In virtual reality (VR) applications, 360° images are crucial for delivering immersive and panoramic experiences. However, the substantial data volumes create significant challenges for network storage and bandwidth. Additionally, transmission channel noise can further degrade the user experience by introducing visual distortions. To address these challenges, in this paper we propose a condensed semantic communication framework, specifically the channel denoising-based latent consistency model (CDLCM), designed for efficient 360° image transmission. The CDLCM compresses transmission data by employing deep neural networks (DNNs) to extract multiscale semantic features, which are then condensed via vector quantization (VQ). While this approach reduces transmission overhead, it may result in the loss of crucial image details and increase vulnerability to noise. To counteract these effects, the CDLCM integrates a spherical attention mechanism to detect and correct VQ errors caused by noise during inverse quantization. Additionally, the latent consistency model (LCM) iteratively denoises and restores lost image details, ensuring high-quality reconstruction. The framework also adapts to estimated channel noise by dynamically adjusting the diffusion steps to improve computational efficiency. Numerical experiments verify that the CDLCM not only reduces transmission overhead but also achieves superior reconstruction quality for 360° images compared to state-of-the-art methods. Overall, CDLCM offers an effective solution that balances compression efficiency and reconstruction fidelity, making it well-suited for immersive 360° image transmission.

Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for sixth-generation (6G) wireless communications, but it suffers from a significant limitation, namely the double-fading attenuation. Active RIS has emerged as a promising solution, effectively mitigating the attenuation issues associated with conventional RIS-assisted systems. However, the current academic work on active RIS focuses on the system-level optimization of active RIS, often overlooking the development of models that are compatible with its electromagnetic (EM) and physical properties. The challenge of constructing realistic, EM-compliant models for active RIS-assisted communication, as well as understanding their implications on system-level optimization, remains an open research area. To tackle these problems, in this paper we develop a novel EM-compliant model with mutual coupling (MC) for active RIS-assisted wireless systems by integrating the developed scattering-parameter (S-parameter) based active RIS framework with multiport network theory, which facilitates system-level analysis and optimization. To evaluate the performance of the EM-compliant active RIS model, we design the joint optimization scheme based on the transmit beamforming at the transmitter and the reflection coefficient at the active RIS to maximize the achievable rate of EM-compliant active RIS-assisted MIMO system. To tackle the inherent non-convexity of this problem, we employ the Sherman-Morrison inversion and Neumann series (SMaN)-based alternating optimization (AO) algorithm. Simulation results verified that EM property (i.e., MC effect) is an indispensable factor in the optimization process of MIMO systems. Neglecting this effect introduces a substantial performance gap, highlighting its significance in the more pronounced the MC effect is, the greater the gap in achievable rates.

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 multiple-input-single-output (MISO) system considering the imperfect channel state information (CSI) case. Then, the constrained stochastic majorization-minimization (CSMM) based AO algorithm address non-convex problems. Simulation results verified that our proposed sub-array based DFA-RIS architecture can benefit the EE of the system more than other RIS architectures.

Heterogeneous unmanned aerial vehicle (UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous benchmarks. To address this, we propose the $ζ$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $ζ$. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor $\mathcal{F}$ distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.

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 to effectively facilitate the inference of different AI tasks. Based on the Generative AI (GAI) techniques, we first propose a Joint Source-Channel Coding (JSCC) that which only extracts and fuses task-related semantic features, and then transmits them to achieve efficient task-oriented semantic transmission. Then, we propose a generative training algorithm to train the proposed JSCC for optimal performance. Furthermore, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed JSCC, which allows the semantic features with significant contributions to the task inference to preferentially occupy limited bandwidth resources for wireless transmission. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks under all considered channel conditions.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

