Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming sixth-generation (6G) mobile networks. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of Signal-to-Noise Ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to Low Earth Orbit (LEO) satellite channels. In response to these constraints, we propose an Importance-Aware Robust Semantic Transmission (IRST) framework, specifically designed for scenarios characterized by bandwidth scarcity and channel variability. The IRST scheme begins by applying a segmentation model enhancement algorithm to improve the granularity and accuracy of semantic segmentation. Subsequently, a task-driven semantic selection method is employed to prioritize the transmission of semantically vital content based on real-time Channel State Information (CSI). Furthermore, the framework incorporates a stack-based, SNR-aware channel codec capable of executing adaptive channel coding in alignment with SNR variations. Comparative evaluations across diverse operating conditions demonstrate the superior performance and resilience of the IRST model relative to existing benchmarks. The code is available at https://github.com/lightwindy-ch/IRST.git.

Deep neural networks have achieved significant progress but face growing security threats, particularly backdoor attacks. Adversaries implant backdoors into pre-trained models and uses specific triggers to activate the backdoor, inducing the model to output incorrect results. To cope with backdoor attacks, we systematically introduces frequency domain perturbations into backdoor defense scenarios. By preserving phase semantics and perturbing amplitude high-frequency features, the repaired model will shift from trigger dependency to semantic dependency, thereby weakening the coupling relationship between backdoor trigger patterns and target categories. We propose Freq-Pret to demonstrate this backdoor defense strategy, which is a novel backdoor defense scheme combining frequency domain perturbations with lightweight retraining. Theoretical analysis and experiments demonstrate that Freq-Pret effectively repairs backdoors without compromising initial accuracy, enabling the repaired model to resist attacks and correctly categorize contaminated samples with high probability. Compared to existing methods, Freq-Pret offers clear advantages.

Semantic communication employs deep learning to transmit semantically meaningful information rather than raw data, thereby improving communication efficiency. To facilitate the adaptation of continuous semantic features to digital transmission systems, vector quantization (VQ) serves as an effective approach for discretizing high-dimensional features into compact codebook indices. However, existing VQ-based systems face a critical dilemma: conventional VQ codebooks demand large index ranges to preserve fidelity, contradicting digital modulation’s need for limited discrete states to ensure noise robustness. To bridge this gap, we design a 2K image transmission framework that jointly considers codebook compactness and transmission robustness. The framework operates in two stages: In Stage 1, we devise MOC-RVQ, a multi-head ordered codebook (MOC) with residual vector quantization (RVQ) to reduce the index range while maintaining image fidelity. In Stage 2, a Swin Transformer-based noise reduction block (NRB) is integrated with feature requantization for further robust restoration. Experiments on 2K-resolution datasets demonstrate that the proposed MOC-RVQ surpasses traditional codecs like BPG, JPEG, and learnable baselines, while maintaining low transmission overhead.

A reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is investigated, where the transmitter (Alice) is a dual-functional radar-communication (DFRC) base station (BS) that aims to sense the location of a potential warden (Willie), while simultaneously transmitting public and covert signals to the legitimate users, Carol and Bob, respectively. Both cases of known and unknown Willie locations are considered. For the known-location case, assuming perfect channel state information (CSI) at Willie, a covert rate maximization is formulated with the joint optimization of active and passive beamforming, which is solved using successive convex approximation (SCA), penalty method, and semidefinite relaxation (SDR). For the unknown-location case, we propose to estimate Willie’s location via radar sensing and develop a sensing-based imperfect CSI model. In particular, the CSI error uncertainty is bounded by the sensing accuracy, which is characterized by the Cramér-Rao bound (CRB). Subsequently, a robust communication rate maximization problem is formulated under the constraints on quality-of-service (QoS) of Carol, sensing accuracy, and covertness level. The Schur complement and S-procedure are employed to handle the non-convex constraints. Numerical results compare the system performance under the two cases, and demonstrate the significant covert performance superiority of the sensing-based imperfect CSI model and NOMA over the general norm-bounded imperfect CSI model and the orthogonal multiple access scheme. Furthermore, the dual yet contradictory effects of sensing on covert communications are revealed. It is also found that Alice primarily utilizes Carol’s signal for sensing, while allocating almost all of Bob’s signal for communication.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

This paper presents WirelessGPT, a generative foundation model designed for multi-task learning in integrated sensing and communication (ISAC) systems. Built upon large-scale heterogeneous wireless datasets including Traciverse, Sensiverse, and DeepMIMO, WirelessGPT learns universal spatio-temporal-frequency representations through self-supervised pretraining with masked channel token prediction. The proposed architecture introduces a multi-scale patch embedding module to capture both local and global channel features, and a triple-axis attention encoder to jointly model temporal, spatial, and frequency-domain dependencies. After pretraining, the model can be efficiently fine-tuned via lightweight adapters for diverse downstream tasks such as channel estimation, channel prediction, human activity recognition, environment reconstruction, and object tracking. Experimental results show that WirelessGPT achieves superior accuracy and generalization under limited labeled data and dynamic ISAC conditions, outperforming traditional and task-specific models in low-SNR and high-mobility scenarios while maintaining efficient inference suitable for edge deployment. By unifying communication and sensing functionalities within a single generative backbone, WirelessGPT establishes a scalable paradigm for AI-native 6G systems, enabling shared representations that support heterogeneous wireless tasks.

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7 K× lower communication volume on OPV2V.

Semantic communication has emerged as a promising paradigm exhibiting improved robustness compared to traditional approaches under low SNR conditions. Precise synchronization is imperative for accurate semantic communication. However, existing synchronization techniques face challenges reliably achieving synchronization at low SNRs, limiting semantic communication development. To improve synchronization performance, especially under low SNR scenarios, this work proposes an image-based semantic synchronization method (SeSy) leveraging inherent image correlations. SeSy is applicable to both semantic and traditional communication systems. Theoretical analysis establishes bounds on the miss detected ratio (MDR) for SeSy. Experimental results demonstrate that SeSy achieves lower MDR and root mean square error (RMSE) compared to traditional methods across various SNR levels, especially at low SNRs.

Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications. However, conventional mono-static ISAC systems, which simultaneously transmit and receive at the same equipment, may suffer from severe self-interference, and thus significantly degrade the system performance. To address this issue, this paper studies a multi-static ISAC system for cooperative target localization and communications, where the transmitter transmits ISAC signal to multiple receivers (REs) deployed at different positions. We derive the closed-form of weighted sum Cramér-Rao bound (CRB) on the joint estimations of both the transmission delay and Doppler shift for cooperative target localization, and the weighted sum CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements for the REs. To solve this problem, we first decouple it into two subproblems for RE selection and transmit beamforming, respectively. Then, a minimax linkage-based method is proposed to solve the RE selection subproblem, and a successive convex approximation algorithm is adopted to deal with the transmit beamforming subproblem with non-convex constraints. Finally, numerical results validate our analysis and reveal that our proposed multi-static ISAC scheme achieves better ISAC performance than the conventional mono-static ones with ideal SI cancellation when the number of cooperative REs is large.

With the rapid advancement of 6G, identity authentication has become increasingly critical for ensuring wireless security. The lightweight and keyless Physical Layer Authentication (PLA) is regarded as an instrumental security measure in addition to traditional cryptography-based authentication methods. However, existing PLA schemes often struggle to adapt to dynamic radio environments. To overcome this limitation, we propose the Adaptive PLA with Channel Extrapolation and Generative AI (APEG), designed to enhance authentication robustness in dynamic scenarios. Leveraging Generative AI (GAI), the framework adaptively generates Channel State Information (CSI) fingerprints, thereby improving the precision of identity verification. To refine CSI fingerprint generation, we propose the Collaborator-Cleaned Masked Denoising Diffusion Probabilistic Model (CCMDM), which incorporates collaborator-provided fingerprints as conditional inputs for channel extrapolation. Additionally, we develop the Cross-Attention Denoising Diffusion Probabilistic Model (CADM), employing a cross-attention mechanism to align multi-scale channel fingerprint features, further enhancing generation accuracy. Simulation results demonstrate the superiority of the APEG framework over existing time-sequence-based PLA schemes in authentication performance. Notably, CCMDM exhibits a significant advantage in convergence speed, while CADM, compared with model-free, time-series, and VAE-based methods, achieves superior accuracy in CSI fingerprint generation.

This paper addresses the challenge of achieving security in semantic communication (SemCom) over a wiretap channel, where a legitimate receiver coexists with an eavesdropper experiencing a poorer channel condition. Despite previous efforts to secure SemCom against eavesdroppers, guarantee of approximately zero information leakage remains an open issue. In this work, we propose a secure SemCom approach based on superposition code, aiming to provide quantifiable and controllable security for digital SemCom systems. The proposed method employs a double-layered constellation map, where semantic information is associated with satellite constellation points and cloud center constellation points are randomly selected. By carefully allocating power between these two layers of constellation, we ensure that the symbol error probability (SEP) of the eavesdropper when decoding satellite constellation points is nearly equivalent to random guessing, while maintaining a low SEP for the legitimate receiver to successfully decode the semantic information. Simulation results demonstrate that the peak signal-to-noise ratio (PSNR) and mean squared error (MSE) of the eavesdropper's reconstructed data, under the proposed method, can range from decoding Gaussian-distributed random noise to approaching the variance of the data. This validates the effectiveness of our method in nearly achieving the experimental upper bound of security for digital SemCom systems when both eavesdroppers and legitimate users utilize identical decoding schemes. Furthermore, the proposed method consistently outperforms benchmark techniques, showcasing superior data security and robustness against eavesdropping. The implementation code is publicly available at:https://github.com/1weixuanchen/A-Superposition-Code-Based-Semantic-Communication.

Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) schemes to confuse semantic eavesdroppers. However, the state-of-the-art SemSteCom schemes for image transmission rely on the pre-selected cover image, which limits the generalization. To address this issue, we propose a Generative Diffusion Model-based Coverless Semantic Steganography Communication (SemSteDiff) scheme to hide secret images into generated stego images. The semantic related private and public keys enable legitimate receiver to decode secret images correctly while the eavesdropper without the completely correct key-pairs fail to obtain them. Simulation results demonstrate the effectiveness of the plug-and-play design in different Joint Source-Channel Coding (JSCC) frameworks. Results under different eavesdropping settings show that, when Signal-to-Noise Ratio (SNR) = 0 dB, the peak signal-to-noise ratio (PSNR) of the legitimate receiver is 4.14 dB higher than that of the eavesdropper.

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. To address this, we perform JSCC over the network layer to combat packet loss and support real deployment. Inspired by the generative latent modeling, we propose Glaris, a generative latent-prior-based resilient speech semantic communication framework that performs resilient transform coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that Glaris consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and error resilience.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decisions, dynamic environmental adaptation, and complex missions. AgentNet has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments, laying the foundation for fully autonomous networking systems. Despite its promise, AgentNet is still in the early stages of development and still lacks an effective networking framework to support automatic goal discovery, multi-agent self-orchestration, and task assignment. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks. SANet can infer the semantic goal of the user and automatically assign agents associated with different layers of the network stack to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet: (the agents' objective) optimization error, (dynamic environment) generalization error, and (multi-objective) conflicting error. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms, static-weighting and dynamic-weighting algorithms, are introduced to optimize the above three metrics. A bandwidth-adaptive compression framework is also proposed to enable different agents to perform in situ compression of their intermediate embeddings, dynamically adjusting to localized resource constraints and task requirements. We derive theoretical bounds for all these performance metrics and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. Finally, to validate our theoretical results, we develop an open-source Radio Access Network (RAN) and core network-based hardware prototype that implements three Transformer-based time-series prediction agents to interact with three different layers of the network. Experimental results show that the proposed MoPS framework achieves performance gains of up to$14.61\%$while requiring only$44.37\%$of the Floating-Point Operations (FLOPs) for inference at each agent compared to state-of-the-art algorithms. Also, compared to the static-weighting algorithm, the dynamic-weighting algorithm achieves up to$83.81\%$reduction in training errors caused by conflicting objectives.

Since the current error detection and correction of semantic information mainly rely on the detection of the final semantic recovery performance to identify the wrong semantic features, this method reduces the efficiency of semantic communication. To address this issue, this paper proposes a semantic communication system based on hybrid automatic repeat request for semantic packets (SPHARQ). Where a novel semantic check code (SCC) is designed as an effective proxy to enable immediate detection of semantic distortion, and the semantic features to be transmitted are selected based on key factors such as feature importance. Based on the SCC, a dynamic retransmission control criterion jointly driven by semantic and physical metrics is established, enabling semantic-aware retransmission. Building upon this criterion, a cooperative retransmission scheme for semantic packets is designed, further enhancing their transmission quality and efficiency. Then, theoretical analysis is conducted on the average number of transmissions and the throughput of semantic packets in this system, and corresponding closed-form expressions are provided. Simulations validate the theoretical analysis, showing that at low signal-to-noise ratio (SNR) the proposed system achieves gains of up to 0.25 in multi-scale structural similarity (MS-SSIM), 4 dB in peak signal-to-noise ratio (PSNR), and enhanced intersection over union (IoU) across multiple segmentation categories over the latest semantic HARQ scheme, with lower overhead.

Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL), where multi-task SemComs that perform multiple tasks simultaneously attach great importance due to its high efficiency. However, the study of robust multi-task-oriented semantics transmission is still in early stages. In this paper, we propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework for the robust and efficient transmissions of multi-task semantic features. First, to improve the robustness and effectiveness of semantic coding, a multi-task semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR) method is investigated to ensure the important features delivery under limited channel resources. Then, to accurately detect the possible transmission errors, a novel feature distortion evaluation (FDE) network is designed to identify the distortion level of each feature, based on which an efficient HARQ method is proposed. Specifically, the corrupted features are retransmitted, where the remaining channel resources are used for incremental transmissions. The system performance is evaluated under different channel conditions in multi-task scenarios in the Internet of Vehicles. Extensive experiments show that the proposed framework outperforms state-of-the-art works by more than 20% in rank-1 accuracy for vehicle re-identification, and 10% in vehicle color classification accuracy in the low signal-to-noise ratio regime.

The existing works on holographic MIMO are mainly based on the time encoding (TE) scheme. Since the continuous aperture of holographic MIMO is able to capture both the temporal and the spatial variation of electromagnetic waves, we propose a space-time encoding (STE) scheme, which relies on the orthogonal basis function representation of the spatial-temporal EM waves. From the perspective of electromagnetic information theory, we derive the achievable information rate of the STE scheme in the narrowband communication systems and prove that the STE scheme achieves a higher information rate than the TE scheme. Firstly, We build the transmission model of the STE scheme based on electromagnetic information theory and investigate the characteristics of the model, including the blocklength of codewords and the signal-to- noise ratio. Specifically, the blocklength is determined through proving the eigenvalue distribution of the space-time-wavenumber-frequency limited operator and the signal-to-noise ratio is obtained based on proposed noise model. Then we derive the achievable information rate of both the STE scheme and the TE scheme by employing the finite blocklength information theory. Closed-form approximations of the rates are further derived, based on which we prove the conclusion that the STE scheme achieves a higher information rate than the TE scheme while utilizing the same spatial and temporal resources. Numerical results verify the accuracy of the approximations and indicate that the STE scheme improves the information rate by 7.96% over the TE scheme.

Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions, combining the demands for both the bit-accurate delivery and end-to-end (E2E) distortion minimization. However, current joint source-channel coding (JSCC) in SemComs is not compatible with the existing communication systems and cannot adapt to the variations of the sources or the channels, while separate source-channel coding (SSCC) is suboptimal in the finite blocklength regime. To address these issues, we propose an adaptive source-channel coding (ASCC) scheme for SemComs over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical deep JSCC and SSCC schemes for both the single- and parallel-channel scenarios while maintaining full compatibility with practical digital systems.

This paper advocates a fluid antenna system (FAS)-assisted long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. In the proposed system, FAS provides spatial diversity gains for LoRa, eliminating the necessity for integrating multiple-input multiple-output (MIMO) technologies into the system. It consists of a traditional LoRa transmitter with a fixed-position antenna and a LoRa receiver employing the FAS (Rx-FAS). The pilot sequence overhead and placement for FAS are also considered. Specifically, we consider embedding pilot sequences within symbols to reduce the impact of pilot overhead on system throughput and the physical layer (PHY) frame structure, leveraging the fact that the pilot sequences do not convey source information and correlation detection at the LoRa receiver need not be performed across the entire symbol. The achievable performance of LoRa-FAS is thoroughly analyzed under both coherent and non-coherent detection schemes. We obtain new closed-form approximations for the probability density function (PDF) and cumulative distribution function (CDF) of the FAS channel under the block-correlation model. Furthermore, the approximate SER, equivalently the bit error rate (BER), of the proposed LoRa-FAS is also derived in closed form. Simulation results indicate that substantial SER gains can be achieved by FAS within the LoRa framework, even with a limited size of FAS. In addition, our analytical results align well with Clarke’s exact spatial correlation model. Finally, when utilizing the block-correlation model, we suggest that the correlation factor should be selected as the proportion of the eigenvalues of the exact correlation matrix greater than 1 for higher accuracy.

Wireless federated learning (FL) is an emerging artificial intelligence (AI) technique capable of leveraging the data and computing capacity of networked wireless devices while ensuring their individual data privacy and security. However, in geographical areas with poor wireless signal coverage, implementing FL is challenging. Additionally, intensive computation and communication put significant strain on resource-limited wireless devices. To address these issues, firstly, we propose a user equipment (UE)-to-network relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes device scheduling, network topology construction, and multi-type resource allocation to achieve low latency and low energy consumption. The second contribution is threefold. 1) For solving the device scheduling problem, we propose a voting-based strategy to identify the most suitable wireless UEs as relays. 2) Regarding the network topology optimization problem, we derive the optimal solutions under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For solving the multi-type resource allocation problem, we analyze its mathematical structure and propose an iterative algorithm that has significantly lower computational complexity than the traditional method. This algorithm is capable of jointly optimizing the usage of transmission time resource, computing capacity, and transmit power. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

As a paradigm shift towards pervasive intelligence, semantic communication (SemCom) has shown great potentials to improve communication efficiency and provide user-centric services by delivering task-oriented semantic meanings. However, the exponential growth in connected devices, data volumes, and communication demands presents significant challenges for practical SemCom design, particularly in resource-constrained wireless networks. In this work, we propose a task-agnostic semantic communication (TASC) framework capable of supporting multimodal data across diverse tasks. To investigate the interplay between communication and intelligent tasks from an information-theoretic perspective, we introduce a distributed multimodal information bottleneck (DMIB) principle, which enables the extraction of minimal sufficient unimodal and multimodal representations by eliminating redundant information while preserving task-relevant semantics. To further reduce the communication overhead, we develop an adaptive semantic feature transmission method under dynamic channel conditions. Then, TASC is trained based on federated meta-learning (FML) to learn a well-initialized model for rapid adaptation and generalization. To gain deep insights, we conduct theoretical analysis and devise resource management to accelerate convergence while minimizing the training latency and energy cost. Moreover, we develop a joint user selection and resource allocation algorithm to address the non-convex problem with theoretical guarantees. Extensive simulation results validate the effectiveness and superiority of the proposed TASC compared to baselines.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves great performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results on different devices, including IoT device JETSON AGX ORIN, show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay. We have released our code and pre-trained models at https://github.com/Wireless3C-SJTU/MambaJSCC, allowing full reproduction of our results.

As a typical scenario for the 6th Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the α - κ - μ channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

Semantic communication emerges as a pivotal technology for realizing the 6G vision. However, existing semantic-aware reconstruction systems rely on syntactic-level loss functions for optimization, failing to focus on the precise recovery of semantic information and resulting in suboptimal semantic fidelity. Meanwhile, the autoregressive decoding architecture adopted in text semantic communication introduces prohibitive high latency. To address these two issues, we propose a robust information bottleneck (RIB) guided non-autoregressive semantic communication (NASC) scheme, named RIB-NASC. First, we pioneer the integration of the RIB criterion into semanticaware reconstruction systems, formulating a direct optimization objective tailored for underlying semantic recovery and establishing an informativeness-robustness trade-off. Second, we derive a tractable variational lower bound for the RIB objective via variational approximation and a novel synonymous mapping-based semantic posterior estimation strategy. Third, we design a lightweight non-autoregressive semantic decoder architecture based on Transformer encoder, enabling high-speed parallel semantic decoding during inference. Extensive simulation results demonstrate that the RIB-NASC scheme significantly outperforms baseline schemes in terms of semantic recovery performance (BLEU score and sentence similarity) and achieves a decoding delay reduction of nearly 96.7% compared to traditional autoregressive decoding.

In this paper, we propose a hybrid learning framework that combines federated and split learning, termed semi-federated learning (SemiFL), in which over-the-air computation is utilized for gradient aggregation. A key idea is to strategically adjust the learning rate by manipulating over-the-air distortion for improving SemiFL’s convergence. Specifically, we intentionally amplify amplitude distortion to increase the learning rate in the non-stable region, thereby accelerating convergence and reducing communication energy consumption. In the stable region, we suppress noise perturbation to maintain a small learning rate for improving SemiFL’s final convergence. Theoretical results demonstrate the antagonistic effects of over-the-air distortion in different regions, under both independent and identically distributed (IID) and non-IID data settings. Then, we formulate two energy consumption minimization problems, one for each region, which implements a two-region mean square error threshold configuration scheme. Accordingly, we propose two resource allocation algorithms with closed-form solutions. Simulation results show that under different network and data distribution conditions, strategically manipulating over-the-air distortion can efficiently adjust the learning rate to improve SemiFL’s convergence. Moreover, energy consumption can be reduced by using the proposed algorithms.

Agentic AI networking (AgentNet) has attracted significant interest due to its promising potential to move traditional AI-based networking solutions beyond closed-loop and passive learning to proactive interaction and goal-driven action, offering a path to self-learning and generally intelligent networking systems. Despite its promise, ensuring the security and trustworthiness of such systems presents significant challenges, particularly concerning identity management, agent capability verification, and data integrity during collaborative learning. To address these issues, this paper proposes TrustAgentNet, a novel consortium blockchain-based framework for unified and trusted agent identification, traceable skillset and tag descriptions, and secure on-chain collaborative learning in AgentNet. In TrustAgentNet, a chain of skillset (CoS) is introduced, consisting of a skillset chain to distributedly store all the verified skillsets and associated tags, and a dedicated training chain for each distinct skillset can be jointly constructed and maintained by the authorized agents using a collaborative learning-based approach. Theoretical analysis suggests that there exists a three-way trade-off among the security level, skillset performance, and resource cost. This tradeoff is also empirically validated by the experimental results obtained from a hardware prototype implemented based on a Hyperledger Fabric-based consortium blockchain. To verify the practical performance of TrustAgentNet, we consider a real-world scenario of multi-agent collaborative learning under malicious attack. Experimental results suggest that TrustAgentNet can effectively guarantee the security of skillset training and enable rapid response and recovery from potential attacks within seconds.

This paper proposes an adaptive source-channel coding (ASCC) scheme for point-to-point digital semantic communications over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical separate and deep joint source-channel coding schemes while maintaining full compatibility with practical digital systems.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm that relies on a large number of specialized AI agents to collaborate and coordinate for autonomous decision-making, dynamic environmental adaptation, and complex goal achievement. It has the potential to facilitate real-time network management alongside capabilities for self-configuration, self-optimization, and self-adaptation across diverse and complex networking environments, laying the foundation for fully autonomous networking systems in the future. Despite its promise, AgentNet is still in the early stage of development, and there still lacks an effective networking framework to support automatic goal discovery and multi-agent self-orchestration and task assignment. This paper proposes SANNet, a novel semantic-aware agentic AI networking architecture that can infer the semantic goal of the user and automatically assign agents associated with different layers of a mobile system to fulfill the inferred goal. Motivated by the fact that one of the major challenges in AgentNet is that different agents may have different and even conflicting objectives when collaborating for certain goals, we introduce a dynamic weighting-based conflict-resolving mechanism to address this issue. We prove that SANNet can provide theoretical guarantee in both conflict-resolving and model generalization performance for multi-agent collaboration in dynamic environment. We develop a hardware prototype of SANNet based on the open RAN and 5GS core platform. Our experimental results show that SANNet can significantly improve the performance of multi-agent networking systems, even when agents with conflicting objectives are selected to collaborate for the same goal.

This paper addresses the difficulty of implementing federated learning (FL) in geographical areas with poor wireless signal coverage, and alleviates the high burden imposed by intensive computation and communication on resource-limited wireless devices. Firstly, we propose a user equipment (UE)-tonetwork relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes resource allocation, network topology construction, and device scheduling, to achieve low latency and low energy consumption. The second contribution is threefold. 1) For allocating resources, we propose a linear-complexity algorithm which is capable of jointly optimizing the transmission time resource and the computing power. 2) For constructing network topology, we derive the optimal closed-form solution under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For scheduling devices, we propose a voting-based device scheduling algorithm that is near-optimal. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

Recent contributions of semantic information theory reveal the set-element relationship between semantic and syntactic information, represented as synonymous relationships. In this paper, we propose a synonymous variational inference (SVI) method based on this synonymity viewpoint to re-analyze the perceptual image compression problem. It takes perceptual similarity as a typical synonymous criterion to build an ideal synonymous set (Synset), and approximate the posterior of its latent synonymous representation with a parametric density by minimizing a partial semantic KL divergence. This analysis theoretically proves that the optimization direction of perception image compression follows a triple tradeoff that can cover the existing rate-distortion-perception schemes. Additionally, we introduce synonymous image compression (SIC), a new image compression scheme that corresponds to the analytical process of SVI, and implement a progressive SIC codec to fully leverage the model’s capabilities. Experimental results demonstrate comparable rate-distortion-perception performance using a single progressive SIC codec, thus verifying the effectiveness of our proposed analysis method.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

This article introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based semantic importance coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and semantic importance coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

The growing demand for reliable and extensive connectivity has made low Earth orbit (LEO) satellites aided Internet of Things (IoT) systems a critical area of research. However, current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial IoT devices to the satellites (T-S) and from the satellites to the Earth station (S-ES), while also accounting for limited satellite coverage and Earth curvature effects. To realistically represent channel conditions, the Nakagami fading model is employed for the T-S links to characterize diverse small-scale fading environments, while the shadowed-Rician fading model is used for the S-ES links to capture the combined effects of shadowing and dominant line-of-sight paths. Furthermore, the analysis incorporates uplink and downlink interference, ensuring a comprehensive evaluation of system performance. The accuracy and effectiveness of our theoretical framework are validated through extensive Monte Carlo simulations. These results provide insights into key performance metrics, such as coverage probability and average ergodic rate, for both individual links and the overall system. Our study also offers an important analytical tool for optimizing the design and performance of IoT-over-LEO satellite systems with the operational constraints that are more realistic.

With the rapid development of Internet of Things (IoT) technology, more sensors are required to operate in complex channel scenarios and under limited communication resources. Semantic communication, as an emerging paradigm, extracts, transmits, and reconstructs information at the semantic level, offering advantages, such as high compression rates and strong noise resistance. These features are expected to find widespread application across various IoT scenarios. However, widely used image similarity evaluation metrics like peak signal-to-noise ratio and multiscale structural similarity index primarily focus on pixel or structural features, making it challenging to accurately measure the loss of semantic-level information during transmission. This limitation poses challenges for the performance evaluation of visual semantic communication systems and restricts the emergence of more novel and efficient systems. To address this issue, we propose a new semantic evaluation metric-semantic similarity score (SeSS). This metric is based on Scene Graph Generation and graph matching techniques, transforming image similarity scores into graph matching scores. By manually annotating thousands of image pairs, we fine-tuned the hyperparameters within SeSS to align it more closely with human semantic perception. The performance of SeSS has been tested across various image datasets and specific IoT visual tasks. Experimental results demonstrate the effectiveness of SeSS in measuring differences in semantic-level information between images, making it a valuable tool for evaluating visual semantic communication systems. This development is expected to encourage the emergence of more robust systems suited for diverse IoT scenarios. The code of SeSS is openly available onhttps://github.com/FSR3340/Semantic_Similarty_ScoreGitHub.

Compatibility between semantic communication and traditional mobile communication systems remains a significant challenge. Therefore, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This mechanism ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. Simulation results demonstrate that the proposed CLESC framework effectively achieves compatibility and adaptability between semantic and traditional communication systems, significantly enhancing channel robustness. Compared to traditional and artificial intelligence (AI)-based video source coding transmission schemes, our proposed CLESC achieves superior transmission performance under low signal-to-noise ratio (SNR) conditions.

As a novel communication paradigm, semantic communication (SemCom) can greatly improve communication efficiency, which has aroused extensive research by scholars worldwide. As one of the important aspects of digital communication nowadays, how to combine channel estimation with SemCom is an important research direction. In this article, based on orthogonal frequency-division multiplexing (OFDM) communication architecture, the semantic importance-aware reordering-enhanced SemCom system (SIARE-SC) is proposed, which utilizes the inequality of semantic symbols combined with channel estimation in OFDM systems to reduce the distortion caused by channel estimation interpolation error (CEIE) and further improve the signal recovery quality. To enhance the generalizability of the system, we extend the verification of the effectiveness of SIARE-SC in various scenarios with different sources, channels, and pilot patterns. Furthermore, the importance reordering method proposed in the SIARE-SC has good applicability and effectiveness, which can be used to be compatible with other SemCom systems and has a significant suppression effect on the peak-to-average power ratio (PAPR). Meanwhile, CEIE has been considered for the first time to be included in the analysis of SemCom distortion, and mathematically derive the performance expressions of SIARE-SC under different channel and pilot pattern scenarios from three perspectives, namely, channel bandwidth ratio (CBR), signal-to-noise ratio (SNR), and CEIE, to obtain the corresponding bound of performance. The proposed SIARE-SC is shown to significantly improve semantic performance in various scenarios by conducting a large number of experimental tests.

