arXiv subjects

Jingqing Wang

Publications and source records attributed to Jingqing Wang.

At least 37 records · Page 2Linked to original sources

arXiv2024

MAC Revivo: Artificial Intelligence Paves the Way

The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasingly complex wireless environments while ensuring network Quality of Service (QoS) performance. This paper explores the potential integration of advanced Artificial Intelligence (AI) methods into the design of Wi-Fi MAC protocols. We propose AI-MAC, an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency. By intelligently predicting and managing interference, AI-MAC aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS. Our experimental results demonstrate that AI-MAC significantly reduces both interference and latency, paving the way for more reliable and efficient wireless communications in the increasingly crowded ISM band.

cs.NI↗

arXiv2024

AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning

The Age of Information (AoI) has recently gained recognition as a critical quality-of-service (QoS) metric for quantifying the freshness of status updates, playing a crucial role in supporting massive ultra-reliable and low-latency communications (mURLLC) services. In mURLLC scenarios, due to the inherent system dynamics and varying environmental conditions, optimizing AoI under such multi-QoS constraints considering both delay and reliability often results in non-convex and computationally intractable problems. Motivated by the demonstrated efficacy of deep reinforcement learning (DRL) in addressing large-scale networking challenges, this work aims to apply DRL techniques to derive optimal resource allocation solutions in real time. Despite its potential, the effective integration of FBC in DRL-based AoI optimization remains underexplored, especially in addressing the challenge of simultaneously upper-bounding both delay and error-rate. To address these challenges, we propose a DRL-based framework for AoI-aware optimal resource allocation in mURLLC-driven multi-QoS schemes, leveraging AoI as a core metric within the finite blocklength regime. First, we design a wireless communication architecture and AoI-based modeling framework that incorporates FBC. Second, we proceed by deriving upper-bounded peak AoI and delay violation probabilities using stochastic network calculus (SNC). Subsequently, we formulate an optimization problem aimed at minimizing the peak AoI violation probability through FBC. Third, we develop DRL algorithms to determine optimal resource allocation policies that meet statistical delay and error-rate requirements for mURLLC. Finally, to validate the effectiveness of the developed schemes, we have executed a series of simulations.

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arXiv2024

FBC-Enhanced ε-Effective Capacity Optimization for NOMA

The advent of massive ultra-reliable and low-latency communications (mURLLC) has introduced a critical class of time- and reliability-sensitive services within next-generation wireless networks. This shift has attracted significant research attention, driven by the need to meet stringent quality-of-service (QoS) requirements. In this context, non-orthogonal multiple access (NOMA) systems have emerged as a promising solution to enhance mURLLC performance by providing substantial enhancements in both spectral efficiency and massive connectivity, particularly through the development of finite blocklength coding (FBC) techniques. Nevertheless, owing to the dynamic nature of wireless network environments and the complex architecture of FBC-enhanced NOMA systems, the research on the efficient design of optimizing the system performance for maximizing system capacity while guaranteeing the tail distributions in terms of new statistical QoS constraints for delay and error-rate is still in its infancy. In an effort to address these challenges, we put forth the formulation and solution of ε-effective capacity problems tailored for uplink FBC-enhanced NOMA systems, specifically catering to ensure statistical delay and error-rate bounded QoS requirements. In particular, we establish uplink two-user FBC-enhanced NOMA system models by applying the hybrid successive interference cancellation (SIC). We also develop the concept of the ε-effective capacity and propose the optimal power allocation policies to maximize the ε-effective capacity and ε-effective energy efficiency while upper-bounding both delay and error-rate. We conduct a set of simulations to validate and evaluate our developed optimization schemes over FBC-enhanced NOMA systems.