The exponential growth of wireless devices and stringent reliability requirements of emerging applications demand fundamental improvements in distributed channel access mechanisms for unlicensed bands. Current Wi-Fi systems, which rely on binary exponential backoff (BEB), suffer from suboptimal collision resolution in dense deployments and persistent fairness challenges due to inherent randomness. This paper introduces a multiagent reinforcement learning framework that integrates artificial intelligence (AI) optimization with legacy device coexistence. We first develop a dynamic backoff selection mechanism that adapts to real-time channel conditions through access deferral events while maintaining full compatibility with conventional CSMA/CA operations. Second, we introduce a fairness quantification metric aligned with enhanced distributed channel access (EDCA) principles to ensure equitable medium access opportunities. Finally, we propose a centralized training decentralized execution (CTDE) architecture incorporating neighborhood activity patterns as observational inputs, optimized via constrained multi-agent proximal policy optimization (MAPPO) to jointly minimize collisions and guarantee fairness. Experimental results demonstrate that our solution significantly reduces collision probability compared to conventional BEB while preserving backward compatibility with commercial Wi-Fi devices. The proposed fairness metric effectively eliminates starvation risks in heterogeneous scenarios.

IEEE 802.11be (Wi-Fi 7) introduces Multi-Link Operation (MLO) as a While MLO offers significant parallelism and capacity, realizing its full potential in guaranteeing strict delay bounds and optimizing Quality of Service (QoS) for diverse, heterogeneous traffic streams in complex multi-link scenarios remain a significant challenge. This is largely due to the limitations of static Enhanced Distributed Channel Access (EDCA) parameters and the complexity inherent in cross-link traffic management. To address this, this paper investigates the correlation between overall MLO QoS indicators and the configuration of EDCA parameters and Acess Catagory (AC) traffic allocation among links. Based on this analysis, we formulate a constrained optimization problem aiming to minimize the sum of overall packet loss rates for all access categories while satisfying their respective overall delay violation probability constraints. A Genetic Algorithm (GA)-based MLO EDCA QoS optimization algorithm is designed to efficiently search the complex configuration space of AC assignments and EDCA parameters. Experimental results demonstrate that the proposed approach’s efficacy in generating adaptive MLO configuration strategies that align with diverse service requirements. The proposed solution significantly improves delay distribution characteristics, and enhance QoS robustness and resource utilization efficiency in high-load MLO environments.

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) structure is widely used to generate or 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 spectrum efficiency given a fixed number of array-elements 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 multiplex transmission schemes based on the corresponding QF-UCA antenna structure with various array-elements layouts. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency.

The vortex electromagnetic wave carrying 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 array-elements as an intriguing question. In this paper, we propose anN-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 theN-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.

The advent of massive ultra-reliable and low-latency communications (mURLLC) has introduced a critical class of time- and reliability-sensitive services in 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 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$\epsilon $-effective capacity problems tailored for uplink FBC-enhanced NOMA systems, specifically catering to ensure statistical delay and error-rate bounded QoS requirements. In particular, we establish uplink two-user FBC-enhanced NOMA system models by applying the hybrid successive interference cancellation (SIC). We also develop the concept of the$\epsilon $-effective capacity and propose the optimal power allocation policies to maximize the$\epsilon $-effective capacity and$\epsilon $-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.

In recent years, reconfigurable intelligent surfaces (RIS) have garnered significant attention for their ability to control the phase shifts in reflected signals. By intelligently adjusting these phases, RIS can establish seamless direct paths between communication devices obstructed by obstacles, eliminating the need for forwarding and significantly reducing system overhead associated with relaying. This capability is crucial in multi-hop ad hoc networks requiring multiple relay steps. Consequently, the concept of incorporating multi-hop RIS into wireless multi-hop relay networks has emerged. In this paper, we propose a novel network model where each UAV communication node is equipped with a RIS, facilitating seamless connections in multi-hop relay wireless networks. We analyze the performance of this model by integrating RIS-assisted physical layer modeling into the seamless connection network framework and conducting a detailed comparative analysis of RIS-assisted and conventional connections. At the medium access layer, we introduce a RIS-DCF MAC protocol based on the IEEE 802.11 distributed coordination function (DCF), modeling the medium access process as a two-hop access scenario. Our results demonstrate that the seamless connections and diversity gain provided by RIS significantly enhance the performance of multi-hop relay wireless networks.

To enable cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to their 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. The integration of spatial, aerial, and terrestrial dimensions is thus regarded as a critical facilitator for accommodating massive Ultra-Reliable Low-Latency Communications (mURLLC) applications. 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 by using Laplace transform. Third, we introduce modeling techniques aimed at defining the$\epsilon $-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.

In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus enhancing the visual experience of the user. 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 an activation map to adaptively quantize semantic features, thereby reducing data distortion caused by quantization. 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.

