The rapid growth in mobile video services has significantly increased network traffic, posing new challenges for efficient utilization of limited network resources. In this paper, we propose an energy-efficient framework for cross-domain resource coordination tailored specifically to scalable video coding (SVC) multicast transmission. Our work jointly considers both optical resources on the wired side and wireless resources on the wireless side, where we introduce advanced techniques such as multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) to boost spectral efficiency and better accommodate high data-rate demands. By leveraging the flexible wavelength allocation capability and energy-saving advantages of time and wavelength division multiplexed passive optical network (TWDM-PON), we formulate a unified optimization problem based on a utility function that balances user quality of experience (QoE) and overall system power consumption. The optimization problem involves selecting suitable SVC enhancement layers for users, assigning wireless resources, and allocating optical wavelengths. To simplify this complex process, we divide the original problem into two subproblems: user grouping and resource allocation. The subproblem is converted into a convex form using the convex-concave procedure (CCP) and solved iteratively. Our proposed solution effectively coordinates optical and wireless resources, addressing repeated requests for identical video content alongside QoE requirements. Simulation results demonstrate that our method achieves improved resource utilization and a better balance between QoE and power efficiency compared to existing approaches.

In this work, we propose a novel integrated sensing and communication (ISAC) framework for connected and autonomous vehicles (CAVs), which incorporates digital semantic communication (SemCom) to achieve both reliable communication and accurate sensing. Within this framework, the transmitting vehicle extracts semantic symbols from the source data and transmits them over orthogonal frequency division multiplexing (OFDM) sub-carriers, while simultaneously utilizing echo signals for radar-based environmental sensing. To achieve reliable SemCom, the transmitter must jointly optimize the quantization bitwidth for semantic symbols, the modulation order, the power allocation across semantic symbol dimensions, and the transmit beamforming strategy. These optimizations must also consider sensing performance, leading to a tradeoff between radar sensing and task-oriented SemCom. To address this joint optimization problem, we decompose it into three subproblems and develop corresponding solutions: 1) a hierarchical constrained proximal policy optimization (H-CPPO) algorithm to determine the quantization bitwidth, modulation order, and power allocation under frequency-flat channels, 2) a joint beamforming strategy to optimize the dual-function radar-SemCom transmit beamforming vector, and 3) a semantic importance-based signal-to-noise ratio (SNR) matching strategy that effectively adapts the optimal power allocation obtained under frequency-flat conditions to fading channels with random gains. Simulation results on a road image segmentation task show that, the proposed SemCom scheme achieves near-optimal segmentation accuracy while reducing radar beamforming error by up to 82% compared to the conventional digital system using the same quadrature phase shift keying (QPSK) modulation.

In this paper, a novel covert semantic communication framework is investigated. Within this framework, a server extracts and transmits the semantic information, i.e., the meaning of image data, to a user over several time slots. An attacker seeks to detect and eavesdrop the semantic transmission to acquire details of the original image. To avoid data meaning being eavesdropped by an attacker, a friendly jammer is deployed to transmit jamming signals to interfere the attacker so as to hide the transmitted semantic information. Meanwhile, the server will strategically select time slots for semantic information transmission. Due to limited energy, the jammer will not communicate with the server and hence the server does not know the transmit power of the jammer. Therefore, the server must jointly optimize the semantic information transmitted at each time slot and the corresponding transmit power to maximize the privacy and the semantic information transmission quality of the user. To solve this problem, we propose a prioritised sampling assisted twin delayed deep deterministic policy gradient algorithm to jointly determine the transmitted semantic information and the transmit power per time slot without the communications between the server and the jammer. Compared to standard reinforcement learning methods, the proposed method uses an additional Q network to estimate Q values such that the agent can select the action with a lower Q value from the two Q networks thus avoiding local optimal action selection and estimation bias of Q values. Simulation results show that the proposed algorithm can improve the privacy and the semantic information transmission quality by up to 77.8% and 14.3% compared to the traditional reinforcement learning methods.

