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.

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.

End-to-end (E2E) semantic communication (SemCom) powered by semantic knowledge base (SKB) is an efficient SemCom framework. However, in practical scenarios, SKB discrepancy among multiple SemCom pairs arises due to dynamic environmental changes (e.g., varying source data or tasks) or system-level alterations (e.g., integration of new SemCom pairs with divergent SKBs). Such discrepancy leads to performance disparity in semantic transmission, where underperforming pairs fail to maintain efficient task execution. To address this challenge, this paper introduces a cooperative SKB update policy, which enables collaborative evolution of SKBs to mitigate SKB discrepancy and improve performance of underperforming pairs. For each SemCom pair endowed with SKB-enabled SemCom, partial local SKB is selected out and uploaded to a mobile edge computing (MEC) server for establishing a global SKB. The global SKB aggregates the advantages of each local SKB, and is broadcasted to all SemCom pairs. Then, each SemCom pair updates their local SKB with the assistance of the global SKB. This process makes the local SKBs more sensible and less ambiguous, thereby enhancing the semantic transmission performance. Furthermore, in order to maximize the cooperative gains under limited uplink budgets of SemCom pairs, a knowledge selection optimization problem is formulated for the selection of the uploaded knowledge. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains, especially for the initially poor-performing pairs, and provide comprehensive performance comparison of the knowledge selection scheme.

Systems combining inertial measurement unit and magnetic field information have been extensively studied for indoor positioning and navigation. However, existing research predominantly addresses static magnetic field distributions, with limited attention to user-environment interactions. Establishing the correlation between magnetic field changes and user behavior presents new challenges for enhancing spatial structure perception. This paper proposes an indoor positioning algorithm based on magnetic semantic extended Kalman filtering (EKF) for inertial navigation system. We analyze the projection characteristics of magnetic field vectors in the carrier coordinate system, revealing the inherent relationship between abrupt changes in magnetic field components and user's dynamic behavior. Based on this, we introduce fuzzy logic methods to model the relationship between abrupt changes in the magnetic field and angular velocity fluctuations, thereby accurately extracting the semantic information of the magnetic field environment. To fully utilize this semantic information, we design an EKF algorithm that integrates magnetic semantic perception information to assist the INS in state updating and error correction. The proposed algorithm not only improves positioning accuracy but also enhances the system's adaptability to dynamic environmental changes. The experimental results indicate that the maximum error of our indoor positioning system is 0.93 meters and the error is controlled within 1 meter.

In this paper, we study a multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) downlink system, where a base station employs beamforming to transmit multiple data streams simultaneously to a central user and a cell-edge user. Over each beam, the orthogonal frequency division multiplexing (OFDM) modulated signals intended for both users are superimposed and then transmitted. Because of beamforming and OFDM modulation, the central user may have weaker beams compared with the cell-edge user. This causes the unsuccessful successive interference cancellation (SIC). To solve this problem, we derive the necessary and sufficient conditions to ensure the successful SIC for arbitrary beamforming matrices. Based on these conditions, we design a specialized beamforming structure where signals over weak beams can be orthogonally separated from those over strong ones. We propose that joint encoding and decoding are applied to signals over weak beams and similarly to those over strong ones. To optimize the beamforming matrices, we propose a constrained convex concave procedure based algorithm and a matrix fractional programming based algorithm. It is verified through simulation results that the proposed scheme performs better than the conventional OFDM-NOMA scheme.

Hybrid reconfigurable intelligent surface (HRIS) as one of the key technologies has the potential to improve the sensing and communication performance in the upcoming sixth-generation systems by creating virtual links among the entities. This paper proposes a HRIS-assisted integrated sensing and communication (ISAC) system to simultaneously perform the sensing and communication by co-designing transmit beamforming at base station and the reflection coefficients at the HRIS. Specifically, we derive the Fisher information matrix on the estimations of transmission delay and angle of departure, and derive the corresponding closedform expression of the Cramér-Rao bound (CRB) to describe the sensing performance. Then, a CRB minimization problem is formulated by taking into account the communication requirements and power constraints, which is non-convex and generally difficult to solve. To address this problem, we propose an algorithm that combines the Schur complement technique with sequential parametric convex approximation to approximate the original problem into a convex version. Finally, numerical results indicate that the proposed method effectively enhances the performance of the ISAC system by appropriately increasing the number of active reflecting elements. It also demonstrates that our proposed HRIS outperforms the conventional passive RIS and active RIS for the ISAC system under limited power budget.