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.

Driven by the tremendous demand for real-time data processing in the Internet of Vehicles (IoV), edge computing is envisioned as a promising solution to alleviate the resource limitation on vehicles. Current works on edge task scheduling simply optimize the total system cost and ignore the various constraints of applications, which will result in the reduction of the task completion rate and even cause security accidents. Although few works studied the multi-task deadline-constrained scheduling problem, their complexity is too high, resulting in the explosive growth of the runtime. Spurred by the above issues, the multi-task scheduling problem is formulated to maximize the task completion rate. Further, a Multi-user Efficient Task Scheduling (METS) algorithm is proposed to solve the formulated problem, which consists of three key components: (1) the dominating set-based network clustering that aims to reduce the network scale, (2) the matching-based task assignment to assign tasks that are modeled by the Directed Acyclic Graph (DAG) to their proper clusters, and (3) the intra-cluster DAG scheduling to schedule DAGs to the proper network nodes. Simulation results show that the proposed METS algorithm can significantly improve the task completion rate and reduce the algorithm runtime in an IoV environment with thousand-level network scale and thousand-level task requests.

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 new dimension of QoS metrics to support time-sensitive applications. Nonetheless, the research efforts directed towards incorporating 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.

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 frequency-selective 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.

Massive ultra-reliable and low latency communications (mURLLC) has emerged to support wireless time/error-sensitive services, which has attracted significant research attention while imposing several unprecedented challenges not encountered before. By leveraging the significant improvements in space-aerial-terrestrial resources for comprehensive 3D coverage, satellite-terrestrial integrated networks have been proposed to achieve rigorous and diverse quality-of-services (QoS) constraints of mURLLC. To effectively measure data freshness in satellite communications, recently, age of information (AoI) has surfaced as a novel QoS criterion for ensuring time-critical applications. Nevertheless, because of the complicated and dynamic nature of network environments, how to efficiently model multi-dimensional statistical QoS provisioning while upper-bounding peak AoI, delay, and error-rate for diverse network segments is still largely open. To address these issues, in this paper we propose statistical QoS provisioning schemes over satellite-terrestrial integrated networks in the finite blocklength regime. In particular, first we establish a satellite-terrestrial integrated wireless network architecture model and an AoI metric model. Second, we derive a series of fundamental statistical QoS metrics including peak-AoI bounded QoS exponent, delay-bounded QoS exponent, and error-rate bounded QoS exponent. Finally, we conduct a set of simulations to validate and evaluate our proposed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.

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.

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.

Tremendous research efforts have been made in conceptualizing 6G mobile wireless networks to support unprecedented scenarios with extremely diverse and challenging delay and error-rate bounded quality-of-services (QoS) requirements for ultra-reliable and low latency communications (URLLC), especially for cell-edge users. However, QoS performance is greatly limited by the computation capacity and finite battery capacity. To address this issue, mobile edge computing (MEC) has been developed by enabling mobile users to offload partial or complete computation-intensive tasks to MEC servers for computing. In addition, leveraging the significant improvements in coverage rate and spectral efficiency, intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) integrated MEC systems, which smartly reconfigure and design wireless propagation environments by bypassing blockage of line-of-sight (LOS) communications, can avoid service starvation of cell-edge users while supporting QoS for URLLC. However, how to statistically upper-bound both delay and error rate for URLLC in multiple-input multiple-output (MIMO)-UAV/IRS-based MEC systems still remains a challenging problem, especially when considering short-packet communications, such as finite blocklength coding (FBC). To overcome these difficulties, in this paper we propose FBC-based joint beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS for URLLC with MEC. First, we develop MIMO-UAV/IRS-based 3D wireless channel models using FBC. Second, we formulate and solve the ϵ-effective energy-efficiency maximization problems by converting non-convex problems into convex problems in both single-user and multiple-user scenarios. Finally, the obtained numerical analyses validate and evaluate our developed MIMO-UAV/IRS-based schemes.