Driven by the severe self-interference (SI) in in-band full-duplex (IBFD) technologies, which creates extremely harsh communication environments, addressing this challenge has become a critical research focus. Semantic communication technologies, meanwhile, exhibit significant advantages in constrained environments by enabling efficient information transmission with reduced data volume and optimized bandwidth utilization. This article proposes an in-band full-duplex semantic communication (IBFD-SC), which combines IBFD with semantic communication to save transmission volume and improve spectral efficiency. The system incorporates a semantic importance mechanism, which is merged with radio frequency (RF) communication links. A semantic importance mapping module is introduced to map semantic symbols to baseband signals, considering both channel conditions and the significance of semantic symbols. Additionally, a nonlinear interference cancellation method is designed to eliminate SI, ensuring the integrity and reliability of key semantic information during communication. Experimental results demonstrate that the integration of semantic importance effectively mitigates interference and improves communication performance, particularly under low signal to interference plus noise ratio (SINR) conditions.

With the rapid development of Internet of Things (IoT) technology, an increasing number of resource-constrained devices operate in dynamic and heterogeneous network environments, posing challenges for efficient image transmission. Multi-user semantic communication (SC) enables reduced bandwidth consumption and enhanced noise resilience by understanding the intrinsic meaning of information and sharing common semantic features across devices, offering great potential for widespread applications in various IoT scenarios. However, current multi-users SC approaches for image transmission lack adaptability and fail to consider both content and style features, leading to degraded image reconstruction quality. Moreover, semantic redundancy among devices remains underutilized, limiting bandwidth efficiency in IoT networks. To address these limitations, in this paper, a novel multi-user content-style adaptive semantic communication system for image transmission in IoT scenarios is proposed. Specifically, a dual-branch semantic information extraction and adaptive recovery scheme is first established, which simultaneously captures and adaptively fuses semantic content and style features to improve reconstruction quality. Secondly, an adaptive common information extraction and enhanced coding module is introduced for resource-limited IoT devices, which dynamically adjusts the transmission rate based on varying channel conditions and the computational capabilities of different users, further optimizing communication performance. Finally, experimental results show that the proposed method improves peak signal-to-noise (PSNR) by at least 10% under poor SNR conditions for multi-users semantic communication, compared to baseline methods.

In this article, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean-square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC (A-MIJSCC) framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance.

In order to satisfy the requirements for service transformation and upgrading toward industrial digitization, networking, and intelligence, sixth generation-enabled industrial Internet of Things (IIoT) imposes new requirements on deterministic delay. However, the existing best-effort communication networks increase the uncertainty of transmission, making it difficult for users to ensure deterministic delay performance. In this article, we propose a deterministic delay guarantee strategy (DDGS) under reconfigurable intelligent surface (RIS)-assisted communication networks to ensure network performance in IIoT scenarios. In particular, we utilize stochastic network calculus (SNCs) to derive the probability that the delay falls within a specific time window, characterizing the probabilistic bounds of deterministic delay. Then, we explore the relationship between delay determinacy and wireless resources by jointly optimizing the transmit power, the channel blocklength allocation, and the phase-shift matrix at the RIS to maximize delay determinacy. Based on the interdependence of action choices among users and past experience, this article proposes a performance guarantee parameterized deep Q-network (PG-PDQN) algorithm to solve the complex problem containing a mixture of discrete and continuous action spaces. Simulation results show that the DDGS strategy significantly improves the delay determinacy compared to other strategies, and the PG-PDQN algorithm has good convergence, thus effectively improving the network performance.

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver’s limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introducesDiffCom, a novel end-to-endgenerative communicationparadigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, ourDiffComleverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes. Furthermore,DiffComincorporates a blind posterior sampling technique to address scenarios with unknown forward transmission characteristics. Extensive experimental validations demonstrate thatDiffComnot only produces realistic reconstructions with details faithful to the original data but also achieves superior robustness against diverse wireless transmission degradations. Collectively, these advancements establishDiffComas a new benchmark in designing generative communication systems that offer enhanced robustness and generalization superiorities.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

In this paper, we propose “SoundSpring”, a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.

Semantic-and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of the next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver. This is particularly advantageous for machine-oriented communication of high-data-rate content, such as images and videos, where the goal is rapid and accurate inference, rather than perfect signal reconstruction. While semantic-and task-oriented compression can be implemented in conventional communication systems, joint source–channel coding (JSCC) offers an alternative end-to-end approach by optimizing compression and channel coding together, or even directly mapping the source signal to the modulated waveform. Although all digital communication systems today rely on separation, thanks to its modularity, JSCC is known to achieve higher performance in finite blocklength scenarios and to avoidcliffand theleveling-off effectsin time-varying channel scenarios. This article provides an overview of the information theoretic foundations of JSCC, surveys practical JSCC designs over the decades, and discusses the reasons for their limited adoption in practical systems. We then examine the recent resurgence of JSCC, driven by the integration of deep learning techniques, particularly through DeepJSCC, highlighting its many surprising advantages in various scenarios. Finally, we discuss why it may be time to reconsider today’s strictly separate architectures and reintroduce JSCC to enable high-fidelity, low-latency communications in critical applications such as autonomous driving, drone surveillance, or wearable systems.

In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility between semantic and digital communications. Most of the current semantic communication systems rely primarily on analog modulation, limiting their integration with digital communication systems, which are more common in practice. In fact, traditional quantization methods are unsuitable for semantic communication because they do not account for semantic information within symbols. These factors block the wide application of the semantic communication. To address these challenges, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. The entire process is independent of any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness.

Rydberg atomic receivers have been envisaged as a revolutionary technology for future wireless communications and sensing. In order to detect the angle of arrival (AoA), researchers have typically constructed arrays comprisingmultipleRydberg atomic receivers. This paper presents a novel finding: The AoA of an incident signal can be accurately recovered even with asingleRydberg atomic receiver, by harnessing the phenomenon of inner-vapor interference. Firstly, we apply the micro-element method to the atomic vapor and derive a closed-form expression for the laser transmission in the presence of interference between the incident and local oscillator (LO) radio frequency (RF) signals. Secondly, we propose a robust method to estimate the AoA based on the particle swarm optimization (PSO) algorithm. Simulation results substantiate the effectiveness of our proposed scheme with practical parameter settings, verifying the applicability of AoA detection using a single Rydberg atomic receiver.

Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, which are prevalent in real-time communications. In this paper, we investigate how to elevate the resilience ability of NICs to combat packet losses. We propose ResiComp, a pioneering neural image compression framework with feature-domain packet loss concealment (PLC). Motivated by the inherent consistency between generation and compression, we advocate merging the tasks of entropy modeling and PLC into a unified framework focused on latent space context modeling. To this end, we take inspiration from the impressive generative capabilities of large language models (LLMs), particularly the recent advances of masked visual token modeling (MVTM). In specific, ResiComp develops a bi-directional masked Transformer to model the contextual dependencies among latents with dual-functionality: 1) iteratively acts as a conditional entropy model to boost compression efficiency; 2) operates latent PLC to improve resilience. During training, we integrate MVTM to mirror the effects of packet loss, enabling a dual-functional Transformer to restore the masked latents by predicting their missing values and conditional probability mass functions. Our ResiComp jointly optimizes compression efficiency and loss resilience. Moreover, ResiComp provides flexible coding modes, allowing for explicitly adjusting the efficiency-resilience trade-off in response to varying Internet or wireless network conditions. Extensive experiments demonstrate that ResiComp can significantly enhance the NIC’s resilience against packet losses, while exhibits a worthy trade-off between compression efficiency and packet loss resilience. Additionally, packet-level simulations, conducted using diverse network models based on real traces, demonstrate that ResiComp exhibits much better robustness to fluctuating network conditions compared to redundancy-based approaches like VTM + FEC.

In this paper, we formulate a Secure Hierarchical Semantic Communication (SH-SC) framework that leverages cloud-edge-device collaboration to enable efficient, robust, and privacy-preserving semantic communications. Firstly, we propose a quantization-aware efficient semantic communication (SemCom) model pre-training scheme running in the cloud. In particular, a semantic quantization method is applied to reduce the data required for transmission, and a quantization-aware multi-splitting points training method is proposed to mitigate the accuracy loss caused by quantization. Secondly, we propose a robust SemCom model deployment strategy in local device and honest but curious edge server for privacy-preserving, where a post-training quantization method on the device is proposed to reduce the computational overhead and enhance privacy preservation. Thirdly, we propose a SemCom model based adaptive device-edge collaborative inferencing mechanism for SemCom quality of AI services (QoAIS), where a Joint Quantization Device-Edge Collaboration Semantic Communication (JQDESC) scheme is formulated. Moreover, we provide a theoretical analysis of the privacy preservation of the proposed quantization scheme against model inversion attack through back-propagation and quantization error accumulation. Experimental results demonstrate that our proposed JQDESC scheme effectively protects privacy under various adversarial capabilities, and has better performance in memory usage and end-to-end latency while maintaining similar accuracy.

Federated learning (FL) has emerged as a promising distributed paradigm that enables collaborative model training while preserving data privacy, but it suffers from performance degradation due to data heterogeneity. Although clustered federated learning (CFL) attempts to address this challenge by grouping clients with similar data distributions, existing methods are inefficient in capturing client data representations, leading to incorrect cluster identities and inferior cluster performance. To overcome these limitations, we propose an efficient prototype-based CFL framework (FedPC). Specifically, we introduce a dual-prototype strategy combining specific prototypes and generalized prototypes to capture class representations for cluster identities, along with a prototype-contrastive training mechanism that maximizes intra-cluster prototype consistency to improve cluster performance. Extensive experiments on medical imaging datasets (BloodMNIST and DermaMNIST) demonstrate that the FedPC outperforms nine state-of-the-art (SOTA) approaches, achieving average improvements of 2.17% and 3.47%, respectively. Furthermore, the FedPC reduces communication overhead by 3.33 to 5.68 times compared to SOTA methods, showcasing its efficiency in real-world FL scenarios.

Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offset mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level fusion method of multi-BS sensing information is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherently accumulated to improve sensing accuracy. Moreover, a low-complexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differential privacy (DP) mechanism. We analyze the convergence upper bounds of individual PL models by considering the impact of the mechanism (i.e., quantization errors and Gaussian DP noises) and imperfect communication channels on the FL of WPFL. By minimizing the maximum of the bounds, we design an optimal transmission scheduling strategy that yields min-max fairness for WPFL with OFDMA interfaces. This is achieved by revealing the nested structure of this problem to decouple it into subproblems solved sequentially for the client selection, channel allocation, and power control, and for the learning rates and PL-FL weighting coefficients. Experiments validate our analysis and demonstrate that our approach substantially outperforms alternative scheduling strategies by 87.08%, 16.21%, and 38.37% in accuracy, the maximum test loss of participating clients, and fairness (Jain's index), respectively

Future mobile wireless networks will catalyze substantial demand for precise distributed cooperative positioning (DCP), especially when the global navigation satellite systems are unavailable. However, conventional message passing based DCP methods may suffer considerable performance degradation due to message approximation and sparsity/mobility of nodes. In this paper, we first present a high-accuracy parametric message approximation method, which achieves closed-form representations of all types of messages involved and reduces the computational complexity of message passing procedures. Using these representations, we propose a model- and data-driven hybrid inference approach, dubbed graph neural network enhanced spatio-temporal message passing (GNN-STMP), which fine-tunes parametric messages passed on factor graph and obtains more accuratea posterioridistribution of nodes’ positions by exploiting GNN-generated messages. Furthermore, we develop a universal framework for the parametric message passing based DCP problem, by integrating GNN-STMP with the extend Kalman filter based node’s state prediction and refinement. This framework significantly reduces the positioning ambiguity caused by insufficient spatial ranging measurements from neighbor nodes. Simulation results and analyses demonstrate that, compared with state-of-the-art methods, our proposed approaches achieve the best and near-best positioning accuracy when insufficient and sufficient spatial ranging measurements are available, respectively, while incurring modest computational complexity.

Terahertz (THz) massive multiple-input multiple-output (MIMO) has recently been expected to address the challenges of extremely high data rates, high reliability and low latency for many future use cases such as immersive communications. This paper investigates the upper bound of the roundtrip interaction delay violation probability (UB-RIDVP) for immersive communications in a THz massive MIMO based communication system through stochastic network calculus (SNC). Specifically, the system design adopts the split rendering introduced in 3GPP TR 26.928, based on which not only the uplink and downlink queuing delays, but also the processing delays at the engine side and the user terminal (UT) side are included in the roundtrip interaction delay of the immersive communications. The traffic arrivals and wireless channels in the uplink and downlink are characterized by carrying out the SNC analysis. We derive the expression of the UB-RIDVP, and solve its parameters through the proposed Solving UB-RIDVP Algorithm. The numerical results show that the theoretical UB-RIDVP can reasonably estimate the trend of its violation probability. When a roundtrip interaction delay bound constraint is given as a performance metric, the proposed analytical approach can be utilized to guide the system design.

In immersive communication, delivering real-time, high-resolution 360-degree panoramic videos imposes extremely high demands on network performance. In this paper, we propose a rate splitting multiple access (RSMA)-enabled adaptive panoramic video semantic transmission (APVST) framework. Specifically, APVST is built based on the deep joint source-channel coding (JSCC) structure and achieves adaptive semantic extraction and variable-length coding of panoramic frames. Additionally, APVST employs an entropy model and a latitude adaptive module to jointly achieve rate control, and utilizes a weight attention module to enhance the panoramic video quality. Given the overlapping field of view (FoV) when users watch panoramic videos, RSMA is integrated into the semantic transmission to further improve system efficiency. Therefore, we introduce an RSMA-enabled semantic stream transmission scheme, and formulate a joint optimization problem for latency and video quality by optimizing power, common rate, and channel bandwidth allocation ratios, aiming to maximize the users’ quality of service (QoS). To address this problem, we develop a deep reinforcement learning (DRL) approach based on the proximal policy optimization (PPO) algorithm, which integrates semantic-level FoV information to effectively adapt to dynamically changing environments. Simulation results indicate that our proposed APVST reduces bandwidth consumption by 20% compared to semantic video transmission schemes and 45% compared to traditional ones. Furthermore, our research validates the effectiveness of RSMA in panoramic video semantic transmission, demonstrating QoS improvements of up to 20% compared to other multiple access schemes.

In this paper, we introduce an end-to-end non-orthogonal multiple access (NOMA) framework for the downlink transmission of correlated information sources in the multi-user scenario, in which the data required or transmitted by multiple users share similar content. To enhance the end-to-end transmission performance, we resort to the semantic communication paradigm and build our system based on the deep joint source-channel coding (D-JSCC) scheme. Inspired by Wyner’s common information, an information theoretical concept, the common information (CI) extraction is proposed to capture the correlation between multiple users effectively. By relaxing the constraint of the object function, equivalency can be established between common information extraction and mutual information maximization. Thereby, the Jenson-Shannon divergence (JSD) is adopted in the loss function for learning the common information representation (CIR). In order to categorize the theoretical performance limit of the proposed system, semantic synonymous mapping (SSM) based information theory is applied for analyzing the effect of correlation level and different decoding schemes on the achievable channel capacity. Specifically, the analytical expression of channel capacity under additive white Gaussian noise (AWGN) and Rayleigh channel is derived and verified by Monte-Carlo experiments. By conducting simulations on three different image datasets, it is verified that our proposed scheme can outperform a series of other state-of-the-art (SoTA) multiple access or distributed source coding (DSC) schemes under up to seven user scenarios. Besides, the visualization and ablation study results validate the effectiveness of the common information extraction.

The emergence of large language models (LLMs) with multi-task generalization capabilities is expected to improve the performance of artificial intelligence (AI)-as-a-service provision in 6G networks. By fine-tuning LLMs, AI services can become more precise and tailored to the demands of different downstream tasks. However, centralized fine-tuning paradigms pose a potential risk to user privacy, and existing distributed fine-tuning methods incur significant wireless transmission burdens due to the large-scale parameter transmission of LLMs. To tackle these challenges, by leveraging the low rank feature in LLM fine-tuning, we propose a wireless over-the-air federated learning (AirFL) based low-rank adaptation (LoRA) framework that integrates LoRA and over-the-air computation (AirComp) to achieve efficient fine-tuning and aggregation. Based on multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), we design a multi-stream AirComp scheme to fulfill the aggregation requirement of AirFL-LoRA. Furthermore, by deriving an optimality gap, we gain theoretical insights into the joint impact of rank selection and gradient aggregation distortion on the fine-tuning performance of AirFL-LoRA. Next, we formulate a non-convex problem to minimize the optimality gap, which is solved by the proposed backtracking-based alternating algorithm and the manifold optimization algorithm iteratively. Through fine-tuning LLMs for different downstream tasks, experimental results reveal that the AirFL-LoRA framework outperforms the state-of-the-art baselines on both training loss and perplexity, closely approximating the performance of FL with ideal aggregation.

Semantic Communication (SemCom) has opened a new paradigm in the 6G system. However, the performance of SemCom can be severely affected by time-varying path loss, channel noises, and other interference in wireless channels. Therefore, we propose a novel Semantic Prior aided Channel-adaptive Equalizing and De-noising SemCom (SP-EDNSC) framework, where adaptive elimination channel impact is regarded as an inverse problem. This inverse problem is addressed through semantic priors learned from score-based generative models cached in knowledge base. To reduce distortion while enhancing perceptual quality, we further combine autoencoders, adversarial learning and diffusion models to develop a latent diffusion-based (SP-Latent-Diff EDNSC) system within the SP-EDNSC framework. In the semantic space, the joint semantic equalizer and de-noiser module utilizes the proposed latent diffusion posterior sampling method. This method iteratively executes a modified reverse stochastic differential equation to sample clean semantic features, using the time-dependent score function of likelihood and semantic priors. The semantic priors are derived from pre-trained latent diffusion models, while the likelihood is approximated by a multivariate normal distribution. Simulations demonstrate that our scheme achieves superior performance in both distortion metrics like PSNR and SSIM, as well as in perceptual performance (LPIPS).

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems.

Semantic communications (SemComs) which utilize the inherent meanings and relationships of data, have shown significant advantages for information transmission in recent years. In this paper, a novel semantic base (Seb) enabled SemCom framework is proposed, where Sebs, the basic units of fine-grained image semantics, are explicitly shared among transceivers to support image transmission. Specifically, first, to improve source coding efficiency, a Seb-based image codec is proposed, where semantics in each image patch are encoded with synchronized Sebs, that are generated from recent images. To ensure semantic consistency among the embeddings of Sebs, Gray coding is used to establish the projection, enhancing the framework’s robustness against channel noise. The details of images are further refined by a residual codec for high-quality reconstructions. Second, to enhance transmission reliability with overhead as small as possible, a semantic-aware fine-grained hybrid automatic repeat request (SAFG-HARQ) is proposed, where only erroneous Sebs are retransmitted to precisely refine corrupted semantics using contextual correlations. Extensive simulations demonstrate that the proposed framework outperforms state-of-the-art works, where the image reconstruction quality is improved by 20% in learned perceptual image patch similarity (LPIPS), with a 60% reduction in transmission costs.

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless networks can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, through server-device collaboration at the wireless network edge. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at the edge server of the base station (BS), while distributing the expert networks across the mobile devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

The memristive crossbar array (MCA) has been successfully applied to accelerate matrix computations of signal detection in massive multiple-input multiple-output (MIMO) systems. However, the unique property of massive MIMO channel matrix makes the detection performance of existing MCA-based detectors sensitive to conductance deviations of memristive devices, and the conductance deviations are difficult to be avoided. In this paper, we propose an MCA-based detector circuit, which is robust to conductance deviations, to compute massive MIMO zero forcing and minimum mean-square error algorithms. The proposed detector circuit comprises an MCA-based matrix computing module, utilized for processing the small-scale fading coefficient matrix, and amplifier circuits based on operational amplifiers (OAs), utilized for processing the large-scale fading coefficient matrix. We investigate the impacts of the open-loop gain of OAs, conductance mapping scheme, and conductance deviation level on detection performance and demonstrate the performance superiority of the proposed detector circuit over the conventional MCA-based detector circuit. The energy efficiency of the proposed detector circuit surpasses that of a traditional digital processor by several tens to several hundreds of times.

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.

In this paper, we propose a stochastic geometry based innovative model to characterize the impact of the limited-size distribution region of terrestrial terminals in terrestrial-to-aerial networks by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the limited-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the terrestrial-aerial (T-A) links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and unmanned aerial vehicles (UAVs), and conduct a thorough analysis of the coverage probability of the T-A links under Nakagami fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic terrestrial-to-aerial networks involving nomadic aerial base-stations and terrestrial terminals confined in a limited-size region.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). In particular, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.

Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face challenges with conflicting requirements between code-book design and digital constellation modulation. Traditional codebooks need wide index ranges, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. In addition, a residual vector quantization (RVQ) mechanism is also integrated for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Finally, to simulate modern image transmission scenarios, we employ a diverse collection of high-resolution 2K images as the test set. The experimental results consistently demonstrate the superior performance of MOC-RVQ over conventional methods such as BPG or JPEG. Additionally, MOC-RVQ achieves comparable performance to an analog JSCC scheme, while needing only one-sixth of the channel bandwidth ratio (CBR) and being directly compatible with digital transmission systems.

Multiple access is one of the primary issues for multi-user semantic communication systems. In this paper, we propose a novel pair of semantic difference (SeD) aware NOMA transceivers for downlink semantic-based image transmission, which mitigates the semantic-level interference among semantic streams. In specific, a SeD-aware superposition coding (SC) technique is proposed to suppress the semantic-level interference by coupling the semantic symbols of higher inter-feature semantic difference, which makes the interfering semantic symbols be identified and filtered out by the corresponding decoding function. The SeD-aware successive interference cancellation (SIC) technique further reduces the semantic-level interference by estimating the transmitted semantic symbols with the joint semantic and channel (JSC) autoencoder. Simulation results show that the proposed transceivers achieve comparable performance with benchmarks of OMA-aided transmission, while outperforming the benchmark of SeD-unaware NOMA transceivers in terms of the quality of reconstructed images and outperforming both benchmarks in terms of semantic transmission efficiency.

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT) devices to extrct their visual information from unstructured data. However, it is hard to deploy a complete DNN model at resource-constrained IoVT devices to fulfill their latency, energy, and inference accuracy demands. Exploiting the reachable and available computing resources of IoVT devices and mobile-edge computing (MEC) servers, we propose an edge-device collaborative DNN inference framework to empower resource-constrained IoVT devices to perform DNN-based inference. Especially, the DNN model partition separates the DNN model into two parts, which are deployed on both the IoVT devices and multiaccess MEC server for performing inference collaboratively. The DNN early exit and computation resource allocation are employed to accelerate the DNN inference while guaranteeing the inference accuracy. Moreover, a metric to measure the inference performance of average latency and accuracy (IPLA) is designed. Joint multiuser DNN partitioning, early exit point selection, and computation resource allocation are optimized to maximize the tradeoff performance of inference latency and accuracy. We model the optimized problem as an Markov decision process and propose a deep deterministic policy gradient-based edge-device collaborative DNN inference algorithm to solve the problem of huge state space and high-dimensional continuous actions. Experiments are conducted with the Alexnet model on the data set of CIFAR-10 and Resnet-50 model on the data set of ImageNet. Simulation results verify that the proposed algorithm speeds up the overall inference execution of IoVT devices while guaranteeing inference accuracy.

With the development of communication in the post-5G era, the combination of communication and cloud computing becomes closer. In order to promote the further development of cloud-network, this paper will study the performance of Multi-server cloud Database under the Communication quality of quasi-static Rayleigh fading channel with Multiple antennas(MC-MD). The CLIENTS with unlimited customers, a COMMUNICATION SYSTEM subject to quasi-static Rayleigh fading, and CLOUD DATABASE with two-phase locking protocol are the three components of the MC-MD model. Transactions are 1)initiated by the CLIENTS, 2)transmitted to the CLOUD DATABASE through the COMMUNICATION SYSTEM for processing, 3)then returned to the CLIENTS. The indicators of the model is mathematically derived by using queuing theory. These include client’s indicators(average concurrent quantity of the system in steady state(CQ), average transactions stay time of the system in steady state(ST), average queue length of the Waiting Area in steady state(QL), and average transactions wait time of the Waiting Area in steady state(WT)) and server’s indicator(average number of service desks in the busy period at steady state(DN)). Under the appropriate conditions, the results indicate that the theoretical value of service performance is basically consistent with the simulation value. Clearly, the high speed improves the service performance of the system and decreases the service pressure. On the basis of this, the optimization strategy is proposed and the simulation indicators Jitter of transactions sojourn time of the system in steady state(STJ) is added. The results show that the transaction scheduling optimization strategy effectively reduce the delay and its jitter.

Reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) has recently emerged as a promising candidate to improve the energy and spectral efficiency of Internet of Things (IoT) systems. This paper aims to develop an efficient channel estimation scheme for RIS-assisted MIMO IoT systems within structured Bayesian learning framework. However, the high-dimensional channel matrix with considering its underlying structured sparsity makes efficient channel estimation scheme design a challenging task. To deal with it, we firstly formulate the cascaded RIS-assisted MIMO channel estimation as a generic sparse signal recovery problem with considering the constructed two-level block-structured sparsity of channels. Secondly, we design a flexible prior model to characterize such structured sparsity of channels, in which hierarchical hyperparameters are introduced, and the iterative Bayesian learning-based method is developed to autonomously estimate channels and the hyperparameters associated with the prior model. Thirdly, to relieve the high-computational complexity involving matrix inversion when calculating the posterior of channels, we develop efficient methods from two perspectives. On the one hand, an inverse-free method is developed by relaxed evidence lower bound (ELBO) maximization with an adjustable factor of reducing the gap between the standard ELBO and relaxed ELBO. On the other hand, a method of reducing the dimension of sparse representation matrix aided by external block-structured sparsity is developed. Finally, the computational complexity and convergence properties of the proposed methods are analyzed in detail. Simulation results are provided to verify the superiority of the devised channel estimation methods.

With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.

In a hybrid scenario of 5G and Industrial Internet of Things (IIoT), there is a lack of a theoretical tool to analyze probabilistic end-to-end (E2E) delay. In this article, we provide a comprehensive procedure, which is based on stochastic network calculus (SNC) with moment-generating functions (MGFs), for calculating the E2E delay violation probability for the target traffic in IIoT. The particularity of the scenario is that the E2E network is composed of two segments: 1) the industrial wireless link challenged by the complex fading channel and 2) the multinode wired network that supports common schedulers. An improved concatenation theorem is proposed to calculate the service capability of the E2E network, and a method based on Meijer G-functions is proposed to calculate the MGF for the service processes of various wireless fading channels. We investigate the impacts of various resource allocation strategies (on both wireless and wired networks) and parameters (e.g., bandwidth, weight, and cycle time) on probabilistic E2E delay and provide numerical performance bounds. We show that the capability joint adaptation of the wireless and wired networks is the key to E2E service guarantee. Moreover, related parameters such as the weights and message sizes should be carefully considered to improve E2E delay.

The wireless control system (WCS) operating with massive ultra-reliable and low-latency communications is viewed as a promising technology for the Industrial Internet of Things (IIoT). However, the co-design of sensing, control, and communications is full of challenges, and the trade-off between reliability and control performance in a closed-loop WCS under blind areas of the wireless network’s coverage is still to be solved. In this paper, we exploit reconfigurable intelligence surface (RIS) to assist the device located in the blind areas of the wireless network’s coverage in transmitting sensing and control information over the wireless channels. Furthermore, we formulate a joint optimization of reliability and control cost for a RIS-assisted closed-loop WCS. Specifically, we apply a linear quadratic regulator (LQR) cost to measure the control performance, and a trade-off performance metric called reliability-to-control efficiency (RCE) is proposed for the WCS. In addition, we maximize the minimum RCE of IIoT devices while meeting the requirements of reliability, control cost, and communication resources. An alternating optimization-based maximum the minimum RCE algorithm (AO-MmRCEA) is formulated to jointly optimize the transmission power, transmission time, beamforming, and reflecting coefficients of RIS. The convergence and complexity of the proposed AO-MmRCEA algorithm are analyzed. Simulation results demonstrate the convergence and effectiveness of the proposed AO-MmRCEA algorithm for closed-loop WCS.

This article proposes a secrecy and energy-efficient device-edge co-inference scheme for resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer security assistance. Our approach leverages split learning, where the AIoT device executes the initial part of the AI model, and the mobile edge computing server (MECs) computes the remainder, reducing energy consumption (EC) and inference delay. We measure secrecy capacity under the finite blocklength regime to address the vulnerability of intermediate feature data (IFD) to eavesdropping over wireless channels and its short block length characteristics. The objective is to minimize the average EC of the device-edge co-inference by jointly optimizing deep neural network (DNN) model partitioning and resource allocation. We formulate a distributed reinforcement learning-based joint DNN model partitioning and resource allocation (DRPA) algorithm, which uses knowledge-based reinforcement learning for optimal DNN partitioning and a convex optimization approach for resource allocation. Simulation results demonstrate that the DRPA algorithm achieves near-optimal performance, closely matching the results of exhaustive search methods.

Integrated sensing and communication (ISAC) base stations can provide communication and wide range sensing for vehicles via downlink (DL) transmission, thus enhancing the driving safety. One major challenge for achieving the high performance of communication and sensing is how to deal with the DL mutual interference among adjacent ISAC base stations, which includes not only communication-related interference but also sensing-related interference. In this article, we establish a DL mutual interference model of adjacent ISAC base stations, and analyze the relationship between the communication and sensing mutual interference channels. To mitigate the mutual interference, we propose a collaborative precoding design for adjacent base stations under the transmit power constraint and constant modulus constraint. To solve the nonconvex collaborative precoding design problem, we first relax the problem into a convex programming by omitting the rank constraint, and propose a joint optimization algorithm to solve the problem. To reduce computational complexity, We further propose a sequential optimization algorithm, which divides the collaborative precoding design problem into four subproblems and finds the optimum via a gradient descent algorithm. Finally, we evaluate the collaborative precoding design algorithms by considering sensing and communication performance via numerical results.

