Request (ARQ) and Maximal Ratio Combination (MRC), have been jointly exploited to enhance the confidentiality of wireless services requested by a legitimate user (Bob) against an eavesdropper (Eve). The obtained security performance is analyzed using Packet Error Rate (PER), where the exact PER gap between Bob and Eve is determined. PER is proposed as a new practical security metric in cross layers (Physical/MAC) security design since it reflects the influence of upper layers mechanisms, and it can be linked with Quality of Service (QoS) requirements for various digital services such as voice and video. Exact PER formulas for both Eve and Bob in i.i.d Rayleigh fading channel are derived. The simulation and theoretical results show that the employment of ARQ mechanism and MRC on a signal level basis before demodulation can significantly enhance data security for certain services at specific SNRs. However, to increase and ensure the security of a specific service at any SNR, adaptive modulation is proposed to be used along with the aforementioned scheme. Analytical and simulation studies demonstrate orders of magnitude difference in PER performance between eavesdroppers and intended receivers.

By combining adaptive modulation (AM) and automatic repeat request (ARQ) protocol, cross-layer design (CLD) scheme of multiuser MIMO system with antenna selection (AS) is presented. By means of exact expression of packet error rate (PER), the switching thresholds (STs) of fading gain subject to a target PER constraint are derived, which is more accurate than the conventional STs based on approximate PER expression. According to these results, and using mathematical manipulation, we obtain the tightly closed-form expressions of the average spectral efficiency (SE) and average PER. Simulation results show that the multiuser MIMO system with CLD-AS scheme using the derived STs provides higher SE than that using existing approximate STs while meeting the PER requirement. Besides, simulations for average SE and PER are in good agreement with the derived theoretical expressions, which demonstrates the validity of the theoretical analysis.