Machine learning based prediction in mobile networks is crucial for optimizing network operations. This paper presents a quantum-enhanced Tiny Time Mixer (QTTM), a novel hybrid model that integrates parameterized quantum circuits with quantum data re-uploading (QDR) into the classical TTM architecture, replacing its gated attention and prediction head modules. QDR enables QTTM to operate effectively on NISQ (Noisy Intermediate Scale Quantum) devices while maintaining enhanced representational power with reduced model size. Experimental results demonstrate that QTTM achieves performance parity with classical TTM in cellular traffic and user count prediction tasks, yet requires 23% fewer model parameters.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection is the premise of achieving the URLLC. In this paper, we propose a multi-view images assisted proactive BS selection scheme that can predict the optimal BS for the user in the next frame. The proposed scheme utilizes vision sensing and thus does not require the entire pilot resources, such that the latency caused by seeding and receiving pilots reduces. In addition, we design a multitask learning strategy and a prior knowledge based fine tuning method to ensure the accuracy and reliability of BS selection. Simulation results in an outdoor environment demonstrate the superior performance of the proposed scheme in terms of both the accuracy and the robustness.

Human-oriented image communication should take the quality of experience (QoE) as an optimization goal, which requires effective image perceptual quality metrics. However, traditional user-based assessment metrics are limited by the deviation caused by human high-level cognitive activities. To tackle this issue, in this paper, we construct a brain response-based image perceptual quality metric and develop a brain-inspired network to assess the image perceptual quality based on it. Our method aims to establish the relationship between image quality changes and underlying brain responses in image compression scenarios using the electroencephalography (EEG) approach. We first establish EEG datasets by collecting the corresponding EEG signals when subjects watch distorted images. Then, we design a measurement model to extract EEG features that reflect human perception to establish a new image perceptual quality metric: EEG perceptual score (EPS). To use this metric in practical scenarios, we embed the brain perception process into a prediction model to generate the EPS directly from the input images. Experimental results show that our proposed measurement model and prediction model can achieve better performance. The proposed brain response-based image perceptual quality metric can measure the human brain's perceptual state more accurately, thus performing a better assessment of image perceptual quality.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection and beam searching are the premises of achieving the URLLC. In this paper, we consider the mmWave communications systems where mobile users are served by the roadside unit (RSU). We propose a multi-camera view based proactive RSU selection and beam searching scheme that can predict the optimal RSU for the user in the next frame and search the corresponding beam pair. The proposed scheme utilizes vision sensing and reduces training resources. In addition, the visual information of multiple views makes the selection of the optimal RSU more accurate and reliable compared to the existing single view technologies. Simulation results in an outdoor environment show the superior performance of the proposed scheme in terms of predicting accuracy and achievable rate.

Scene graph generation (SGG) has been developed to detect objects and their relationships from the visual data and has attracted increasing attention in recent years. Existing works have focused on extracting object context for SGG. However, very few works have attempted to exploit implicit contextual correlations among relationships of the objects. Furthermore, most existing SGG schemes rely on high-level features to predict the predicates while overlooking the potential inherent association of low-level features with the object relationships. We present in this article a novel scheme to capture enhanced contextual information for both objects and relationships. We design a Dual-branch Context Analysis Transformer (DCAT) architecture to extract both object context and relationship context from the visual data with dual transformer branches and then effectively fuse both high-level and low-level features by an adaptive approach to facilitate relationship prediction. Specifically, we first conduct feature representation learning to enrich relation representations by the visual, spatial, and linguistic feature extractors. Next, two transformer branches are designed to leverage the modeling of global associative interaction and mine the hidden association among objects and relationships. Then, we devise a novel feature disentangling method to decouple contextualized high-level features with guidance from the visual semantics. Finally, we develop a refined attention module to perform low-level feature recalibration for the refinement of the final predicate prediction. Experiments on Visual Genome and Action Genome datasets demonstrate the effectiveness of DCAT for both image and video SGG settings. Moreover, we also test the quality of the generated image scene graphs to verify the generalizability on downstream tasks like sentence-to-graph retrieval and image retrieval.

Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.