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing mobile wireless network architectures for ultra-reliable and low-latency communications (URLLC). One of the major design issues raised by URLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying wireless fading channels. To efficiently accommodate statistical QoS provisioning for URLLC traffic, it is crucial to model and investigate wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, outage capacity, etc., in the non-asymptotic regime. However, how to rigorously and efficiently characterize the stochastic dynamics of mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither well understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling frameworks and controlling mechanisms for statistical delay and error-rate bounded QoS provisioning in the non-asymptotic regime. First, we establish FBC-based system models by characterizing various information-theoretic specifications. Second, we characterize the outage-probability and outage capacity functions in the non-asymptotic regime. Third, we develop a set of new statistical delay and error-rate bounded QoS metrics and control mechanisms including delay-bound-violation probability, QoS-exponent functions, and the -effective capacity in the non-asymptotic regime. Finally, the obtained simulation results validate and evaluate our proposed controlling mechanisms for statistical QoS in supporting URLLC.

In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.

Taking advantage of the broadcast nature of radio frequency (RF) wave propagation, simultaneous wireless information and power transfer (SWIPT) has recently gained significant research attention since it can prolong the battery-life of energy-constrained and low-power-supported mobile devices. In addition, due to the potential benefits of favorable propagation and channel hardening, cell-free (CF) massive multi-input multi-output (m-MIMO) can significantly enhance the QoS performance of SWIPT in terms of the achievable data rate and energy efficiency. On the other hand, finite blocklength coding (FBC) has been proposed to guarantee stringent QoS requirements while reducing the access latency using short-packet communications. However, how to efficiently integrate these new techniques using FBC based statistical delay-bounded QoS theory has imposed many new challenges not encountered before. To overcome these difficulties, in this paper we propose and develop statistical delay and error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G wireless networks in the finite blocklength regime. In particular, we establish SWIPT-enabled CF m-MIMO based system models by using FBC. We also formulate and solve the optimization problems for the tradeoff between the E-effective capacity and harvested energy for our proposed statistical delay and error-rate bounded QoS provisioning mechanisms. The obtained simulation results validate and evaluate our developed schemes.

To support the new and dominating traffic services – ultra-reliable and low latency communications (URLLC), the short-packet data communication techniques, such as finite blocklength coding (FBC), have been developed to guarantee the stringent delay and error-rate bounded quality-of-services (QoS) requirements for delay/age-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information for the delay/age-sensitive data transmissions. Since the status updates normally only consist of a small number of information bits and require ultra-low latency, integrating AoI with FBC creates another promising solution for supporting delay/age-sensitive URLLC services. However, how to characterize the relationships between AoI and delay over URLLC-enabled wireless networks has neither been well understood nor thoroughly studied in the finite blocklength regime. To overcome these challenges, we propose the joint optimization and tradeoff modeling for both peak AoI and delay-bound violation probabilities to support URLLC in the finite blocklength regime. First, we build up FBC based AoI-driven system models in the finite blocklength regime. Second, we apply the stochastic network calculus (SNC) to upper-bound both the peak AoI violation probability and delay-bound violation probability. Third, we jointly optimize the peak AoI violation probability and delay-bound violation probability and characterize their tradeoff in the finite blocklength regime. Finally, we conduct the extensive simulations to validate and evaluate our proposed AoI-driven schemes in the finite blocklength regime.

Massive ultra-reliable and low latency communications (mURLLC) has been developed as a new and dominating 6G standard traffic service to support statistical delay and error-rate bounded quality-of-services (QoS) provisioning for real-time data-transmissions. Inspired by mURLLC, finite blocklength coding (FBC) has been proposed to upper-bound both delay and errorrate by using short-packet data communications. On the other hand, to solve the massive connectivity problem imposed by mURLLC, the unmanned aerial vehicle (UAV)-enabled systems are developed by leveraging their deploying flexibility and high probability of establishing line-of-sight (LoS) wireless links while guaranteeing various QoS requirements. In addition, the age of information (AoI) has recently emerged as a new QoS performance metric in terms of information freshness. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning over 6G standards has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support mURLLC over UAV-enabled 6G wireless networks in the finite blocklength regime. Specifically, first, we develop the UAV-enabled 3D wireless networking models with wireless-link channels using FBC. Second, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Third, taking into account the peak AoI violation probability, we formulate and solve the AoI-driven ε -effective capacity maximization problems to support statistical delay and error-rate bounded QoS provisioning. Finally, we conduct the extensive simulations to validate and evaluate our developed schemes.