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arXiv2024

Performance Boundary Analyses for Statistical Multi-QoS Framework Over 6G SAGINs

To enable the cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to its exceptional 3D coverage and the ability to guarantee rigorous and multidimensional demands for quality-of-service (QoS) provisioning, including delay and reliability across vast distances. In response to the complex, heterogeneous, and dynamic serving scenarios and stringent performance expectations for 6G SAGINs, it is crucial to undertake modeling, assurance, and analysis of the key technologies, aligned with the diverse demands for QoS provisioning in the non-asymptotic regime, i.e., when implementing finite blocklength coding (FBC) as a new dimension for error-rate bounded QoS metric. However, how to design new statistical QoS-driven performance modeling approaches that accurately delineate the complex and dynamic behaviors of networks, particularly in terms of constraining both delay and error rate, persists as a significant challenge for implementing mURLLC within 6G SAGINs in the finite blocklength regime. To overcome these difficulties, in this paper we propose to develop a set of analytical modeling frameworks for 6G SAGIN in supporting statistical delay and error-rate bounded QoS in the finite blocklength regime. First we establish the SAGIN system architecture model. Second, the aggregate interference and decoding error probability functions are modeled and examined through using Laplace transform. Third, we introduce modeling techniques aimed at defining the$ε$-effective capacity function as a crucial metric for facilitating statistical QoS standards with respect to delay and error-rate. To validate the effectiveness of the developed performance modeling schemes, we have executed a series of simulations over SAGINs.

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arXiv2024

Location-Driven Beamforming for RIS-Assisted Near-Field Communications

Future wireless communications are promising to support ubiquitous connections and high data rates with cost-effective devices. Benefiting from the energy-efficient elements with low cost, reconfigurable intelligent surface (RIS) emerges as a potential solution to fulfill such demands, which has the capability to flexibly manipulate the wireless signals with a tunable phase. Recently, as the operational frequency ascends to the sub-terahertz (THz) bands or higher bands for wireless communications in six-generation (6G), it becomes imperative to consider the near-field propagation in RIS-assisted communications. The challenging acquisition of channel parameters is an inherent issue for near-field RIS-assisted communications, where the complex design is essential to acquire the informative near-field channel embedded with both the angle and distance information. Hence, in this paper we systematically investigate the potential of exploiting location information for near-field RIS-assisted communications. Firstly, we present the progresses in the near-field RIS-assisted communications, which are compatible with existing wireless communications and show the potential to achieve the fine-grained localization accuracy to support location-driven scheme. Then, the Fresnel zone based model is introduced, with which the location-driven beamforming scheme and corresponding frame structure are developed. Also, we elaborate on four unique advantages for leveraging location information in RIS-assisted communications, followed by numerical simulations. Finally, several key challenges and corresponding potential solutions are pointed out.

cs.IT↗

arXiv2024

Delay Tradeoff and Adaptive Finite Blocklength Framework for URLLC

With various time-sensitive tasks to be served, ultra-reliable and low-latency communications (URLLC) has become one of the most important scenarios for the fifth generation (5G) wireless communications. The end-to-end delay from the sub-millisecond-level to the second-level is first put forward for a wide range of delay-sensitive tasks in the future sixth generation (6G) communication networks, which imposes a strict requirement on satisfying various real-time services and applications with different stringent quality of service (QoS) demands. Thus, we need to find out new delay reduction framework to satisfy the more stringent delay requirements. In this article, a state-of-the-art overview of end-to-end delay composition and delay analysis combined with access protocols are elaborated. We reveal the tradeoff relationship among transmission delay, queuing delay, and retransmission times with the change of blocklength in the finite blocklength (FBL) regime. Based on the tradeoff and combining the grant-free (GF) random access (RA) scheme, we propose the adaptive blocklength framework and investigate several effective algorithms for efficiently reducing the over-the-air delay. Numerical results show that our proposed framework and schemes can significantly reduce the over-the-air delay for URLLC.

cs.IT↗

arXiv2024

RIS-Based Self-Interference Cancellation for Full-Duplex Broadband Transmission

Full-duplex (FD) is an attractive technology that can significantly boost the throughput of wireless communications. However, it is limited by the severe self-interference (SI) from the transmitter to the local receiver. In this paper, we propose a new SI cancellation (SIC) scheme based on reconfigurable intelligent surface (RIS), where small RISs are deployed inside FD devices to enhance SIC capability and system capacity under frequencyselective fading channels. The novel scheme can not only address the challenges associated with SIC but also improve the overall performance. We first analyze the near-field behavior of the RIS and then formulate an optimization problem to maximize the SIC capability by controlling the reflection coefficients (RCs) of the RIS and allocating the transmit power of the device. The problem is solved with alternate optimization (AO) algorithm in three cases: ideal case, where both the amplitude and phase of each RIS unit cell can be controlled independently and continuously, continuous phases, where the phase of each RIS unit cell can be controlled independently, while the amplitude is fixed to one, and discrete phases, where the RC of each RIS unit cell can only take discrete values and these discrete values are equally spaced on the unit circle. For the ideal case, the closed-form solution to RC is derived with Karush-Kuhn-Tucker (KKT) conditions. Based on Riemannian conjugate gradient (RCG) algorithm, we optimize the RC for the case of continuous phases and then extend the solution to the case of discrete phases by the nearest point projection (NPP) method. Simulation results are given to validate the performance of our proposed SIC scheme.