This paper proposes a novel integrated sensing and communication (ISAC) framework incorporating digital semantic communication (SemCom) to resolve the tradeoff between sensing and communication performance. In particular, to accomplish task-oriented semCom, the base station (BS) extracts semantic symbols and transmits each dimension over different orthogonal frequency division multiplex (OFDM) subcarriers. To achieve sensing objective, the BS broadcasts OFDM signals and receives echoes via a uniform linear array (ULA) to estimate echo channel state information (CSI) and obtain target parameters. Given the varying task-related importance of each dimension, the framework allocates quantization bits, modulation order, and transmission power accordingly to meet SemCom requirements. On the other hand, sensing performance is evaluated using the Cramér-Rao Bound (CRB) of echo CSI, with transmission power allocation optimized to enhance sensing. The problem is formulated to minimize the sensing CRB while satisfying SemCom task loss, total resources, and transmission efficiency constraints. To solve this problem, we introduce a Hybrid Action Space Proximal Policy Optimization (H-PPO) algorithm, which can simultaneously determine the power allocated for each dimension from a continuous action space, and select a proper number of quantization bits and modulation order from discrete action spaces. Simulations show that the proposed method enhances SemCom task performance by up to 77% and reduces sensing error by up to 58% compared to conventional digital systems.

In this paper, a novel covert semantic communication framework is investigated for image transmission. Within this framework, a server extracts and transmits the semantic information, i.e., the meaning of image data, to a user. An attacker seeks to detect and eavesdrop the semantic transmission to acquire the details of the original image. To secure the semantic communications from such eavesdropping attack, a friendly jammer is deployed to transmit jamming signals so as to interfere the attacker. To evaluate the quality of the received and the eavesdropped semantic information, we introduce a semantic similarity metric called graph-to-nearest-triple (GNT). The privacy level of the system is quantified as the difference between the GNT of the received semantic information at the user and the attacker. The server and the jammer collaboratively manage their transmit power to maximize the privacy level of the semantic communication. To solve this non-convex power management problem, we propose a step-wise dense reward function guided recurrent Q learning algorithm to jointly optimize the transmit power at the server and friendly jammer with no inter-device communication, such that the considered problem is solved in a spectrally, computationally and space compact way. Simulation results show that the proposed method can improve privacy and the semantic transmission quality by up to 17.2% improvement compared to the traditional RL based solutions.

Low-earth orbit (LEO) satellite networks has attracted a lot of attention, since it is able to provide high-quality services worldwide. With the assistance of NFV technology, the flexibility and the quality of service (QoS) of LEO satellite networks can be further improved. However, most existing researches have not considered virtual network functions (VNFs) reusing during the process of service function chain (SFC) placement in NFV-enabled satellite networks. In this paper, we investigate the problem of SFC placement, and propose a joint VNF reusing and routing algorithm. By considering both of the initialization delay and sharing of VNFs, the service deployment delay can be effectively reduced, and the network profit can be largely improved. In our setting, simulation results show that our proposed algorithm outperforms baseline algorithms in profit, with an improvement of at least 23 %.

Semantic communication systems enhance communication efficiency and task performance by understanding the semantics of transmitted information, offering broad application prospects. Nevertheless, existing semantic communication systems struggle to handle information with complex relational semantics. Motivated by this limitation, this paper proposes the n-ary relation semantic communication system with correction and reasoning (NRSC-CR). In NRSC-CR, semantic information is extracted from transmitted texts into n-ary relations. To enhance robustness against wireless channel influence, a correction algorithm based on the rationality of primary triplets and auxiliary information pairs (CPA) is introduced at the receiver. Furthermore, by deeply learning the interactions within and between n-ary relations, a reasoning algorithm based on global and local dependency (RGL) is proposed to address the performance degradation caused by excessive influence under low signal-to-noise ratio (SNR) conditions. Extensive simulation results show that the proposed NRSC-CR has advantages in understanding complex relational semantics, channel robustness, and data transmission efficiency.

In this paper, a multi-modal data based semi-supervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained.

