Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech transmission which transmits only the semantic-relevant information over the channel for the speech recognition task, and a compact additional set of semantic-irrelevant information for the speech reconstruction task. We propose a novel end-to-end DL-based transceiver which extracts and encodes the semantic information from the input speech spectrums at the transmitter and outputs the corresponding transcriptions from the decoded semantic information at the receiver. In particular, we employ a soft alignment module and a redundancy removal module to extract only the text-related semantic features while dropping semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a semantic correction module to further correct the predicted transcription with semantic knowledge by leveraging a pretrained language model. For the speech to speech transmission, we further include a CTC alignment module that extracts a small number of additional semantic-irrelevant but speech-related information, such as duration, pitch, power and speaker identification of the speech for the better reconstruction of the original speech signals at the receiver. We also introduce a two-stage training scheme which speeds up the training of the proposed DL model. The simulation results confirm that our proposed method outperforms current methods in terms of the accuracy of the predicted text for the speech to text transmission and the quality of the recovered speech signals for the speech to speech transmission, and significantly improves transmission efficiency. More specifically, the proposed method only sends 16% of the amount of the transmitted symbols required by the existing methods while achieving about a 10% reduction in WER for the speech to text transmission. For the speech to speech transmission, it results in an even more remarkable improvement in terms of transmission efficiency with only 0.2% of the amount of the transmitted symbols required by the existing method while preserving the comparable quality of the reconstructed speech signals.

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In this paper, we focus on intellicise wireless networks from semantic communication (SemCom). We present a comprehensive framework of intellicise wireless networks, including components such as brain for intellicise wireless networks (BIWN), intellicise signal processing, intellicise information transmission, intellicise network organization, and intellicise service bearing. We also investigate the enabling technologies and driving factors of intellicise wireless networks. Subsequently, we introduce the applications of intellicise wireless networks and envision new services. Finally, we outline the challenges of implementing intellicise wireless networks from a broad perspective and discuss possible solutions.

The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.

Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in improved communication efficiency. However, existing learning-based approaches still face limitations in defining semantic level loss and often struggle to find a good trade-off between preserving semantic information and preserving intricate details. In addition, the existing semantic communication approaches cannot effectively train semantic encoders and decoders without the support of downstream models. To address these limitations, this paper proposes a contrastive learning (CL)-based semantic communication system. First, inspired by practical observations, we introduce the concept of semantic contrastive loss and propose a semantic contrastive coding (SemCC) approach that treats data corruption during transmission as a form of data augmentation within the CL framework. Moreover, we propose a semantic re-encoding (SemRE) operation, which uses a duplicate of the semantic encoder deployed at the receiver to guide the entire training process when the downstream model is inaccessible. Further, we design the training procedure for SemCC and SemRE approaches, respectively, to balance the semantic information and intricate details. Finally, simulations are performed to demonstrate the superiority of the proposed approaches over competing approaches. In particular, our approaches achieve a significant accuracy improvement of up to 53% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/24, and also obtain comparable image reconstruction quality as the bandwidth compression ratio is improved.

Cache-aided coded content delivery is studied for devices with diverse quality-of-service (QoS) requirements, specified by a different average distortion target. The network consists of a server holding a database of independent contents, and users equipped with local caches of different capacities. User caches are filled by the server during a low traffic period without the knowledge of particular user demands. As opposed to the current literature, which assumes that the users request files in their entirety, it is assumed that the users in the system have distinct distortion requirements; and therefore, each user requests a single file from the database to be served at a different distortion level. Our goal in this work is to characterize the minimum delivery rate the server needs to transmit over an errorfree shared link to satisfy all possible demand combinations at the requested distortion levels, considering both centralized and decentralized cache placement. For centralized cache placement, the optimal delivery rate is characterized for the two-file two-user scenario for any pair of target distortion requirements, when the underlying source distribution is successively refinable. For the two-user scenario with more than two successively refinable files, the optimal scheme is characterized when the cache capacities of the users are the same and the number of files is a multiple of 3. For the general source distribution, not necessarily successively refinable, and with arbitrary number of users and files, a layered caching and delivery scheme is proposed, assuming that scalable source coding is employed at the server. This allows dividing the problem into two subproblems: the lossless caching of each layer with heterogeneous cache sizes, and cache allocation among layers. A delivery rate minimization problem is formulated and solved numerically for each layer; while two different schemes are proposed to allocate user caches among layers, namely, proportional cache allocation (PCA) and ordered cache allocation (OCA). A decentralized lossy coded caching scheme is also proposed, and its delivery rate performance is studied. Simulation results validate the effectiveness of the proposed schemes in both settings.