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We derive the closed-form expressions of Cramér-Rao bound (CRB) for the distance and velocity estimations, and obtain the communication rate under the mean square error of channel estimation. Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values. To solve this problem, some numerical methods are typically adopted to obtain the optimal solutions, whose computational complexity grow exponentially with the number of symbols and subcarriers of OFDM. Therefore, we propose a relaxation and approximation method to transform the original discrete problem into a continuous convex one and obtain the sub-optimal solutions. Finally, our proposed scheme is compared with the exhaustive search method in numerical simulations, which show slight gap between the obtained sub-optimal and optimal solutions, and this gap further decreases with large weight factor.

Score-based diffusion models represent a significant variant within the family of diffusion models and have found extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the ScoreBased Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to better align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.

With the proliferation of edge computing, efficient artificial intelligence inference on edge devices has become essential for intelligent applications, such as autonomous vehicles and virtual/augmented reality. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an optimization framework to tackle the challenges posed by dynamic channel conditions and device mobility in end-to-end communication systems. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multilevel feature transmission, accounting for temporal factors, state transitions, and dynamic elements throughout the transmission process. Additionally, we enhance the multilevel feature transmission policy by introducing an additional fifth-level edge-assisted semantic communication, which maximizes recognition performance by leveraging a large semantic knowledge base on the edge server. Formulated as an online optimization problem, our framework aims to simultaneously minimize semantic loss and adhere to specified transmission latency thresholds. To achieve this, we design a soft actor-critic-based deep reinforcement learning system with a carefully designed reward structure for real-time decision making. This approach overcomes the optimization difficulty of the NP-hard problem while fulfilling the optimization objectives. Numerical results showcase the superiority of our approach compared to traditional greedy methods across various system setups using open-source datasets.

This paper investigates deploying semantic edge inference systems for performing a common image clarification task. In particular, each system consists of multiple Internet of Things (IoT) devices that first locally encode the sensing data into semantic features and then transmit them to an edge server for subsequent data fusion and task inference. The inference accuracy is determined by efficient training of the feature encoder/decoder using labeled data samples. Due to the difference in sensing data and communication channel distributions, deploying the system in a new environment may induce high costs in annotating data labels and re-training the encoder/decoder models. To achieve cost-effective transferable system deployment, we propose an efficient Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can maintain high inference accuracy in a new environment without the need for labeled samples. Specifically, DASEIN exploits the task-relevant data correlation between different deployment scenarios by leveraging the techniques of unsupervised domain adaptation and knowledge distillation. It devises an efficient two-step adaptation procedure that sequentially aligns the data distributions and adapts to the channel variations. Numerical results show that, under a substantial change in sensing data distributions, the proposed DASEIN outperforms the best-performing benchmark method by 7.09% and 21.33% in inference accuracy when the new environment has similar or 25 dB lower channel signal to noise power ratios (SNRs), respectively. This verifies the effectiveness of the proposed method in adapting both data and channel distributions in practical transfer deployment applications.

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.