Multimedia computational communications towards 6G can improve the transmission efficiency significantly by introducing intelligent computation in the communication process. This intelligent/smart communication architecture includes multimedia representation, coding, transmission and other parts from the perspective of semantics, where multimedia semantic representation is the core part and is mainly utilized to reduce the amount of multimedia data. In this paper, sketch graph is proposed as an effective representation of images to describe the pixel variations, geometric feature distribution and structural information and has potential applications in multimedia computational communications. Specifically, we developed a learning-based method to extract sketch graphs with edge detection, sketch point detection and sketch line detection by deep neural networks (DNNs). Moreover, we designed an end-to-end extraction method and achieved real-time processing. The experimental results on several datasets demonstrated the advanced performance in terms of classification and generation tasks. Image classification results on the HumanSketch, ImageNet, and Caltech datasets showed that sketch graphs extracted by our method had better describing ability than those extracted by traditional methods and other traditional image compression methods. On the other hand, image generation results on the Cityscapes dataset indicated the potential of the sketch-graph-based image compression codec.

Scene graph generation (SGG) is designed to represent images by objects and their relationships. Existing works mainly attempt to strengthen object pair representations for SGG. However, most methods ignore the significant semantic information implied in union regions, which refers to the surrounding area of object pairs. In this paper, we propose a new union message based architecture, named as USGG, to profoundly exploit the relational semantics of unions to facilitate SGG. Concretely, we employ sufficient feature extraction to enhance the features of objects and unions. Next, we devise the Union Embedding Network to model the relational representations through two symmetric encoder-decoder branches. Moreover, the Union Fusion Network is designed to integrate the refined semantics by two-stage feature fusion. Extensive experiments are conducted on Visual Genome dataset, which demonstrates that the proposed approach achieves competitive performance against state-of-the-art methods on Recall, mean Recall and Zero Shot Recall metrics.

In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aidednetwork architecturefor mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network. With images taken from street cameras and user’s identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy.

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.

mmWave communications systems usually rely on analog or hybrid analog/digital architectures and thus need a predefined codebook to perform beamforming. Traditional codebooks are designed for universal environments, although in practice a particular BS will only serve a particular environment. In this paper, we propose novel site-specific codebook design methods by utilizing the visual information captured through cameras. Different from other site-specific codebook design methods that require a large amount of measured channel state information (CSI), the proposed ones need only a simple snapshot of the environment followed by efficient computer vision (CV) techniques. Thus the proposed CV-aided codebook design reduces the overhead of communications system, such as the cost of time, human resources, as well as the hardware installation and calibration. Specifically, we propose a CV-based approach that detects the LOS area around the BS and reconstructs the LOS channel vectors set (CVS). With this knowledge, we build a vision-based beam codebook using Lloyd algorithm. Further, we design a FusionNet to generate the codebook that can serve the non-line-of-sight (NLOS) users. The simulation results demonstrate the effectiveness of the proposed CV-aided codebook design methods and their superiority compared to the conventional methods.

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST.

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.

Audiovisual synchronization is an essential indicator of video quality. The degradation in the quality of experience caused by such synchronization errors is often measured using the mean opinion score (MOS). However, this method is susceptible to emotion and bias. Electroencephalography (EEG), as an objective tool, overcomes the drawbacks of subjective testing for evaluating human perception. In this work, we measure the human perception of audio and video synchronization based on the EEG approach through a series of experiments. A scoring model was developed for audio and video under multilevel synchronization errors by extracting the power spectral density (PSD) of EEG signals as features. The use of EEG signals for perceptual ability assessment of audiovisual distortion provides a potential neurally informed approach that is more objective than the high-level cognitive activity in subjective data.

Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.

Integrated Sensing and Communication (ISAC) is an emerging technology that realizes both communication and sensing functionalities on the same sets of hardware as well as same frequency bands. Such technique will bring tremendous benefits such as spectrum efficiency improvement and cost reduction. In this paper, we firstly define three different classes of ISAC with the integration of both resources and functionalities for communication and sensing as the ultimate form. Furthermore, target at the full-integration level, we propose a downlink-pilot-based OFDM ISAC signal processing algorithm for base-station-orientated sensing. The OFDM architecture guarantees forward compatibility, and downlink reference signals including DMRS and CSI-RS ensure the communication throughput and address privacy concerns as well. Simulation results show that the proposed method is able to achieve outstanding sensing performance with range estimation error of 0.15m and velocity estimation error of 0.2m/s in the low SNR region.