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arXiv2024

Performance Analysis and Blocklength Minimization of Uplink RSMA for Short Packet Transmissions in URLLC

Rate splitting multiple access (RSMA) is one of the most promising techniques for ultra-reliable and low-latency communications (URLLC) with stringent requirements on delay and reliability of multiple access. To fully explore the delay performance enhancement brought by uplink RSMA to URLLC, in this paper, we evaluate the performance of two-user uplink RSMA and propose the corresponding blocklength minimization problem. We analyze the impact of finite blocklength (FBL) code on the achievable rate region and the effective throughput of uplink RSMA. On this basis, we propose the problem of minimizing the blocklength for uplink RSMA with power allocation under constrained reliability and effective throughput. Then, we present an alternating optimization method to solve this non-convex problem. Simulation results show that different from the infinite blocklength (IBL) regime, the achievable rate region of the uplink RSMA is not always larger than that of uplink non-orthogonal multiple access (NOMA) in the FBL regime. But with the help of our proposed blocklength minimization scheme, uplink RSMA can achieve the same achievable rate with a smaller blocklength compared to uplink NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) without the need for time sharing in the FBL regime, showing the potential of uplink RSMA to achieve low delay for URLLC.

cs.IT↗

arXiv2024

Scalable Extraction Based Semantic Communication for 6G Wireless Networks

Due to the challenges of satisfying the demands for communication efficiency and intelligent connectivity, sixth-generation (6G) wireless network requires new communication frameworks to enable effective information exchange and the integrated Artificial Intelligence (AI) and communication. The Deep Learning (DL) based semantic communication, which can integrate application requirements and the data meanings into data processing and transmission, is expected to become a new paradigm in 6G wireless networks. However, existing semantic communications frameworks rely on sending full semantic feature, which can maximize the semantic fidelity but fail to achieve the efficient semantic communications. In this article, we introduce a novel Scalable Extraction based Semantic Communication (SE-SC) model to support the potential applications in 6G wireless networks and then analyze its feasibility. Then, we propose a promising the SE-SC framework to highlight the potentials of SE-SC model in 6G wireless networks. Numerical results show that our proposed SE-SC scheme can offer an identical Quality of Service (QoS) for the downstream task with much fewer transmission symbols than the full semantic feature transmission and the traditional codec scheme. Finally, we discuss several challenges for further investigating the scalable extraction based semantic communications.

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arXiv2024

Generative AI Driven Task-Oriented Adaptive Semantic Communications

Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full semantic features of source data and learning low-dimensional channel inputs to transmit them within limited bandwidth resources. Although transmitting full semantic features can preserve the integrity of data meaning, this approach does not attain the performance threshold of the TOSC. In this paper, we propose a Task-oriented Adaptive Semantic Communication (TasCom) framework, which aims to effectively facilitate the execution of AI tasks by only sending task-related semantic features. In the TasCom framework, we first propose a Generative AI (GAI) architecture based Generative Joint Source-Channel Coding (G-JSCC) for efficient semantic transmission. Then, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed G-JSCC, which allows the semantic features with significant contributions to the AI task to preferentially occupy limited bandwidth resources for wireless transmission. Furthermore, we propose a generative training algorithm to train the proposed TasCom for optimal performance. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks at all considered channel conditions.