Carrier-sense multiple access with collision avoidance (CSMA/CA) is one of the fundamental medium access control (MAC) protocols for ad hoc networks. As a network’s size increases, its throughput degrades substantially because of packet collisions. To reduce the collision probability, combining non-orthogonal multiple access (NOMA) with CSMA/CA is a promising solution. However, existing NOMA-CSMA/CA protocols adopt distributed power selection and channel inversion power control, resulting in a high power collision probability and limiting the number of power levels. To address these issues, we propose a power back-off NOMA-based CSMA/CA (PBN-CSMA/CA) protocol for ad hoc networks. The proposed protocol achieves centralized power allocation, avoiding power collisions by employing the Zadoff-Chu (ZC) sequence and the power level allocation (PLA) frame. Additionally, power back-off (PB) control is used to set the transmission power, which expands the number of power levels and gives full play to the performance advantages of NOMA. To analyze the performance comprehensively, the closed-form expressions of the average outage probability, normalized saturation throughput, average packet delay and transmission energy consumption are theoretically analyzed. Both the analytical and simulation results demonstrate that the PBN-CSMA/CA protocol outperforms the existing NOMA-CSMA/CA and traditional CSMA/CA protocols, with significant throughput gains and delay reductions.

Semantic communication has emerged as a promising communication paradigm and there have been extensive research focusing on its applications in the increasingly prevalent multi-user scenarios. However, the knowledge discrepancy among multiple users may lead to considerable disparities in their performance. To address this challenge, this paper proposes a novel multi-pair cooperative semantic knowledge base (SKB) update policy. Specifically, for each pair endowed with SKB-enabled semantic communication, its well-understood knowledge in the local SKB is selected out and uploaded to the server to establish a global SKB, via a score-based knowledge selection scheme. The knowledge selection scheme achieves a balance between the uplink transmission overhead and the completeness of the global SKB. Then, with the assistance of the global SKB, each pair’s local SKB is refined and their performance is improved. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains with minimal transmission overhead, especially for the initially poor-performing pairs.

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.

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

Cross-technology communication (CTC) is a promising technique that enables direct communications among incompatible wireless technologies without needing hardware modification. However, it has not been widely adopted in real-world applications due to its inefficiency and unreliability. To address this issue, this paper proposes a deep joint source-channel coding (DJSCC) scheme to enable efficient and reliable CTC. The proposed scheme builds a neural-network-based encoder and decoder at the sender side and the receiver side, respectively, to achieve two critical tasks simultaneously: 1) compressing the messages to the point where only their essential semantic meanings are preserved; 2) ensuring the robustness of the semantic meanings when they are transmitted across incompatible technologies. The scheme incorporates existing CTC coding algorithms as domain knowledge to guide the encoder-decoder pair to learn the characteristics of CTC links better. Moreover, the scheme constructs shared semantic knowledge for the encoder and decoder, allowing semantic meanings to be converted into very few bits for cross-technology transmissions, thus further improving the efficiency of CTC. Extensive simulations verify that the proposed scheme can reduce the transmission overhead by up to 97.63% and increase the structural similarity index measure by up to 734.78%, compared with the state-of-the-art CTC scheme.

This paper studies a multi-static integrated sensing and communications (ISAC) system for multi-user downlink communications and cooperative target sensing, where the base station transmits ISAC signal and the users deployed at different positions receive it. We analyze the joint estimation of transmission delay and Doppler shift of cooperative users for target sensing, and derive the corresponding Cramér-Rao bound (CRB) in closed form. Then, a CRB minimization problem is formulated by considering the cooperative cost and communication rate requirements among these users. To solve this problem, we propose a minimax linkage-based cooperative method for user selection, and then design an approximation non-convex transforming algorithm for transmit beamforming. Finally, simulation results validate our analysis and reveal significant performance improvement of our proposed methods over the state-of-the-art benchmarks.

Mobile-edge computing (MEC) networks bring computing and storage capabilities closer to edge devices, which reduces latency and improves network performance. However, to further reduce transmission and computation costs while satisfying user-perceived quality of experience, a joint optimization in computing, pushing, and caching is needed. In this article, we formulate the joint-design problem in MEC networks as an infinite-horizon discounted-cost Markov decision process and solve it using a deep reinforcement learning (DRL)-based framework that enables the dynamic orchestration of computing, pushing, and caching. Through the deep networks embedded in the DRL structure, our framework can implicitly predict user future requests and push or cache the appropriate content to effectively enhance system performance. One issue we encountered when considering three functions collectively is the curse of dimensionality for the action space. To address it, we relaxed the discrete action space into a continuous space and then adopted soft actor–critic learning to solve the optimization problem, followed by utilizing a vector quantization method to obtain the desired discrete action. Additionally, an action correction method was proposed to compress the action space further and accelerate the convergence. Our simulations under the setting of a general single-user, single-server MEC network with dynamic transmission link quality demonstrate that the proposed framework effectively decreases transmission bandwidth and computing cost by proactively pushing data on future demand to users and jointly optimizing the three functions. We also conduct extensive parameter tuning analysis, which shows that our approach outperforms the baselines under various parameter settings.