As the proliferation of sophisticated task models in 5G empowered digital twin, it yields significant demands on fast and accurate model training over resource-limited wireless networks. It is vital to investigate how to accelerate the training process based on the salient features of practical systems, including heterogeneous data distributions and system resources both across devices and over time. To study the non-trivial coupling between participating device selection and their appropriate training parameters, we first characterize the dependency of convergence performance bound on system parameters, i.e., statistical structure of local data, mini-batch size and gradient quantization level. Based on the theoretical analysis, a training efficiency optimization problem is formulated subject to heterogeneous communication and computation capabilities among devices. To realize online control of training parameters, we propose an adaptive batch-size assisted device scheduling strategy, which prioritizes the selection of devices that offer good data utility and dynamically adjust their mini-batch sizes and gradient quantization levels adapting to network conditions. Simulation results demonstrate our proposed strategy can effectively speed up the training process as compared with benchmark algorithms.

The randomness and complexity of the wireless channel is challenging to meet the various quality of service (QoS) for different wireless communication application scenarios. Reconfigurable intelligent surface (RIS) technology has been proposed to achieve dynamic control of signal propagation over the wireless medium, and thus enables intelligent reconstruction of the channel environment. Moreover, age of information (AoI) has been proposed to quantify the timeliness of status update information accurately, which is new QoS metric. However, the AoI-driven statistical QoS guarantee problem in the RIS-enabled wireless network is not trivial and needs to be solved. In this paper, we employ the AoI violation probability to measure the reliability requirement for maintaining the freshness of status updates and derive its upper bound. Then, we formulate the AoI-driven effective capacity maximization problem. Finally, we transform the formulated problem into a signal-to-noise ratio (SNR) maximization problem, and further propose a twin delayed deep deterministic policy gradient (TD-DDPG) based joint optimization algorithm for obtaining the effective decisions on transmission power of device and the phase shift of RIS. Simulation results show that the TD-DDPG-based scheme has better performance than other traditional schemes.

The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, Physical Layer Authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low Signal-Noise Ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multi-attacker detection architecture based on multi-dimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multi-locations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but can not precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor datasets demonstrate the superiority of the designed detection system in Adjusted Mutual Information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.

We propose a novel task-oriented and semantic-aware heterogeneous networks (TOSA-HetNets) framework for multitype Artificial Intelligence of Things (AIoT) devices with various requirements, where the dense edge servers with different transmission capabilities, computing resources, and power consumption are divided into different layers to provide on-demand collaboration for AIoT devices located in accessible areas. Moreover, we propose a device–edge collaboration intelligent tasks inference scheme between edge servers and AIoT devices in TOSA-HetNets, it includes AIoT devices performing semantic features extraction and uploading the corresponding semantic features to the associated edge servers, multiple layers of edge servers collaborating with AIoT devices to execute the intelligent tasks and transmit the intelligent task results back to AIoT devices. To investigate the performance of TOSA-HetNets in supporting device–edge collaboration intelligent tasks inference, we adopt stochastic geometry to obtain the closed-form expressions of average task success probability, power consumption, and network throughput in the downlink transmission. Furthermore, we define a metric of average achievable task back-transmission energy efficiency (TBT-EE) to measure the information bit of successfully transmitted correct intelligent task results with unit power consumption, which is a function of average task success probability, average network throughput on the unit area, and the total power consumption. Meanwhile, we maximize the average achievable TBT-EE by optimizing the density of edge servers and the average semantic compression ratio. Simulation results verify the correctness of the obtained closed-form expressions and show that the edge servers’ density and average semantic compression ratio have different influences on the performance of TOSA-HetNets.

Symbiotic radio (SR) is a promising technology to support ultralow-power or even zero-power Internet of Things (IoT) devices in the sixth-generation mobile networks. In this article, we propose an energy-aware symbiotic transmission in a reconfigurable intelligent surface (RIS) enhanced SR system, in which an IoT network embeds its own data passively over cellular downlink signals by backscattering. The base station (BS) serves multiple cellular users (CUs) through time division multiple access (TDMA) and each IoT device is associated with one CU. We formulate the BS’s energy minimization problem subject to the constraints of the minimum amounts of transmission bits required by IoT devices and CUs. The user association, the active transmit beamforming at the BS, the passive reflecting beamforming at the RIS, and the frame division policy are jointly optimized. The formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard and nonconvex. We decouple the problem and solve the subproblems alternatively. First, we design a many-to-one swap-matching-based algorithm to solve the user association subproblem. Then, we develop a joint cooperative beamforming and time allocation optimization algorithm based on the alternative optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that the proposed joint user association and cooperative beamforming algorithm brings significant performance gain in reducing the energy consumption of the BS with fast convergence speed compared with other schemes.

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization root mean square error (RMSE) by about 6 dB compared with the maximum likelihood esimation (MLE)-based benchmark method.

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.

In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.

Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

传统无线通信系统广泛利用了远场空间资源。随着6G网络的出现, 近场资源的探索和利用势在必行。这些资源为无线通信系统引入了新的物理空间维度。通过利用更高频段并结合智能超表面(RIS)、超大规模多入多出(XL-MIMO)和无蜂窝网络等技术, 近场通信将成为6G网络的关键推动因素。这种范式转变挑战了传统的远场平面波假设, 需要重新评估空间资源管理策略。 尽管传统系统已有效利用远场空间资源, 但在6G网络中采用近场空间资源为重新定义无线通信系统提供了机会。这种向近场通信的转变促进了对创新技术范式的研究。近场通信有可能显著提高频谱效率、数据传输速率和空间分辨率, 从而在增强现实、高精度定位、通感一体化以及安全无线能量传输等领域实现先进应用。影响近场通信开发和应用的关键因素包括近场传播和信道建模、提高空间资源利用率、硬件挑战以及工程实践与标准化。这些领域强调了近场通信的多面性, 反映了在标准化工作的同时, 对建模、技术、硬件开发和工程实践进步的需求。近场通信具有推动无线技术发展的变革潜力, 为消费者、工业和安全等领域的应用提供了新的可能。 在此背景下, 中国工程院院刊《信息与电子工程前沿(英文)》邀请张平院士担任主编, 赵亚军总工、戴凌龙教授、Marco di Renzo教授担任执行主编, 组织出版了“近场通信理论与应用”专刊。专刊收录12篇文章, 包括2篇综述、5篇研究、5篇通讯, 内容涵盖近场传播基本原理、信道模型的发展、传统机制在近场环境中面临的限制等, 此外包含XL-MIMO信道研究、同时无线信息和能量传输(SWIPT)系统以及RIS的最新研究进展, 以及它们在增强通信系统中的应用。

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE’s AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.

Semantic communication shows great promise in reducing network traffic and alleviating spectrum shortage. While many semantic theories have been put forward, how to measure the importance of semantic information theoretically remains an open issue. In this paper, we propose semantic value, a metric that measures the importance of semantic information, for text transmission. First, we model a semantic communication system for text transmission, in which semantic information is represented by semantic triplets. Then, we propose a hybrid communication mechanism to ensure the success of text transmission. Finally, we compare the performances of the conventional mode and the semantic mode in terms of latency and derive conditions leading to minimum latency.

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-layer authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multiobservation-multichannel-attribute (MOMCA) based multiuser authentication architecture, which considers both the observations of multireceivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multiusers. To better fit the channel features of multiobservations, we proposed two gradient boosting optimization-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid overfitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose aresidualenergy andwirelessaware PS design for efficientFLtraining over mobile devices (REWAFL). REWAFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REWAFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REWAFL is effective in improving training accuracy and efficiency, while avoiding flat battery of mobile devices.

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modalGenerativeSemanticCommunications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

The continuous aperture of Holographic MIMO enables us to encode and transmit information spatially. This paper investigates the achievable rate of linear Holographic MIMO with arbitrary aperture-length using the finite blocklength information theory. Specifically, we first employ the prolate spheroidal wave functions to expand the received wavenumber band-limited electromagnetic field. This orthogonal representation enables two schemes to convey information related to the normal additive white Gaussian noise (AWGN) channel and the non-normal AWGN channel, namely the NA and NNA schemes, respectively. Then we derive the accurate achievable rates and the converse bounds of the two schemes by extending the$\kappa \beta $bound in finite blocklength information theory. Moreover, we derive an approximate closed-form expression of the achievable rate in the large aperture-length regime based on normal approximation. The approximation indicates that for a given space efficiency, the error probability decreases rapidly as the aperture length L increases, with the rate of decline determined by$Q\left ({{O\left ({{\sqrt {L}}}\right)}}\right)$. Finally, we obtain the asymptotic results when the aperture-length tends to infinity. Numerical results demonstrate that the NA scheme outperforms the NNA scheme when the blocklength is small, while the NNA scheme excels in the large blocklength regime. The accuracy of the approximation and the validity of the asymptotic results are verified.

The scope of the 6G wireless communication system is envisioned to expand beyond delivering data to humans and towards connecting machines that constantly upload computation-intensive status updates to obtain real-time situational awareness. Under dynamic environments, the amount of useful information contained in status updates degrades over time, which could be measured based on the concept of age of information. In this paper, we develop an analytical framework to investigate the temporal value of status updates, in terms of the peak age of information. Given the temporal dynamics of observed physical process, the procedure of transmission and computing is modeled as tandem queues for both parallel processing and series processing modes at the edge server. The obtained closed-form expressions explicitly characterize the coupling among information generation, transmission, and usage, which can be exploited as performance metrics for task-oriented resource optimization. The accuracy of our analysis is verified with simulation results. Based on the theoretical analysis, we formulate an optimization problem to simultaneously minimize the age of status updates and energy consumption for multiple devices. Numerical results reveal that the computation and transmission time could be traded off to obtain timely status updates at low energy cost.

Multiple access technologies are critical technologies in every communication era. As a promising paradigm for next-generation mobile communication, semantic communication has explored new semantic information space resources. Based on the characteristic that different semantic models cannot understand semantic information generated by other models, we propose the concept of semantic orthogonal signals. Combining the advantages of Deep joint source and channel coding (DeepJSCC), an Orthogonal-Model Division Multiple Access (O-MDMA) technology that can be applied to any semantic model is proposed. The essence of O-MDMA is to migrate the anti-interference capability of DeepJSCC to the multi-user capacity. Compared with Non-Orthgonal Multiple Access (NOMA) and Model Division Multiple Access (MDMA) technologies, O-MDMA has better performance. The O-MDMA can be integrated with NOMA, and experimental results show that the combined technique can save more bandwidth.

Remote zero-shot object recognition, which involves offloading the zero-shot recognition task from one mobile device to a remote mobile edge computing (MEC) server or another mobile device, is crucial for 6G. To address this challenge, this paper presents a lightweight semantic knowledge base (SKB)-enabled multi-level feature extractor that projects the image into visual, semantic, and intermediate feature spaces. Then, this paper proposes a novel SKB-enabled multi-level feature transmission framework, which utilizes SKB and multi-level feature extractor at both transmitter and receiver. The semantic loss and required transmission latency at each level are characterized, and a multi-level feature transmission optimization problem is formulated to minimize the semantic loss under transmission latency constraint. However, this optimization problem is a multi-choice knapsack problem, which is challenging to solve optimally. To overcome this issue, an enhanced convex concave procedure is proposed to obtain an efficient solution. Furthermore, this paper theoretically analyzes the effects of SKBs on the communication performance when the feature extractors at both ends are the same. Numerical results demonstrate that the proposed design outperforms the benchmarks and provide insights into the impact of SKBs at both ends on performance as well as the tradeoff between transmission latency and zero-shot classification accuracy.

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [$ {\mathrm {O(}}1/V{\mathrm {)}} $,$ {\mathrm {O(}}V{\mathrm {)}} $] with$ V $being a control parameter.

In this paper, we propose a novel interference-suppressed semantic-aware non-orthogonal multiple access (IS-SNOMA) framework for downlink image transmission in the semantic-aware communication networks. The proposed IS-SNOMA is able to mitigate the inter-user interference in the non-orthogonal transmission for multiple semantic-oriented users (SU) or the coexistence of SUs and bit-oriented users (BU). 1) For the homogeneous transmission of semantic streams, we propose a pair of novel semantic difference (SeD) aware IS-SNOMA transceivers to accommodate multiple SUs over the same resource block. A novel SeD-aware superposition coding (SeDSC) technique and SeD-aware successive interference cancellation (SeDSIC) technique are specially designed to mitigate the semantic-level interference. 2) For the heterogeneous transmission of semantic-bit streams, we develop a pair of syntactic difference (SyD) aware IS-SNOMA transceivers to multiplex channels for SUs and BUs. The heterogeneous semantic symbols and bit sequences are superposed and separated with the proposed SyD-aware SC technique (SyDSC) and SyD-aware SIC technique (SyDSIC), respectively. Simulation results demonstrate the advantages of the proposed frameworks in improving the communication efficiency of both SUs and BUs, compared with OMA and NOMA-aided transmission benchmarks.

Remote zero-shot object recognition, i.e., offloading zero-shot object recognition tasks from one mobile device to remote mobile edge computing (MEC) server or another mobile device, has become a common and important task to conquer for 6G. With this goal, this paper first establishes a zero-shot multi-level feature extractor, which projects the image into the visual, semantic, as well as intermediate feature space in a lightweight way. Then, this paper proposes a novel multi-level feature transmission framework powered by a semantic knowledge base (SKB), and characterizes the semantic loss and required transmission latency at each level. Under this setup, this paper formulates the multi-level feature transmission optimization problem to minimize the semantic loss under the end-to-end latency constraint. Such a problem, however, is a multi-choice knapsack problem, and thus very difficult to solve. To resolve this issue, this paper proposes an efficient algorithm based on the convex concave procedure to find an efficient solution. Numerical results show that the proposed design outperforms the benchmarks, and illustrate the tradeoff between the transmission latency and zero-shot classification accuracy, as well as the effects of the SKBs at both the transmitter and receiver on classification accuracy.

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of non-differentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR).

Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence. In this paper, a novel scalable multi-task semantic communication system with feature importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations are investigated by a joint semantic encoder to extract relevant features. Then, a new scalable coding method is proposed based on feature importance ranking, which dynamically adjusts the coding rate and guarantees that important features for semantic tasks are transmitted with higher priority. Simulation results show that SMSC-FIR achieves performance gain w.r.t. individual intelligent tasks, especially in the low SNR regime.

In this paper, we propose a new class of high-efficiency semantic coded transmission methods to realize end-to-end speech transmission over wireless channels. We name the whole system as Deep Speech Semantic Transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we set an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semantic features of different importance can be reasonably allocated with different coding rates, which maximizes the system coding gain. Furthermore, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism such that a single model can be applied over various channel states. The end-to-end optimization of our model leads to a flexible rate-distortion (RD) tradeoff, supporting an adaptive rate wireless speech semantic transmission. Experimental results verify that our DSST system clearly outperforms current engineered speech transmission systems on both objective and subjective metrics. Compared with existing neural speech semantic transmission methods, our model saves up to 75% of channel bandwidth costs when achieving the same quality. Audio samples are available at https://ximoo123.github.io/DSST.

In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resulting in degraded end-to-end transmission performance especially for high-resolution images. To tackle this, the proposed WITT employs Swin Transformers as a more capable backbone to extract long-range information. Different from ViTs in image classification tasks, WITT is highly optimized for image transmission while considering the effect of the wireless channel. Specifically, we propose a spatial modulation module to scale the latent representations according to channel state information, which enhances the ability of a single model to deal with various channel conditions. As a result, extensive experiments verify that our WITT attains better performance for different image resolutions, distortion metrics, and channel conditions. The code is available at https://github.com/KeYang8/WITT.

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (DID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.

The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted communication in FANET. Nevertheless, the conventional methods only utilize the untrusted information that UAV nodes passively “heard” from the “auditory” domain (AD), resulting in severe communication disruptions and even collision accidents. In this paper, we present a novel VA-matching solution that matches the neighbors observed from both the AD and the “visual” domain (VD), which is the first solution that enables UAVs to accurately correlate what they “see” from VD and “hear” from AD to detect the Sybil attacks. Relative entropy is utilized to describe the similarity of observed characteristics from dual domains. The dynamic weight algorithm is proposed to distinguish neighbors according to the characteristics' popularity. The matching model of neighbors observed from AD and VD is established and solved by the vampire bat optimizer. Experiment results show that the proposed VA-matching solution removes the unreliability of individual characteristics and single domains. It significantly outperforms the conventional RSSI-based method in detecting Sybil attacks. Furthermore, it has strong robustness and achieves high precision and recall rates.

We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effectively capture the dependencies within speech frames and estimate the probabilistic distribution of the speech feature more accurately, giving rise to better compression performance. In particular, the speech signals are analyzed and synthesized by a pair of nonlinear transforms, yielding latent features. An entropy model with hyperprior is built to capture the probabilistic distribution of latent features, followed by quantization and entropy coding. The proposed waveform codec can be optimized flexibly towards arbitrary rate, and the other appealing feature is that it can be easily optimized for any differentiable loss function, including perceptual loss used in semantic communications. To further improve the speech quality, we incorporate residual coding to mitigate the degradation arising from quantization distortion at the latent space. Results indicate that achieving the same perceptual quality score, the proposed method saves up to 27% coding rate than widely used adaptive multi-rate wideband (AMR-WB) codec as well as emerging neural waveform coding methods.

The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) is a promising solution to provide flexibility in deploying the networks while reconstructing the wireless propagation environment proactively and cost effectively. We propose a UAV-RIS-assisted finite blocklength transmission framework for machine-type communications (MTCs), where downlink nonorthogonal multiple access (NOMA)-based coordinated multipoint (CoMP) is considered to mitigate intercell-interference and improve cell-edge transmission performance. Considering the cell-edge performance, we aim to maximize the minimum achievable rate of cell-edge devices (CEDs) by jointly optimizing the base stations’ transmission power allocation ratio, subchannel-device matching scheme, RIS reflecting coefficient, and UAV trajectory. To solve the highly coupled nonconvex optimization problem, we propose a double-layer alternating optimization algorithm for maximizing the minimum rate (DLAO-MM) in an iterative manner. Specifically, in theinner layer, we first derive the closed-form solution of power allocation, the propose a low-complexity priority-based subchannel-device matching scheme, and finally solve the RIS phase optimization subproblem. In theouter layer, we propose a successive convex approximation (SCA)-based optimization algorithm for the UAV trajectory planning subproblem. The convergence and effectiveness of the proposed DLAO-MM scheme for UAV-RIS-aided CoMP transmission are evaluated by simulations, which show that: 1) the proposed DLAO-MM scheme is capable of improving the cell-edge performance compared to the benchmark schemes; 2) the combination of UAV and RIS improves the cell-edge performance compared with RIS deployed in a fixed location; and 3) adopting the NOMA scheme in the UAV-RIS-aided CoMP system achieves a higher minimum CED rate than orthogonal multiple access.

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers’ prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named “hierarchical variational autoencoder (HVAE)” for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers’ prior channel information even when trained on a few data in the complex environment. HVAE consists of an autoencoder (AE) module for CIR characteristics extraction and a variational AE (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distributions are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this article, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the tradeoff between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatial-temporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Traditional communication systems treat messages’ semantic aspects and meaning as irrelevant to communication, revealing its limitations in the era of artificial intelligence (AI), such as communication efficiency and intent-sharing among different entities. Through broadening the scope of the traditional communication system and the AI-based encoding techniques, in this manuscript, we present a novel semantic communication system, which involves the essential semantic information exploration, transmission and recovery for more efficient communications. Compared to other state-of-the-art semantic communication-related works, our proposed semantic communication system is characterized by the “flow of the intelligence” via the propagation of the model. Besides, the concept of semantic slice-models (SeSM) is proposed to enable flexible model-resembling under the different requirements of the model performance, channel situation and transmission goals. Specifically, a layer-based semantic communication system for images (LSCI) is built on the simulation platform to demonstrate the feasibility of the proposed system and a novel semantic metric called semantic service quality (SS) is proposed to evaluate the semantic communication systems. We evaluate the proposed system on Cityscapes and Open Images datasets, resulting in averaged 10% and 2% bit rate reduction over JPEG and JPEG2000, respectively. In comparison to LDPC, the proposed channel coding scheme can averagely save 2dB and 5dB in AWGN channel and Rayleigh fading channel, respectively.

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated, which can maximize the computation offloading capacity and suppress inter-functionality interference. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush–Kuhn–Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixed-integer optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multi-tier computing architecture when the BS is resource-limited, and ii) the proposed NOMA-aided JSCMC framework is superior in inter-functionality interference management and can achieve high-quality sensing and computing performance simultaneously compared with other benchmark schemes.

Abstract Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is a promising passive device that contributes to full-space coverage via transmitting and reflecting the incident signal simultaneously. As a new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization (CCO) problem in STAR-RIS-assisted networks, a multi-objective proximal policy optimization (MO-PPO) algorithm is proposed to handle long-term effects. To strike a balance between each objective, the MO-PPO algorithm provides a set of optimal solutions to approach a Pareto front (PF), where the solution on the approximate PF is regarded as an optimal result. Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated. For the AVUS, the improved point is to integrate the action values of both coverage and capacity and then update the loss function. For the LFUS, the improved point is only to assign dynamic weights for both loss functions of coverage and capacity, while the weights are calculated by a min-norm solver at every update. The numerical results demonstrated that the investigated update strategies outperform the fixed weights MO optimization algorithms in different cases, which include a different number of sample grids, the number of STAR-RISs, the number of elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the STAR-RIS-assisted networks achieve better performance than conventional wireless networks without STAR-RISs. Moreover, with the same bandwidth, a millimetre wave is able to provide higher capacity than sub-6 GHz, but at a cost of smaller coverage.

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure finite blocklength transmission framework in machine-type communications (MTC) networks, where the integration of millimeter-wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology is considered to alleviate the problem of insufficient spectrum resources caused by massive MTC devices (MTCDs). For improving the ability of anti-eavesdropping, we aim to maximize the achievable sum secrecy capacity (SC) by jointly optimize the MTCDs’ transmission power, RIS phase coefficient and receive beamforming design. To handle the nonconvexity of the proposed optimization problem, we decouple it into three sub-problems, where the first two are solved by successive convex approximation (SCA) method. A minimum mean squared error successive interference cancellation (MMSE-SIC) scheme is proposed to tackle the receive beamforming problem for uplink NOMA networks. Furthermore, an alternating optimization based joint power, phase, and beamforming allocation (AO-JPPBA) algorithm is developed to implement joint optimization. Simulation results show that: 1) the security performance of the proposed AO-JPPBA is improved by 612.26% than the baseline scheme; 2) the proposed MMSE-SIC beamforming scheme is more effective in improving sum-SC of uplink NOMA networks; 3) RIS’s location has an obvious impact on sum-SC when considering eavesdroppers with strong wiretapping ability.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

With the prosperity of emerging applications, the$6^{th}$Generation mobile communication systems (6G) is coming at an unimaginable speed. It is expected to provide more intelligent, flexible, and secure services. As an essential pillar of 6G networks, ultra-Reliable Low Latency Communication (uRLLC) has promoted the vigorous development of intelligent communications. However, the existing networks cannot fully satisfy the strict and various requirements of uRLLC services, including delay, reliability and security. Considering the interaction between the physical layer and the upper layer, we propose a Cross-layer Flexible Security Solution (CFSS), which includes initiative waiting strategy, flexible transmission time interval scheduling strategy, and flexible pre-backup transmission strategy. While considering secure communication, CFSS could flexibly provide customized services to the users through cross-layer parameters configuration and resource allocation. In addition, we extend the Stochastic Network Calculus (SNC) modeling to the security field, and use Finite Blocklength Coding (FBC) to analyze the service process of uRLLC. Two cases of FBC are considered comprehensively, namely, given decoding error probability and given transmission rate. Finally, Experienced Meta-Asynchronous Advantage Actor-Critic (EM-A3C) algorithm is proposed to solve the complex optimization problem, the establishment of experience pool effectively improves the algorithm efficiency.

The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. A weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. Fi-nally, numerical results show the significant performance gain achieved by the proposed schemes over the benchmark schemes.

Deep learning (DL) enabled semantic communications have been developed to improve the offline communication efficiently and intelligently by exploring the semantic information, while constraining their applications in real-time online scenarios. In this work, we propose SemAudio, the first DL-based streaming semantic communication system for real-time audio processing. To better extract the semantic features of the audio signal, SemAudio employs the Transformer-XL due to its potential to capture long-distance dependency. Moreover, the system works based on a chunk-based mask attention strategy to enable real-time streaming. By incorporating the novel Transformer-XL and chunk-wise approach, SemAudio can effectively learn and extract semantic features from real-time audio data. Furthermore, to alleviate the channel distortion and attenuation, the semantic and channel encoder/decoder are jointly designed by minimizing the mean error in both time and frequency domains rather than the merely time domain. The extensive experimental results suggest that our proposed SemAudio outperforms the traditional communications. Besides, the proposed SemAudio compromises the quality and latency to meet real-time requirements, which obtains satisfactory performance with significantly higher accuracy and lower latency under multiple channel conditions for real-time audio communication.

Abstract With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

As one of the key communication scenarios, ultrareliable low-latency communication (uRLLC) has become an important pillar to promote the vigorous development of intelligent mobile communications. In the practical scenarios, uRLLC services have strict and diverse Quality-of-Service (QoS) requirements. However, the existing networks are difficult to meet the various delay and reliability requirements of uRLLC services. Moreover, the improvement of performance should not ignore the shortage of resources. A flexible and on-demand network solution is quite necessary, which could provide customized services according to the specific requirements and maximize the utilization efficiency of network resources. In this article, we propose an intelligent and flexible network solution (IFNS) based on the stochastic network calculus (SNC) model. Three key technologies are considered in the IFNS, that are flexible transmission time interval scheduling, flexible packet duplication transmission, and rate-adaptive reliable transmission. While providing customized services for users with various requirements, it realizes the balance between system energy efficiency and spectral efficiency and improves the resource utilization efficiency of the network. Based on the basic domain knowledge and the past experience, we propose the knowledge-assistance meta actor–critic (K-MAC) algorithm to solve the complex optimization problem caused by SNC modeling. Finally, simulation results show that the performance of the IFNS is improved 23.15%, the K-MAC algorithm has good convergence performance and reduces the complexity up to 89.4% compared with common learning algorithms.

With the continuous popularization and deepening of the Internet-of-Things (IoT) technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth of wireless communication traffic and the surge of energy consumption make it a great challenge to support various requirements of IoTDs, such as ultrareliable and low latency. The 6th-generation (6G) network has put forward new goals and visions for green communication, network flexibility and intelligence, which are expected to solve these key challenges. In this article, we propose a cross-layer optimization scheme to achieve the trade-off between energy efficiency (EE) and spectral efficiency (SE) of the 6G enabled IoT networks, where the ultrareliable and low-latency applications are considered. Flexible self-organization of three parameters is realized, namely, transmission time interval (TTI), packet duplication (PD), and resource block (RB) allocation. The key technology of flexible TTI scheduling guarantees the reduction of latency, and the PD transmission can effectively improve the reliability. Furthermore, based on machine learning (ML) method, we propose the transfer asynchronous advantage actor–critic (TA3C) algorithm to realize parameter configuration and resource allocation. The simulation results show that the EE and SE tradeoff performance of our proposed flexible scheme is improved by at least 39.29% compared with the fixed parameter configuration. In addition, the TA3C algorithm has better convergence performance and reduces the algorithm complexity by up to 91.23% compared with other ML algorithms.

Two key impediments to reconfigurable intelligent surface (RIS)-aided vehicular communications are, respectively, the double fading experienced by the signal on RIS-aided cascaded links and the high-mobility-induced intractability of acquiring channel state information (CSI). To overcome these challenges, a novel kind of RIS is presented in this paper, namely active reconfigurable intelligent omni-surface (RIOS), each element of which is supported by active loads, that concurrently transmits and reflects the incident signal amplified rather than just reflecting it as compared to the case of a passive reflecting-only RIS. We consider the use of an active RIOS to a vehicular communication system for mitigating double fading effect. Specifically, the active RIOS is mounted on the vehicle window to enhance transmission for users in the vehicle and for adjacent vehicles. We aim to jointly optimize the transmit precoding matrix at the base station (BS) and RIOS coefficient matrices to minimize the BS’s transmit power relying exclusively upon the imperfect knowledge of the large-scale CSI. To significantly relax the frequency of channel information updates, initially an efficient transmission protocol is put forward to reap the high active RIOS beamforming gain with low channel training overhead by appropriately tailoring the time-scale of CSI acquisition. Then, two algorithms, namely an alternating optimization (AO)-based algorithm and a constrained stochastic successive convex approximation (CSSCA)-based algorithm, are developed to tackle with the investigated resource allocation problem, whose pros and cons are elaborated, respectively. Simulation results substantiate the significant performance improvement of active RIOS as well as determine the validity and robustness of our proposed algorithms over various benchmark schemes.

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel coding (NTSCC). In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding. Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features and provide side information for source-channel coding. Unlike existing conventional deep joint source-channel coding methods, the proposed NTSCC essentially learns both the source latent representation and an entropy model as the prior on the latent representation. Accordingly, novel adaptive rate transmission and hyperprior-aided codec refinement mechanisms are developed to upgrade deep joint source-channel coding. The whole system design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under established perceptual quality metrics. Across test image sources with various resolutions, we find that the proposed NTSCC transmission method generally outperforms both the analog transmission using the standard deep joint source-channel coding and the classical separation-based digital transmission. Notably, the proposed NTSCC method can potentially support future semantic communications due to its content-aware ability and perceptual optimization goal.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Multi-path transmission control protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose theReinforcementLearning-based mobile AR/VR multipath transmission with streamingPowerSpectrumDensity analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams.