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arXiv2024

Dynamic Energy-Saving Design for Double-Faced Active RIS Assisted Communications with Perfect/Imperfect CSI

Although the emerging reconfigurable intelligent surface (RIS) paves a new way for next-generation wireless communications, it suffers from inherent flaws, i.e., double-fading attenuation effects and half-space coverage limitations. The state-of-the-art double-face active (DFA)-RIS architecture is proposed for significantly amplifying and transmitting incident signals in full-space. Despite the efficacy of DFA-RIS in mitigating the aforementioned flaws, its potential drawback is that the complex active hardware also incurs intolerable energy consumption. To overcome this drawback, in this paper we propose a novel dynamic energy-saving design for the DFA-RIS, called the sub-array based DFA-RIS architecture. This architecture divides the DFA-RIS into multiple sub-arrays, where the signal amplification function in each sub-array can be activated/deactivated dynamically and flexibly. Utilizing the above architecture, we develop the joint optimization scheme based on transmit beamforming, DFA-RIS configuration, and reflection amplifier (RA) operating pattern to maximize the energy efficiency (EE) of the DFA-RIS assisted multiuser MISO system considering the perfect/imperfect channel state information (CSI) case. Then, the penalty dual decomposition (PDD) based alternating optimization (AO) algorithm and the constrained stochastic majorization-minimization (CSMM) based AO algorithm address non-convex problems in the perfect/imperfect CSI case, respectively. Simulation results verified that our proposed sub-array based DFA-RIS architecture can benefit the EE of the system more than other RIS architectures.

cs.IT↗

arXiv2024

Self-Sustainable Active Reconfigurable Intelligent Surfaces for Anti-Jamming in Wireless Communications

Wireless devices can be easily attacked by jammers during transmission, which is a potential security threat for wireless communications. Active reconfigurable intelligent surface (RIS) attracts considerable attention and is expected to be employed in anti-jamming systems for secure transmission to significantly enhance the anti-jamming performance. However, active RIS introduces external power load, which increases the complexity of hardware and restricts the flexible deployment of active RIS. To overcome these drawbacks, we design a innovative self-sustainable structure in this paper, where the active RIS is energized by harvesting energy from base station (BS) signals through the time dividing based simultaneous wireless information and power transfer (TD-SWIPT) scheme. Based on the above structure, we develop the BS harvesting scheme based on joint transmit and reflecting beamforming with the aim of maximizing the achievable rate of active RIS-assisted system, where the alternating optimization (AO) algorithm based on stochastic successive convex approximation (SSCA) tackles the nonconvex optimization problem in the scheme. Simulation results verified the effectiveness of our developed BS harvesting scheme, which can attain higher anti-jamming performance than other schemes when given the same maximum transmit power.

cs.IT↗

arXiv2024

Statistical Delay and Error-Rate Bounded QoS Provisioning for AoI-Driven 6G Satellite-Terrestrial Integrated Networks Using FBC

As one of the pivotal enablers for 6G, satellite-terrestrial integrated networks have emerged as a solution to provide extensive connectivity and comprehensive 3D coverage across the spatial-aerial-terrestrial domains to cater to the specific requirements of 6G massive ultra-reliable and low latency communications (mURLLC) applications, while upholding a diverse set of stringent quality-of-service (QoS) requirements. In the context of mURLLC satellite services, the concept of data freshness assumes paramount significance, as the use of outdated data may lead to unforeseeable or even catastrophic consequences. To effectively gauge the degree of data freshness for satellite-terrestrial integrated communications, the notion of age of information (AoI) has recently emerged as a novel dimension of QoS metrics to support time-sensitive applications. Nonetheless, the research efforts directed towards defining novel diverse statistical QoS provisioning metrics, including AoI, delay, and reliability, while accommodating the dynamic and intricate nature of satellite-terrestrial integrated environments, are still in their infancy. To overcome these problems, in this paper we develop analytical modeling formulations/frameworks for statistical QoS over 6G satellite-terrestrial integrated networks using hybrid automatic repeat request with incremental redundancy (HARQ-IR) in the finite blocklength regime. In particular, first we design the satellite-terrestrial integrated wireless network architecture model and AoI metric model. Second, we characterize the peak-AoI bounded QoS metric using HARQ-IR protocol. Third, we develop a set of new fundamental statistical QoS metrics in the finite blocklength regime. Finally, extensive simulations have been conducted to assess and analyze the efficacy of statistical QoS schemes for satellite-terrestrial integrated networks.