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.

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 support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and computation cost. To achieve this, we propose a framework based on deep reinforcement learning that enables the dynamic orchestration of these three activities for the MEC network. The framework can implicitly predict user future requests using deep networks and push or cache the appropriate content to enhance performance. To address the curse of dimensionality resulting from considering three activities collectively, we adopt the soft actor-critic reinforcement learning in continuous space and design the action quantization and correction specifically to fit the discrete optimization problem. We conduct simulations in a single-user single-server MEC network setting and demonstrate that the proposed framework effectively decreases both transmission load and computing cost under various configurations of cache size and tolerable service delay.

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.

Intelligent reflecting surface (IRS) has been regarded as an emerging technology, which can significantly enhance the coverage and capacity of next-generation wireless networks. An accurate and easy-to-use channel model is essential to the deployment and evaluation of IRS-assisted communication systems. In this paper, we propose a 3D geometry-based channel model for the IRS-assisted multiple-input-multiple-output (MIMO) communications, and its corresponding parameters are obtained by channel measurement to better capture the underlying propagation characteristics. First, a cascaded channel model considering the relationship between the reflection amplitude and the angle of signal is presented. With the cascaded channel model, a channel coefficient generation procedure based on the 3GPP technical report 38.901 is proposed for IRS-assisted communication systems. To generate the cascaded channel, a response function that satisfies the reciprocity of uplink and downlink channels is derived. Then a channel measurement campaign at 3.7GHz is carried out to obtain some small-scale parameters for the IRS scenario. Finally, the proposed statistical channel model is implemented on a system-level simulation platform and some channel characteristics are analyzed to validate its rationality.

This paper considers smart-duplex (SD) powered user-centric ultra dense networks (UC-UDN), which shifts the conventional access point-centric paradigm to the user-centric one by de-cellular concept, to provide good quality-of-service for a large number of users via flexibly designing the user association, power allocation, and duplex mode. The maximization average ratio of satisfied users for the considered SD UC-UDN in the long-term time scale is firstly formulated as a Markov decision process (MDP) problem with large discrete action space. To reduce the action space, the user association and power allocation processes are modeled as a two-layer tree structure, and then selecting an action is equivalent to finding the path from root to one of the leaf nodes of the tree. A multi-agent tree-structured policy gradient (MATSPG) based deep reinforcement learning (DRL) algorithm is proposed to solve this problem by directly mapping the action space for user association and power allocation to the two layers of the tree, respectively, whose training is shown to be equivalent to the training of neural networks on two-layer paths. The time and space complexity for searching one action in the proposed MATSPG is also proved to be lower than other conventional DRL algorithms. Simulations show that the proposed MATSPG algorithm significantly improves the average ratio of the satisfied users than the conventional DRL methods in typical scenarios.

The emergence of 5G and 5G-Advanced (5G-A) communication networks brings about new features and functionalities, such as simultaneous usage of multiple bands. Multi-band cooperation provides substantial improvements in reducing pilot overhead and beam management latency, which is a hot research topic in 5G-A systems. Accurate and reasonable channel models in real propagation environments are key technology for evaluating multi-band assisted cooperative algorithms. In this paper, we first measure and analyze the propagation channels at 2.3 GHz and 3.7 bands under line-of-sight (LoS) and non-LoS (NLoS) conditions in urban macro (UMA) scenarios. Furthermore, the consistency and dissimilarity of the two channels are analyzed in the angle and delay domains. Finally, we compare the multi-frequency channel correlation using the measurement results with the 3GPP channel models. It is found out that the multi-frequency channel correlation generated by the 3GPP model differ significantly from the measured results, especially in NLoS scenario. Based on the measured data, we propose a modified channel model and compare the model with the measured data. The simulation results show that our modified model can be well matched with the measurement data and capture the multi-frequency channel characteristics. Compared to the standard model, our proposed new model ensures more accurate simulation results, which are consistent with the measurement results.