The cross-dataset problem in modulation recognition that may arise from some practical factors, such as unknown channel environment and the transmitter radio frequency characteristics, can severely reduce the recognition accuracy. To tackle this challenge, we propose a hybrid approach that leverages the cross-attention mechanism to combine the received signal’s IQ features, the statistical features, and the transform-domain features to improve recognition accuracy. Specifically, we first introduce multiple delay vectors in the cyclic cumulant (CC) and exploit their varying sensitivities to different channel environment and transmitter characteristics to improve robustness in cross-dataset scenarios. Furthermore, the proposed hybrid approach fuses in-phase and quadrature (IQ) features and time-frequency (TF) graph features with the improved CC features, where IQ features and TF graph features enhance the recognition accuracy, while CC features ensure the robustness in cross-dataset scenarios. In addition, the proposed hybrid approach introduces a flexible design that utilizes transfer learning pretraining on the large dataset which applies few-shot learning on the new dataset to adapt to varying data lengths while ensuring the recognition accuracy. Simulation results verify that the proposed solution achieves better recognition accuracy than baselines.

In this paper, a semantic communication framework where the transmitter and the receiver possess different knowledge (i.e., different methods to extract semantic information and regenerate source data) is investigated. In the proposed framework, the transmitter extracts semantic information according to its knowledge, and the receiver processes the received semantic information based on its own knowledge. To ensure the receiver can understand the semantic information as anticipated, the transmitter will ask a series of questions to estimate the receiver’s knowledge and adjust the method of semantic information extraction according to the estimation. Due to the limited wireless resources and communication time, the size of the extracted semantic information and the number of questions that the transmitter can ask are limited. This problem is formulated as an optimization problem whose goal is to maximize worse case answer similarities over semantic generation and question selection decisions. More specifically, the transmitter aims to ask the questions to pinpoint the largest knowledge divergence and adjusts its semantic generation method to minimize this divergence. An adversarial reinforcement learning (ARL) inspired algorithm, combined with a matching network, is designed to achieve such opposite goals by searching the optimal semantic information generation scheme and question selection scheme in an adversarial manner. Simulation results demonstrate that the proposed framework can improve the semantic similarity of answers by up to 5.5% gain and can achieve up to 10.7% gain in terms of the average similarity of texts compared to the algorithm without knowledge estimation.

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

In this paper, a semantic communication framework for a scenario where the transmitter and the receiver have different knowledge is proposed. In the proposed framework, the transmitter extracts semantic information from the data according to its knowledge and sends it to the receiver. The receiver requires to process the received semantic information based on its knowledge. Here, the knowledge implies a method which the transmitter or the receiver can use to process the semantic information. Since the transmitter or the receiver have different knowledge, they may have different understandings for the same information. To ensure the receiver can understand the semantic information, the transmitter requires to estimate the receiver's knowledge by asking a series of questions about the transmitted data. By evaluating the difference between the answers of the receiver and the answers of the transmitter, the transmitter can adjust the method of semantic information extraction. Since the size of the extracted semantic information and the number of the questions that the transmitter can ask are limited, the transmitter must adjust the method of semantic information extraction and select appropriate questions to transmit. This problem is formulated as an optimization problem whose goal is to maximize the similarity of answers of the transmitter and the receiver by determining semantic information while minimizing the semantic similarity of answers by determining the questions to be transmitted. To solve this problem, an adversarial reinforcement learning (ARL) algorithm is proposed. The proposed algorithm, which consists of a semantic information RL and a question RL, can find the optimal semantic information generation scheme and question selection scheme by a competitive game between two agents. Simulation results demonstrate that the proposed framework can improve the semantic similarity of answers by up to 3.7% gain and can achieve up to 15.2 % gain in terms of the average similarity of texts compared to the algorithm without the estimation of the knowledge of the receiver.

The utilization of millimeter-wave in vehicle-to-vehicle (V2V) communications can ensure high system capacity. However, in dense high-mobility environment, V2V communications will encounter severe resource collisions and require fre-quent beam training resulting in substantial signaling overhead. To address the above issues, we study the joint optimization problem of beam management and resource allocation in the millimeter-wave V2V communication system. Specifically, we propose a dual-layer deep reinforcement learning (DRL) archi-tecture that combines beam management and resource allocation into two interconnected tasks. Leveraging this dual-layer DRL architecture, we obtain a solution that involves interactive work between a communication module and an adaptive learning module. This approach is able to collect channel state information in real time and adapt to the ever-changing environment sufficiently. Simulation results show that the joint optimization scheme exhibits fast convergence, improves the effective achievable rate, and reduces the signaling overhead.