The new and important service class of massive Ultra-Reliable Low-Latency Communications (mURLLC) is defined in the 6G era to guarantee very stringent quality-of-service (QoS) requirements, such as ultra-high data rate, super-high reliability, tightly-bounded end-to-end latency, etc. Various 6G promising techniques, such as finite blocklength coding (FBC) and Terahertz (THz), have been proposed to significantly improve QoS performances of mURLLC. Furthermore, with the rapid developments in nano techniques, THz wireless nano-networks have drawn great research attention due to its ability to support ultra-high data-rate while addressing the spectrum scarcity and capacity limitations problems. However, how to efficiently integrate THz-band nano communications with FBC in supporting statistical delay/error-rate bounded QoS provisioning for mURLLC still remains as an open challenge over 6G THz wireless nano-networks. To overcome these problems, in this paper we propose the THz-band statistical delay/error-rate bounded QoS provisioning schemes in supporting mURLLC standards by optimizing both the transmit power and blocklength over 6G THz wireless nano-networks in the finite blocklength regime. Specifically, first, we develop the FBC-driven THz-band wireless channel models in nano-scale. Second, we build up the THz-band interference model and derive the channel capacity and channel dispersion functions using FBC. Third, we maximize the ϵ-effective capacity by developing the joint optimal resource allocation policies under statistical delay/error-rate bounded QoS constraints. Finally, we conduct the extensive simulations to validate and evaluate our proposed schemes at the THz band in the finite blocklength regime.

Inspired by the new and dominating traffic services - ultra-reliable and low latency communications (URLLC), finite blocklength coding (FBC) has been developed to support delay and error-rate bounded quality-of-services (QoS) provisioning for time-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information. Since the status updates normally consist only of a small number of information bits but warrant ultra-low latency, exploring AoI in the finite blocklength regime creates another promising solution for supporting URLLC services. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning in the finite blocklength regime has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the AoI-driven statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support URLLC in the finite blocklength regime. First, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Second, we characterizes the upper-bounded peak AoI violation probability. Third, we formulate and solve the peak AoI violation probability minimization and E-effective capacity maximization problems to support our proposed statistical delay and error-rate bounded QoS provisioning. Finally, we conduct the simulations to validate and evaluate our developed schemes in the finite blocklength regime.

In supporting the new 6G standard traffic services-massive ultra-reliable low-latency communications (mURLLC), several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning theory and finite blocklength coding (FBC), have been developed to upper-bound both delay and error-rate for time-sensitive multimedia applications. On the other hand, cell-free (CF) massive multi-input multioutput (m-MIMO), where a large number of distributed access points (APs) jointly serve a massive number of mobile devices using the same time-frequency resources, has emerged as one of the 6G key promising techniques to significantly improve various QoS performances for supporting mURLLC. However, it is challenging to statistically guarantee stringent mURLLC QoS-requirements for transmitting multimedia traffics over CF m-MIMO and FBC based 6G wireless networks. To overcome these problems, we develop analytical models to precisely characterize the delay and error-rate bounded QoS performances while considering non-vanishing decode-error probability for CF m-MIMO based schemes. In particular, we develop FBC based system models and apply the Mellin transform to characterize arrival/service processes for our proposed CF m-MIMO modeling schemes. Then, we formulate and solve the delay violation probability minimization problem and obtain the closed-form solution of the optimal rate adaptation policy for each mobile user over 6G CF m-MIMO mobile wireless networks in the finite blocklength regime. Our simulation results validate and evaluate our proposed schemes for statistical delay and error-rate bounded QoS provisioning.