Abstract Unmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely remote monitoring by collecting information from monitoring devices and transferring the collected information to base station. The freshness of delivered information is critical to system performance, which can be quantified by the concept of age of information (AoI). Nevertheless, the fresher information comes at the cost of higher energy consumption at UAVs. Considering the limited onboard energy, it is essential to strike a balance between the age of delivered information and the required energy budget. here, both straight trajectory and circular trajectory of UAV are considered, and study a problem with the goal of supporting timely data collection while minimizing the energy consumption at UAV. The problem is formulated as a multi‐criteria optimization problem by jointly considering the aging of collected information, the trajectory planning of UAV and energy consumption at UAV. To solve the formulated problem, a solution procedure to find a sequence of Pareto‐optimal points is proposed. Simulation results demonstrate the Pareto‐optimal curve, which yields the energy‐efficient UAV trajectory for timely data collection.

This article considers a smart-duplex (SD) powered user-centric ultra dense networks (UC-UDNs), where each user is served cooperatively by multiple access points (APs) adopting the de-cellular concept to achieve desired Quality-of-Service (QoS). The average QoS satisfaction ratio maximization problem for the considered SD UC-UDN is formulated as a Markov decision process (MDP) with large discrete action space by designing the user association and power allocation. To reduce the action space, user association and power allocation are modeled as a two-layer tree, and selecting an action for each user is equivalent to finding the path from the root to one leaf of the constructed tree. Then, a multiagent tree-structured policy gradient (MATSPG)-based deep reinforcement learning (DRL) algorithm is proposed to solve the MDP problem, whose training process is shown to be equivalent to that of the two-layer neural networks. Next, the time and space complexity of searching one action in the proposed MATSPG are also proved to be lower than the conventional DRL algorithms. Finally, simulations show that the proposed MATSPG algorithm significantly improves the average QoS satisfaction ratio than the conventional multiagent deep deterministic policy gradient and multiagent deep Q-network methods in typical scenarios.

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.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, Due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

The ubiquitous computing resource at UAVs and IoT devices can be exploited in conjunction to form a UAV-IoT edge computing system for low-cost and responsive environmental monitoring. Under stochastic computational task arrival at IoT devices, one major challenge is how to realize path control for multiple UAVs and energy efficient computation offloading in real time. Moreover, the freshness of obtained updates is of critical importance to the system performance under such time-critical scenarios. In this paper, we employ the concept of age of information (AoI) to quantify the timeliness of updates at IoT devices, and formulate an energy minimization problem by jointly considering UAV path planning, energy consumption of computation offloading and age evolution of updates. To solve the formulated problem, we propose a deep reinforcement learning based solution to achieve fast decision making. Simulation results show that the performance of proposed solution is competitive in terms of obtaining fresh updates at low energy cost.

Key generation rate and bit disagreement ratio are two main indicators to evaluate the performance of physical layer key generation (PLKG). This work explores the key generation performance in reconfigurable intelligent surface (RIS)-assisted PLKG, where a concrete scheme of key generation is proposed. Specifically, we first deduce the key generation rate expression based on estimation theory in RIS-assisted PLKG. Then we optimize the RIS reflecting coefficients with instantaneous channel state information to maximize the key generation rate. Finally, aiming at minimizing transmit power while guaranteeing key generation rate target, we formulate a power minimization problem in RIS-assisted PLKG. An alternating optimization algorithm is applied to solve the non-convex mixed-integer nonlinear programming. Simulation results demonstrate that the key generation rate of the proposed scheme is up to 197.5% higher than the existing relay-assisted scheme, and we successfully reduce the required transmit power by at least 6.6 dBm than without power minimization scheme.

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.