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms.

In this paper, a covert semantic communication framework is proposed for image transmission over wireless networks. In the proposed framework, devices extract and selectively transmit semantic information of image data to a base station (BS). The semantic information consists of the objects in the image and a set of attributes of each object. A warden selects a device to detect and eavesdrops the semantic information. To ensure the security of semantic communications, a jammer, acts as the defender, requires to find a vulnerable device and transmits jamming signals to the vulnerable device. The metric to measure the performance of the covert semantic communications is defined as the difference in the average accuracy of the BS and the warden answering a set of questions for each image. To maximize the performance of covert semantic communications, each device and the jammer must jointly optimize their transmit power, determine the vulnerable device to be protected, and determine the partial semantic information that each device needs to transmit. To solve this problem, we propose a multi-agent policy gradient (MAPG) algorithm. The proposed algorithm enables each device and the jammer to cooperatively discover the vulnerable devices as well as find the semantic information transmission and power control policies that maximize the performance of the covert semantic communication system. Simulation results show that the proposed algorithm can improve the communication performance by up to 14.5% compared to the independent reinforcement learning.

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.

In this paper, a semantic communication framework for image transmission is investigated. In the framework, a server transmits image data to a set of users utilizing semantic communication techniques, which enable the server to transmit only the semantic information that accurately captures the meaning of an image. To evaluate the performance of the studied semantic communication system, we propose a multimodal metric called image-to-graph semantic similarity (ISS). The significance of this new metric is that it can measure the correlation of the meaning between semantic information and the original image. To meet the ISS requirement of each user, the server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem whose goal is to minimize the average transmission latency while reaching the ISS requirement. To solve this problem, we propose a model-based actor critic deep reinforcement learning (DRL) algorithm. Compared to traditional actor critic DRL, in the proposed algorithm, we design a novel value function to improve the action exploration thus improving the probability of finding an optimal solution. Simulation results show that the proposed method can reduce the transmission delay by 16.4% and improves the convergence speed by up to 50% compared to the traditional actor critic DRL.

Direct communications among vehicles to exchange massive sensor data is an essential component to achieve fully autonomous driving, which can be enabled by Vehicle-to-Everything (V2X) sidelink communications in the mmWave frequency band. However, in a dynamic vehicular environment, frequent beam tracking for obtaining time-varying channel information and maintaining accurate beam alignment incurs high training overhead. To reduce the overhead, a multi-frequency coordination-based beam tracking (MFC-BT) scheme is proposed and formulated as a compressed sensing recovery problem. In the training phase, the optimal training beams are designed by minimizing the Cramér-Rao lower bound (CRLB) of channel spatial angle parameters estimation. The prior angle probability distribution is determined by the spatial congruence with sub-6 GHz channel as well as the temporal correlation of V2X channel. In the estimation phase, sub-6 GHz channel spatial information and previous angle information are extracted as weights to estimate the best beam pair. Simulation results show that compared to the conventional tracking method, the proposed scheme can reduce the training overhead by 48% to achieve a 38.9% effective rate improvement, which is significant at low SNR.

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution. Simulation results show that the proposed framework can reduce by 41.3% data that the BS needs to transmit and improve by two-fold the total MSS compared to a standard communication network without using semantic communication techniques.

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the average number of successfully served VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with large numbers of users, a low-complexity dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the average number of successfully served users as well as 81.2% and 87.5% gains in the convergence speed, respectively.

In this paper, a semantic communication framework is proposed for wireless networks. In the proposed framework, a base station (BS) extracts the semantic information from textual data, and, transmits it to each user. This semantic information is modeled by a knowledge graph (KG) and hence, the semantic information consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the studied semantic communication system, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS can only transmit partial semantic information to each user so as to satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user and determine partial semantic information to be transmitted. This problem is formulated as an optimization problem whose goal is to maximize the total MSS by optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm integrated with the attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Simulation results demonstrate that the proposed semantic communication framework can reduce the size of data that the BS needs to transmit by up to 46% and yield a two-fold improvement in the total MSS compared to a standard communication network that does not consider semantic communications.