One of most important techniques for enabling the sixth-generation (6G) mobile wireless network lies in how to efficiently guarantee various stringent quality-of-service (QoS) performance-metrics to support the emerging massive Ultra-Reliable Low-Latency Communications (mURLLC) in 6G. Correspondingly, finite blocklength coding (FBC) has been developed as an effective technique to significantly improve various QoS indices for mURLLC through implementing short-packet communications. On the other hand, Terahertz (THz) band wireless nano-communications have been widely envisioned as a promising 6G technique to efficiently support utra-high data-rate (up to 1 Tbps). One of the major constraints over THz-band nano-networks is the severely limited energy that can be accessed by nano devices. Towards this end, various novel energy harvesting (EH) mechanisms have been proposed to remedy the energy scarcity problem. However, how to accurately characterize the relationships among THz wireless channels, energy consumption, and EH models for FBC based nano communications remains a challenging problem to support statistical delay and error-rate bounded QoS provisioning over FBC based 6G THz wireless nano-networks. To overcome these challenges, in this paper we propose optimal resource allocation policies to achieve the maximum ε-effective capacity in the THz band over FBC-EH-based nano-networks. Particularly, we establish nano-scale system models and characterize wireless channel models in the THz band using FBC. In order to support statistical delay and error-rate bounded QoS provisioning, we formulate and solve the ε-effective capacity maximization problem under several different EH constraints for our proposed schemes. Simulation results are included, which validate and evaluate our proposed schemes in the finite blocklength regime.

Massive ultra-reliable and low latency communications (mURLLC) has emerged as new and dominating 6G-standard services to support statistical quality-of-services (QoS) provisioning for delay-sensitive data transmissions. To measure the freshness of updated information, age of information (AoI) has recently formed as the new dimension of QoS metric. Since status updates usually consist of a small number of information bits but warrant ultra-low latency, integrating AoI withfinite blocklength coding(FBC) creates an alternative promising solution for mURLLC. On the other hand, to solve the massive connectivity issues imposed by mURLLC,unmanned aerial vehicle(UAV) has been developed to significantly enhance the line-of-sight (LOS) coverage while guaranteeing various QoS requirements. However, how to efficiently integrate the above new techniques for statistical delay and error-rate bounded QoS provisioning in UAV systems has been neither well understood nor thoroughly studied. To overcome these challenges, we propose FBC based statistical delay and error-rate bounded QoS provisioning schemes which leverage AoI as a key QoS provisioning technique for mURLLC over UAV mobile networks. First, we develop FBC based UAV system models. Second, we build up AoI-metric based modeling frameworks to upper-bound peak AoI violation probability using FBC. Third, we formulate and solve FBC based peak AoI violation probability minimization problem. Forth, we jointly optimize peak AoI violation probability and$\epsilon $-effective capacity and characterize their tradeoffs. Finally, our simulations validate and evaluate our developed schemes.

While 5G is being widely deployed around the world, the efforts from both academia and industry have started to investigate various promising 6G techniques, among which Terahertz (THz) systems have drawn much research attention. Recent developments in nanotechnology have enabled electromagnetic nano-communications in the THz band for supporting very large bandwidths with ultra-high data rates over 6G big-data-driven nano-networks. One of the major bottlenecks over such networks is the very limited energy that can be accessed by nano devices. Towards this end, novel energy harvesting (EH) mechanisms have been proposed to remedy this energy scarcity problem. However, how to accurately model and characterize the relationships among THz-band wireless channel, energy consumption, and EH models still remains a challenging and open problem. To solve the abovementioned problems, we propose to develop a joint optimal resource allocation policy for self-powered nano devices to achieve the maximum channel capacity in the THz band over EH-based nano-networks. Particularly, using the Time-Spread On-Off Keying (TS-OOK) modulation mechanism, we establish the wireless communication and EH models in the THz band. Then, we formulate and solve the channel capacity maximization problem under several different constraints for our proposed THz-band EH-based schemes. Simulation results are included, which evaluate and validate our proposed EH-based nano-communication schemes in the THz band.

While 5G is being widely deployed around the world, the efforts from both academia and industry have started to propose and investigate various promising 6G techniques, among which Terahertz (THz) wireless ad hoc networks have drawn much research attention. With the recent development in nanomaterials, THz systems have been developed to support rapidly increasing demand for ultra-high data rates while addressing the spectrum scarcity and capacity limitation problems of current wireless communication systems. Towards this end, THz wireless techniques have been envisioned as one of the key technologies of 6G wireless networks, which boosts the range of applications of nanotechnology. However, due to the complexity in accurately characterizing THz-band wireless channels and interference models in the nanoscale scenarios, a number of technical challenges, such as capacity and mutual information modelling problems, need to be overcome for achieving such ultra-high-speed data rates in the THz band. To solve the above problems, we propose to maximize the mutual information in the THz band over wireless ad-hoc nano-networks. Particularly, we establish THz-band nano-communication system models. Then, we characterize the interference model and formulate and solve the mutual information maximization problem for our proposed THz-band nano-communication schemes. Simulation results are included, which validate and evaluate our proposed schemes in the THz band.