An accurate energy efficiency analytical model based on a two-mode circuitry was recently proposed; and the model showed that the use of this circuitry can significantly improve a system's energy efficiency. In this paper, we use this analytical model to develop a new power control scheme, a scheme that is capable of allocating a minimum transmission power precisely within the delay-outage probability constraint. Precision brings substantial benefits as numerical results show that the energy efficiency using our scheme is much higher than other schemes. Results further suggest that data rate values affect energy efficiency non-uniformly, i.e., there exists a specific data rate value that achieves maximum energy efficiency.

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.

Deterministic methods in indoor-localization systems based on the received signal strength (RSS) almost utilize the average value of the RSS, such as the k- nearest neighbor (KNN) algorithm. However, the distribution of RSS is not always normal Gaussian in the real complex indoor environment so the average value may not represent the location well. To solve this problem, we present a novel algorithm, named as probabilistic KNN (pKNN) algorithm. The algorithm uses the probability of RSS in the Radio-map as a weighting to calculate the Euclidean distance, and it filters the RSS value whose probability is less than 3%. At the same time, we propose a new application environment called as passive indoor-localization scenario. In this scenario, the access point (AP) collects the RSS when the mobile terminal (MT) is not connecting to the AP. Experiment and results analysis for different k values show that p-KNN algorithm is feasible and effective in passive indoor- localization scenario. Finally, comparing to the KNN algorithm, p-KNN algorithm can achieve a better average location accuracy.

This paper analyzes the optimal power and density allocation for D2D (Device-to-Device) communication in heterogeneous networks on multi-bands with target of maximizing D2D achievable transmission capacity. The heterogeneous networks contain one or several cellular systems, and D2D communication shares uplink resources with them. By utilizing stochastic geometry, the problem is formed as sum capacity optimization for D2D network with constraints that guarantee outage probabilities of both cellular and D2D transmissions. The original problem is non- convex but we find the D2D transmission capacity has a remarkable property: it is sectional continuous with limited discontinuous point in the feasible region of power and density allocation and the maximum capacity is reached on the critical point. Based on this, we propose a algorithm as follows: first we figure out all of critical points as a candidate set and then obtain the optimal solution from them. The numerical results demonstrate the effectiveness of the algorithm and it shows that optimal parameters of D2D transmission is affected by outage constraints as well as the interference from cellular systems.

This paper analyzes the optimal density and power allocation for D2D (Device-to-Device) communication in heterogeneous networks on multi-bands with target of maximizing D2D transmission capacity. The heterogeneous networks contain one or several cellular systems, and D2D communication shares uplink resources with them. By utilizing stochastic geometry, it is formed as a sum capacity optimization problem for D2D network with constraints that guarantee outage probabilities of both cellular and D2D transmissions.Since the original problem is non-convex, we divide the proof into two steps: first we prove the power allocation problem is convex when the D2D density is fixed, which can be solved by lagrangian method; then we prove that the optimal D2D density exists in a semi- closed interval.We propose a linear searching algorithm based on the former conclusions:with discretizing the interval of D2D density, a series of solutions can be obtained by solving the optimization problem of each D2D density; hence the global optimal capacity can be selected from them.The simulation results demonstrate the effectiveness of the proposed algorithm and it also shows that optimal parameters of D2D transmission is affected by outage constraints as well as the interference from cellular systems.

This paper analyzes the optimal D2D user allocation over multi-bands in the heterogeneous networks. The heterogeneous networks contain one or several cellular systems and D2D communication shares uplink resource with them. By allocating D2D users on different bands, it can reduce the interference between D2D and cellular systems and improve D2D transmission capacity at the same time. Through utilizing stochastic geometry, the problem is formed as sum D2D transmission capacity on each band with constraints that guarantee outage probilities of both cellular and D2D transmission. The primal problem is first proved to be convex and then solved by constructing Lagrange function and KKT conditions. The optimal D2D user densities over multi-bands are derived and we propose a D2D scheduling algorithm base on this conclusion for the dynamic D2D access process. Simulation results show the superiority of optimal user allocation over average allocation method.