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over the air. This paper presents a novel framework to balance the accuracy and integrity of AirFL, where multi-antenna devices and base station (BS) are jointly optimized with a reconfigurable intelligent surface (RIS). The key contributions include a new and non-trivial problem jointly considering the model accuracy and integrity of AirFL, and a new framework that transforms the problem into tractable subproblems. Under perfect channel state information (CSI), the new framework minimizes the aggregated model’s distortion and retains the local models’ recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner. Under imperfect CSI, the new framework delivers a robust design of the beamformers and RIS configuration to combat non-negligible channel estimation errors. As corroborated experimentally, the novel framework can achieve comparable accuracy to the ideal FL while preserving local model recoverability under perfect CSI, and improve the accuracy when the number of receive antennas is small or moderate under imperfect CSI.

In this paper, the problem of neural compression based image transmission over wireless channels is studied. Since all procedures are considered over wireless links, the quality of training is affected by wireless factors such as packet errors. In the considered model, compressed data given by the neural source encoder (NSE) are fed into an error-control channel encoder and modulated as discrete symbols sent over a memoryless channel. In the receiving end, the channel decoder and the neural source decoder (NSD) forms an iterative structure to reconstruct the original image. Since all neural compressed data are transmitted over wireless channels, the training of NSD is affected by wireless channel factors such as residual bit errors given by the channel decoder. Meanwhile, during outer-loop iterations, the NSD needs to match the variant of information reliability output by the channel decoder so as to build a global optimal receiver. To this end, a refiner neural network is first attached after the NSD to adjust its output as the format of a priori information sent into the channel decoder. Then, the extrinsic information transfer (EXIT) functions of channel decoder and NSD are derived. At each iteration, the reliability of messages sent into the NSD is explicitly predicted by using the EXIT chart. By this means, the NSD can be trained in a residual bit error aware manner, and we realize a joint learning and iterative decoding framework to ensure the quality of neural image transmission over realistic wireless channels.

Different from public 4G/5G networks that are dominated by downlink (DL) traffic, emerging 5G non-public networks (NPNs) need to support significant uplink (UL) traffic to enable emerging applications such as industrial Internet of things (IIoT). The UL-DL spectrum sharing is becoming a viable solution to enhance the UL throughput of NPNs, which allows NPNs to perform the UL transmission over the time-frequency resources configured for DL transmission in coexisting public networks. To deal with the severe interference from the DL public base station (BS) transmitter to the coexisting UL non-public BS receiver, we propose an adaptive asymmetric successive interference cancellation (SIC) approach, in which the non-public BS is enabled to have the capability of decoding the DL signals transmitted from the public BS and cancelling them for interference mitigation. In particular, this paper studies a basic UL-DL spectrum sharing scenario when a UL non-public BS and a DL public BS coexist in the same area, each communicating with multiple users via orthogonal frequency-division multiple access (OFDMA). Under this setup, we aim to maximize the common UL throughput of all non-public users, under the condition that the DL throughput of each public user is above a certain threshold. The decision variables include the subcarrier allocation and user scheduling for both non-public and public BSs, the receiver mode of the non-public BS over subcarriers, as well as the rate and power control. Numerical results show that the proposed design significantly improves the common UL throughput as compared to benchmark schemes without such consideration.

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.

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.

The ever-growing number of Internet-of-Things (IoT) devices makes multiaccess edge computing (MEC)-assisted status update system more and more attractive, which can be deployed to enable remote data acquisition and analysis from urban space. The ambient computing resource at edge automatically extracts valuable status update information from the data collected by IoT devices, which supports the real-time remote monitoring applications. In this article, we employ the concept of Age of Information (AoI) to quantify the freshness of status updates. To combat the limited battery capacity at IoT devices, energy harvesting (EH) is leveraged to capture the green energy from ambient environment. Specifically, we investigate an age minimization problem by considering the randomness in energy arrivals, heterogeneity in harvesting mode, and the stochasticity in transmission and computing process. The formulated problem is a long-term stochastic optimization problem. Then, we transform the original problem into a series of per-time slot deterministic optimization problem. An online scheduling policy is proposed to obtain the energy management decisions at devices, and the transmission and computing scheduling decisions among multiple devices without any prior knowledge on the network dynamics, which is facilitated to be implemented. Simulation results show that the performance of our proposed algorithm is competitive when compared with other existing schemes.

The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter$V$. Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as$[O(1/V), O(V)]$. Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Thanks to the inherent characteristics of flexible mobility and autonomous operation, unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this article, we employ Age of Information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the Internet of Things (IoT) devices, and investigate an AoI-energy-aware data collection scheme for UAV-assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov decision process. To cope with the multidimensional action space, we develop a twin-delayed deep deterministic (TD3) policy gradient-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep$Q$-network and actor–critic-based algorithms in terms of achievable AoI and energy efficiency.

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned-aerial-vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the 3-D mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are threefold: 1) a new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network; 2) a specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical CA, which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA; and 3) a codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning-enabled UAV position/attitude prediction is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios.

Federated learning-based automotive navigation has recently received considerable attention, as it can potentially address the issue of weak global positioning system (GPS) signals under severe blockages, such as in downtowns and tunnels. Specifically, the data-driven navigation framework combines the position estimation offered by the high-sampling inertial measurement units and the position calibration provided by the low-sampling GPS signals. Despite its promise, the privacy preservation and flexibility of the participating users in the federated learning process are still problematic. To address these challenges, in this article, we propose an efficient, flexible, and privacy-preserving model aggregation scheme under a federated learning-based navigation framework named FedLoc. Specifically, our proposed scheme efficiently protects the locally trained model updates, flexibly supports the fluctuation of participants, and is robust against unregistered malicious users by exploiting a homomorphic threshold cryptosystem, together with the bounded Laplace mechanism and the skip list. We perform a detailed security analysis to demonstrate the security properties in terms of privacy preservation and dishonest user detection. In addition, we evaluate and compare the computational efficiency with two traditional schemes, and the simulation results show that our scheme greatly improves the computational efficiency during participant fluctuation. To validate the effectiveness of our scheme, we also show that only part of the model update is excluded from aggregation in the case of a dishonest user.

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.

In this paper, to investigate sparse channel estimation in OFDM communication systems, we propose a novel binary particle swarm optimization (BPSO) based pilot pattern design scheme and develop an efficient sparse Bayesian learning (SBL) scheme for sparse channel recovery. First, through modifying the mutual incoherence property (MIP) criterion, we outline a new penalty function for optimizing pilot pattern design, which comprehensively takes into account the overall coherence of the measurement matrix. Second, we modify the conventional BPSO algorithm by proposing a new adaptive inertia weight scheme, in which the inertia weight varies with the current state of particle swarm and the number of iterations. Furthermore, we map the pilot pattern design into the framework of the modified BPSO algorithm. Third, we develop a partitioned matrix iterative mechanism to compute matrix inversion in each iteration involved in SBL techniques. Finally, our numerical and simulation results illustrate the efficacy of the BPSO based pilot pattern design scheme and our proposed SBL algorithms in terms of mean-square estimation (MSE) error performance.

We address the problem of sparse channel representation for downlink channel estimation in multi-user frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Existing methods typically adopt discrete Fourier transform (DFT) matrix as a sparse basis to represent sparse channels. However, a sparse basis constructed through dictionary learning method has proven to have strong sparse channel representation. In this work, we develop a discriminative dictionary learning-based sparse channel representation for downlink channel estimation in a multi-user FDD massive MIMO systems. Considering partially shared support between near users, we present a new discriminative dictionary learning (DDL) method for sparse channel representation, based on which the channel estimation scheme is developed. Compared with learning a shared dictionary for all users, it can provide a better representation, thus improving the performance of recovery in the compressive sensing process. Numerical results demonstrate the superior performance of discriminative dictionary as compared to the shared dictionary in terms of normalized mean square error (NMSE) and symbol error rate (SER).

In this letter, we explore the problem of DOA estimation using neural networks for two closely spaced sources. Since the traditional high-resolution techniques based on classical algorithms cannot achieve high-accuracy DOA estimation in the presence of two closely spaced sources, especially at low signal-to-noise ratios (SNR), we propose a novel DOA estimation method based on a cascaded neural network to address this problem. Specifically, this network comprises two parts: the SNR classification network and the DOA estimation network. The latter network contains two estimation subnetworks, which are appropriate for different SNRs by training with noisy data and activated by the output of the SNR classification network. Simulation results demonstrate that the estimation performance of our proposed method achieves much better than that of the existing algorithms under various conditions, especially for the scenes with low SNRs or small snapshot number.

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul.

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.

As the versatile applications emerge, traffic offloading is an urgent issue to improve the performance for the fifth generation (5G) and beyond networks. We focus on the scenario where a device is enabled to transmit to more than one device simultaneously. The device-to-device (D2D) enabled traffic offloading scheme is studied by employing non-orthogonal multiple access (NOMA) and unlicensed access technologies. Our target is to maximize the capacity of the D2D network by optimizing subchannel assignment and power control while guaranteeing the capacity of NOMA-based cellular links and the WiFi system. The formulated problem is a non-convex mixed integer programming problem, which is hard to solve within a rational time. The problem is decomposed into subchannel assignment and power control subproblems. A matching based licensed subchannel allocation algorithm and an unlicensed subchannel access mechanism are proposed. Furthermore, we propose a centralized power control algorithm and a distributed power control algorithm based on global and local information, respectively. Besides, the unlicensed resource management scheme based on Stackelberg game is proposed to achieve the near-optimal utility of both D2D links and the WiFi system. The simulations illustrate that the proposed scheme can increase the throughput of D2D networks efficiently compared with other works.

Spectrum efficiency can be greatly improved through high-accuracy spectrum sensing in cognitive unmanned aerial vehicle (UAV) networks. However, the traditional centralized cooperative spectrum sensing (CCSS) methods are not applicable to the spectrum sensing of cognitive UAV networks, since the mobility of nodes and the dynamicity of network topology make it challenging to gather all the sensing information into a fusion center (FC) quickly enough. To overcome the challenge, this paper proposes a clustering-based distributed cooperative spectrum sensing (c-DCSS) scheme. Specifically, the considered cognitive UAV network is first clustered based on Max-Min distance clustering methods by jointly taking the position, velocity, and moving direction of UAVs in account, and then a two-stage fusion scheme is adopted to execute hierarchical sensing information fusion. Simulation results show that compared to the unclustered DCSS (u-DCSS) scheme, the proposed scheme significantly enhances the spectrum detection performance of cognitive UAV networks, especially when the number of UAV nodes is relatively large.

UAV has attracted a significant amount of attention for its low-cost and diverse applications like video surveillance, auxiliary communication, etc. In this paper, the UAV fire surveillance network is proposed and the maximization of the UAV-centric energy efficiency (EE) is investigated by jointly taking the source/channel rate control and flow routing into account. The design is cast into a cross-layer optimization problem, which is proven to be difficult to solve. In light of it, a parametric transformation approach is adopted to convert the original problem into a tractable form and further decouple it into two independent subproblems. An efficient algorithm consisting of a two-layer iterative algorithm with an inner loop and an outer loop is proposed to solve the transformed problem. Simulation results show the impact of the network configuration on the network-wide EE and the performance of the proposed algorithm.

Fountain codes are promising for unmanned aerial vehicle (UAV) communications with intermittent transmission links caused by high UAV mobility. However, it is challenging to estimate the transmission delay of UAV communication systems with fountain codes due to the uncertainty of the coding rate and the dynamic channel quality. In this paper, we propose a delay estimation method based on a joint buffer-decoder queuing model for UAV communication systems with LT codes, and show that the complexity of the proposed delay estimation method can be reduced from O(n3) to O(n2). Simulation results validate the effectiveness of the proposed delay estimation method.

The performance of coordinated multi‐point (CoMP) transmission system depends heavily on the quality of information exchange via backhaul links. However, the limited capacity of backhaul leads to quantisation error and delay of shared channel state information. In this study, the authors propose a novel hybrid CoMP transmission strategy to mitigate the impacts of the backhaul delay for data sharing and switch the transmission modes adaptively based on the current arrivals of the shared user data. Furthermore, they present analytical expressions for the average sum rate in an interference alignment network, in which the combining effects of quantisation from limited capacity and connection uncertainty introduced by finite delay are considered. With the analytical results, they evaluate the sum rate performance under limited backhaul for the conventional CoMP transmission modes and the proposed strategy. The simulation results demonstrate that the proposed strategy functions better on overcoming the impacts of capacity limited backhaul than the conventional ones.

Artificial intelligence (AI) and big data analytics enable autonomous vehicles (AVs) to dramatically change future intelligent transportation in smart cities. AVs are envisaged to evolve to a service rather than a product in the future. To provide best user experience of such services, three primary factors, namely, waiting time, travel time, and supply of AV services, are taken into consideration in a multiobjective optimization. Conventional optimization of services relies on traffic flow analysis over a queuing network model. However, due to the mobility of vehicles and the transfer uncertainty of road networks, the queuing network analysis is too complicated and practically intractable. For accuracy and convenient processing, network calculus (NC) is extended to model the queueing problem in this paper. The optimal number of available AVs can be identified by guaranteeing the waiting time of customers. The satisfaction of AV services can be viewed as a supply and demand problem, and optimized by bipartite graph matching. In order to reduce the average travel time, especially for rush hours with heavy traffic, we further propose a new online AVs fleet management scheme with congestion control for smart cities. It is shown that the intelligent management of AV fleet can be efficiently achieved, outperforming the cases of traditional vehicles. NC-assisted AI enables an efficient intelligent transportation paradigm in smart cities, while achieving substantial energy saving.

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Things (IoT) devices will be supported by the massive machine type communication (mMTC). Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have the advantages of improving network capacity, reducing MTC devices' (MTCDs) latency and enhancing quality of service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs' on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading.

In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.

Indoor fingerprint location is widely used in passenger flow analysis, location based service, and security monitoring. RSS is usually used as signal fingerprint. However, in these applications the acquisition process relies on the uplink data transmission of the target device. These signals are often non-normally distributed and fluctuate greatly when the target moves. In order to cope with the above difficulties, this paper proposes a hidden markov model (HMM) framework based on targets maximum speed. It combines the speed parameters with path constraints to improve the calculation of state transition matrix. The kernel density estimation method based on the Wiener process is also used to solve the confusion matrix of HMM, which improves the accuracy of estimating the RSS distribution of the uplink signal. The performance of the algorithm are compared with the traditional HMM and NB methods in an actual indoor scenario. The influence of the target moving speed and the length of the observation sequence on the positioning accuracy is also analyzed.

In this paper, we propose a novel three-dimensional (3D) multi-confocal ellipsoid simulation model with uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) communication systems. Firstly, by employing the spherical wavefront, we characterize near-field effects including the angle of arrival (AoA) shifts and Doppler frequency variations in both space and time domains, and we derive the closed-form expressions of impulse responses of the theoretical model. Secondly, we develop a corresponding simulation model with finite and discrete scatterers within a cluster for the theoretical model. Additionally, we develop the cluster evolution algorithm with a 3D extension of our previously proposed scheme for modeling non-stationary properties of clusters. Their impacts on the proposed channel model are investigated via key statistical properties, e.g., the spatial-temporal cross-correlation function. Moreover, we also discuss the impacts of the range of offset angles and the number of scatterers within a cluster on statistical properties of the proposed simulation model. Finally, our numerical and simulation results show that our proposed simulation channel model is able to capture characteristics of massive MIMO channels while well agreeing with the results obtained from the theoretical modeling.

LTE/WiFi Aggregation (LWA) provides a promising approach to relieve data traffic congestion in licensed bands by leveraging unlicensed bands. Critical challenges arise from provisioning quality-of-service (QoS) through heterogenous interfaces of licensed and unlicensed bands. In this paper, we minimize the required licensed spectrum without degrading the QoS in the presence of multiple users. Specifically, the aggregated effective capacity of LWA is firstly derived by developing a new semi-Markov model. Multi-band resource allocation with the QoS guarantee between the licensed and unlicensed bands is formulated to minimize the licensed bandwidth, convexified by exploiting Block Coordinate Descent (BCD) and difference of two convex functions (DC) programming, and solved efficiently with a new iterative algorithm. Simulation results demonstrate significant performance gain of the proposed approach over heuristic alternatives.

The new short-length single-direction frame structure for ultra-dense small cells (SCs) in the next- generation communication systems allows the dynamic time division duplex implemented by flexible transmit direction selection in each frame. Meanwhile, asymmetric uplink (UL) or downlink (DL) modes will introduce more complex and diverse interference in the adjacent SCs. Instead of treating the interference as noise as the conventional schemes do, we utilize some of the interference properties and propose a rate- splitting (RS) strategy to split each message into a common part and a private part. The two parts are decoded using successive interference cancellation (SIC). To maximize the time-averaged sum rate, we design the transmit direction and the covariances of the private and common messages jointly. The problem is NP-hard, as it subjects to mixed Boolean variables, transmit power and SIC decodability constraints. This paper achieves the suboptimal solutions numerically by difference of concave programming and convex-relaxed linear programming. The results show that the proposed strategy brings gain in time-averaged sum rate compared to the conventional schemes using symmetric UL-DL transmission without RS for different users' locations and network sizes. And the results indicate that the proposed strategy improves the performance of the users at the cell boundary and is superior for dense SC networks.

Over-the-air (OTA) radiated testing for multi-input multi-output (MIMO) capable mobile terminals has been actively discussed in the standardization in recent years, where multiprobe anechoic chamber (MPAC) method has been selected. Setting up a multiprobe configuration is costly, so finding ways to limit the number of probes will make the implementation of the test system simpler and cheaper. In this paper, two probe subset selection algorithms for three dimensional (3D) MPAC and fading emulator are proposed, namely, decremental selection algorithm (DSA) and error threshold selection algorithm based on alternating search (SAAS), where the goal is to minimize the number of probe antennas while ensuring the accuracy of the target channel emulation. Simulation results show that a small number of probe sets are selected under the given error threshold by the two algorithms, which greatly saves the cost of setup configuration. The performance of SAAS generally outperform that of DSA, especially when there are fewer probes selected.

Orthogonal matching pursuit (OMP) and its improved algorithms are widely used as hybrid precoding solutions in millimeter wave massive MIMO systems. The existing modified hybrid precoding schemes reduce computational complexity, however, they cause the loss of spectral efficiency to some extent. In this paper, we propose a novel generalized orthogonal matching pursuit (gOMP) algorithm based on order-recursive least squares (ORLS) in order to balance both computational cost and spectral efficiency. Compared to OMP algorithm, the maximum iteration of gOMP-ORLS algorithm can be reduced by choosing more than one vector in each iteration. Meanwhile, it has much lower implementation complexity due to avoiding matrix inversion operation. More importantly, the realized spectral efficiency of the proposed algorithm is higher than other existing algorithms. The simulation results as well as our detailed analysis demonstrate that a) the proposed gOMP-ORLS algorithm can achieve the approximately same spectral efficiency as OMP algorithm; b) it can reduce computational complexity and improve precoding efficiency prominently.

This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio (CR) network that contains relay and massive multi-input multi-output (MIMO). The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the k th user’s asymptotic signal to interference and noise ratio (SINR) is independent of fast fading. When the ratio between the base station (BS) antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users (PUs) and secondary users (SUs) in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information (CSI), including the end-to-end SINR and achievable sum rate. When the numbers of primary base station (PBS) antennas, secondary base station (SBS) antennas, and relay antennas become infinite, the asymptotic SINR of the k th PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled asymptotically. Transmission performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K -user system without fast fading. Next, the PUs’ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna number, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underlay CR network. The primary and secondary networks can operate independently.

The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1-ϵ)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ϵ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(ϵ), O(1/ϵ)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

It is envisioned that the Internet of Things (IoT) will provide promising opportunities to users, manufacturers, and service providers with a wide applicability in many fields. By employing social networking and device-to-device (D2D) communications in the IoT, the resulting social IoT can potentially provide services more effectively and efficiently. This paper focuses on content sharing among smart objects (devices) in the social IoT with D2D-based cooperative coded caching. Generally, complete content items or coded fragments are allowed to be delivered via multi-hop cooperative D2D communications. First, aiming at maximizing the overall success rate of multi-hop-based content sharing, the interplay between coding parameter optimization and wireless resource allocation is investigated by considering both physical and social characteristics. Moreover, a Roth and Vande Vate-based distributed scheme is proposed to solve the dynamic matching problem between the content helpers and content requesters. Numerical results demonstrate that the proposed scheme can achieve a good tradeoff between system performance and computational complexity.

This paper presents a new approach to efficiently maximizing the energy efficiency (EE) of hybrid arrays under a practical setting of non-ideal power amplifiers (PAs) and non-negligible circuit power, where coherent and non-coherent beamforming are considered. As a key contribution, we reveal that a bursty transmission mode can be energy-efficient to achieve steady transmissions of a data stream under the practical setting. This is distinctively different from existing studies under ideal circuits and PAs, where continuous transmissions are the most energy-efficient. Another important contribution is that the optimal transmit duration and powers are identified to balance energy consumptions in the non-ideal circuits and PAs, and maximize the EE. This is achieved by establishing the most energy-efficient structure of transmit powers, given a transmit duration, and correspondingly partitioning the non-convex feasible region of the transmit duration into segments with self-contained convexity or concavity. Evident from simulations, significant EE gains of the proposed approach are demonstrated through comparisons with the state of the art, and the superiority of the bursty transmission mode is confirmed especially under low data rate demands.

This work investigates content sharing among smart objects (devices) via device-to-device (D2D) communications in the social Internet of Things (IoT) by exploiting distributed content coding schemes. Aiming at minimizing the overall transmission cost, the resource matching between content transmitters and content receivers is optimized by applying matching theory. Particularly, the dynamic distributed Roth and Vande Vate algorithm is employed to optimize the matching problem, achieving a lower computational complexity and a higher system stability. In addition, social characteristics among mobile users are also exploited in the optimization problem to further increase system reliability and robustness. Numerical results demonstrate the effectiveness of the proposed scheme.

In this paper, we present a secrecy outage performance analysis of wireless powered communication networks with multiple eavesdroppers, where an energy-limited information source with multiple antennas harvests the radio frequency (RF) energy from a dedicated power beacon (PB) before transmission. To exploit the benefits of multiple antennas at source, two popular multi-antenna transmission schemes, i.e., maximal ratio transmission and transmit antenna selection, are investigated for two intercepting ways at Eves, i.e., non-colluding and colluding scenarios, respectively. Specifically, adopting the time-switching protocol at PB, we derive exact and asymptotic closed-form expressions of the secrecy outage probability for both two transmission schemes taking into account the outdated channel state information (CSI). From our analysis, several important concluding remarks are obtained as follows: a) Full secrecy diversity order can be achieved by both two transmission schemes with no feedback delay, however, it reduces to zero in the presence of feedback delay; b) MRT scheme always outperforms TAS scheme with no feedback delay. However, TAS scheme achieves a similar performance as MRT scheme or even better in moderate and even serious feedback delay conditions.

This paper investigates secrecy efficiency (SE) optimization in a friendly full-duplex (FD) decode- and-forward (DF) relay network with a passive eavesdropper. Targeting on SE maximization, the power allocation is optimized subject to the total power and minimum secrecy rate constraints adapting to the assumption of unknown eavesdropper's channel state information (CSI). By exploiting the properties of fractional programming (FP) and Difference of Convex functions (DC) programming, the resulting nonconvex optimization problem can be relaxed into a more tractable equivalent problem. Simulation results demonstrate that our proposed scheme can reach a better trade-off between security performance and energy consumption.

In this paper, we investigate the average throughput of a multi-antenna wireless powered communication network where an energy-constrained user harvests energy from a hybrid access-point (AP) equipped with multiple antennas in the downlink, and then transmits information to the AP in the uplink using the harvested energy. Specifically, we consider a more practical scenario, i.e., nonlinear energy harvester, as compared with the traditional linear model. In order to evaluate the key parameters, such as the transmit power, antenna numbers, time-splitting, channel fading severity, on the performance of the considered system, we derive closed-form expressions of the average throughput for both delay tolerant and delay intolerant transmission modes in Nakagami-m fading channel. In addition, to further exploit the insights on the application of the considered system, the asymptotic analysis for the achievable throughput are also provided in two special cases, i.e., high transmit power regime and high saturation threshold regime. Finally, our results demonstrate that the considered system exhibits the throughput saturation phenomenon, and the parameters of channel fading severity produce a different impact on the average throughput in the two transmission modes.

By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with 56 × 32 antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2-5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.

Recognizing object location by taking a photo with smartphones is useful for many location-based services. However, start-of-the-art technologies for both localization and location recognition have difficulty in achieving satisfactory performance. Moreover, it is a challenging issue to construct and maintain a large-scale image database for existing visual-based location recognition systems. To cope with these issues, we introduce CrowdOLR, a crowdsourcing based object location recognition system, which collects one location image together with various rich sensory data (GPS coordinates, azimuth angle, tilt angle, etc.) as a fingerprint of a location query and matches it to a fingerprint database crowdsourced from users' smartphones. We designed a simple and efficient user action mode and proposed a series of fingerprint extracting, searching, and matching methods, so that CrowdOLR satisfies five desirable properties: high recognition accuracy, user friendliness, quick response, no/little site survey, and timely update. We implemented CrowdOLR and collected 8100 location fingerprints of 162 objects for performance evaluation. Extensive experiments demonstrate that CrowdOLR achieves promising results in various complicated and realistic scenarios.

This paper aims at improving the system energy efficiency of orthogonal frequency division multiple access small cell networks under user data rate requirements. Differently, in this paper, we take into account the effect of both forward link and backhaul link when formulating the system energy efficiency optimization problem. The optimal solution to the system energy efficiency problem needs the joint consideration of the scheduling, the transmission power optimization, and the backhaul link data rate control, which is NP-hard and generates huge signaling overhead. To this end, we first compute and obtain the lower bound and upper bound of the optimal system energy efficiency based on the property analysis of the formulated system energy efficiency problem. Then, to reduce the signaling overhead and calculation complexity, we propose a forward and backhaul link energy efficiency optimization scheme (FBEEOS) to approach the achieved system energy efficiency bounds. The system energy efficiency is proved to be convergent in a non-decreasing manner in FBEEOS. The simulation results confirm the properties of FBEEOS. In addition, our results show that the transmission power optimization is necessary to achieve higher system energy efficiency though its contribution to total energy consumption is negligible. This is a result of the effect of transmission power on interference and throughput, and needs to be taken into consideration when we optimize the system energy efficiency.

To reduce the huge consumption of traditional sensing, a multi-centers estimation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group, the states of other channels (estimated channels, ECs) are estimated according to their correlations with the DCs and the dependence on history to save sensing time. Specifically, number of groups (Ng) and number of DCs in each group (NDC) can be adjusted jointly to improve sensing performance. Moreover, two Hidden Markov Model (HMM) based estimation methods, namely joint estimation (JE) and cooperative estimation (CE), are formulated. In JE, the DCs are modeled as the observed vectors and utilized jointly to estimate ECs' states. While in CE, each DC estimates ECs' states separately and a weight-based cooperative algorithm is designed to merge their results. Tested with real-world measurement data, results show the reduced sensing consumption is considerable at the expense of slight sensing accuracy loss. On these bases, it is significant to note that NdC should be adjusted according to sensing consumption to optimize performance.

Most Wi-Fi based localization algorithms are cooperative as user device is required to associate with an AP. However, user may not associate with AP in scenarios such as supermarkets which calls for non-cooperative localization. In this paper, the probe request (PR) frame sent by device is analyzed and the weighted kernel density estimation assisted Bayes (w-KAB) algorithm is utilized for localization. The PR frame is sent in a sparse manner in time and the probability distribution of its receiving signal strength is complicated due to channel misalignment. Therefore kernel density estimation is adopted in the training stage to estimate the distribution of signal strength accurately with a limited amount of training data. In the localization stage, a weighted naive Bayes algorithm is used to estimate the location of user. Experiments are also conducted using off the shelf devices to validate the performance of the proposed algorithm.

This paper considers the uplink of a single-cell large-scale multiuser multiple-input multiple-output (MU-MIMO) system. The transmitted data is detected by linear receivers at the base station (BS), that is, maximum ratio combining (MRC), zero-forcing (ZF) and minimum mean square error (MMSE). New non-asymptotic outage expressions that are valid for any number of BS antennas are derived. It is shown that the MMSE scheme always achieves the best outage performance, and the ZF scheme outperforms the MRC scheme in the high signal-to-noise-ratio (SNR) regime, while the opposite holds in the low SNR regime. We also demonstrate that when the number of BS antennas is very large and the number of users is fixed, the asymptotic outage probability tends to zero, providing that the channel state information (CSI) is perfect. However, if both the number of BS antennas and the number of users grow large, it would be a different matter. Extensive numerical results are presented to verify the theoretical analysis.

This paper considers a heterogeneous ad hoc network with multiple transmitter-receiver pairs, in which all transmitters are capable of harvesting renewable energy from the environment and compete for one shared channel by random access. In particular, we focus on two different scenarios: the constant energy harvesting (EH) rate model where the EH rate remains constant within the time of interest and the i.i.d. EH rate model where the EH rates are independent and identically distributed across different contention slots. To quantify the roles of both the energy state information (ESI) and the channel state information (CSI), a distributed opportunistic scheduling (DOS) framework with two-stage probing and save-then-transmit energy utilization is proposed. Then, the optimal throughput and the optimal scheduling strategy are obtained via one-dimension search, i.e., an iterative algorithm consisting of the following two steps in each iteration: First, assuming that the stored energy level at each transmitter is stationary with a given distribution, the expected throughput maximization problem is formulated as an optimal stopping problem, whose solution is proven to exist and then derived for both models; second, for a fixed stopping rule, the energy level at each transmitter is shown to be stationary and an efficient iterative algorithm is proposed to compute its steady-state distribution. Finally, we validate our analysis by numerical results and quantify the throughput gain compared with the best-effort delivery scheme.