As a new and dominating 6G mobile-networks' service class for time-sensitive traffics, massive ultra-reliable and low latency communications (mURLLC) has received tremendous attention. One of key 6G enabling-techniques for achieving mURLLC lies in how to efficiently support statistical delay and error-rate bounded quality-of-services (QoS) provisioning for real-time data-transmissions over time-varying wireless networks. Towards this end, several emerging wireless techniques, including finite blocklength coding (FBC), hybrid automatic repeat request with incremental redundancy (HARQ-IR) protocol, millimeter wave (mmWave), cell-free (CF) massive multiple-input multiple-output (m-MIMO), etc., have been shown to be 6G promising enablers to significantly improve various QoS performances. However, integrating these techniques with the statistical delay and error-rate bounded QoS provisioning theory for mURLLC has imposed many new difficulties not encountered before. To overcome these challenges, in this paper we propose the statistical delay-and-error-rate-bounded QoS provisioning system architecture over mmWave user-centric CF m-MIMO and FBC-HARQ-IR based 6G wireless networks. First, we establish the comprehensive system models by accurately characterizing the integrations of above-described 6G promising techniques with statistical QoS provisioning theory. Then, we integrate FBC with HARQ-IR protocol to derive the channel capacity as a function of error probability. Finally, we obtain the closed-form expressions for effective capacities under our proposed schemes. We also conduct a set of simulations to validate and evaluate our proposed FBC-HARQ-IR based mmWave user-centric CF m-MIMO schemes.

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for exposure-path prevention in supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to required coverage rate constraint due to extensive power consumption over time-varying wireless channels. To overcome the above-mentioned problems, in this paper we propose the AQ-DBPSK/DS-CDMA (alternating quadratures differential binary phase shift keying/direct-sequence code division multiple access) based cooperative MIMO energy-efficient and interference-mitigating scheme under the constraint of optimal tradeoff between power consumption and coverage rate over multi-hop clustered WCSNs. In particular, we build sensing models to characterize the minimum coverage rate constraint and formulate the exposure-path prevention problem using the percolation theory. Then, we derive the critical density of camera sensors subject to minimum exposure-path prevention probability constraint. We develop the AQ-DBPSK/DS-CDMA scheme and also derive the optimal data rate to optimize transmit-power and data-rate trade-off. By deriving the critical density of camera sensors over 3D WCSNs, we apply the cooperative MIMO based NEW LEACH architecture for our multi-hop cooperative MIMO scheme. Also conducted is a set of simulations which show that our proposed scheme can outperform other existing schemes in terms of energy efficiency and interference-mitigation over multi-hop 3D clustered WCSNs.

In the last few decades, the statistical delay- bounded quality-of-service (QoS) technique has been proposed and investigated to support the delay-bounded 5G multimedia wireless services In addition, the energy harvesting (EH) based systems, which enable the mobile devices to harvest energy from various external sources, have been designed to address the energy scarcity problem. Researchers have recently integrated EH based systems with short-packet communications for their potential benefits of small payloads and low latency to support reliable multimedia data transmissions. Under the short delay requirements for the low-latency 5G multimedia wireless services, it is not ideal to apply the traditional Shannon capacity for modeling the data rate under the error-rate bounded in the non- asymptotic regime. Accordingly, researchers have developed the finite wireless data transmission rate in the non-asymptotic regime. However, it is challenging to determine the convexity of the effective-capacity maximization problem subject to the statistical delay-bounded and error-rate bounded QoS constraints due to the complexity of analyzing the maximization problem in the non- asymptotic regime. To overcome these challenges, we design the EH based cross- layer optimization scheme in supporting the statistical delay-bounded and error-rate bounded QoS requirements in the finite blocklength regime in the following steps. First, we characterize the EH based system models under finite blocklength coding (FBC). Second, we derive and analyze the convexity of the /spl epsilon/-effective capacity maximization problem for our proposed EH scheme in the finite blocklength regime. Finally, we conduct several simulation evaluations which validate our proposed EH scheme under the statistical delaybounded and error-rate bounded QoS constraints in the finite blocklength regime.