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for VR users using VLC. Here, VR users move in real time and their movement patterns change over time according to their application. Both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and build THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the sum successful transmission probability of all VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm using meta-learning framework is proposed. The proposed algorithm can effectively solve the formulated problem and enable the trained policy to quickly adapt to new user movement patterns. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed meta-learning solution yields a 78% improvement in the convergence speed and about 16.4% improvement in the sum successful transmission probabilities of all VR users.

Information retrieval in the cloud is common and convenient. Nevertheless, privacy concerns should not be ignored as the cloud is not fully trustable. Fully Homomorphic Encryption (FHE) allows arbitrary operations to be performed on encrypted data, where the decryption of the result of ciphertext operation equals that of the corresponding plaintext operation. Thus, FHE schemes can be utilized for private information retrieval (PIR) on encrypted data. In the FHE scheme proposed by Ducas and Micciancio (DM), only a single homomorphic NOT AND (NAND) operation is allowed between consecutive ciphertext refreshings. Aiming at this problem, an improved FHE scheme is proposed for efficient PIR where homomorphic additions and multiplications are based on linear operations on ciphertext vectors. Theoretical analysis shows that when compared with the DM scheme, the proposed scheme allows multiple homomorphic additions and a single homomorphic multiplication to be performed. The number of allowed homomorphic additions is determined by the ratio of the ciphertext modulus to the upper bound of initial ciphertext noise. Moreover, simulation results show that the proposed scheme is significantly faster than the DM scheme in the homomorphic evaluation for a series of algorithms.

The cellular vehicle-to-everything (C-V2X) networks can support direct vehicle-to-vehicle (V2V) communications via sidelink/PC5 interface without cellular infrastructure support. The sensing-based semipersistent scheduling (SPS) scheme is to allow vehicles to autonomously reserve and select radio resources. However, the common sensing nature of the distributed SPS algorithm gives rise to that C-V2X distributed communications can be challenged by the resource selection collisions, especially in aperiodic traffic, which results in low reliability. In this article, we propose a short-term sensing-based resource selection (STS-RS) scheme to reduce packet collisions due to resource contention, where a short-term sensing duration is configured at the beginning of the resource unit right before resource selection, and whether the packet is ultimately transmitted on the selected resource depends on the sensing result. Furthermore, analysis models of the performance for the proposed STS-RS scheme and SPS scheme defined in C-V2X mode 4 are investigated. Finally, simulations and numerical results show that the STS-RS scheme significantly reduces the packet collisions and increases the C-V2X direct communication performance compared to the SPS scheme.

In the case where the private data is not equally important, personalized local privacy preservation based on randomized response (RR) is studied in the collection of sensitive data. So far, the existing RR mechanisms for multiple discrete private sources, which are termed as conventional randomized response (CRR) mechanisms, focus on a universal approach that exerts the same amount of privacy preservation for all sensitive values, without catering for their concrete privacy requirements. An immediate consequence is that they may be offering insufficient protection to a subset of data contributors with relatively higher privacy requirements, while applying excessive privacy control to another subset with relatively lower privacy requirements. Motivated by this, a novel perturbation framework, which is termed as personalized randomized response (PRR) mechanism, is proposed to achieve personalized privacy preservation (Personalized-PP) by designing the statistical privatization mechanism for multiple sensitive values. The proposed PRR technique introduces the weights for different sensitive values according to their sensitivity, and then introduces the weights into the decision of PRR by considering the concrete requirements for privacy, and thus, attains a higher data utility with respect to the quality of statistics while guaranteeing Personalized-PP. The estimate error of the private distribution is used to measure the quality of statistics for the two RR mechanisms. Theoretical study shows that the estimate error of PRR mechanism is smaller than that of the CRR mechanism for a certain same subjective privacy leakage degree. In particular, simulation results reveal the circumstances where CRR mechanism fails to provide Personalized-PP, and then establish the superiority of PRR mechanism.

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association.

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the longterm historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLCenabled UAVs.