In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization framework with full traffic buffers, we formulate a dynamic optimization problem by explicitly considering the effects of random traffic arrivals and time-varying channel fading. The stochastic formulation can quantify the tradeoff between power consumption and queuing delay. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed into a per-slot penalized weighted sum rate maximization problem, which is shown to be nondeterministic polynomial-time hard. Based on the equivalence between the penalized weighted sum rate maximization problem and the penalized weighted minimum mean square error (WMMSE) problem, the group sparse beamforming optimization-based WMMSE algorithm and the relaxed integer programming-based WMMSE algorithm are proposed to efficiently obtain the joint RRH activation and beamforming policy. Both algorithms can converge to a stationary solution with low-complexity and can be implemented in a parallel manner, thus they are highly scalable to large-scale C-RANs. In addition, these two proposed algorithms provide a flexible and efficient means to adjust the power-delay tradeoff on demand.

In order to investigate the water hammer problem of the filling pipeline during the rocket propellant filling process of the spaceflight launch site, the simulation calculation model and the real experimental system is established. It researches the water hammer characteristics of the filling pipeline, and analyses the law of pressure change when water hammer occurs. The improved schemes are proposed in this paper, and the simulation calculation and real experiment are carried through for the proposed schemes. It also carries through data analysis for the simulation and experimental results. The results show that the proposed scheme can effectively reduce the water hammer effect of the pipeline during the filling process, improve the rocket propellant filling accuracy and enhance the security and reliability of the system.

In this paper, we provide a Gamma distribution to approximate composite Gamma-Gamma (Generalized-K or KG) distribution by using the moment matching method. Based on the approximate distribution, we propose a novel closed-form approximate upper bound of D-MIMO system. The proposed upper bound, which enable us to go further manipulations, only involves some simple functions. Starting from the approximate upper bound, we perform a high-SNR analysis and investigate the asymptotic behavior of the capacity in the following two cases: i) the number of receive antennas grows into infinity for the fixed average and total transmit power, and ii) the number of antennas at both ends grow large at a fixed ratio. It is demonstrated that the proposed approximate and the asymptotic upper bounds match accurately with the exact analytical expression.

The heterogeneous smart grid (SG) poses two major challenges for wireless networks, namely, providing sufficient bandwidth for a wide variety of applications and high reliability for critical real-time applications. To address these challenges, the impact of communication outage on the demand response management as a typical SG application is analyzed in this paper. A dynamic spectrum management (DSM) technique is proposed to allocate resources, considering the QoS and application priorities. Vacant digital TV frequency bands are utilized to support SG applications. An algorithm to estimate the SG capacity is introduced, which can be applied to various user distributions and SG environments. This is used in conjunction with a low-complexity coloring theory algorithm to allocate the spectrum. The results presented show that DSM provides better performance than traditional fixed spectrum management, in terms of QoS and secondary spectrum utilization.

This paper addresses the energy-efficient resource allocation problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) networks with multiple users. The joint optimization of power and subcarrier allocation to maximize bits-per-Joule is formulated constrained by the maximum transmit power and the minimum required system data rate. Due to the prohibitive computational complexity, this paper proposes an iteration-based sub-optimal scheme with superlinear convergence based on the Dinkelbach method by relaxing the constraints and exploiting the properties of non-linear fractional programming. The analytical solutions for the power and subcar-rier allocation in each iteration are derived by the subgradient method according to Karush-Kuhn-Tucker (KKT) conditions and Lagrange multipliers. Finally, the simulation results validate the convergence and energy efficiency performance of the proposed scheme.

In this paper, an implementable channel quantization scheme that can effectively exploit both spatial and temporal channel correlation for massive MIMO systems is proposed. In limited feedback systems, differential quantization conducted by applying skewing and rotations on a differential codebook is effective on improving the overhead efficiency practically. To apply these techniques in massive MIMO systems, we adopt noncoherent trellis coded quantization (NTCQ) for Rayleigh channel as a foundation. The inherent codebook of NTCQ is defined and investigated thoroughly. Then we propose a scheme that can produce a differential inherent codebook, which makes adaptive skewing and rotations applicable. In numerical simulations, compared to previous approaches on differential NTCQ, superiority of the proposed scheme is significant. It needs no prior statistical knowledge of channel correlation, while high overhead efficiency can also be achieved. The results reveal that in massive MIMO systems, if ideal channel state information at user terminals is assumed to be available, precisely feeding them back to base stations is practical within affordable overhead and computations.

Unrelenting demand of mobile data can be met by intensive spectrum and spatial reuse which can be achieved by femtocells and multi-stream MIMO transmission. However, random deployment of femtocells may cause severe co-channel interference (CCI). In this paper we propose an algorithm, where femto base stations form a cluster and cooperatively generate precoding matrix for interference mitigation to nearby macro user (MUE) and other femto users (FUEs). We propose a modified version of conventional block diagonalization (cBD) linear precoding, where some antennas in femto cluster are de-activated for interference alignment in the null space of MUE. The cBD involves two singular-value- decomposition (SVD) operations which introduces high computational complexity. Therefore we propose a low complexity precoding algorithm which involves generalized zero forcing channel inversion (ZF-CI), QR decomposition and lattice reduction (LR) transformation. Simulation and analytical results show the superior performance of our proposed scheme in terms of sum-rate and computational complexity.

We consider a multipair two-way relay network where multiple communication pairs simultaneously exchange information with the help of a single relay. Each terminal has only a single antenna, while the relay is equipped with a very large antenna array. We further assume that channel state information is available at the relay node. We investigate the power efficiency of this network when very simple signal processing, i.e., maximum ratio combining (MRC) or zero-forcing (ZF), is used at the relay. When the number of relay antennas grows infinite, the transmit power of each terminal or relay (or both) can be made inversely proportional to the number of relay antennas while maintaining a given quality-of-service. We show that with very large antenna arrays, the two-way relaying scheme outperforms both the orthogonal scheme and the one-way relaying scheme.

Powering wireless cellular system by green renewable energy cuts greenhouse gas emissions and electricity bill. However, green energy sources have the limitation of unsustainable availability and capacity. Therefore, the criteria of optimizing network planning for such system should involve both static and dynamic aspects. In this paper, the network planning scheme aims to jointly maximize cell coverage and energy sustainability, which indicate static and dynamic system performance respectively. Firstly, the energy buffer describing green energy evolution is modeled as a G/G/1 queue to investigate energy sustainability. Based on this model, the closed-form expression of service outage probability, which characterizes energy sustainability, is given. Secondly, effective coverage is introduced as a comprehensive parameter which balances cell coverage and energy sustainability. The network planning problem turns to be an effective coverage maximizing problem and solved by automatic cell management. Finally, simulation results are illustrated to validate the proposed automatic cell management.

This paper focuses on linear precoding in a cognitive radio (CR) multi-input multi-output (MIMO) network, where a secondary broadcast channel shares the same spectrum with a licensed primary user (PU). Considering cooperative feedback from PU to the secondary transmitter (ST), a linear precoding scheme is proposed to minimize the expected mean square error (MSE) and null out the interference to the PU. The proposed scheme is an improved approach, which is robust to the channel uncertainties caused by quantization errors and the lack of channel quality information (CQI). Furthermore, interference power control (IPC) is applied at the ST to adjust the transmit power under the tolerable interference threshold of the PU. Simulation results show the effectiveness of the proposed scheme compared with the zero-forcing (ZF) and the conventional minimum mean square error (C-MMSE) criteria.

This paper considers the use of energy harvesters for cooperative relaying in a large relay network, which consists of N energy-harvesting (EH) relays and one source-destination pair. In particular, a threshold-based “save-then-transmit” scheme is employed at the relays, where each relay transmits only when both the backward and forward link channel coefficients are above certain thresholds. We assume that the time scale of EH is much larger than that of communication blocks. For general channel fading models, we derive the asymptotic average throughput for the case with many relays, by using the amplify-and-forward (AF) relaying scheme. The throughput maximization is cast as a joint optimization problem over the transmission thresholds corresponding to all possible harvested energy rate states, which is shown to be non-convex in general. By applying a convexification technique via randomization, the original problem is transformed into a new formulation with a generalized threshold-based transmission scheme, which is shown to be efficiently solvable by bisection search, with the help of an offline look-up table only related to the channel statistics. Finally, with some numerical experiments, we demonstrate the performance gain of the proposed threshold-based transmission scheme against some suboptimal ones.

This paper considers joint estimation with multiple sensors powered by energy harvesters in wireless sensor networks. In particular, we focus on a network with K sensor nodes, which communicate with a fusion center via K orthogonal channels and power themselves by harvesting energy from the environment. Assuming a deterministic energy-harvesting model under which the harvested energy profile is known prior to transmission, the worst-case mean-square error (MSE) minimization problem over a finite horizon of T estimation periods is investigated. We consider the cases that the sensors have either infinite or finite battery capacity, and develop efficient iterative algorithms to compute the optimal power allocation strategy, with numerical results presented to validate our analysis.

We propose decentralized relay coordination algorithms and corresponding channel state information (CSI) signaling concepts for weighted sum rate maximization, via linear downlink transceiver optimization in multiuser multi-relay MIMO network. Two decentralized algorithms are developed with the local CSI requirements at the base station and the relay nodes. In the first proposed algorithm, the generalized channel inversion precoding is utilized at the base station to avoid the second hop channel sharing requirements among the relay nodes. Further, decentralized transceiver optimization of the relay nodes and users are developed to maximize the weighted sum rate. For the second proposed approach, we provide a novel structure of the precoding matrix of the base station. Then decentralized joint BS, relay and user transceiver design is developed to further improve the performance. Moreover, CSI signaling and decentralized processing in TDD system are analyzed. Simulation results show the effectiveness of the decentralized relay coordination compared to the existing algorithms.

There is a growing interest around the world in supplying the communication networks with green energy from natural resources, e.g., solar, wind, and hydro, to reduce carbon footprints. However, the green energy sources and the energy buffer have the limitation of unstable availability and capacity. It is challenging to ensure that the fluctuant energy supply meets the demands of dynamic traffic loads. In this paper, we study the problem of how to ensure the sustainability of green energy powered cellular networks (i.e., green cellular networks). We firstly construct a generalized model to describe the energy evolution process of green energy powered cells. Then, energy dynamics metrics, which are energy level transfer time and energy outage probability, are analyzed by adopting diffusion approximation. Based on the results obtained in the analysis, a liquid cell management scheme is proposed to ensure the sustainability of green energy powered cells by adjusting the cell radii. The scheme performs excellently in improving both the lifetime and green energy utilization of green HeNBs.

White space and spectrum holes in the TV bands bring potential opportunities to relieve the apparent spectrum scarcity. Because cellular networks are now universally deployed and propagation characteristics of the TV band are superior to those of the existing cellular bands, it is essential to consider the behavior of cellular network in the TV bands. As interference is one of most important issues we need to study in TV white space (TVWS), we investigate the behavior of aggregate interference from cellular network generated by cognitive radio (CR) in TVWS. Three models of CR cellular network are proposed in this paper. We find that the behavior of aggregate interference is mainly determined by the keep-out distance which is used to protect TV receivers from cellular networks interference and the radius of the cellular network. The numerical results can help in obtaining a conceptually and computationally improved understanding of adjacent channel interference aggregation in cellular network and guiding the deployment of cellular network in the TV bands.

In this paper we study the joint price and power allocation for interference management in macro- femto spectrum sharing network based on the Stackelberg game. In our model, the macro base station (MBS) works as leader and overlaid femto base stations (FBSs) as followers. The MBS allocates its power and interference price to the FBSs to guarantee its user's minimum rate requirement and reap the revenue from the femto network. Based on this price, FBSs calculates their power in distributed way. The game is formulated for joint utility maximization of both types of players. We consider the two cases of fixed and dynamic MBS power and propose uniform price for all FBSs. In this game model, first of all the number of players (FBSs) is calculated that can participate in the game against different macro user rate requirement and then price and power are determined. We propose unique closed form solution to the both cases based on convex optimization which always guarantee the convergence. The numerical results validate the effectiveness of our proposed solutions.

In this paper, we focus on detecting, estimating and tracking spatial multi-paths in radio propagation, based on elaborated channel sounding. An algorithm based on least-square criterion is proposed, and the multi-paths are estimated by an iterative method. The particle swarm optimizer(PSO) is used to guarantee both global optimum and computational efficiency in each iteration. Realistic system influence, such as system response, non-isotropic array gain, are considered and mitigated. The precision is theoretical analysed and experimental verified. It is shown that in this scheme, the spatial multipath separating and tracking ability can be significantly improved, and reliable results can be obtained. Moreover, the computational efficiency is significantly raised, and the trade- off of precision and complexity can be flexible.

Green radios have drawn much attention in recent years. This paper derives the energy per good-bit expressions for semi-blind relay networks with and without maximum ratio combining under Rayleigh fading channels considering the energy consumed per bit as well as the link reliability and retransmission probabilities. The optimal constellation size is investigated constrained by a given bit error rate. The energy consumed in the transmission, reception and idle modes are all involved. Numerical simulations demonstrate the energy efficiency performance of semi-blind relay networks in several cases with the direct transmission as the benchmark. Finally some practical implications can be made from these observations. The analysis and results will be a good reference for the real green cellular communication design.

Green radios have drawn much attention in recent years. This paper derives the energy per good-bit expressions for MQAM and MPSK in DF relay networks under AWGN and Rayleigh fading channels. The energy per good-bit is defined as the energy per correctly received bit, which indicates that the energy consumed per bit as well as the link reliability and retransmission probabilities are considered. An energy efficient mode switching transmission is proposed. Constrained by a given probability of bit error, the optimal constellation size of MQAM and MPSK to achieve energy efficiency is obtained. Finally, computer simulations are also carried out to analyze the energy efficiency performance with respect to system parameters such as relay position and transmission distance.

Besides energy-efficient technologies, increasing attention is paid on powering cellular networks with renewable energy sources, concerning climate change, fossil fuel prices and energy security. In this paper, we not only aim to reduce the absolute energy consumption of cellular networks, but also provide a guideline to utilize the renewable energy efficiently in cellular networks. The renewable energy sources have the limitation of unstable availability and capacity. It is thus challenging to improve renewable energy efficiency while maintaining the energy supply sustainability. The energy supply sustainability problem is modeled as an optimization problem aiming to maximize the network energy residue ratio (ERR), which is NP-hard. To solve the optimization problem in polynomial time, the network ERR maximization algorithm is proposed after analyzing the relation between energy efficiency and energy depleting rate (EDR). The algorithm maximizes link energy efficiency via power control at physical (PHY) layer and maximizes network ERR via access control at media access control (MAC) layer jointly in a cross-layer manner. The network ERR maximization algorithm performs excellently in improving both the lifetime and the number of users served by renewable energy.

In conventional amplify-and-forward relay networks, relays retransmit signals with predetermined transmit power and amplification factors assuming perfect channel knowledge. This paper proposes a relay selection scheme with amplification factor optimization according to the minimum mean square error criterion when the channel state information is imperfect due to the channel estimation error. Relays can adaptively adjust their amplification factor and transmit power according to the quality of the channels. The relay selection includes both single and multiple relay selection schemes. Finally, numerical results prove the advantage of our proposed scheme compared with the relay selection on the basis of the normalized amplification factor. The impact of channel estimation error is analyzed. We also investigate how the number of selected relays has an impact on the system performance.

In this study, the authors address the problem of transceiver design in an amplify‐and‐forward multiple‐input multiple‐output relay system with full or partial channel state information (CSI), where linear processing is applied to maximise the average received signal‐to‐noise ratio (SNR) at source, relay and destination, respectively. This joint design problem is a non‐convex optimisation problem, so the authors convert it into a scalar problem and derive the optimal unitary decoder under average power constraint. The precoder at source and relay can be obtained by employing generalised Rayleigh quotient method. Specifically, two CSI assumptions are considered: (i) the authors first derive the precoder and decoder with full CSI; (ii) assumimg the statistical independence between the channel estimated value and channel estimation errors, the problem with partial CSI can be solved as the same way as full CSI case. Numerical results show a great improvement on the average received SNR by applying the proposed scheme. On the other hand, the authors also illustrate the impact on received SNR arose by the channel estimation errors.

In cognitive radio (CR) systems, efficient spectrum sensing ensures secondary users (SUs) to successfully access the spectrum hole. Typically, the detection problem has been studied separately from the optimization of throughput of secondary network. However, due to non-zero probabilities of miss detection and false alarm, the sensing phase has an impact on the throughput of CR networks as well as on the transmission of primary users (PUs). In this paper, using energy detection, we maximize the total throughput of all SUs by jointly optimizing the detection threshold and resource allocation in multiband CR systems. Efficient algorithms including online and offline solutions are proposed to solve the proposed mix-integer programming problem, which show better performance compared with traditional uniform detection threshold selection algorithm.

Spectrum sensing, as a key technology of cognitive radio (CR), needs to reliably and efficiently detect spectrum holes in wireless environments, which challenges the traditional spectral estimation methods typically operating at or above Nyquist rates. This paper develops a novel compression ratio allocation (CRA) method for wideband spectrum sensing in CR networks. In our scheme, each CR terminal performs compressed sensing with sub-Nyquist rate samples to scan a wide spectrum range at practical signal-acquisition complexity. It can greatly reduce the sensing measurements through fewer sample numbers. Meanwhile, the cognitive base station optimizes the compression ratio at each CR terminal according to their local signal-to-noise ratio (SNR), so the total sample number can be further cut down. Simulation results show that the CRA algorithm provides an optimal performance while requiring a relatively low complexity of sensing process.

In this paper, we study joint spectrum sensing and resource allocation for heterogeneous services in multi-band cognitive radio systems. Two types of services are considered: delay-sensitive (DS) services and delay-tolerant (DT) services. Considering the influence of the probabilities of miss detection and false alarm, the detection threshold, power and sub-channel allocation are jointly optimized to maximize the total data rate of DT services while satisfying the delay requirement of DS services. For the protection of primary transmission, a new criterion referred to as rate loss constraint is introduced. With the queue theory, the delay requirements of DS services are transformed into constant rate requirements. The optimization problem is formulated as a three-variable non-convex problem under constraints. Moreover, by dividing the optimization problem into two stages, an iterative dual decomposition method is proposed to solve it. The effectiveness of our proposed algorithm is evaluated by extensive simulations and compared with existing algorithms.

In this paper we proposed a novel cochannel interference avoidance strategy for outdoor remote medical monitor network. We employ simulated annealing to design the channel allocation algorithm, which is the core of the strategy. We implemented the strategy in the experimental we built at Beijing outer suburb and took a series of experiments to examine the effect of the strategy. The experimental results showed that the interference level in the network was well controlled after the strategy is used.

In this paper, we investigate a novel slotted ALOHA-based distributed cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU. Based on that, the analytical average system throughput is derived in both saturation and non-saturation networks. Second, the relationship between the average system throughput and the number of sensing channels is attained. We show that the optimal number of sensed channel in a given number of SUs is dependent on the number of licensed channels, the number of idle channels, and the transmission probability of each SU. Finally, the analytical results are validated by substantial simulations.

In this paper, we investigate the power saving issue in cognitive radio (CR) multi-hop relay network. Due to the dynamic property of the wireless channel, the quality of service (QoS) guarantee for multi-hop transmission is quite challenging. To deal with these problems, automatic repeat-request (ARQ) protocol in an end-to-end manner is incorporated. For multi-hop transmission evaluation purpose, the end-to-end packet delivery probability is put forward as a QoS indicator in this paper. Besides, by underlay spectrum sharing, each relay is possessed of a power budget (i.e., maximum transmit power) to protect primary user from suffering intolerable interference. This paper addresses the power saving problem under each relay's power budget constraint, which means the end-to-end QoS constraints can be satisfied with the minimum total power consumption for relays along the optimal path. Motivated by this, we propose a joint Lagrange dual method based power allocation and exhaustive search based relay selection algorithm to obtain the solution. Numerical simulations are presented to validate the theoretical analysis. The results show that the proposed algorithm achieves a good performance in power saving.

As one of the essential characteristics for CRN, the cognitive reconfiguration can automatically adjust the cross-layer parameters to meet the user requirements, realize interoperability between heterogeneous networks and adapt to the time-varying environment. However, the cross-layer parameters reconfiguration implementation is still challenging due to its need for complex environment cognition and multi-objects optimization. In this direction, ant colony optimization (ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the adaption. The aim of this paper is to present a generic cross-layer parameters reconfiguration framework including indispensable function entities for autonomous reconfiguration decision making with regard to the multiple and complex objectives. Finally, numerous results prove the effective performance improvements of ACO based reconfiguration solution in CRN.

This paper studies a spectrum sharing cognitive radio (CR) network coexisting with a primary network. In particular, the channel state information (CSI) between the secondary transmitter (STx) and the primary receiver (PRx) is assumed to be outdated due to channel feedback latency. We assume that the secondary user (SU) shall satisfy a given delay quality-of-service (QoS) constraint as well as the average interference power constraint. Our aim is to obtain the maximum arrival rate of the SU under aforementioned constraints with the outdated CSI. In this respect, we derive the optimal power allocation scheme to achieve the maximum effective capacity, and further derive the effective capacity. The closed-form expressions for the lower and upper bounds on the effective capacity are also provided. Numerical and simulation results are presented to show the effects of the outdated CSI. It is shown that the effective capacity of the SU is insensitive to the channel correlation coefficient especially under low channel correlation coefficient.

In this paper, we address a two-way relay network (TWRN) where two source nodes exchange their information through a relay node (RN) in a bi-directional manner and propose a receiver design method at RN and source nodes under variable-gain amplify-and-forward (VG-AF) relay system. A two-period transmission protocol is employed: in the training period, source node and RN will send their own training signals to obtain channel state information (CSI); in the transmission period, two source nodes exchange their information based on the CSI obtained in the training period. We develop the Maximum-Likelihood (ML) estimator and Linear-Minimum-Mean-Square-Errors (LMMSE) estimator and derive the expression of channel estimation errors. Numerical results show that VG relay system outperforms fixed gain (FG) system in terms of mean square errors (MSE) of channel estimation and bit error rate (BER) performance. Besides, in contrast to the high Peak-to-Average-Power-Ratio (PAPR) in FG relay system, simulation results show that VG relay system has a more stable transmission power.

Considering the inevitable trends for heterogeneous network convergence, Cognitive Radio Network (CRN) concept has been proposed with some essential characteristics to achieve adaptation and global end-to-end goals. This motivates a more flexible and effective dynamic channel assignment scheme which can utilize the licensed spectrum effectively through reusing idle licensed spectrum opportunistically. This paper focuses on the dynamic channel assignment which offers optimal resource allocation mechanism to satisfy the requirement of users and networks in transmission. Owing to the optimization problem of channel assignment is constituted as a nonlinear programming, we propose the use of Ant Colony Optimization (ACO) algorithm as a way to manage and assign channel resource dynamically in CRNs. The ACO, as an intelligent technique, has the capacity to solve the complex multi-objective optimization problem and simplify the computational process. Finally, the dynamic channel assignment algorithm is simulated, and the numerical results with detailed are analyzed.

In this paper, a mini-slot sensing with selective coordinator in cognitive radio is investigated. In traditional sensing, a whole sensing duration is only used to sense one channel. In proposed mini-slot sensing, a sensing duration is divided into many mini-slots and each mini-slot can be used to sense one channel independently, then data from all slots are been sent to a coordinator for final decisions. A proof is put up to demonstrate that mini-slot sensing has a better performance than the traditional sensing. In this research, the ability of different secondary users sensing different channels are not equal. Based on this assumption, we prove that in mini-slot sensing, not all slots do benefits to CR system. Slots can be divided to two groups: contributor (slots which improve system performance) and destroyer (slots which decrease the performance). A selective coordinator is proposed to assist mini-slot sensing which receive data from all slots and reject the destroyers in making final decisions. Based on the analysis above, an optimization problem is formulated to find the optimal assignment for slots to channels. A Greedy based two stage assignment method is proposed as the sub-optimal solution. Simulation results show that our proposed method has a better performance than traditional methods.

In this paper, we consider the power saving issue in the cluster based multi-hop cognitive radio (CR) network with one pair of primary user (PU) in presence. By underlay spectrum sharing, the transmit power of CR nodes are strictly restricted to protect PU from suffering severe interference. Moreover, the end-to-end outage probability is put forward as an essential QoS indicator for multi-hop transmission and has been carefully studied in the article. The objective of this paper is to minimize the total power consumption of CR transmitters along relay path. Both the end-to-end outage requirement and power budget of relays are incorporated as constraints. To solve the formulated problem and obtain the optimal solution, we propose a joint Lagrange dual method based power allocation and objective oriented optimal relay selection algorithm, and give thorough evidences and illustrations as well. Finally, numerical simulations are made and results demonstrate that the proposed algorithm has a good performance in power saving.

In this paper, a novel MAC-layer spectrum sensing scheme base on continuous-time semi-Markov process is investigated for the purpose of improving spectrum sensing efficiency of cognitive radio (CR) systems. The scheme focuses on identification of the optimal sensing sequence of channel by modeling a group of licensed channels' usage pattern as a continuous-time semi-Markov process. Experimental results show that the proposed algorithm has the potential to achieve noticeably improved performance in terms of reduction the sensing overhead and second user's average throughput when compared to the conventional non-prioritization spectrum sensing approach, thus is suitable for CR networks.

Cognitive radio is considered to be a key technology for future heterogeneous networks. Cognitive radio network is an evolution of the cognitive radio by extending the radio link scope to network scope, and is defined as a network that can observe its environment, make decisions based on the observations, and then reconfigure according to the decisions, all while taking into account the end-to-end goals. This paper proposes a high level abstraction of the cognitive radio network architecture. The operation of the proposed architecture is guided by the end-to-end goals. The proposed architecture consists of four components: end-to-end goals management, cognition management, self-organization management, and reconfiguration management. The proposed architecture provides the functionality to manage these components, enable communication between them, and facilitate the interfaces between cognitive radio network and its surrounding environment. In order to demonstrate the functionality of the proposed architecture, we present a use case of ubiquitous wireless access services and show that the proposed architecture enables ubiquitous connectivity with harmonized networks and integrated services.

This paper addresses joint source-relay precoder and decoder designs for a single data flow transmission in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay networks with imperfect channel state information (CSI). First, the precoder is obtained by improving the lower bound of the received SNR under power constraints at source and relay. Then, we derive the decoder to maximize the average received signal-to-noise ratio (SNR). Numerical results show a great improvement on the received SNR by applying our proposed schemes. Besides, we also make a discussion about the impact which brought by the channel correlation coefficients based on our derived received SNR expression and the numerical results.

With the trend of technology innovations in recent years, network heterogeneity and inefficient spectrum usage are the great challenges in Cognitive Radio Networks (CRNs). As one of the solutions for efficient heterogeneous network information delivery in CRNs, a common broadcast signaling channel named Cognitive Pilot Channel (CPC) is proposed with its large coverage and easy implementation characteristics in contrast to usually inefficient and time-consuming spectrum sensing techniques. In order to improve the accuracy of network information delivery, the geographical regions are divided into small meshes and the network information in each mesh is broadcast one by one. This paper proposes an efficient coding scheme for broadcast CPC, called Differential Mesh Information Coding (DMIC), to reduce the redundancy of similar network information among different meshes. The strategies of choosing the basic mesh with popular commonality and quantizing the differential information among meshes are also proposed and proved by numerous results.

Considering the inevitable trends for heterogeneous network convergence and self-adaptation ability, Cognitive Radio Network (CRN) concept has been proposed with some essential characteristics to achieve global end-to-end goals. CRNs are composed of cognitive devices which have the capable of changing network configurations based on the dynamic environment. This capability opens up the possibility of designing flexible and dynamic topology control strategies with the purpose of opportunistically reusing idle licensed spectrum and effectively achieving data transmission. This work focuses on the problem of designing effective topology reconfiguration algorithm to offer optimal routing solutions. We analyze the topology control for CRNs finding the topology problem can be formularized as a multi-objective optimization problem. In this context, as an intelligent technology for complex multi-objective optimization, the ant colony optimization (ACO) techniques are applied in topology reconfiguration for optimal decision making in this paper. Finally, the reconfiguration algorithm is simulated, and the simulation results with detailed are analyzed.

In spectrum sharing systems, a secondary user (SU) is permitted to share frequency bands with a primary user (PU) as long as its transmission does not interfere with the PU's communication. In this paper, the outage probability is investigated for the cognitive relay system over Nakagami-m fading channel. By applying the interference temperature constraints at the source nodes and relay nodes in secondary systems, we analyze the outage performance in two-hop underlay spectrum sharing with the best relay selection criterion. The probability density function (PDF) and cumulative distribution function (CDF) of the signal to noise ratio (SNR) at the SU's receiver are derived to obtain the closed-form upper bound of the outage probability of the secondary relay system. Simulations results demonstrate the validity and accuracy of the theoretical analysis.

This letter addresses the joint linear processing issues for 3-phase two way cooperative MIMO relay systems, aiming to minimize the mean squared error (MMSE). Considering the difficulty to acquire the channel state information (CSI) of the direct link at relay node, we first derive an iterative linear processing scheme for the relay node ignoring the direct link between two source nodes. Then the receive processing matrix combining the signal from direct link and relay link can be easily given according to MMSE orthogonality principle. Finally by comparing other schemes that can accomplish bidirectional data exchange, simulation results show that our proposed 3-phase cooperative scheme can achieve better tradeoff of link reliability and spectrum efficiency especially when the direct link is in medium quality.

In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations.

