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Robust Continuous-Time Beam Tracking with Liquid Neural Network
May 01, 2024
Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Apr 30, 2024Pre-trained Language Models (PLMs) have shown excellent performance on various downstream tasks after fine-tuning. Nevertheless, the escalating concerns surrounding user privacy have posed significant challenges to centralized training reliant on extensive data collection. Federated learning, which only requires training on the clients and aggregates weights on the server without sharing data, has emerged as a solution. However, the substantial parameter size of PLMs places a significant burden on the computational resources of client devices, while also leading to costly communication expenses. Introducing Parameter-Efficient Fine-Tuning(PEFT) into federated learning can effectively address this problem. However, we observe that the non-IID data in federated learning leads to a gap in performance between the PEFT method and full parameter fine-tuning(FFT). To overcome this, we propose FeDeRA, an improvement over the Low-Rank Adaption(LoRA) method in federated learning. FeDeRA uses the same adapter module as LoRA. However, the difference lies in FeDeRA's initialization of the adapter module by performing Singular Value Decomposition (SVD) on the pre-trained matrix and selecting its principal components. We conducted extensive experiments, using RoBERTa and DeBERTaV3, on six datasets, comparing the methods including FFT and the other three different PEFT methods. FeDeRA outperforms all other PEFT methods and is comparable to or even surpasses the performance of FFT method. We also deployed federated learning on Jetson AGX Orin and compared the time required by different methods to achieve the target accuracy on specific tasks. Compared to FFT, FeDeRA reduces the training time by 95.9\%, 97.9\%, 96.9\% and 97.3\%, 96.5\%, 96.5\% respectively on three tasks using RoBERTa and DeBERTaV3. The overall experiments indicate that FeDeRA achieves good performance while also maintaining efficiency.
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
Apr 18, 2024Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels renders them vulnerable to eavesdropping, posing a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach, which embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Experiments also show that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach.
Resource Allocation for Green Probabilistic Semantic Communication with Rate Splitting
Mar 31, 2024In this paper, the energy efficient design for probabilistic semantic communication (PSC) system with rate splitting multiple access (RSMA) is investigated. Basic principles are first reviewed to show how the PSC system works to extract, compress and transmit the semantic information in a task-oriented transmission. Subsequently, the process of how multiuser semantic information can be represented, compressed and transmitted with RSMA is presented, during which the semantic compression ratio (SCR) is introduced to directly measure the computation overhead in a transmission task, and communication overhead is indirectly described as well. Hence, the problem of wireless resource allocation jointly considering the computation and communication consumption for the PSC system with RSMA is investigated. Both conventional wireless resource constraints and unique constraints on semantic communication are considered to maximize the energy efficiency (EE). Simulation results verify the effectiveness of the proposed scheme.
Evolving Semantic Communication with Generative Model
Mar 29, 2024
Recently, learning-based semantic communication (SemCom) has emerged as a promising approach in the upcoming 6G network and researchers have made remarkable efforts in this field. However, existing works have yet to fully explore the advantages of the evolving nature of learning-based systems, where knowledge accumulates during transmission have the potential to enhance system performance. In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency. The system features a novel channel-aware semantic encoder that utilizes a pre-trained Semantic StyleGAN to extract the channel-correlated latent variables consisting of serval semantic vectors from the input images, which can be directly transmitted over a noisy channel without further channel coding. Moreover, we introduce a semantic caching mechanism that dynamically stores the transmitted semantic vectors in the local caching memory of both the transmitter and receiver. The cached semantic vectors are then exploited to eliminate the need to transmit similar codes in subsequent transmission, thus further reducing communication overhead. Simulation results highlight the evolving performance of the proposed system in terms of transmission efficiency, achieving superior perceptual quality with an average bandwidth compression ratio (BCR) of 1/192 for a sequence of 100 testing images compared to DeepJSCC and Inverse JSCC with the same BCR. Code of this paper is available at \url{https://github.com/recusant7/GAN_SeCom}.
Probabilistic Semantic Communication over Wireless Networks with Rate Splitting
Mar 01, 2024
In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) system with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the large-sized data. The semantic communication is enabled by shared probability graphs between the BS and the users. The probability graph can be used to further compress the transmission data at the BS, while the received compressed semantic information can be recovered through using the same shared probability graph at each user side. The semantic information compression progress consumes additional computation power at the BS, which inevitably decreases the transmission power due to limited total power budget. Considering both the effect of semantic compression ratio and computation power, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method, and the semantic compression ratio subproblem is addressed using a greedy algorithm. Numerical results validate the effectiveness of the proposed scheme.
A Nearly Information Theoretically Secure Approach for Semantic Communications over Wiretap Channel
Jan 25, 2024This paper addresses the challenge of achieving information-theoretic security in semantic communication (SeCom) over a wiretap channel, where a legitimate receiver coexists with an eavesdropper experiencing a poorer channel condition. Despite previous efforts to secure SeCom against eavesdroppers, achieving information-theoretic security in such schemes remains an open issue. In this work, we propose a secure digital SeCom approach based on superposition codes, aiming to attain nearly information-theoretic security. Our proposed method involves associating semantic information with satellite constellation points within a double-layered constellation map, where cloud center constellation points are randomly selected. By carefully allocating power between these two layers of constellation, we ensure that the symbol error probability (SEP) of the eavesdropper decoding satellite constellation points is nearly equivalent to random guessing, while maintaining a low SEP for the legitimate receiver to successfully decode the semantic information. Simulation results showcase that the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) for the eavesdropper's reconstructed data, using our proposed method, can range from decoding Gaussian-distributed random noise to approaching the variance of the data. This validates the ability of our method to achieve nearly information-theoretic security, demonstrating superior data security compared to benchmark methods.
Point Cloud in the Air
Jan 01, 2024Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission.
Deep Learning Enabled Semantic Communication Systems for Video Transmission
Dec 08, 2023Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system's robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3\% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Nov 22, 2023Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is further developed to ensure fast and efficient distributed training. In addition, we propose a novel backend scheduler to allocate different attention heads to heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the compute resource utilization on each edge device. Our preliminary experimental results show that Confidant achieves at most 45.3% memory reduction and 8.03x inference speedup in practical settings.