In order to adapt to different vehicle densities, a path connectivity probability based cross-layer routing handoff mechanism is proposed under software defined vehicle ad hoc networks (SDVN). In this mechanism, we employ the derived path connectivity probability model to predict the density degree of a path, which will be used to decide whether to use base stations (BSs) assisted routing or V2V cross-layer routing. Particularly, in the process of performing V2V cross-layer routing, we utilize the information such as connectivity probability, transmission rate, and load to further improve the effectiveness of the proposed mechanism. Simulation results show that our proposed routing algorithm achieves significant gains in terms of delivery ratio and average delay, compared with several existing routing protocols.

The vehicle to everything communication in long term evolution (LTE V2X) supports vehicle-to-vehicle (V2V) communication directly via PC5 interface. In LTE V2X mode 4, vehicles autonomously select radio resources based on a distributed scheduling scheme. However, some researches show that this sensing based semi-persistent scheduling (SPS) scheme incurs low transmission reliability due to resource contention especially when the transmissions are aperiodic. Therefore, a cluster-based resource selection scheme is proposed to reduce the resource collision, where resources are divided into different resource sets and each cluster head selects its available resource set based on measurement. Simulation results show that, compared with LTE V2X mode 4, the proposed scheme has better performance for both periodic and aperiodic traffic.

Achieving both simplicity and efficiency in fully homomorphic encryption (FHE) schemes is important for practical applications. In the simple FHE scheme proposed by Ducas and Micciancio (DM), ciphertexts are refreshed after each homomorphic operation. And ciphertext refreshing has become a major bottleneck for the overall efficiency of the scheme. In this paper, we propose a more efficient FHE scheme with fewer ciphertext refreshings. Based on the DM scheme and another simple FHE scheme proposed by Gentry, Sahai, and Waters (GSW), ciphertext matrix operations and ciphertext vector additions are both applied in our scheme. Compared with the DM scheme, one more homomorphic NOT AND (NAND) operation can be performed on ciphertexts before ciphertext refreshing. Results show that, under the same security parameters, the computational cost of our scheme is obviously lower than that of GSW and DM schemes for a depth-2 binary circuit with NAND gates. And the error rate of our scheme is kept at a sufficiently low level.

Two novel heterogeneous LTE-Vehicle networks (LTE-V HetNets) are proposed to improve the transmission performance of beacon messages. LTE-V HetNets with uplink-downlink decoupling mechanism are the focus of the study. Software defined network (SDN) architecture is introduced into the LTE-V network to achieve centralized control. Under the SDN framework, a centralized algorithm based on the maximum utility function is proposed, which can achieve global load balancing by autonomously assigning the number of vehicles connected to each base station. Simulation results show the LTE-V HetNets achieve valuable gains compared to single-tier MBS in terms of average uplink rate, total number of the uploaded beacons and the downloaded beacons. In addition, SDN based LTE-V HetNets with decoupling uplink-downlink and load balancing approaches can further improve these performances when the number of vehicles is small.

Software defined network (SDN) attracts wide attentions from both research and industry fields because of its properties such as programmability, high flexibility, and controllability. However, the energy waste problem still exists in SDN as well as in the traditional networks. Fortunately, because of the centralized control, energy saving strategies will be easy to be deployed in SDN. Aiming at improving the energy efficient of SDN, a green scheme named GreSDN is designed in this paper. Different from the previous works, GreSDN advocates that minimizing the energy consumption of the network without changing the transmission paths of the traffic flow until they finished. To this end, we propose two heuristic routing algorithms that can be deployed in GreSDN. Simulation results show that the algorithms can improve energy efficiency of the network largely, and have a little impact on the network performance.

In this paper, we propose a cellular cognitive-radio vehicular ad hoc network (CCR-VANET) which consists of cellular network (primary network) and vehicular ad hoc network (secondary network). The two coexisting networks share the uplink spectrum of cellular network. The moving pattern of all vehicles is described as the classic Car-Following model. A cognitive carrier sense multiple access with collision avoidance (cognitive- CSMA) protocol with two-stage decision is investigated to opportunistically access the uplink spectrum. Based on this model and protocol, the transmission opportunity is derived by using stochastic geometry tools. Finally, simulation results show that, with cognitive-CSMA protocol and invariable transmission power of coexisting networks, the CCR-VANET transmission opportunity increases with the increasing of maximum received beacon power threshold, predefined carrier sensing threshold and the number of subchannels, while decreases with the increasing of the density of active primary transmitters.