As one of the promising technologies of the future 6G network, Integrated Sensing and Communication (ISAC) is able to provide tremendous benefits such as performance improvement and cost reduction through integrating the two systems as a whole. The use of ISAC technology will introduce new sensing abilities and enhance information processing capabilities at base stations, and makes the interaction between base stations and vehicles more frequent, which brings huge benefits to connected automated vehicles(CAV). If the BS only transmits the single beam at a given period, the sensing functions will not be guaranteed as there are blind zone and other issues. Therefore, we propose a multi-beam model for the ISAC downlink transmission. Furthermore, considering the uneven distribution of resources among different users, an ISAC multi-beam power allocation algorithm is proposed. Such convex optimization problem is solved using CVX toolbox and optimal solutions are obtained. The simulation results show that compared with the traditional average power allocation and water filling algorithms, proposed algorithm will improve the total communication rate for multi-user scenario, and provide a satisfied sensing accuracy of less than 1m sensing error.

In this paper, a novel multiple access method is proposed and evaluated for integrated sensing and communication (ISAC) enabled UAV ad hoc network, in which the UAVs can perform sensing and communicating simultaneously. With integrated signal, a novel spatial division method is proposed based on a multi-beam framework with tunable analog antenna arrays for ISAC system. With the implementation of such spatial division method, we design a new time-frequency resource allocation scheme by dividing the integrated signal into Radar (R) mode and Radar Communication (RC) mode. Moreover, according to the packet arrival rate, to make full use of spectrum resources, a novel procedure to assign channels is proposed. The performance of medium access method is analyzed by using Markov model. Simulation results shows that the multiple access method proposed in this paper has improved the throughput of UAV nodes with the assistance of sensing information.

Positioning is of importance to Unmanned Aerial Vehicle (UAV) networks, which is mainly realized using Global Navigation Satellite System (GNSS). However the GNSS signals are not always available in environments such as denied or indoor areas. To address this issue, in this paper, a joint neighbor discovery and positioning method for UAV network is proposed to deal with the scenarios with poor GNSS signals. Specifically, during the neighbor discovery process, the Two-Way Ranging (TWR) and Angle of Arrival (AoA) algorithm is utilized. In addition, an approximate optimal multi-hop positioning method is proposed. The performance of the proposed method is analyzed. Numerically, the results demonstrate the positioning accuracy and the convergence of the neighbor discovery process. This paper verifies the value and feasibility of multi-hop positioning, providing a basis for further research in joint positioning and communication in UAV networking.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Mobile traffic is projected to increase 1000 times from 2010 to 2020. This poses significant challenges on the 5thgeneration (5G) wireless communication system design, including network structure, air interface, key transmission schemes, multiple access, and duplexing schemes. In this paper, full duplex (FD) is discussed, aiming to highlight some insights on various issues including deployment scenarios, frame structures, reference signals (RS), interference mitigation, transceiver structures/calibration, and extension of time division duplex (TDD)/frequency division duplex (FDD) to full duplex. It is anticipated that with future standardization and deployment of FD systems, TDD and FDD will be harmoniously integrated, supporting all the existing half duplex mobile phones efficiently, and leading to a much enhanced 5G system performance.

In real environments, channel reciprocity cannot be directly exploited between uplink and downlink in time-division duplex (TDD) due to antenna gain mismatch. Previous work about antenna calibration mainly focused on single cell. This work proposes an adaptive scheme for antenna calibration between two cooperative BSs. The proposed scheme aims at achieving an equal ratio between antenna transmitter and receiver analog gains among cooperative BSs in flat fading channels. The essential idea is to relay calibration parameter through a calibration path. This procedure can be controlled by a certain BS dubbed primary BS. Evaluation of the proposed scheme is carried out by computer simulation.

We investigate linear detection and precoding for denoising-based physical network coding (D-PNC) in two-way multi-input multi-output relay channels. We propose an MMSE-based detector, which first gives a coarse detection to the two source messages using MMSE detector and then detects the product of the two coarse detected messages. The advantage of such detector is to randomize the interference. A simple precoder is also proposed for relay message transmission, which provides fairness. Simulation results show that D-PNC with the proposed detector and precoder has similar pair error rate performance to other schemes while keeps simple structure and lower complexity.

Considering the latest development after introducing Relay Nodes (RNs) into the cellular networks and the effect of inter-cell interference coordination in the cellular networks, we propose a novel method of inter-cell interference coordination for relay enhanced cellular networks. Firstly, for each cell, all available frequency resource is divided into two parts. The first part is for the users connected to the BS directly, and the other part is allocated to the users connected indirectly to the BS through Relay Nodes. Furthermore, the second part of frequency resource is divided into three parts which are utilized by cell-edge users. System-level simulation results show that the proposed scheme performs better on system capacity and spectrum efficiency than the Soft Frequency Reuse (SFR) scheme.