In this paper, we study the cross-layer resource allocation for the secondary users (SUs) supporting heterogeneous services in interference-limited spectrum sharing system. Two classes of SUs are considered: delay-tolerant SUs (DT-SUs) and delay- sensitive SUs (DS-SUs). With the queue theory, the delay requirements of DS-SUs are transformed into constant rate requirements. Unlike most previous works, this paper formulates the optimization problem by taking heterogeneous Quality of Service (QoS) of both primary users (PUs) and SUs into consideration. Moreover, based on the convex optimization theory, we propose the dual decomposition method in which the joint subcarrier assignment and power allocation are performed to achieve the optimal solution. To simplify the computation complexity, a suboptimal algorithm is proposed to decouple the optimization problem into two sub-problems. Simulation results show that the system performance achieved by using the proposed suboptimal algorithm is close to that achieved by the dual decomposition method.

In this paper, we consider a cognitive radio (CR) multicast network sharing spectrum with a primary network. To protect the primary transmission, interference power constraint is applied to restrict the transmit power of the cognitive base station (CBS). The objective is to minimize the weighted aggregate outage probability for given target rates for the CR multicast network. Specifically, two types of outage probability are concerned, that is, group outage probability and individual outage probability. For each type of outage probability, the optimal power/rate control scheme is derived. The simulation results are illustrated to validate the proposed power/rate control schemes.

This paper addresses the linear processing issues for two way non-regenerative MIMO relay systems with multiple antennas at each node. Based on theoretical derivation, we first propose an optimal joint iterative linear processing scheme for the relay node and receiving source nodes, aiming to minimize the total mean squared error (MSE). The convergence of this iterative algorithm is proved in terms of analysis and simulation. Then in order to reduce the practical complexity in real systems, a closed form suboptimal solution is also derived. Finally, numerical results are provided to show the performance gain of the proposed schemes.

With the increasing demands for high data rate applications with high quality of service in the next generation networks, OFDMA based femtocell technology is a promising solution for indoor coverage extension and network capacity boosting with its automatic and easy deployment features in contrast to the high CAPital Expenditure (CAPEX) and OPerating EXpense (OPEX) investments for macrocell deployments and operations. Lots of researches have been done on interference mitigation and resource allocation in femtocell networks. However, little attention has been paid to the analysis of the upper bound of macro/femtocell hierarchical network capacity by using the joint femtocell scheduling and optimization strategies. Therefore, this paper has formulated the problem by optimizing the weighted sum capacity of the macro/femtocell hierarchical network with two levels of solutions: one is the optimal solution; the other is a suboptimal local solution, which is raised to approximate the optimal setting with reduced complexity. And a distributed Soft Frequency Reuse (SFR) based approach named Soft Control (SC) is proposed for the fully localized solution. Simulation results verify that the proposed suboptimal and distributed solutions can provide the good performance and approximate the optimal solution with low complexity, with only small loss of the weighted capacity.

OFDMA femtocells are generally accepted as a very promising solution for indoor coverage with high data rate. However, the lack of systematic schemes to effectively mitigate macro/femtocell hierarchical interference, fully utilize radio resources, provide quality-of-service (QoS) and fairness guarantee among users suffocate the performance realization of femtocells. In this paper, an Adapted Soft Frequency Reuse (ASFR) approach is raised to combat traditional inter-cell interference (ICI)1by inheriting the conventional soft frequency reuse (SFR) functionality and to mitigate inter-tier interference (ITI) of macro/femtocells by applying an orthogonal spectrum reuse between macro/femtocells. Moreover, a powerful inter-femtocell interference (IFI) coordination mechanism is provided to complete the interference solution for the three types of interference in the macro/femtocell hierarchical networks. While the interference handling propositions are targeted at optimized spectrum partition for base stations (macro/femtocells included), to further increase spectrum efficiency, a Maximum Weighted Sum Capacity (MWSC) based scheduling design is adopted, to optimize spectrum allocation for users, with restraints to QoS and fairness guarantees. In this way, spectrum efficiency is enhanced at both the BS and UE sides. Simulation results show that the proposed solutions provide fairly good performance in comparison with conventional co-channel macro/femtocell utilizing Proportional Fair (PF) scheduling method.

The two-tier femtocell network is comprised of a central macrocell underlaid with shorter range femtocell hotspots. Due to the universal frequency reuse, this kind of new system architecture brings about urgent problems of the interference management and the resource allocation. Motivated by these problems, the following contributions are made in this paper: 1) a novel joint power and subchannel allocation problem for Orthogonal Frequency Division Multiple Access (OFDMA) downlink based femtocells is formulated on the premise of minimizing Femto BSs' radiating interference; 2) a pseudo-handover based scheduling information exchange method is proposed to avoid the collision interference; 3) an iterative scheme of subchannel allocation and power control is proposed to solve the formulated problem, which is an NP-complete problem. Through simulations and comparisons with three other schemes, the proposed scheme shows better performance in reducing interference and the Femto BS's transmit power, and improving the spectrum efficiency.

This paper considers a secondary user (SU) sharing the spectrum licensed to a primary user (PU) if limited interference caused to the latter can be guaranteed. In particular, besides the interference power constraint at the PU to protect the PU, the transmit power of the SU is also considered. Under such a setup, we consider the average bit error rate (BER) as the performance metric for the SU, and then derive the optimal power allocation strategies to achieve the minimum average BER of the SU. Simulation results are presented and discussed. It is shown that the optimal power allocation strategies can achieve substantial performance gain for the SU over the water-filling method.

In the spectrum sharing systems, an unlicensed (secondary) user may share a frequency band with its licensed (primary) user as long as its transmission does not interfere with the primary user's communications. For the cognitive wireless relay networks, this interference regulation from the primary user may affect its cooperative scheme. In this paper, we investigate the outage performance of cognitive wireless relay networks in a spectrum sharing environment, in which the secondary users including the source and relays may take advantage of a frequency band of the primary user to transmit data to the receiver while not interfering with the primary user's communication. In particular, we quantify the relation between the outage performance of the secondary relay link and the interference inflicted on the primary user.

In this paper, we consider a four-node relay-based OFDMA system. The expression of the upper bound for the achievable rate in Successive Relaying (SUR) protocol is derived by using the cut-set theorem for half-duplex systems. Based on this expression, the near-optimal solution of the achievable rate is obtained in the joint power and subcarrier allocation constraint, according to the dual problem decomposition approach and the subgradient method. Moreover, we make a comparison on the achievable rate between SUR and Simultaneous Relaying (SIR) in the pathloss model accepted by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) Advanced system. The simulation results demonstrate the enhancement of the achievable rate in SUR protocol is expanded in high signal-to-noise ratio (SNR) compared with the achievable rate in SIR protocol under both the symmetric and asymmetric cases. And for SUR protocol , the achievable rate in symmetric case performs better in high SNR.

Spectrum sensing is the key functionality of cognitive radio. To combat with the effects of destructive channels, cooperative spectrum sensing technique among multiple secondary users has been proposed in cognitive radio networks. In this paper, we present an optimal cooperative spectrum sensing strategy to maximize the sensing efficiency, which not only concerns with the system overhead of spectrum sensing but also fulfills the interference restriction from the primary networks. The proposed scheme results in the optimal sensing parameters according to the channel-usage characteristic of single primary channel. Then we extend the work into the multichannel environment. Simulation results verified the performances of our strategies.

As the spatial dimension is exploited to further improve the spectrum utilization for cognitive radio (CR), the position of the primary user transmitter has become very useful information for the CR system. Most existing localization algorithms require transmitting power of the primary user, making them less practical. In this paper, we propose a practical semi range-based (PSRB) localization algorithm in which the primary user transmitting power is another parameter to estimate. Meanwhile, highly reliable decisions of the primary user occupancy status are made by virtue of cooperative spectrum sensing, so the possibility of detection of the sensing nodes are more accurate, leading to an improved performance of the algorithm. Simulation results show that the performance of the PSRB exceeds the iterative semi range-based algorithm.

In order to obtain a more accurate assessment of relay performance in real-world outdoor propagation environment and to provide guidelines for the relay-based system deployment in the IMT-Advanced frequency band, the performance of a variety of relay schemes is investigated based on wideband measurements. The measurements were conducted at 2.35 GHz with 50 MHz bandwidth, which is within the frequency bands allocated to the IMT-Advanced system. We pay attention to two aspects: 1) the performance evaluation of a variety of transmission schemes in real propagation environment, and 2) the impact of propagation environment on the relay performance. Based on the measured channel transfer matrix, the achieved signal-to-noise ratio (SNR), spatial diversity and capacity of different transmission schemes are analyzed and compared. The measurement results reveal that in the NLOS region of the base station (BS) or in the region far away from the BS, the decode-and-forward (DF) relaying can significantly enhance the system performance.When the quality of the link between the BS and the relay station (RS) is good, the DF can provide larger performance improvement than the amplify-and-forward relaying. It is also found that propagation condition in the link between the RS and MS has major impact on the SNR, but minor impact on the spatial diversity.

The problem of resource allocation in multiuser OFDM system is a combinatorial optimization problem, difficult to solve in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, ant colony optimization, a typical algorithm of metaheuristic methods, is applied to solve the problem of resource allocation in multiuser OFDM system. The system model for the application of ACO on the problem, as well as two algorithms based on ACO, is proposed. Comparing to traditional strategies, it is indicated by numerical results that the proposed algorithms can significantly increase the throughput of the system and simultaneously guarantee fairness.

This comment points out several errors in the above letter. The correct frequency offset estimator is then proposed. Monte Carlo simulation results validate our analytical observations.

In this paper, we propose a relay selection scheme based on cooperative MIMO (multi-input-multi-output) communication for network lifetime maximization in energy-constrained sensor networks. Compared with existing work, our distributions are: cooperative nodes choice is not based on minimizing energy consumption per communication but balancing remaining energy between participants including source and cooperators based on one special application. We validate the efficiency of the proposed algorithm by simulation by comparing it with the direct communication and minimizing energy consumption communication schemes.

The adaptive transmission jointly considering adaptive modulation and coding (AMC) and automatic repeat request (ARQ) has been applied to MIMO systems to provide high spectral efficiency and link robustness. However, the adaptive transmission requires knowledge of the channel state information (CSI) and in practice perfect CSI is rarely available. In this paper, under the condition of finite-length buffer, the queuing characteristics of the Zero-Forcing (ZF) detection MIMO systems combining with adaptive transmission in the presence of imperfect CSI are analyzed. We use Markov-chain-based analytical framework to investigate the performance of the systems and derive the close form expressions for the throughput, packet loss rate and average delay, from which we can maximize the overall system throughput under the specified QoS constraints through numerical search. The simulation results demonstrate the verification of the performance analysis.

With the wide deployment of wireless local area networks (WLANs), the problem of configuration and maintenance of access points (APs) arises. The traditional manual configuration method greatly increases the cost of configuration and maintenance of the WLANs system and hinders the scalability of WLANs. The plug-and-play (PnP) of APs, which means that APs auto-configure themselves based on the network environment, is a good solution to this problem. The PnP of APs can reduce both the complexity and cost of configuration and maintenance for WLANs and also improve the system performance. The selection of working mode and channel is one of the major challenges for the PnP of multi-mode APs. In this paper, we propose a mode and channel selection scheme that combines analytic hierarchy process (AHP) with grey relational analysis (GRA). The goal of our proposed scheme is to select the most suitable mode and channel for multi-mode AP to improve the performance of WLANs and offer better service to users. In the proposed scheme, AHP is responsible for deciding the weights of criteria factors according to their contributions to the final goal. GRA combines the weights of criteria factors with the values of criteria factors to rank all alternative modes and channels combination and make a decision. It is seen from the simulation results that our proposed scheme can select the most suitable working mode and channel for AP in different scenarios.

In this paper, we propose a novel spatial autocorrelation model of the shadow fading process in urban macro environments. The proposed model is based on the empirical results obtained from extensive wideband radio channel measurement campaigns at 2.35 GHz in an urban area of a typical medium-sized Chinese city. The shadow fading component was extracted assuming a single-slope log-distance path loss model. The consistency with the level crossing theory of Gaussian processes is achieved by an implicit constraint on the parameters of the model. The proposed model gives a better fit to the empirical results in individual measurement routes than the widely reported exponential and double exponential models. An heuristic explanation of the proposed autocorrelation property is also presented.

This paper focuses on adaptive resource scheduling for multiple services per user at the downlink of multiple input multiple output (MIMO) - orthogonal frequency division multiplexing (OFDM) system. In future wireless networks, one user will simultaneously require multiple homogeneous or heterogeneous services. Then, the scheduling algorithm is responsible for not only assigning resource blocks to different users but also distributing the assigned resource blocks among multiple services for one user. This paper firstly formulates this integrated optimization problem based on utility function in homogeneous service system. As the solution to the optimization problem requires high computational complexity, a sub-optimal and low-complexity algorithm is proposed with two theorems for practical implementation. Moreover, the algorithm is extended to heterogeneous services system by classifying delay sensitive services according to the head-of-line packets delay. The design goal of the algorithm is to fully exploit multiuser diversity gain while guaranteeing the quality of service (QoS) for delay sensitive services. Numeric results show that the algorithm outperforms traditional algorithm in terms of system spectral efficiency and fairness criterion.

Multiple input multiple output (MIMO) system has been recognized as a promising candidate for future wireless communication. The adaptive modulation which adjusts the transmitter parameters, such as modulation order, transmit power or coding rate, to time-varying channel conditions has been applied to MIMO system and shown a good average spectral efficiency performance. In this paper, the channel estimation error (CEE)'s effect on the effective spectral efficiency of the MIMO system with zero-forcing receiver is investigated, when the transmitter adopts adaptive modulation. To reduce CEE's negative effect, a dynamic adaptive modulation scheme is proposed. This scheme can dynamically adjust the signal to noise ratio (SNR) thresholds for the different modulation orders according to the feedbacks from the receiver. The numerical results show that the system performance of the proposed scheme is near optimal with acceptable implementation complexity.

An optimal multi-user MIMO linear precoding scheme based on particle swarm optimization is proposed in this paper. The proposed scheme aims to maximize the system capacity of multi-user MIMO system. This paper explores a simplified function to measure the optimal problem. With the adoption of particle swarm optimization algorithm, the optimal linear precoding vector could be easily searched according to the simplified function. The proposed scheme outperforms the multiuser MIMO linear precoding schemes based on channel inversion and channel block diagonalization methods.

The multiple-input-multiple-output (MIMO) technique, combined with multi-level quadrature amplitude modulation (M-QAM), has been considered a potential scheme for high data rate transmission in next generation mobile communication systems. In this paper, the symbol error rate (SER) of M-QAM MIMO system based on spatial multiplexing is analyzed and closed-form results are given under rich scattering Rayleigh fading channel for zero-forcing (ZF) receiver and zero-forcing perfect successive interference cancellation (ZF-PSIC) receiver. Furthermore, these two precise analytical SER expressions are taken as the upper bound and lower bound of zero-forcing successive interference cancellation (ZF-SIC) receiver without optimal ordering. Monte Carlo simulations validate the analytical results and prove the conclusions.

In this paper the optimal threshold is proposed to derive channel impulse response on the multi-path taps and eliminate noise on the other taps. The choice of threshold, which is crucial for the accuracy of the estimator, is analyzed theoretically and the optimal threshold introducing the least MSE is derived. Simulation results show that the performance of our proposed channel estimation with the optimal threshold has significant improvement over the conventional channel estimation algorithm.

The IEEE 802.16 standard supports the multi-hop mesh mechanism, which focuses on high throughput, fast and cost-effective deployment. The standard defines the scheduling scheme in mesh mode, and many researchers have contributed greatly to this area. However, most of the existing research work is designed for single-radio single-channel environment and some heavy traffic nodes become the bottleneck of the network performance's improving. In this paper, we propose a flexible two-radio and single-radio combined deployment (TSCD) scheme for multi-channel transmission using interference free scheduling algorithm in IEEE 802.16 mesh networks. The simulation results show that the proposed scheme increases the overall thoughput and reduces delay of the network compared to all single-radio network. What's more, TSCD network gets almost the same performance compared to all two-radio network with less cost.

In the future wireless communication environment, many radio access technologies will coexist in the same or border areas to provide users with ubiquitous access. The heterogeneous infrastructure makes reconfigurability an important characteristic which is very necessary in the mobile terminals. Moreover, the reconfiguration technique is also introduced to the design of protocol stack to meet the ever-changing link conditions and service requirements. Hence, in order to facilitate the realization of the protocol reconfiguration capabilities, a component-based protocol architecture is introduced by end-to-end reconfigurability (E2R) II project. This paper focuses on the supply of a customized protocol stack for each application according to the communication environment. To achieve this goal, an evaluation mechanism for protocol components is proposed. Besides, we put forward an optimization scheme that finds better protocol components from the network-side library to replace ill-suited ones running in the current system. The major advantage of the proposed protocol management schemes is that users may enjoy a better quality of service due to the protocol reconfigurability.

The pouring of more and more attractive radio access technologies (RATs) with complementary characteristics has inspired the trends of interworking and convergence in future wireless systems. End-to-end reconfigurability (E2R) is an approach toward the convergence of diverse RATs. This paper aims at investigating an effective seamless vertical handover (VHO) scheme for E2R systems. The system model supporting the VHO scheme is brought forward. A network sort algorithm is developed enabling terminals to select the most appropriate network to handover. Particularly, we propose a novel handover trigger algorithm based on quality of service (QoS) evaluation allowing terminals to initiate a handover when the QoS is lower than their expected value. Simulation results reveal that the proposed VHO scheme enhances the user satisfaction as well as optimizes the overall network performance.

To meet the increasing demand of wireless services associated with the scarcity of the radio spectrum and the trend to provide end to end quality of service (QoS), on the one hand advanced technologies that harness the available resource efficiently should be developed, on the other hand the collaboration of different layers such as physical (PHY) layer and medium access control (MAC) layer is needed. In this paper, we propose a joint space-frequency-power scheduling algorithm (JSFP) for real time service in multiuser cellular MIMO-OFDM system which jointly optimizes the subcarrier, bit and power allocation in the PHY layer along with the scheduling in the MAC layer to exploit the multiuser diversity. This algorithm considers both the user equipments (UE)' QoS requirements (such as packet delay and packet loss ratio) and the UEs' channel conditions and includes three parts: packet scheduling, subcarrier allocation and antenna selection, power allocation. Numerical results show that the proposed algorithm achieves significant system performance improvement compared with the conventional methods.

The scarcity of spectrum resource in future wireless networks has inspired the demand of dynamic spectrum management (DSM). This paper investigates a cell based dynamic spectrum management (CBDSM) scheme to enhance the spectrum utilization and maximize the profit of operators for cognitive networks. In this scheme, the economic factor of the spectrum is taken into account in order to guarantee the rationality for spectrum trading. Especially, we focus on the interference mitigation method, which is considered as a fundamental issue for applying DSM to wireless systems. As a potential tool for promoting the distributed autonomous radio resource optimization algorithms, game theory is applied in the DSM scheme to investigate a win-win solution for spectrum trading between RATs. The simulation results reveal that the proposed CBDSM scheme improves the spectrum utilization and the profit of operators while effectively mitigating mutual interference between wireless networks.

A low-density parity-check (LDPC) coded adaptive modulation and coding (AMC) strategy is presented for orthogonal frequency division multiplexing (OFDM) systems. Since system performance is improved as the maximum iteration times for LDPC decoding increase, iteration times for decoding reflect channel quality. We first propose an AMC strategy to utilize the information of iteration times for decoding to adopt higher order modulation and coding schemes (MCS) than the conventional strategy, so as to improve system throughput. Since it may bring worse reliability, hybrid automatic repeat request (H-ARQ) is used in a second strategy. Simulation under typical urban channel is carried out, and the results show the proposed strategies can bring higher throughput, and the strategy with H- ARQ can improve throughput and guarantee reliable transmission simultaneously.

Link adaptation technique is applied in an amplify and forward (AF) - orthogonal frequency division multiplexing (OFDM) relaying system, and adaptive bit and power allocation (ABPA) is investigated for such system. ABPA problem is presented first, and the optimal solution is proposed with aid of Greedy algorithm. Because channel transfer functions for two hops are independent, which limits throughput of a conventional AF-OFDM system, we take sub-carrier coupling (SQ into account. Two solutions are proposed for the problem with ABPA and SC; the first adopts a concatenated method to realize SC and ABPA with two steps, and the second utilized a joint way to adjust SC and allocated bits and power. Numeral simulation results show that the proposed ABPA solution can improve throughput greatly while guaranteeing bit error rate (BER) requirement very well. When SC is involved, more improvement can be obtained; the joint solution outperforms the concatenated solution.

This paper presents a simplified V-BLAST (vertical bell lab layered spaced-time) detection algorithm from the hardware implement perspective. Simulation shows that the BER (Bit Error Rate) performance is close to Golden detection algorithm, but the complexity is greatly less. Then the paper provides an efficient hardware structure to implement this algorithm in FPGA (field programmable gate array), which can be used in the B3G TDD-MIMO-OFDM (Beyond 3G Time-Duplex-Division Multi-Input Multi-Output Orthogonal frequency division multiplexing) system. By applying bit-width reduction technique, the fabrication area it takes can be significantly reduced. In uplink, we adopted 4 transmit and 8 receive antennas. The implementation with Virtex square Pro Series FPGA was verified to be worked well in B3G system.

In this paper, the performance of discrete Fourier transform (DFT) based channel estimation (CE) for orthogonal frequency division multiplexing (OFDM) systems with transmit antenna diversity is analyzed. Conventional DFT-based CE suffers from the dispersive distortion of an estimated channel impulse response (CIR) due to the existence of virtual carriers (VCs). We analyze the mean squared error (MSE) performance and provide a concise expression which reveals the leakage effect due to VCs. Further, analytical expression for bit error rate (BER) with the effect of VCs is also derived. Simulation results illustrate the accuracy of the theoretical analysis.

Multiple input multiple output (MIMO) systems provide dramatic capacity gain through an increased spatial dimension. However, the capacity gain is reduced if the channel state information (CSI) is not perfect. The more exact the CSI is, the higher capacity gain we can obtain. In order to obtain higher capacity gain, this paper analyzes the two factors which mainly affect the reliability of CSI: the quality of channel estimation and the feedback delay, and proposes a channel modification strategy, which can optimally reduce the effect of the two factors and obtain more exact CSI. Simulation shows that the channel modification strategy is effective.

In codebook based precoding MIMO system, the precoding codebook affects the system performance remarkably. Consequently, it is a crucial technology to design the optimum codebook for precoding MIMO system. Codebook design is related to the channel fading, antenna number, spatial correlation etc. So specific distributed channel corresponds to respective optimum codebook. In this paper, in order to design the optimum codebooks for the precoding MIMO system, a universal unitary space vector quantization (USVQ) codebook design criterion is provided, which can design the optimum codebooks for various spatial correlated channels with arbitrary antenna configurations. The unitary space K-mean(USK) algorithm is also provided to generate the USVQ codebook, which is iterative and convergent. Simulations show that the capacities of the precoding MIMO schemes using the USVQ codebooks are very close to those of the ideal precoding cases and outperform those of the schemes using the traditional Grassmannian codebooks.

Seamless mobility in heterogeneous networks is difficult to be achieved because of various QoS requirements and complex heterogeneous network environments. Media independent handover (MIH) is used to handle such problem in IEEE 802.21 standard. However, only link layer dependent information is involved for the mobility decision. In this paper, an enhanced media independent handover (EMIH) framework and mobility management mechanism are proposed, in which new function entities (FEs) and modules are defined and used to provide link layer and application layer information from client side and network side to mobility decision engine. Compared with MIH, the EMIH provides more sufficient and comprehensive trigger events. The static and dynamic information are collected flexibly at mobile node (MN) and within the network infrastructure. Various handover types are designed to make use of such information to optimize the handover, which is illustrated by an example in this paper. The proposed EMIH architecture can benefit not only mobile users, but also network operators.

In this paper, a generic framework for validating wideband MIMO channel models based on channel measurement results is proposed. The framework is formulated as a series of continuous functions (metrics) and a definition of distance of continuous function space (degree of approximation). The metrics characterize the MIMO channel from different perspectives, and the distance provides a quantitative measure of the degree of approximation for the specified model. Several fundamental metrics which reflect the spatial multiplexing gain, diversity capability, time and frequency variability, are derived for exploring the frequency-selective fading property. Based on an extensive measurement campaign at 5.25 GHz, the propagation channel is reconstructed by a WINNER-like model. The metrics are calculated from both the model generated channel realizations and the measured impulse response as a demonstration. The proposed framework can be applied to compare different channel models and to evaluate the simplified version of channel models.

In this paper, we propose an accurate and efficient timing synchronization method for distributed multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in both AWGN and multi-path Rayleigh fading channels. A modified cyclic delay synchronization pattern (MCDSP) is proposed and it is verified to be especially suitable for distributed system which needs to separate the signal arriving time of each transmit antenna. At the receiver, two additional methods termed antenna cluster separation window (ACSW) and multi-path backward searching (MPBS) are employed to combat the multi-path effect.

This paper considers zero-forcing beamforming (ZFBF) at the transmitter for a downlink multi-user multiple-input single-output (MU-MISO) system. Transmit beamforming as a simple yet efficient technique can exploit the benefits of multiple transmit antennas provided that the instantaneous channel state information (CSI) is known at both sides of transmission link. In order to have such improvements, the CSI at both link ends must be updated timely. However, the updating process is always subject to non-ideality such as feedback delay and estimation error, which destroy the orthogonality of the parallel channels and cause the mismatch between the actual channel characteristic and the modulation matrices used. By analyzing the effect of feedback delay on the performance of capacity with spectral efficiency and outage probability as benchmarks, we design a cross-layer scheduler combining semi-orthogonal user selection (SUS) algorithm at the medium access control (MAC) layer to reduce the inter-user interferences and an adaptive proportional weighted modulation (APWM) algorithm at the physical (PHY) layer to address the problem of such mismatch, and compare the proposed scheduler (APWM-SUS) with the naive scheduler (the rate modulation matrices designed for the perfect CSI). Finally, simulation and numerical results reveal significant gains and high feasibility.

Wireless broadcast systems provide the users with high bandwidth while 3G cellular systems provide complementary service to support personality and interactivity. In this paper, we develop a novel scheduling algorithm for the integrated Wireless Broadcast/3G system. The proposed algorithm combines Analytic hierarchy process (AHP) and Grey relational analysis (GRA). Simulation results are presented to demonstrate that the proposed algorithm could effectively support data dissemination with low response time, request drop rate, and the unfairness of request drop.

Future wireless systems are characterized by the pouring of diversified services supported by heterogeneous radio access technologies (RATs). In parallel with this, the tremendous demand for spectrum has brought the requirement of dynamic spectrum allocation (DSA) into our sight. This article aims at designing a centralized DSA scheme to enhance the spectrum utilization and maximize the profit of operators for cooperative wireless networks. In this scheme, the economic factor of the spectrum of wireless systems is considered in order to guarantee the fairness for the spectrum allocation. RATs are considered as cooperative players participating in cooperative games for spectrum allocation. As an attractive solution for n-person cooperative game with transferable utility, the Shapley value is adopted to handle the profit allocation among RATs. Particularly, we focus on the interference mitigation method, which is a fundamental issue for applying DSA to real wireless systems. The simulation results reveal that the proposed DSA scheme not only improves the spectrum utilization and the profit of operators, but also effectively restrains the inter-system interference between wireless networks under an acceptable level.

In this paper, we propose a novel cross layer packet scheduling algorithm for real time (RT) traffics in multiuser downlink MIMO-OFDMA wireless systems. The algorithm dynamically allocates the resources in space, time and frequency domain based on the channel state information (CSI), users' quality of service (QoS) requirements and queue state information (QSI). To provide high data rate and high spectrum efficiency, adaptive modulation and coding (AMC) is employed on every eigenmode subchannel. The proposed algorithm can improve the cell throughput, increase the number of users that can be supported and guarantee users' QoS requirements and the fairness among all users. Simulation results indicate that the proposed algorithm is able to get superior performances.

In this paper, dynamic resource allocation problems for multi-user downlink MIMO-OFDMA are investigated. With the goal of minimizing the total transmit power under condition that QoS of different users can be guaranteed, a novel dynamic resource allocation algorithm is proposed. First of all, a new subcarrier allocation policy is given based on the perfect CSI. And then a spatial subchannels grouping mechanism is proposed for bit allocation. In order to utilize the spatial resource efficiently, the proposed algorithm exploits all non-zero spatial subchannels to transmit data. The proposed scheme reduces the computational complexity significantly at little expense of the system performance. Numerical simulations show that the proposed algorithm is a good tradeoff between system performance and computational complexity.

In this paper, we propose a non-unitary codebook based precoding scheme for multi-user MIMO (MU-MIMO) downlink transmission with limited feedback The proposed MU- MIMO scheme will perform scheduling and precoding just according to the limited feedback of the signal to interference and noise ratio (SINR) of each user to enhance the system capacity. A precoder codebook design method is related to Grassmannian line packing criterion. We group vectors from the Grassmannian codebook into precoding matrices according to the correlation coefficient of the vectors to suppress the multi-user co-channel interference (CCI). It terms as non-unitary precoding and outperforms the traditional single user MIMO system as well as the unitary codebook based MU-MIMO scheme with limited feedback and low complexity.

Efficient radio resource allocation is essential to provide quality of service (QoS) for wireless networks. In this paper, a cross-layer resource allocation scheme is presented with the objective of maximizing system throughput, while providing guaranteed-QoS for users. With the assumption of a finite queue for arrival packets, the proposed scheme dynamically allocates radio resources based on user's channel characteristic and QoS metrics derived from a queuing model, which considers a packet arrival process modeled by discrete Markov Modulated Poisson Process (dMMPP), and a multirate transmission scheme achieved through adaptive modulation(AM). The cross-layer resource allocation scheme operates over two steps. Specifically, the amount of bandwidth allocated to each user is first derived from a queuing analytical model, and then the algorithm finds the best subcarrier assignment for users. Simulation results show that the proposed scheme maximizes the system throughput while guaranteeing QoS for users.