For safety applications of Vehicular ad-hoc Network (VANET), many valuable broadcast protocols have been proposed nowadays, most of which are based either on sender or receiver. In fact, sender-based protocols would fall into invalidation owing to high mobility of vehicles, while receiver-based ones would generate extra delay. Combining both, this paper proposes a broadcast protocol with the minimum delay forwarding (BP-MDF) optimization for disseminating safety messages in city scenario. Based on the presented delay model, considering both of the static and dynamic routing attributes of urban road, BP-MDF firstly specifies the only one neighbor node as forwarder at sending end, and other receivers assist in forwarding if specified forwarder rebroadcasts unsuccessfully. Consequently, both reliability and timeliness performance of broadcast are satisfied. The simulation results show that the delay and dissemination efficiency of BP-MDF outperforms Slotted-1 protocol, accompanying with the achievement of more than 95% packet delivery ratio.

The IEEE 802.11p VANET has been studied by academia and industry for many years. In this paper the upper bound of transmission capacity in linear VANET is deduced and evaluated. The maximum of simultaneous transmitters within dedicated areas, which is widely accepted as the indicator of transmission capacity, is constrained by the CSMA/CA or EDCA mechanism in 802.11p. To estimate the upper bound, a linear road scenario is built and one determined uniform distribution of transmitting vehicles is proved to contain maximal number of simultaneous transmitters; then the upper bound of transmission capacity is proposed; finally simulation results show that the bound offers a good constraint for the transmission capacity in linear VANET.

We study broadcast transmission capacity with secrecy outage constraints in a one-dimension vehicular ad hoc network model. We develop our model on the basis of a highway scenario and extend both ends of the highway to infinity, and vehicles are assumed to follow a homogeneous Poisson point process. We divide the fixed total bandwidth into a large number of sub-bands. In intuition, the increasing number of sub-bands theoretically can support more parallel communications simultaneously. However, the broadcast transmission capacity is not always increased with the increasing number of sun- bands. Hence, we study the relationship between broadcast transmission capacity and the number of sub-bands, and then deduce the optimum number of sub-bands. After that, we also derive the equation of the broadcast transmission capacity over Nakagami fading channels and the transmission capacity with the secrecy outage constraint, respectively. Finally, numerical results verify that there is an optimal number of sub-bands in different scenarios.

ABSTRACT Half‐duplex amplify‐and‐forward (AF) transmissions may result in insufficient use of degrees of freedom if they always use the cooperative mode regardless of the fading states. In this paper, we investigate the conditions under which cooperation offers better performance and the corresponding optimal power allocation during cooperation. Specifically, we first derive an expression of ergodic capacity and its upper bound for an AF cooperative communication system with n relay nodes. Secondly, we propose a novel quasi‐optimal power allocation (QOPA) scheme to maximize the upper bound of the derived ergodic capacity. For the QOPA scheme, the cooperative mode is only adopted when the channel gain of source‐to‐destination is worse than that of relay‐to‐destination. Moreover, we analyze the performance of the system with QOPA scheme when the relay moves, which is based on the random direction model, in a single‐relay wireless network. For a multi‐relay AF network, we compare the ergodic capacity and symbol error rate, corresponding to the proposed QOPA and equal power allocation schemes, respectively. Extensive simulations were conducted to validate analytical results, showing that both ergodic capacity and symbol error rate of the system with QOPA scheme are better than those of the system with equal power allocation scheme in a multi‐relay AF network. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, a decode-and-forward (DF) cooperative diversity scheme is considered for dynamic relay networks, where each node is equipped with a single antenna. A randomized space-time block coding (STBC) cooperative relaying scheme has been proposed to get spatial diversity gains without centralized control information. However, it can not get maximum diversity gain when the number of active relay nodes is small. In this paper, we propose a limited feedback scheme to improve the bit error rate (BER) performance of the randomized STBC scheme. Simulation results reveal that the proposed feedback scheme can achieve almost the same BER performance as the centralized STBC scheme in some channel conditions. In addition, it does not degrade the performance of the randomized STBC scheme even with a large feedback delay.