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arXiv2024

Movable Antenna Assisted OAM Wireless Communications With Misaligned Transceiver

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arXiv2024

Achieving High Capacity Transmission With N-Dimensional Quasi-Fractal UCA

The vortex electromagnetic wave carried by multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) is widely used to generate and receive vortex electromagnetic waves with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based on UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes to achieve higher channel capacity with a fixed number of arrayelements as an intriguing question. In this paper, we propose an N-dimensional quasi-fractal UCA (ND QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-elements number on OAM-modes number. We develop the N-dimensional OAM modulation (NOM) and demodulation (NOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-elements number, which is beyond the traditional concept of multiple antenna based wireless communications. Then, we investigate different dimensional multiplexing transmission schemes based on the corresponding QF-UCA antenna structure with various array-element layouts and evaluate the optimal layout type and dimension to obtain the highest channel capacity with a fixed number of array-elements. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency, surpassing those of alternative array-element layouts of QF-UCA and the traditional multiple antenna systems.

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arXiv2024

Statistical QoS Provisioning Architecture for 6G Satellite-Terrestrial Integrated Networks

The emergence of massive ultra-reliable and low latency communications (mURLLC) as a category of time/reliability-sensitive service over 6G networks has received considerable research attention, which has presented unprecedented challenges. As one of the key enablers for 6G, satellite-terrestrial integrated networks (STIN) have been developed to offer more expansive connectivity and comprehensive 3D coverage in space-aerial-terrestrial domains for supporting 6G mission-critical mURLLC applications while fulfilling diverse and rigorous quality of service (QoS) requirements. In the context of these mURLLC-driven satellite services, data freshness assumes paramount importance, as outdated data may engender unpredictable or catastrophic outcomes. To effectively measure data freshness in satellite-terrestrial integrated communications, age of information (AoI) has recently surfaced as a new dimension of QoS metric to support time-sensitive applications. It is crucial to design new analytical models that ensure stringent and diverse QoS metrics bounded by different key parameters, including AoI, delay, and reliability, over 6G satellite-terrestrial integrated networks. However, due to the complicated and dynamic nature of satellite-terrestrial integrated network environments, the research on efficiently defining new statistical QoS schemes while taking into account varying degrees of freedom has still been in their infancy. To remedy these deficiencies, in this paper we develop statistical QoS provisioning schemes over 6G satellite-terrestrial integrated networks in the finite blocklength regime. Particularly, we firstly introduce and review key technologies for supporting mURLLC. Secondly, we formulate a number of novel fundamental statistical-QoS metrics in the finite blocklength regime. Finally, we conduct a set of simulations to evaluate our developed statistical QoS schemes.

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arXiv2024

Activation Map-based Vector Quantization for 360-degree Image Semantic Communication

In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus improving user Quality of Experience (QoE). However, the voluminous data generated by 360-degree images poses challenges in network storage and bandwidth. To address these challenges, we propose a novel Activation Map-based Vector Quantization (AM-VQ) framework, which is designed to reduce communication overhead for wireless transmission. The proposed AM-VQ scheme uses the Deep Neural Networks (DNNs) with vector quantization (VQ) to extract and compress semantic features. Particularly, the AM-VQ framework utilizes activation map to adaptively quantize semantic features, thus reducing data distortion caused by quantization operation. To further enhance the reconstruction quality of the 360-degree image, adversarial training with a Generative Adversarial Networks (GANs) discriminator is incorporated. Numerical results show that our proposed AM-VQ scheme achieves better performance than the existing Deep Learning (DL) based coding and the traditional coding schemes under the same transmission symbols.

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arXiv2024

Throughput and Fairness Trade-off Balancing for UAV-Enabled Wireless Communication Systems

Given the imperative of 6G networks' ubiquitous connectivity, along with the inherent mobility and cost-effectiveness of unmanned aerial vehicles (UAVs), UAVs play a critical role within 6G wireless networks. Despite advancements in enhancing the UAV-enabled communication systems' throughput in existing studies, there remains a notable gap in addressing issues concerning user fairness and quality-of-service (QoS) provisioning and lacks an effective scheme to depict the trade-off between system throughput and user fairness. To solve the above challenges, in this paper we introduce a novel fairness control scheme for UAV-enabled wireless communication systems based on a new weighted function. First, we propose a throughput combining model based on a new weighted function with fairness considering. Second, we formulate the optimization problem to maximize the weighted sum of all users' throughput. Third, we decompose the optimization problem and propose an efficient iterative algorithm to solve it. Finally, simulation results are provided to demonstrate the considerable potential of our proposed scheme in fairness and QoS provisioning.

cs.IT↗