In this paper, an efficient resource management scheme applied to heterogeneous services scenario in multiple- input and multiple-output/orthogonal frequency division multiplexing (MIMO/OFDM) system is proposed. The design of the scheme is to maximize the system throughput while guaranteeing QoS requirements for users. At medium access control (MAC) layer, an urgency-factor based scheduler is designed to utilize the properties of packet data system. An efficient resource allocation algorithm at physical (PHY) layer, which jointly adapt subcarrier allocation, power distribution and bit loading according to instantaneous channel conditions, is proposed. The scheduler and the resource allocator are tightly coupled together through the cross-layer approach. The simulation results show that the proposed scheme improves the system throughput and makes use of resource more efficiently, while guaranteeing QoS for heterogeneous services.

Vertical handover decision making is one of key problems in heterogeneous network environment. In IEEE 802.21 standard, a Media Independent Handover (MIH) framework is presented to facilitate handover with measurements and triggers from link layers. However, vertical handover decision making can benefit from the information more than link layers. In this paper, an Enhanced Media Independent Handover (EMIH) framework is proposed by integrating more information from application layers and user context information. Given such information, the issue becomes how to select a favorite network. In this paper, two novel weighted Markov chain (WMC) approaches based on rank aggregation are proposed, in which a favorite network is selected as top one of rank aggregation result fused from multiple ranking lists based on decision factors. The proposed approaches can easily integrate a priori knowledge and/or human experiences into vertical handover. Simulation results demonstrate the effectiveness of the proposed approaches.

P2P SIP is proposed to leverage Peer-to-Peer computing to control multimedia sessions in a decentralized manner. The deployment and maintenance cost of P2PSIP is reduced compared to conventional SIP. In this paper, we propose a hierarchical P2PSIP system to address the connectivity and overhead problems which haven't been solved in the P2PSIP literature. The hierarchical P2PSIP system is implemented under Linux, which demonstrates the feasibility of the proposed scheme. Finally, exhaustive simulations are performed to evaluate the performance of various P2PSIP schemes. Results indicate that the hierarchical approach not only solves the connectivity problem caused by heterogeneous overlays, but also performs more efficiently than the flat scheme when the percentage of nodes in the upper level overlay is less than 10%.

The traditional channel estimation for orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels is usually carried out in two steps. Firstly obtain the least-square (LS) estimate over the pilot sub-carriers, and then interpolate it over the entire frequency-domain. In this paper, we propose a high precision channel estimation method by adding an intermediate step, which is based on the strong correlation in each path of the channel during the coherent time and can distinguish valid path taps and noise taps effectively, to improve the accuracy of the preliminary estimate over the pilot sub-carriers. The simulation results in the frequency band of 2.4 GHz show that the proposed method can obtain refined channel functions more efficiently and achieve a good bit error rate (BER) performance close to the theoretical bound of ideal channel estimation.

Mobile IPv6 (MIPv6) standardized by IETF, is expected to support the global IP mobility. Fast Handovers for Mobile IPv6 (FMIPv6) is further proposed to improve the performance of MIPv6. However, FMIPv6 only concentrates on the protocol operation while it does not address other critical issues, such as the L2 and L3 identifiers mapping problem and precise L2 triggers generation. This paper proposes an efficient service-oriented framework for FMIPv6. In this framework, FMIPv6 is integrated with candidate access router discovery (CARD) mechanism, specified L2 triggers and different handover processes will be performed for different service types according to QoS requirements. The analytical and simulation results both show that the proposed scheme could achieve better performance in terms of handover latency.

Priority scheduling has been widely used in mobile ad hoc networks. However, most of the prior work related to priority scheduling is designed only for a single data channel. In addition, without providing certain mechanisms to incorporate priority scheduling, existing multi-channel MAC protocols can not provide differentiated service. In this paper, we propose a multi-channel priority MAC protocol which can significantly increase the throughput of high priority flows and reduce their average delay. Our protocol consists of four mechanisms: control phase contention mechanism, priority- oriented channel assignment mechanism, data phase contention mechanism and flow interpolation mechanism. Simulation results demonstrate the effectiveness of the proposed protocol.

In this contribution, an optimal inter-antenna and subblock shifting and inversion (IASSI) scheme exploiting additional degrees of freedom brought by multiple antennas and subblocks is proposed to alleviate peak-to-average power ratio (PAPR) problem of MIMO-OFDM systems. In order to reduce the complexity of proposed scheme, two suboptimal schemes based on sequential search and transformation are presented. Besides, combination of IASSI and matrix transform is proposed, which takes advantage of low autocorrelation characteristic of constant amplitude zero autocorrelation (CAZAC) sequence to further reduce PAPR. Simulation results show that IASSI improves PAPR reduction performance compared with existing PAPR reduction scheme. And when combined with orthogonal matrix transform utilizing CAZAC sequence, further gain can be obtained. All these proposed methods provide flexible tradeoff between satisfied PAPR reduction performance and cost of implementation.

Orthogonal frequency division multiplexing (OFDM) systems encounter performance degradations due to the time varying (TV) channels in wireless environments. As delay spread increases, symbol duration should also increase, and then OFDM systems become more susceptible to time-variations. Time-variations introduce inter-carrier interference (ICI), which must be mitigated to improve the performance in high delay and Doppler spread environments. In this paper, we propose a method to estimate the TV channel. The method is designed for the comb pilot pattern. It performs a multi- symbol processing of the LS estimate over the pilot sub- carriers to detect the active paths of the channel and uses a piece-wise linear model to approximate the time-variations characteristic of each active path during each OFDM symbol to mitigate ICI. Theoretical analysis and simulation result show performance improvement in high delay and Doppler spread environments.

Distributed cellular network (DCN) is regarded as one of the most promising architectures likely to be used in next generation mobile communication systems. Under this network architecture, co-channel interference among multi- users in adjacent cells could not be neglected. In this paper, based on detailed error analysis, several useful conclusions for the maximum Doppler shift estimation in multi-user environments are obtained and validated. Furthermore, in order to combat co-channel interference, a novel ACF-based algorithm with adaptive iteration (AI-ACF) is proposed for multi-user Doppler shift estimation. Simulation results indicate that, with moderate complexity, the proposed algorithm could reduce multi-user co-channel interference effectively and improve Doppler shift estimation performance significantly.

A new noise variance estimation algorithm is presented for a multiuser OFDM system. In this estimator , the noise variance is derived from the traditional channel estimation results and no additional system cost is needed. Its performance is analyzed theoretically. The computational complexity is small and its performance is robust to noise power. Simulation results show that under multi-path fading channel the proposed estimator can obtain accurate real time measurements of the noise variance.

Power allocation problem in Decode and Forward (DF) wireless cooperative multiplexing communication systems is considered in this paper. We first derived an analytical bit error rate (BER) expression with BPSK modulation at the relays and Zero Forcing Successive Interference Cancellation (ZF-SIC) algorithm at the destination, which gives deep insight to how different power assignments among the source and the relays influence the BER performance. Based on this expression, a new power allocation algorithm is developed by minimizing BER under different power constraints, the effectiveness of which has been validated by both theoretical analysis and Monte Carlo simulations.

Wideband channel measurements have been performed at 5.25 GHz in hotspot areas in Beijing with a multiple-input multiple-output channel sounder. The measured scenarios include indoor line-of-sight (LOS) and non-line-of-sight, and outdoor LOS and obstructed-line-of-sight. Statistical results of channel characteristics are presented which include the path loss models, root mean square delay spread, circular azimuth spread and average envelope correlation with respect to antenna separation. Comparative analysis of the results are provided. These information are of significance for the design and performance evaluation of international mobile telecommunications (IMT)-advanced systems.

Amplify and forward (AF) is an effective mode for relaying systems with ease for implementation. In a conventional orthogonal frequency division multiplexing (OFDM) based AF system, relay stations simply amplifies the received signals and forward them to the destination station. Due to the fact that channel transfer function for the first hop (source-relay) and the second hop (relay-destination) may vary a lot, system capacity is limited. This contribution introduces a method named sub-carrier coupling, which couples sub-carriers for the two hops in a certain rule to improve system capacity. Firstly, system with a single relay station and its capacity is discussed, and the coupling scheme to maximize the system capacity is proposed. Then system with multiple relay stations is investigated, and two solutions are proposed. The solution with sub-carrier coupling can bring to much higher capacity since it determines the sub-carrier coupling scheme and assigns relay stations to forward signal for all sub-carriers jointly. Numerical simulation is carried out to show the efficiency of the proposed solutions.

The non-line-of-sight (NLOS) propagation has become a key obstacle to high accuracy location in urban environment. In this paper, a novel algorithm is proposed based on classical scattering models to mitigate the NLOS errors in location estimation. With distributed multi-antenna, the location of the scatterer that causes the NLOS errors is estimated, and the distance between the mobile station (MS) and the scatterer is also obtained. Then the estimated scatterers are used as virtual base stations (BSs) to locate MSs. Simulation results show that the proposed NLOS mitigation algorithm outperforms other algorithms, which indicates our NLOS mitigating method can be applied in NLOS environment effectively.

A novel location model for 4G mobile communication networks is proposed in the paper. The model utilizes the more precise relative distance estimated by short-range signals between the destination mobile terminal to be located (abbr. DT) and the other mobile terminals near DT to be the reference mobile terminal assisting DT (abbr. RT) to improve the location accuracy of DT. Besides, Triangle Similarity Theorem is firstly introduced into the model to search the desired optimal /suboptimal triangle. It is shown that by analysis and simulation that more accurate estimated position for DT in stationary status can be achieved based on the model above.

In this paper, the statistical analysis of cluster is presented based on the outdoor wideband multiple-input multiple-output (MIMO) channel measurements to facilitate IMT-Advanced system design. Clusters are found in multi-dimensional space, i.e., on the azimuth of arrival-azimuth of departure-elevation of departure-delay domain. We identify clusters with an automatic cluster identification algorithm and a new method is developed to improve the algorithm in terms of convergence rate and accuracy, which can also be used in calculating the angular spread (AS) to avoid the ambiguity caused by the origin of the coordinate system. All cluster parameters are described by a set of probability density functions (pdfs) derived from the measured data. The cluster numbers are well fitted with Poisson distribution plus a minimum number of clusters, and both the delay spread and angular spread exhibit the Lognormal distribution. It is concluded that the elevation angle should not be neglected when terminals are rounded by rich scatterers in outdoor scenario. We also find that clusters with smaller delays have in general a higher cross-polarization discrimination (XPD) than that with larger delays.

Radio frequency allocation is of great importance in the multihop cellular network, because some extra resources (frequency or time slot) seem to be allocated to the relay station. Generally, the tradeoff must be made between the frequency reuse factor and the inter-cell interferences during establishing frequency allocation scheme. A "pre-configured and fixed (PreF)" frequency allocation scheme has been proposed in [1]. In this paper, the concept of "soft frequency reuse (SFR)" in [2] is first borrowed, and applied to the multihop cellular network. After that, the improvement is made to the SFR scheme, and a more efficient "modified SFR (MSFR)" frequency allocation scheme is proposed. Through the simulations, it is demonstrated that, almost the same cell throughput can be achieved in the PreF and SFR schemes for the uniform traffic distribution, and the MSFR scheme can provide nearly twice cell throughput as large as that in the aforementioned two schemes. Moreover, the SFR and MSFR schemes both outperform the PreF scheme for the non-uniform traffic distribution because of the support to dynamic frequency resource allocation in the proposed schemes.

A modeling method for wireless packet networks based on network calculus (NC) theory, employing the concatenation of NC components to represent the effects of the modules constituting wireless packet networks model, is proposed in this paper. Three well-known scheduling algorithms are modified to agree with the proposed NC model. A new scheduling algorithm for packet networks is proposed in the context of NC. Simulations then are done to study the end-to-end QoS characteristics of the networks with different scheduling algorithms. The result shows that, in terms of the criterions defined in NC model, the scheduling algorithm proposed in this paper outperforms the existing three algorithms.

This paper presents a context-aware infrastructure for the easy creation and flexible deployment of the context aware application. We introduce the concept of plane in this infrastructure to manage the sensors which are distributed in the communication environment and used to collect the specific context information. A layered structure of the context information is designed based on this infrastructure. Especially, the Demspter-Shafer evidence theory is applied in the design of the reasoning mechanism in this infrastructure for its superiority over Bayesian method in handling the uncertainty problem, and the inferencer is constructed based on the combination rule of this theory. We also choose the rough set theory and genetic algorithm to design the aggregating mechanism and construct the aggregator.

Orthogonal frequency division multiplexing (OFDM) is a very promising transmission technology for the beyond 3G (B3G) wireless communication system with orthogonal frequency division multiple access (OFDMA) as its major multiple access technology. And in the multi-cell scenario, the management of inter-cell interference has significant impact on the performance of the wireless network. This paper proposes an optimal and a sub-optimal inter-cell scheduling strategy both of which coordinate the transmission of interfering cells. In addition, the coordinated scheduling strategies are proposed to be implemented by an distributed and coordinated network architecture such as the radio on fiber (RoF) system. The utility function is used to balance the efficiency and fairness of wireless resource allocation. Simulation results show that, the proposed inter-cell scheduling strategy provides significant gain in fairness over the single-cell scheduling strategy which doesn't involve multi-cell coordination of transmission.

In this paper, we address the problem of the design of pilots aiming at mitigating the inter-cell interference in OFDM systems. Based on the conventional pilot design used in the intra-cell, we design the pilots which can make the power of inter-cell interference distribute in all paths of the channel uniformly, and result in a variation coefficient of zero, then the better performance on system level will be derived. The properties of these pilots are described, and the simulation results are presented.

In this contribution, spatial coding diversity is combined with spatial multiplexing to get higher joint gains on ensuring both low error rate and high spectrum efficiency. A general mathematical model of joint spatial coding and multiplexing (JSCM) is proposed and analyzed. Optimal criteria and a suboptimal iterative algorithm on the design of JSCM are studied. Performance of JSCM is proved by simulations from various aspects.

We consider superimposing pilot signals onto data signals for channel estimation in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. This paper presents a channel estimation method, which is based on orthogonal pilot-constant amplitude zero autocorrelation code (CAZAC) for space-time block code (STBC) OFDM system. In this system, the proposed method has low-complexity and CAZAC codes have the minimum peak-to-average power ratio (PAPR). At the same time, the superimposed pilot approach can increase bandwidth efficiency and data rate compared with other classical approaches, where pilot signals and data signals are disjoined, at nearly the same system performance by the computer simulation.

Wideband Multiple-Input Multiple-Output (MIMO) channel measurements were preformed in outdoor-indoor scenario for peer-to-peer system at 5.25 GHz with 100 MHz bandwidth. Based on the measured data, a new power delay profile (PDP) model is proposed which is applicable regardless of the first path is the strongest or not. Time dispersion parameters and empirical model are presented. The RMS delay spread (RDS) is log-Gumbel distributed and the path number well obeys the Gao's distribution. The relationship between RDS and mean excess delay is also studied. Finally, the propagation pathways are reconstructed under the assumption of two-bounce scattering according to the geography of the measurement site and the spatio-temporal information provided by a high resolution algorithm.

In this paper, we propose a new cooperative random access strategy for future cooperative networks. This scheme considers the different requirements of services, and exploits the cooperative relaying nature for multi-channel environments, e.g. orthogonal frequency division multiplexing access (OFDMA). Due to differentiation of services and definition of special channels, those users with real-time (RT) request can reserve the access channels in advance, while others with non-real-time (NRT) services can access to the BS or the nearest distributed relay stations (DRSs), which are introduced for cooperative relaying, through sharing those channels remained. The analyses and numerical results demonstrate that our scheme can achieve high throughput, low collision probability and low access delay compared with conventional slotted Aloha.

This paper investigates a spatial multiplexing MIMO scheme with beamforming for downlink transmission. The proposed scheme combines spatial multiplexing MIMO technique with beamforming, which has the advantages of both techniques. The proposed scheme utilizes the uplink direction of arrival (DOA) to perform downlink beamforming. It is able to transmit parallel data streams as well as providing beamforming gain and improve the system performance significantly. The proposed scheme provides better BER performance than the traditional spatial multiplexing and beamforming techniques under the same simulation environment.

In this paper, a QoS differentiation adaptive retransmission limits ARQ (QDARL-ARQ) is proposed to improve the efficiency of retransmission in conventional SR-ARQ for the IEEE 802.16e BWA systems. With a simple algorithm implemented based on the conventional SR-ARQ, QDARL-ARQ scheme is able to dynamically adjust the retransmission limits for services with different characteristics by considering their QoS requirements as well as the current system states simultaneously. This scheme aims to achieve lower packet error rate with restrained end-to-end delay in the time-variable and error prone wireless environment in comparison with conventional SR-ARQ. Several performance metrics of QDARL-ARQ are compared with conventional SR-ARQ in both single service scenarios and multiple services scenarios. The performance improvement due to QDARL-ARQ is evaluated through the IEEE 802.16e system level simulation, and the results clearly show that it can improve the performance of mean end-to-end delay, packet error rate and throughput, especially the retransmission efficiency. It can also be found that the conventional SR-ARQ is in fact a special instance of the QDARL-ARQ designed here.

A random access protocol, particularly suitable for OFDMA system, is proposed and analyzed in this paper. The protocol adopts a new dynamic RACH assignment algorithm and a new adaptive access probability scheme. Under the condition of light load, Base Station (BS) will adjust the number of RACHs to improve the channel utilization. Under the condition of heavy load, BS will take effective measure to guarantee QoS requirements of high priority traffics. In addition, the article introduces a statistical model to estimate system load, which is one of the features of the protocol. The simulation results fully indicate that the proposed random access protocol is efficient and reliable at conditions of both high and low load, and not only provides excellent transmission quality guarantee for higher priority traffics, but also supports lower priority traffics transmission efficiently in the integration traffic environment.

In this paper, a new QoS-Guarantee resource allocation scheme is proposed for multi-user MIMO- OFDM systems. Based on channel state information (CSI) , the base station (BS) employs singular value decomposition (SVD) to transmit the MIMO channel into parallel eigenmode subchannels for each subcarrier, and then subcarriers, bits and power are dynamically allocated to users according to user's support subcarrier rate, the queue status of traffics and user's quality of service (QoS) requirement. Moreover, subcarrier reallocation is executed in order to heighten user's transmission rate and improve the utilization of system's resources. Simulation results demonstrate the superior performance of our proposed scheme, which can not only guarantee user's QoS requirement, keep user fairness, but also can greatly improve the cell throughput.

In this paper, we propose a low complexity scheme for adaptive MIMO-OFDM system. A new method for calculating the process gain is developed to reduce the complexity and avoid pseudo inverse operations in the transmitter. The computation method is derived for the adaptive V-BLAST architecture which is based on the ZF-SIC detection algorithm and adaptive modulation and bit loading algorithm. The proposed process gain algorithm is proved equal to the ZF-SIC based process gain computation algorithm. So the proposed algorithm can achieve the same result as the ZF-SIC algorithm, which is proved through simulation methods in the paper. Also the complexity analysis is presented.

Relaying transmission is a candidate way to combat wireless channel fading and enlarge the coverage, and efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, an adaptive multiuser radio resource allocation model is proposed for the downlink of OFDM-relay system, which exploits performance gain in both frequency domain and time domain. According to the different transmission modes, two QoS-oriented scheduling algorithms based on the feedback of the channel state information (CSI) of two hops are investigated. One is enhanced proportional fairness (EPF) algorithm, and the other is improved priority (IPRI) algorithm. Both of them can achieve high system throughput and better resource fairness due to the adaptive allocation, especially in the QoS-guarantee aspect for cell edgy users compared with conventional scheduling schemes. The priority strategy is a novel scheme, because of considering resource fairness with artificial starve (AS) state in IPRI, which yields higher spectral efficiency and achieve better data rate requirements for the users.

Based on 5.25 GHz wideband channel measurements performed in indoor office environment, empirical path loss models in in-room line-of-sight (LoS), room-corridor, and room-room non-line-of-sight (NLoS) propagation conditions are developed for future wireless radio systems. One-slope and dual-slope log-distance models are adopted in in-room LoS and room-corridor conditions, respectively. In room-room NLoS condition, we propose the enhanced attenuation factor models: AF-extended and AF-linear models to further explore the effect of medium walls, heavy walls and doors. This work offers valuable propagation measurements in a frequency range that is being considered allocating to IMT-advanced systems.

Generalized distributed antenna is one of the most promising architectures likely to be used in next generation mobile communication systems. In this paper, based on the architecture, multi-user co-channel interference in Doppler spread estimation is analyzed and several useful conclusions are obtained. Moreover, in order to combat this interference, a novel ACF-based multi-user joint Doppler spread estimation (M- JDSE) algorithm is proposed, taking advantage of centralized signal processing characteristic in generalized distributed antenna systems. Both theoretical analysis and simulation results demonstrate the significant performance improvement of proposed M-JDSE algorithm in multi-user environments.

In this paper, we investigate a novel MIMO transceiver with space-time-hopping code combing pulse position modulation (STH-PPM) for UWB time division multiple access (TDMA) system. Moreover, we propose a new approach called second-order statistics (SOS) with unknown channel state information (CSI) for receiver detection. This new transceiver does not require any training or pilot symbols or decision feedbacks, and it performs well in flat fading channel.

The Non-Line-of-Sight (NLOS) propagation effect has been considered as one of the most important issues in the location estimation especially in urban environment with serious blocking of direct paths. In this paper, we develop a new algorithm to mitigate the NLOS errors in location estimation by distributed multi-antenna. Utilizing the relativity between TOAs obtained from different antennas in a multi-antenna array, the positions of scatterers that cause the NLOS propagation, as well as the distances between mobile stations (MSs) and scatterers, are estimated. Then with appropriate location algorithm, the locations of MSs are obtained with information of scatterers. Simulation results show that this algorithm can mitigate NLOS errors effectively and improve the performance of TOA/TDOA based location algorithm in NLOS environment significantly.

Through exploiting the multiuser diversity, the capacity of MIMO-OFDM system can increase dramatically. In multiuser singular value decomposition (MU-SVD) based MIMO-OFDM system, the scheduler can allow multiple users to simultaneously transmit independent data to base station (BS) on the same subcarrier and the data can be separated in space domain at the BS. In this paper the effect of the correlations between the singular vectors in a MU-SVD based system was investigated, and propose a greedy scheduling algorithm based on MU-SVD. Given a set of users, the algorithm finds the best and most orthogonal spatial subchannels, in order to exploit the multiuser spatial diversity. The simulation results show that if the user data are transmitted on the maximum singular mode (MSM), the amplified noises on the spatial subchannels may lead to the degradation of the system performance, however the proposed algorithm can greatly increase the system capacity.

To provide mobile users with seamless wireless access and guaranteed quality of service (QoS) anywhere and anytime, it is a trend to integrate CDMA cellular networks and wireless local area networks (WLANs) into heterogeneous networks in next generation all-IP wireless networks. Network selection and QoS support for different services are two of the major challenges in integrated CDMA cellular/WLAN networks. In this paper, a net utility-based network selection scheme was proposed, which takes into account the network resource, QoS requirements of applications, user mobility, and vertical handoff. The goal of the proposed scheme is to guide users to select the most suitable access network, where the users' QoS requirements can be satisfied and the cost that user paid for is the lowest. Simulation results demonstrate the effectiveness of the proposed network selection scheme in integrated CDMA cellular/WLAN networks.

A novel preamble sequence design for random access using complete complementary code sets in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is proposed. By elaborate design, both the optimum cross-correlation function (CCF) property and sufficient available number of preamble sequences can be achieved. The generation method and the bound of extension code number are addressed to satisfy the requirement of practical system. In addition, the comparison between proposed code and constant amplitude zero auto-correlation (CAZAC) code which is promising in the long term evolution of 3rd generation systems as preamble sequences was presented. The numerical results show that the application of preamble sequences with complete complementary code sets can obtain high detection probability.

For the Evolved UTRA (EUTRA), in OFDMA uplink, the Inter-cell interference cannot be predicted precisely, so scheduling becomes a puzzling problem and directly affects the uplink performance in OFDMA based system. Previous works (Galdata, et. al., 2003) did not concern such a problem or just consider single cell environment. In this paper, a novel scheme is proposed that could efficiently solve this problem in multi-cell circumstance. It predefines UEs distributed in the whole cell adopt fixed modulation and coding scheme (MCS) according to the distance from Node B. It employs initial average predictive interference to create the first-loop Inter-cell interference and perform scheduling according to the historical information in the following. Fast power control is adopted in order to compensate the imprecisely predictive inter-cell interference. Soft frequency reuse scheme is adopt in order to improve the cell-edge UE performance. To guarantee the UE fairness, proportional fairness (PF) scheduling is adopted in single input and single output (SISO) environment in this paper.

This driven by the need to promote a more efficient use of radio resources and improve the operators' profits, resource allocation has turned into a joint technical and economical problem. At the same time, as a possible enabling solution, game theory has been applied to either dynamic spectrum access (DSA) or joint radio resource management (JRRM) in wireless communication research recently. In this paper, we propose a novel DSA and JRRM combined approach to resource allocation in cooperative networks. With the scenario that distributed reconfigurable radio access networks (RAN) are controlled by different operators, the emerging concept of resource trading is introduced and new entities, such as trading agents (TA), are described. Meanwhile, Shapley value in cooperative game as well as its economic model is exploited to share the profits among the trading RANs. Numerical results show that comparing with existing DSA or JRRM methods, our scheme has better effect in maximizing the individual operator's profits and improving the efficiency of radio resources utilization.

In this paper, we first overview the least square (LS) space-time joint channel estimation in filtered multi-tone (FMT) system in multi-input multi-output (MIMO) system. Then the design of pilots aiming at mitigating the inter-cell interference in MIMO FMT systems is addressed. It is shown that if the pilots which result in a zero variation coefficient of the estimation error are used, the better performance on system level was obtained. The properties of these pilots are described, and the simulation results are presented.

Abstract B3G–TDD uplink in China's FuTURE project is introduced in the paper. By taking advantage of MIMO–OFDM and special design for the TDD frame, peak data rate of 100 Mbps can be achieved. The system is required to support service in both indoor and outdoor environments, and guarantee high data rate communication even with mobility up to 250 kmph. Detail theoretical analysis was carried out to give the principle of designing frame structure for the system under different environments. In order to reduce the influence of Doppler Effect, adaptive frame structure is proposed for the uplink. By special design on the frame for different environments, the channel information can be tracked accurately with low overhead, which ensures the system performance. Through simulation based on the platform of Future Technologies for Universal Radio Enviroment (FuTURE) project, bit error rate (BER) can achieve lower than 10−6 when Eb/N0 is 3 dB for various mobility case in both indoor and outdoor environments, which satisfies the requirement of FuTURE project. By employing it into B3G–TDD uplink, robustness to Doppler Effect is strengthened greatly. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract With current research focusing on beyond 3G (B3G)/4G mobile systems, many advanced techniques are investigated by world‐wide research institutes and standard organization, such as multi input multi output (MIMO), orthogonal frequency division multiplex (OFDM), and multi‐antenna distributed cellular network architecture. Based on these novel techniques, the radio resource management (RRM) strategies, such as access control, also need to be developed. This paper proposes the maximum utility principle access control (MUPAC) basing on Dijkstra's Shortest Path Algorithm for multi‐antenna cellular network architectures. In the accessing process of the proposed algorithm, the shortest path in Dijkstra's Algorithm is replaced by the cost of accessing process, which is represented by utility function. Taking Generalized Distributed Cellular Architecture—Group Cell as an example, MUPAC is described in details with the utility function, maximum utility principle, flow chart of accessing process. Performance evaluation and analyses verify the merits of MUPAC algorithm in improving system capacity, accessing success probability, and efficiency of system resources usage. Copyright © 2007 John Wiley & Sons, Ltd.

Wireless communications systems are developing from the 3G toward the beyond 3G or 4G systems aiming to support high data rate services with higher spectrum efficiency than previous systems over broadband channels with acceptable quality of service (QoS). A lot of technical innovations are initiated to satisfy the challenging requirements from the physical layer to the network layer. In this context, Asia-Pacific countries are playing significant roles in promoting the reality of the future B3G wireless mobile communications by lots of research and development (R&D) activities. Extensive mobile and wireless systems research activities are ongoing in China, Japan, and Korea and other countries in this area. This special issue aims to present the recent developments in the emerging wireless communications networks and technology, with an emphasis on research, development, deployment, application, and market issues toward B3G or 4G systems, in the Asia-Pacific countries. The high quality papers from the academia and the industry are gathered. Some advanced 4G system concepts appear in this special issue. For example, in ‘Network and Access Technologies for New Generation Mobile Communications—Overview of National R&D Project in NICT’, the seamless and secure integration of various heterogeneous wireless networks, such as 3G and B3G cellular networks, the IEEE 802.11-compliant high speed wireless access systems, advanced home networks, ITS networks, and digital broadcasting networks is introduced based on a national R&D project carried out in NICT. And in ‘3G Evolution Scenario toward 4G—Super 3G concept’ by NTT DoCoMo, the super 3G concept is introduced for the long-term evolution of 3G mobile communications systems. Some fundamental research works on the different layers of future wireless systems are also included. For example, in ‘Maximum Utility Principle Access Control for B3G Mobile System’ from BUPT, the Maximum Utility Principle Access Control for multi-antenna cellular network architectures is proposed. And in ‘Design of Spreading Codes with High Diversity Gain for Multicarrier CDMA Systems’, a criterion for evaluating the spreading codes of the MC-CDMA systems is proposed and a new full-diversity code constructed by offsetting the phase of the conventional orthogonal codes is introduced. The guest editors hope the collected papers in this special issue will partially reflect the R&D activities and technology innovations in the Asia-Pacific area.