This paper investigates asymmetric two-way relay channels (TWRCs), where a couple of source nodes exchange data with different flow rates via a relay node. A novel mapping codebook-based physical network coding scheme is proposed for improving the performance of such channels. The proposed network coding scheme introduces a mapping codebook that contains several subcodebooks and adaptively selects a subcodebook from the mapping codebook in each data exchange based on the information of signal phase difference. Moreover, distributed transmission power control is also introduced to simplify codebook design and facilitate physical network coding. Simulation results show that it can significantly improve the BER performance as compared to analog network coding. It is also robust to the phase estimation error and the power control error, in particular, in a lower SNR region.

We investigate the relay multiple access channels (RMAC) with two users and a destination. We determine the lower bound and upper bound on the capacity of RMAC under time-division (TD) decode-and-forward (DF) mode by using superposition modulation. A relay scheme with resource allocation is proposed to achieve the lower bound. Analytical results and simulation results show that the proposed scheme can achieve larger capacity region than that of direct transmission (DT) for the fixed channel gain case. But they provide the same maximum sum capacity. Whereas, the proposed relay scheme can provide higher outage capacity than DT scheme due to the fact that the two sources can share the resources from each other.

In this letter, we propose a novel quadratic programming model for soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detection that is compact for QAM constellations and easy to analyze. A semidefinite relaxation for this model is derived that can be solved by the interior-point method. We also give the sufficient conditions and necessary conditions to speed up the interior-point method.

Abstract In this paper, we investigate the cooperative channels with one source‐destination pair and multiple partners (relays), where only the destination is equipped with multiple antennas. The achievable capacity with the optimal power allocation and partner selection is analyzed with the total transmit power constraints under the different cooperation modes, including amplify‐and‐forward (AF) and decode‐and‐forward (DF). With the partial channel state information (CSI) at the destination, we develop three algorithms to choose the possible best partner(s) for AF, repetition‐coded DF and Gaussian‐coded DF, which are called Selective AF (SAF), Selective repetition‐coded DF (SRDF), and Selective Gaussian‐coded DF (SGDF), respectively. In these algorithms, only one partner will be employed as relay for SAF and SRDF to maximize the capacity while multiple partners are selected for SGDF. An efficient quasi‐distributed protocol to support SAF, SRDF, and SGDF is also involved. We study the impact of the number of receive antennas and SNR on SAF, SRDF, and SGDF. Numerical results show that SAF and SRDF have higher spectral efficiency than direct transmission (DT), especially in low SNR regime and for the small number of receive antennas and large number of users cases, while SGDF outperforms DT evidently in every scenario. Copyright © 2007 John Wiley & Sons, Ltd.

The problem of random access (RA) in a wireless SIMO-OFDM system is addressed and a decentralized medium access control (MAC) strategy is proposed based on the so called random orthogonal frequency division multiple access (ROFDMA). By employing receive beamforming, the problem of packet collisions at the same subcarrier are solved by using random spatial division multiple access (RSDMA), which therefore provides a multipacket reception (MPR) capability. First, we study the optimal number of users allowed to transmit at the same subcarrier and the same slot to maximize the separable packets subject to their BER requirements. Then the transmission probability based on user's channel gain is designed to achieve the desired number of users with the highest probability. Finally, a MPR scheme under average transmit power constraint is proposed with modulation selection. The performance of the proposed scheme is analyzed analytically and evaluated through simulations.

This paper addresses the optimal resource allocation problem for multiuser MIMO-OFDM systems. We apply an optimization algorithm to obtain a joint subcarrier and power allocation scheme based on orthogonal frequency division multiple access (OFDMA) combined with dirty paper coding (DPC) assuming instantaneous channel state information (CSI), which is called as DPC-OFDMA. The ultimate objective is to minimize the total transmit power subject to individual required data rates constraints. To reduce the complexity of the optimal solution, the analysis is considered in two stages. The first stage addresses subcarriers allocation, in which users are allowable to share subcarriers. The second stage employs DPC technology to deal with simultaneous transmissions of the users sharing the same subcarriers. An efficient algorithm to choose the best possible ordering for DPC and the optimal precoding design of each user are also involved. Simulation results show that DPC-OFDMA scheme has high spectral and power efficiency than conventional fixed schemes, where fixed power and subcarriers are allocated to each user.