Relay stations (RSs) are usually used to enhance the signal strength of the mobile stations (MSs) close to the cell boundary. However, the introduction of RSs to the cellular networks increases the interference to the MS served by the base station (BS) under spatial multiplexing mode. In this paper, we propose a method which can cancel the interference through a novel use of dirty paper coding (DPC). Then, under a practical spatial multiplexing frame structure, we evaluate the cellularrelay system performance under three scheduling algorithms, Round Robin (RR) algorithm, Maximum Signal to Interference and Noise Ratio (MaxSINR) algorithm and Proportional Fair (PF) algorithm. Simulation and analytical results show that the spectral efficiency (SE) of cellular-relay system can be dramatically increased by 30% by the proposed interference cancellation method, and RR algorithm achieves the highest gain, while the gain of MaxSINR algorithm is limited gain in our scheme.

In this paper, a cooperative spatial multiplexing scheme is considered for dynamic relay networks. We assume that each relay node is equipped with a single antenna. To obtain spatial multiplexing gains, the distributed multiple-input multiple-output (MIMO) scheme, in which cooperating nodes act as elements of a multi antenna system, has been proposed in many papers. Since the nodes need to know their specific stream and pilot index, either internode communication or a central control unit is required. Our design objective is to obtain spatial multiplexing gains while eliminating the need for stream and pilot allocation. We introduce a novel randomized strategy that decentralize the transmission of multi streams from a set of distributed relay nodes. We analyze the constraints on the random scheme, and propose a limited feedback scheme to further improve its performance.

In this comment, we give a detailed proof to get the power allocation solution for an amplify-and-forward cooperative communication system with n relay nodes, which is based on analyzing. Our conclusions show that the solution with "+" sign is optimal although the solution with "-" sign is also valid for equations (41) and (42) in.

Cognitive radio (CR) is a dynamic spectrum access technology as a solution to spectrum under-utilization problem in some licensed bands. Operating over an exceedingly wide spectrum, CR systems usually adopt multi-carrier modulation (MCM) to implement flexible channelization. Consequently, efficient channel allocation scheme becomes extremely important to an MCM based CR (MCM-CR) system. In this paper, a maximum likelihood detection model is developed to detect the presence and locations of licensed users (LUs) signals in the frequency domain. Performance of the detection model, including the optimal detection region, detection probability and false alarm probability, is analyzed. A one-order two-state Markovian chain model is proposed to predict channel status information. In particular, a novel subcarrier allocation scheme for MCM-CR systems is proposed, taking into account the confidence of channel estimation, quality of services (QoS) of rental users (RUs) and throughput. To validate the analytical results, simulations have been conducted to show effectiveness of the proposed scheme.

In this paper, we first discuss multicarrier modulation (MCM) based cognitive radio (CR) systems. A maximum likelihood ratio spectrum detection model is then presented to detect the occurrence and spectrum gap of licensed users (LUs) signals. Next, we theoretically study the proposed model by deducing an optimal decision region and the detection probability and false alarm probability. Simulation results validate the derived performances of the proposed model for MCM based CR systems.

Space-time codes are the joint design of channel coding, modulation, transmit and receive diversity to provide the best tradeoff between data rate, diversity advantage, and code complexity. Specific codes improve the transmission performance in wireless fading channel by providing both the diversity advantage and the coding advantage, where channel state information (CSI) is available at the receiver. But in practice, it is very difficult to estimate the CSI accurately because of the characteristic of wireless fading channel: multi-path fading and time-variance. Hence, a theoretical study of the symbol error rate (SEK) for orthogonal space-time block codes (OSTBC) is presented in this paper, in the absence of perfect CSI. It is proved that the squaring method used to simplify the decoding of OSTBC in [Li Xiangming et al., 2001] could also be used when there is no perfect CSI at receiver. Using this squaring method, a closed-form expression of both SNR and SER for OSTBC can also be derived under these conditions. Simulation results show that performance in the presence of the channel estimation errors is less than that with ideal condition, and that more estimation errors, means more performance loss. It is also proved through the simulations that the BER bound increases slowly when the variance of channel estimation errors is less than some little value, e.g. 0.2. So in practice, it is important to keep the variance of estimation errors less than this value.