It is an obvious trend that computer network and robot technology will more and more closely combine with each other. And the robot, especially the service robot such as house robot, entertainment robot, etc, will provide different kinds of services to the mankind. This paper introduces a service-oriented network robotic management system (SONRMS) to coordinate and manage the behaviors between robots and their users so as to provide high reliability and safety measures to users. Then SONRMS' architecture is put forward. SONRMS reliability model is described by use of stochastic reward net, reliability block datagram and hierarchical model. The influence among parameters of the system upon SONRMS reliability is analyzed in the end

In this paper, we investigate the coexistence issue of two type of ultra-wideband (UWB) systems under IEEE 802.15.3a, i.e. Direct-Sequence UWB and MultiBand-Orthogonal Frequency Division Multiplexing UWB. The mutual interference of such coexistence scenarios is analyzed by physical layer Monte Carlo simulations. We observe severe and asymmetric performance degradation due to the mutual interference between the two systems. Therefore, we propose the goodput-oriented utility-based transmit power control (GUTPC) algorithm to improve the performance. The feasible condition and the convergence property of GUTPC are investigated, and the choice of the coefficients is discussed for fairness and efficiency. Numerical results demonstrate that GUTPC improves the goodput of the coexisting systems effectively and fairly.

In this paper we propose and discuss an innovative infrastructure-assisted routing protocol for the ad hoc network overall or partial under the coverage of the infrastructure network. The basic idea of this design is to make full use of the infrastructure network's notable features to support the implementation of route discovery and maintenance for the purpose of not only highly reducing time, communication and storage complexity but also balancing traffic load of the ad hoc network. In this protocol, each node detaches link state with the neighbor nodes and reports the changes to the infrastructure network using the triggered update mechanism, while the infrastructure network stores the full knowledge of topology, computes routes on-demand with latency as the edge weight, and distributes the sub-route records to relevant relay nodes using the multi-sub- route relay mechanism. Those sub-routes are typically, but not necessarily, of the same length. Since not all nodes communicate with the infrastructure network directly, the ad hoc network are divided into a set of zones, the light zones and the dark zones, and different strategies, i.e. the link state algorithm and the source routing algorithm, are employed in different zones. We refer to the protocol as the light dark routing (LDR) protocol.

The goal of handoffs in a 4G wireless network is not only to keep the data traffic from being disrupted due to user mobility, but also to switch the connection to the network which best satisfies users' requirements. In this work, we propose a scheme which dynamically switches a mobile user's connection between different access networks. In each handoff, a user will adjust its bandwidth requirement according to the utilization of the current access network. A profitability function is established to evaluate the profits gained from a handoff and to select the target network accordingly. Through vertical handoffs, traffic load will be balanced among the access networks and radio resources will be efficiently utilized. Our simulation result shows that the scheme will effectively decrease call blocking and dropping rate. System throughput and users' experience will also be improved.

Synchronization is a challenging task in ultra wideband (UWB) communications, and becomes more difficult in UWB based wireless sensor networks (WSNs) in the presence of multi-user interference (MUI). For such a system, we develop a synchronization scheme including a novel design of transmitted reference (TR) signal model to avoid MUI and an energy-efficient synchronization algorithm. The synchronization signal of cluster-head is designed to be TR symbols with normal reference pulse and the interfering signal of common neighbour nodes is designed to be TR symbols with alternant anti-polar reference pulse, thus the MUI-free operation is obtained. Furthermore, the synchronization algorithm employs sample mean and energy detection relying on the periodicity in the mean of synchronization signal. Theoretic analysis and simulative results prove that the proposed scheme is both reliable and scalable

The cluster-based wireless sensor network (WSN) can enhance the whole network lifetime. In each cluster, the cluster head (CH) plays an important role in aggregating and forwarding data sensed by other common nodes. A major challenge in the WSN is the appropriate cluster head selection approach. In this paper, we propose a centralized cluster head selection approach using analytical hierarchy process (AHP). Three factors contributing to the network lifetime are considered and they are energy, mobility and the distance to the involved cluster centroid respectively. Simulation results demonstrate that the proposed approach is effective in prolonging the network lifetime

Large progress has been made in the research and realization of the ldquoB3Grdquo (Beyond 3G) TDD mode demonstration system in ldquoB3Grdquo TDD group of Chinese FuTURE (Future Technologies for Universal Radio Environments) project, which is introduced in this paper. Since the ldquoB3Grdquo demonstration system aims at evaluating the radio transmit technologies such as MIMO, OFDM etc. to bear a high data rate up to 100 Mbps, the previous results presented in several literatures coming from the FuTURE project are focusing on the high data rate services transmission. However, services with low data rate such as voice services etc. should also be transmitted in the ldquoB3Grdquo system, which has a relatively smaller bandwidth. In this paper the technologies for voice service are introduced and the performance is analyzed in the whole-link demonstration system, since voice service is a basic service in the mobile communication systems.

As the evolution of 3G, long term evolution (LTE) standardization activity is issued in 3GPP and 3GPP2. In previous paper OFDMA is proposed for downlink of LTE. As the channel reciprocity can be obtained in TD-SCDMA LTE system, the channel status information (CSI) can be exploited at the transmitter to obtain the spatial-frequency multiuser diversity by joint spatial-frequency subcarrier and antenna assignment of MIMO OFDMA for the independent fading of different user in spatial and frequency domain. To guarantee the user fairness, a joint spatial-frequency proportional fairness (PF) scheduling is proposed in this paper. Further, no inter-cell interference mitigation capability can be observed from the current MIMO OFDMA schemes in downlink, the soft frequency reuse is adopted in this paper to avoid the inter-cell interference. Finally the downlink performance of the TD-SCDMA LTE with no-real time service is evaluated with spatial frequency PF scheduling and soft frequency reuse in multi-cell scenario

OFDMA will be the dominant multi-access method in 3GPP long term evolution (LTE) system. Multiuser scheduler plays an important role in optimizing its shared data channel (SDCH). This paper proposes and investigates a multi-carrier gradient scheduling algorithm with minimum/maximum rate constraints (MC-GMR) for downlink SDCH. The objective of MC-GMR scheduler is to maximize the system utility as well as provide quality of service (QoS) guarantee for data service. Performance is evaluated through system level simulations.

This paper presents a comparative study of two receiver schemes for interleaved OFDMA uplink. In the first scheme, called the post-DFT processing based interference cancellation receiver, the CFOs are compensated in frequency domain and an iterative interference-cancellation scheme is used to reduce the inter carrier interference (ICI) and/or multiuser interference (MUI). The other scheme, the signal structure-based MMSE receiver, exploits the signal inner structure on the interleaved OFDMA uplink and separates single user signal waveform by compensates the effective carrier frequency offsets. Simulation results show that the signal structure-based MMSE receiver is robust to large CFOs but its computation complexity increases considerably with the number of users. Furthermore, it assumes to know the signal power. When the CFOs are small, the post-DFT processing based interference cancellation receiver is preferable because its computation is easy with the number of users and it needs not know signal power

In this paper, we propose a zero correlation zone (ZCZ) sequences-based carrier frequency offset estimation algorithm for interleaved OFDMA uplink. By introducing the effective carrier frequency offset (CFO) and designing an appropriate training sequence for each user, we propose a training sequence-based CFO estimation algorithm thus avoiding the computationally complex subspace-based method proposed by Cao & Tureli (2002). Zero correlation zone sequence set is used as training sequence to mitigate multipath inference and multiuser inference. Simulation results show that the proposed algorithm is effective in multi-path fading channel

The inter-cell interference of orthogonal frequency division multiple access (OFDMA) based multi-cell distributed network architecture is analyzed. Based on generalized distributed cellular architecture-group cell, the interference condition without power control and with power control is analyzed respectively, and the system outage probability compared to traditional cellular structure is evaluated. Analyses and simulation results indicate that the inter-cell interference of group cell architecture does not increase more than traditional cellular structure. Moreover, the system resources of group cell architecture are centralized scheduled and allocated by the access point (AP), which makes it flexible to apply centralized SRA power control algorithm to improve the system performance further.

Orthogonal frequency division multiplexing access (OFDMA) which can make full use of frequency resources by using adaptive modulation and coding (AMC) and multi-user diversity, is a promising technology for the next generation wireless communication system. The flexibility of OFDMA also makes the radio resource management (RRM) more complicated. This paper proposes a modified largest weighted delay first (M-LWDF) packet scheduling algorithm with subcarrier allocation for the real time service in the multiuser OFDMA systems. The simulation result shows that it can maximize the system throughput and guarantee the QoS (quality of service) of different users.

According to the features of TD-SCDMA system, a novel multi-antenna pre-processing model and algorithm is proposed. Meanwhile, the more precise uplink channel estimation and downlink channel prediction are put forward for TD-SCDMA system to improve the performance of basic multi-antenna pre-processing algorithm. Simulation results show that: by using the proposed multi-antenna pre-processing technology, the capacity and performance of TD-SCDMA system is significantly improved. Meanwhile, the improved multi-antenna pre-processing algorithm performs better than the basic algorithm under fast varying channel condition.

In this paper we propose a multipath delay estimation scheme for MIMO-OFDM system with the code division preamble. The algorithm is composed of three steps: the delays of L1stronger paths are firstly estimated by coarse multipath delay estimation module. Then the channel coefficients of L1paths are estimated and fed back to interference reconstruction module, by which the interference from other antennas will be recovered. After interference cancellation, fine multipath delay estimation module is used to get the delays of L paths and accurate estimation of delays could be taken advantage by channel estimation module. It is verified by simulation that the proposed algorithm can correctly estimate all paths for 4 antennas once SNR is higher than 6 dB. As antenna number is increased to 16, the correct estimation probability of former 4 paths is still about 90% when SNR is higher than 3 dB. So the proposed multipath delay estimation scheme with interference cancellation can efficiently improve the accuracy of multipath delay estimation in MIMO-OFDM system.

In multiuser MIMO system, multiuser multiplexing and diversity gain can be achieved by spatial scheduling. However, the limited battery life and terminal size of the User Equipment (UE) in a cellular system put a constraint on MIMO when the tranmitter has more antenna than the receiver in downlink, the conventional MIMO schemes can only exploit partial multiplexing gain and diversity gain. In a multiuser MIMO OFDMA system, multiuser diversity gain can be achieved by exploiting the independent frequency and spatial selective fading one another by joint spatial and frequency scheduling. In this paper, different multiuser multiple access schemes with greedy scheduling of MIMO OFDMA are investigated, e.g. TDMA, FDMA/TDMA and SDMA/FDMA/TDMA. For full spatial-frequency multiuser diversity gain and spatial multiplexing gain can be achieved, SDMA/FDMA/TDMA obtains highest spectrum efficiency. Its gain exceeds that of TDMA and FDMA/TDMA at least 80% and 40% respectively. The more antennas configured, the more gain can be observed from SDMA/FDMA/TDMA.

Next-generation wireless networks will be a conglomeration of different networks technologies and will support multiple radio access technologies (Multi-RATs). This will put high demands on radio resource management support. In this paper, we describe an intelligent multiagent radio resource management system, which is self-organized and distributed to ensure the coexistence of Multi-RATs. This paper discusses how to implement macro control and management by using control factors instead of micro control concerning individual users, and also describes the mechanism of coexistence of Multi-RATs. Further, message flow and open questions are discussed.

Coexistence of heterogeneous networks such as cellular, wireless local area network (WLAN), ultra-wideband (UWB) etc. brings new challenges. It is perceived that current radio resource management mechanism cannot meet the requirements of multi-radio access technologies (multi-RATs). This paper proposes a novel intelligent multi-agent radio resource management system, which is self-organized and distributed to ensure the coexistence of multi-RATs. Radio resource is managed by a macro control and management system using control factors and validation mechanism, instead of micro control for individual users. The goal is to increase radio resource utilization efficiency, maximize system capacity and meet the QoS requirements of different services

For optimal radio resource utilization in heterogeneous wireless networks, joint radio resource management (JRRM) is required. In distributed JRRM, each radio each access network (RAN) adjusts network parameters to affect user's RAN selection, thereby indirectly implementing joint radio resource allocation. The mathematical method for instructing such adjustment is lacking. In this article, the relationship between different RANs is mapped into the competition between species in the well-known L-V model developed by ecologists. Based on this model, an adjustment algorithm of distributed joint radio resource allocation is proposed. The simulation results show that compared with no adjustment or over adjustment, our adjustment algorithm can: 1) obtain proper resource allocation; 2) guarantee network coexistence

Our objective is to build a distributed terminal system to provide smart, context-aware, rich-experienced applications upon personal environment networking technologies, such as WLAN, IEEE 802.15.3 series, ZigBee, Bluetooth, etc. For the proliferation of smart devices with autonomous applications, the users can get much more service experiences than before. While smart devices facilitate human operation, the coordination of devices through networks may provide applications proactively by gathering much more service context, which indicates the emergence of the pervasive computing age. Hence, we proposed the distributed terminal system for the cooperating of smart devices. In this paper, we analyzed the organization architecture of universal service terminal (UST), the distributed terminal system proposed by us before. In UST project, we have abstracted and encapsulated the capabilities of devices as servers for remote invocation by applications, moreover, the framework functionalities have been introduced for the organization of the distributed system. Though the architecture of UST has been validated feasible in a demonstration, the centralized control mechanisms in the heterogeneous environment are inefficient and unreliable. Thus, we propose an evolved scheme by introducing decentralized mechanisms in this paper. The devices around the user are organized in an overlay peer-to-peer network, and some powerful nodes of them provide the decentralized mechanisms for resource management, service discovery, etc

In this article we present a novel architecture of mobile networks referred to as mobile ubiquitous service environment (MUSE), which aims to bring users always best experiences (ABE) employing heterogeneous networks and personal area networks. We aim to provide a uniform ubiquitous service environment, which consists of detectable, representable, evaluable, expansible, and adaptable abstract features. Thus, it appears as homogeneity to the services. We aim for the joint utilization of the capabilities provided by novel terminals and heterogeneous networks. As a result of this view, we introduce two interactive environments, terminal service environment (TSE) and network service environment (NSE); which provide the MUSE capabilities to bring experiences to users. In this paper, the design principles of MUSE and the ABE concept are presented, and the QoS framework and the reconfiguration, which play key roles in MUSE, are presented in more detail.

This paper introduces a general framework of B3G (Beyond 3G) mobile communication system in China TDD (time division duplex) Special Work Group. System architecture is described that allows the integration of multiple antenna techniques, e.g., MIMO (multiple input multiple output), in physical layer and the novel cellular architecture, e.g., group cell, in network layer. MIMO techniques based on group cell architecture, including spaced MIMO, distributed MIMO and virtual MIMO, are presented. What's more, a multi-MIMO matrix detection algorithm for distributed MIMO structure in Group Cell B3G TDD system is also presented and analyzed with the corresponding mathematical model. Link-level and system-level simulation results show the performance and capacity of this group cell MIMO system respectively

Resource allocation schemes for traditional MIMO system have been widely studied. However, when the MIMO technique is applied to relaying systems, where several separated terminals form a virtual receive array (VAA) and relay the signal from the transmitter to the receiver, these issues, which have a close relationship with the system performance, should be solved before a MIMO relaying system is deployed on a large scale. The channel capacity for a 2-hop MIMO relaying system is derived from the perspective of information theory. An optimal resource allocation under an aggregate power constraint between relaying nodes is proposed in closed form for the case with two relaying nodes. Numerical results indicate that a MIMO relaying system can achieve a higher channel capacity than traditional MIMO does and that the positions of the relaying nodes affect the system performance directly.

The average rate updating mechanism was first promoted for the starvation problem emerging when some user experiences a sudden drop in channel quality or just keeps on moving backward from the base station under the proportional fair scheduler in the HDR system. Although proportional fair scheduling has received much theoretic research attention recently for its attractive capability on the tradeoff between system utility and fairness, the influence of average rate updating mechanism to the performance of scheduler has been neglected. We point out here that the previous rate update mechanism based on fixed time window is insufficient in keeping the users from starvation. Furthermore, updating the average rate to users with no data to send may not get the overall maximized system utility. We also promote a new average update mechanism as the basis of the proportional fair scheduling algorithm. Simulation and analysis show that this novel design with starvation supervision has better performance over the traditional design.

The paper discusses the key technologies of an all IP architecture for a B3G mobile communication system. The aim is to contribute to the technical innovation of 3G systems by exploiting the potential of an IP-based B3G wireless communication system. The discussion focuses on the realization of an all IP core network. An all IP network architecture, improvement of end-to-end QoS, and flexible service provision are among the major challenges toward the B3G communication system. However, an all IP network architecture is the goal of the evolution of wireless networks on the way to the B3G system. The paper first reviews the development of the 3G core network, and proposes technologies for the all IP architecture for communication between 3G systems and the Internet, such as the assumed all IP network architecture and the design of protocol stacks. The paper describes the architecture of a protocol stack based B3G system, and then elaborates on the functionality of the MPPP, RLC, LLC and MAC layers. Finally, for fully supporting an all IP solution, the paper suggests further research work required based on the IPv6 core network.

A cooperative relaying system is presented to allow the application of multiple-input-multiple-output (MIMO) capacity enhancement techniques, such as space-time block codes (STBC), to mobile terminals (MT) with a limited number of antenna elements. The adaptive optimal power allocation (PA) scheme among relay stations (RS) is an important issue to achieve power efficiency and maximum performance improvement. This paper analyzes the non-regenerative (NR) relaying system based on the STBC technique, and investigates the optimal PA scheme between two non-regenerative RS under power constraint. Two receiving methods, with or without direct path signal, are also discussed in this paper. Numerical results indicate that compared with the uniform PA scheme, the proposed PA scheme can obtain the maximum instantaneous signal to noise ratio (SNR) and improve the system performance despite the RS's position.

In this contribution, some important issues toward B3G (beyond 3G) mobile communication systems are analyzed, including the developing tendency, promising technologies, and demonstration system construction. Advanced technologies for B3G application are introduced comprehensively from physical layer techniques to network layer strategies. The B3G demonstration system developed in WTI (wireless technology innovation) lab is termed as TD-MC-CDMA system. Technical scheme and simulation results of TD-MC-CDMA demonstration system are presented. Discussions for the prediction of next generation mobile communication systems are given.

In this contribution. we propose a novel space-frequency block coded adaptive modulated OFDM (SFBC-AOFDM) scheme to provide high data rate and high spectrum efficiency, this scheme is very effective to combat last lading anyway. Its robustness to frequency-selective fading could be adjusted by changing the SFBC coding size and adaptation unit. In this scheme, we extend the application of adaptive modulation into a MIMO-OFDM system in a simple way. Furthermore, a novel SFBC detection algorithm is proposed to reduce complexity for high order QAM employments. We simulate for SFBC-AOFDM with various MIMO settings and analyse the performance of both BCR and transmission efficiency.

The practical DOA estimation method for uniform circular smart antenna (UCSA) is considered. DOA estimation with unitary root-MUSIC algorithm, which exploits a real-valued eigendecomposition, greatly reduces its computational complexity. However, its application is limited to the uniform linear smart antenna (ULSA). In this contribution, we propose a novel signal converter through which the low cost unitary root-MUSIC algorithm can be exploited for uniform circular smart antenna. The converter is easy to implement by IFFT transformation and real-valued weighting. Simulation results show this method is feasible to provide high resolution and high capacity.

In this paper, we present a new handover scheme, which could improve the quality of service (QoS) in mobile cellular systems supporting multi-rate traffics. In this scheme, the released channels are reserved for the handover call with the highest priority, determined by its dwelling time in the handover area. The performance of a cellular system employing the proposed handover policy is evaluated and compared in terms of blocking and forced termination probabilities, average queuing time. The results show that from an overall performance point of view, the new scheme is a better choice in multi-rate traffics environment.

For the third generation systems' low frequency efficiency and small system capacity, there should be greatly changed in physical techniques in fourth generation mobile communication systems while many advanced techniques suited for high bite rate, high frequency efficiency and large dynamic range, such as JT, STC, MIMO, OFDMA, distributed antenna, are taken into considerations. But besides physical techniques, basic network layer techniques, e.g. the constructions of cellular and the modes of handover, must be broken through in the fourth generation mobile communication systems. The group cell structure proposed in WTI is a novel cellular construction method, which is fit well for these new advanced physical techniques. And slide handover (intelligent group handover) in multicell infrastructure is also novel handover mode emphasized in this paper. According to the current research, the slide handover in multicell infrastructure could improve the system capacity dramatically.

In this paper, we present a novel adaptive incremental redundancy (AIR) scheme. Conventional IR transmits new packets with a fixed rate. Our AIR scheme adjusts the original transmission rate and coding method according to the channel condition. Simulation results show that the throughput and the number of retransmission are improved effectively.

A novel joint-optimization idea for uplink multicarrier CDMA (MC-CDMA) system is presented in this paper. This idea can greatly mitigate the multiple-access interference (MAI) and improve the system performance. Compared to traditional MC-CDMA systems the optimum system has much better bit error rate performance but also large feedback cost. Then some suboptimum systems are introduced which still have good performance similar to the optimum system and less feedback amount.

Low-density parity-check (LDPC) codes have been a focus in the research of error-correcting coding in recent years. Moreover, it may be desirable to combine these marvelous codes, which have exhibited an excellent performance, with spectral efficient modulations to improve the transmission capacity in bandwidth limited channels. In this paper we present a new coding and modulation scheme in this way, using LDPC codes, and in the bit-interleaved coded modulation (BICM) style. Several simulation results show that this scheme can provide a substantial coding gain both on Gaussian channels and Rayleigh channels.

For the TDD system, we propose a novel channel estimation method based on the third-order interpolation, the least-square approximation and the partition of the training sequence. We also put forward a novel time slot, based on which the interpolation of the channel estimation can be performed in every TDD time slot. By using the novel channel estimation method and the novel time slot, the channel estimation can be completed in every TDD time slot instead of across several continuous time slots. For the modulation modes such as QPSK and 16QAM, the simulation results show the novel channel estimation method can obtain performance gain compared with the conventional method.

Multi-antenna OFDM system calls the consideration of coding across antennas and OFDM tones. Derived from the design criteria for space-time block codes (STBC), the coding scheme termed space-frequency block codes (SFBC) is conceived to achieve the maximum diversity order for a given number of transmit and receive antennas. In this contribution, a practical space-frequency block coded OFDM scheme (SFBC-OFDM) is proposed for fast fading broadband channels. Besides, this novel scheme is rather flexible to combat the frequency-selective fading by decreasing the coding size. Simulation and analysis show that our scheme outperforms the STBC-OFDM scheme in the scenario with high vehicle speed and large delay spread when the number of subcarriers in the OFDM system is large.

Space-time block code (STBC) has a good BER performance. In 3GPP FDD specifications, space time transmit diversity (STTD) employs STBC to maintain orthogonality between the two antennas in order to avoid self-interference in fading channels. However, the capacity of multiuser STBC systems is still severely affected by multiple access interference (MAI) and interpath interference (IPI). Fortunately, nonlinear parallel interference cancellation (NPIC) can effectively reduce MAI and IPI with simple signal processing. And for the small spreading factor systems, weighted NPIC (WNPIC) technique provides a good solution. In order to alleviate the influence of the MAI and IPI of multiuser STBC systems under the frequency-selective Rayleigh fading channel, WNPIC based space-time block code (WNPIC based STBC) scheme is proposed firstly in this paper. And then the error probability of the WNPIC based STBC is analyzed theoretically. From our analyses, RAKE based STBC can be regarded as 0th stage WNPIC based STBC whose weighted factors are one. Later, to further evaluate the proposed scheme and verify our analyses, some simulation resulted are achieved. These simulation results show us the optimum weighted factors under different user environments and the multiuser BER performance of simple (RAKE) STBC systems, NPIC based STBC, WNPIC based STBC systems.

For the efficient implementation of a turbo decoder with Log-MAP (logarithm-maximum a posteriori) algorithm, we propose in this paper a solution with three highlights: the general core for forward and backward recursions, the simple branch metric calculation and a simplified rescaling of the path metrics. An FPGA (field programmable gate array) implementation with the proposed solution reduces area consumption to half and shows favorable performance.

Based on the coding/multiplexing scenario specified for W-CDMA FDD (wideband-code division multiple access, frequency division duplex), CRC (cyclic redundancy check)-aided early stopping method is chosen for hardware implementation of the Log-MAP (logarithm-maximum a posteriori) turbo decoder for use in W-CDMA Node B. Modifications to decoder structure for efficient incorporation of the CRC-aided method are proposed. With negligible cost and no performance degradation, our modified decoder works more efficiently.

One property, bandwidth uneven distributed with time slots (BUDTS) in the adaptive modulation wireless system destroys an implicit assumption of traditional traffic scheduling algorithm that the bandwidth is evenly distributed with time slots. Therefore, new scheme fit for such circumstance is needed. We propose a novel algorithm, named adaptive differentiated compensation fair queuing (ADCFQ), to provide basic QoS requirement and fair residual bandwidth sharing to all backlogged flows, as well as the compensation mechanism to the lagged flows due to burst channel error. Simulation shows that in ADCFQ, the effect of uneven bandwidth distribution is well considered.

This paper analyzes the performance of the adaptive modulation system over Nakagami-m fading channels, which utilizes four modulation modes. The optimization criterion of the setting of the modulation switching levels is to maintain the target BER which is desired by many data services. Under this condition, the performance analysis of the throughput and the transmission outage probability of the adaptive modulation system are presented. We do so by adopting the numerical analysis methods. Finally, the simulation results show that the adaptive modulation system can theoretically achieve good performance with high throughput and low transmission outage probability.

The joint transmission (JT) technique was mainly presented by Baier et al. (2000). JT used in the downlink channel does not require a training sequence in the downlink in theory. Moreover, the mobile station (MS) does not demand channel estimation. However, JT requires a huge dynamic range and good linearity of the power amplifier of the transmitter because of the high peak to average ratio of transmitted signals of JT. In this paper, a new scheme called pre-distortion based joint transmission is proposed. Two pre-distortion approaches are also presented. One is based on the pre-clip technique, the other on a pre-nonlinear power amplifier. Finally, simulation results show that the pre-distortion based JT scheme has better BER performance and the BER performance of pre-distortion based JT under nonideal power amplifier is better than that of JT under ideal power amplifier. This is to say, maybe it is not worth compensating the deep channel shading.

As an important space-time code, the layered space-time (LST) code has been studying widely since it was firstly proposed by Foschini in 1996. To exploit its potential, the Bell Lab Layered Space-Time (BLAST) structure of the LST was proposed by Bell Lab. There are two types of BLAST architectures: vertical BLAST (V-BLAST) and diagonally BLAST (D-BLAST). Detection algorithms of V-BLAST were proposed by Golden (see Electronics Letters, vol.35, no.1, 1999). His detection process uses linear combination nulling and successive symbol cancellation (SSC) based on inversing and ordering. However, inversing (Moore-Penrose pseudoinverse) and ordering operation for each iteration bring a huge computation complexity. To detect M transmit antenna signals, Golden's detection algorithm needs M inversing and M ordering operations. Aiming at this shortcoming, a new detection algorithm for layered space-time code is proposed. This new sub-optimal detection algorithm is based on Greville inversing process, with two inversing and one ordering process. Simulation results show us the proposed sub-optimal scheme still has a good BER performance.

In interference limited CDMA systems, call admission control plays a very important role because it directly controls the number of users. We introduce a interference-based CAC algorithm according to the characteristics of HCS. This algorithm adopts a global strategy in macro layer, while using a local strategy in the micro layer. Meanwhile, in order to make full use of resources in the macro layer, different threshold values in the two layers are used. Simulation results show that the performance has been greatly improved.

In the HCS system when different frequencies are applied, compressed mode is used so that interfrequency measurement can be obtained. When and how to trigger compressed mode is a crucial problem because the compressed mode may have a bad effect on the system performance: The performance of the periodic and the event-triggered compressed mode is compared with the help of system simulations. Moreover, the impact of some of the parameters is evaluated in this paper.

This paper compares two different handover criteria in WCDMA through simulations. The simulations are carried out in a WCDMA HCS system where the hexagonal macro and the Manhattan-like micro layer use different frequencies. Two possible handover triggering schemes i.e. CPICH RSCP and CPICH Ec/No, are compared under the same simulation environments and various cell loads. The handover performance is observed in terms of system efficiency including signalling and QoS such as call dropping probability and call blocking probability.

This paper discusses some issues in designing space-time trellis codes of high complexity, i.e., when the total number of candidate codes is large or the total number of pairwise error events is large. By adding windows in the search domain and combining the equal states in error state trellis, the complexity of search is greatly reduced. And at the same time, we can still find some codes which have very good performance. We present some new codes of high complexity at the end of this paper and show that our codes have better performance than many codes before.

Space-time code techniques have been widely studied in wireless communications, for they provide better signal qualities in fading channel environments and increase the system capacity. As an important space-time code, Bell Lab Layered Space-Time (BLAST) code has been paid more attention. However, the traditional detection algorithm of V-BLAST space-time code needs much time to perform linear combination nulling and successive symbol cancellation. The greater the number of transmit antennas, the greater the time delay. To overcome this disadvantage, a new scheme based on linear combination nulling and parallel symbol cancellation is proposed. The new scheme has a lower time delay. The simulation results of this new scheme are obtained by COSSAP simulation in flat Rayleigh channels. These simulation results are presented to verify the BER performance of the new scheme based on parallel symbol cancellation.

In 3GPP FDD specifications, space time transmit diversity (STTD) employs space-time block code (STBC) to maintain orthogonality between the two antennas in order to avoid self-interference in fading channels. However, the STTD technique is just open loop transmit diversity, and we investigate whether we can obtain further gain if closed loop STBC is used. A novel scheme based on closed loop STBC is proposed first. And then an optimized allocation scheme of a fixed transmitter power budget between two transmit antennas is presented. Finally, COSSAP simulation results show a significant performance improvement between closed loop STBC and traditional STBC. Moreover, these simulation results also show a good agreement with our theoretical analyses.