The integration of millimeter wave (mmWave) and multiple-input and multiple-output (MIMO) techniques has been designed to provide reliable communications with large degrees of freedom while supporting the explosively growing number of mobile users. Under stringent requirements in terms of latency and reliability, due to the infinite blocklength assumption of the Shannon's capacity result, researchers have investigated new methods to characterize wireless data transmissions considering the block error probability. The finite blocklength coding (FBC) technique has been developed to model the finite blocklength coding rate in the non-asymptotic regime while supporting short-packet communications over 5G wireless ad-hoc networks. However, because of the design complexity when characterizing the second-order coding rate over mmWave MIMO based wireless channels while being integrated with FBC, how to accurately derive the finite blocklength coding rate over mmWave MIMO wireless fading channels is still an open problem over 5G wireless ad-hoc networks. To tackle the above-mentioned challenges, we propose and develop a system model that can efficiently integrate mmWave-MIMO techniques with finite blocklength coding over 5G wireless ad-hoc networks. In particular, we derive system equations that characterize the foundational informationtheoretic relationship between the finite blocklength channel capacity and the coding rate over our proposed mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime. Also conducted is a MATLAB-based performance evaluation, which validates and analyzes our proposed schemes over mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime.

The non-orthogonal multiple access (NOMA) has been designed to significantly enhance the spectral efficiency for the massive connections of the mobile devices. Moreover, to guarantee the short latency requirements for 5G multimedia wireless services, researchers have designed the statistical delaybounded quality-of-service (QoS) provisioning for supporting the video transmissions over the statistically varying wireless channels. Accordingly, researchers have proposed the finite blocklength coding (FBC) techniques to model the relationship between data transmission rate and channel capacity in the non- asymptotic regime while supporting short-packet communications under the QoS constraints. Due to its potential to significantly improve spectral efficiency and reduce the transmission latency, a NOMA system can be exploited while being integrated with FBC to guarantee the QoS requirements for both the latency and reliability over mobile wireless ad-hoc networks. However, due to the complexity of the effective-capacity maximization problem in the non-asymptotic regime, the FBC based NOMA scheme have imposed new challenges for determining the convexity of the optimization problem and deriving optimal resource allocation policies for NOMA subject to the statistical delay-bounded and error-rate bounded QoS constraints. In order to solve the abovementioned problems, we define a new concept of /spl epsilon/-effective capacity and propose a corresponding system architecture model for NOMA system under FBC. In particular, we characterize the FBC based NOMA system models in wireless ad hoc networks. Considering the statistical delay- bounded and error-rate bounded QoS constraints, we formulate and solve the max- min fairness problem under FBC. Simulation results are included, which evaluate and validate our proposed FBC based NOMA scheme subject to the statistical delay-bounded and error-rate bounded QoS constraints.

To support the delay-bounded multimedia services for 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been developed to jointly guarantee statistically delay-bounded video transmissions over different timevarying wireless channels, simultaneously. On the other hand, as one of the 5G promising candidate techniques, energy harvesting (EH) is designed to solve the energy supply problem while bringing new challenges due to the stochastic nature of the harvested energy in supporting the heterogeneous statistical delay-bounded QoS provisionings. However, due to the unknown dynamics of the distributions for energy and data arrival processes, it is challenging to design the optimal EH and resource allocation policies under the heterogeneous statistical delay-bounded QoS constraints. Towards this end, the reinforcement learning algorithms have been designed to find the optimal EH and resource allocation policies by allowing the mobile users to learn from the different network states and historical behaviors until the optimal response set is reached. To overcome the aforementioned problems, in this paper we propose the learning based algorithm for designing the optimal EH and resource allocation policies while satisfying the heterogeneous statistical delay-bounded QoS constraints over EH based 5G mobile wireless networks. In particular, we establish the EH based system model. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate the effective-capacity optimization problem over EH based 5G mobile wireless networks. Then, we apply the learning based EH algorithm for deriving the optimal resource allocation policy. Also conducted is a set of simulations which validate and evaluate the system performances and show that our proposed learning based EH scheme outperforms the other existing schemes under the heterogeneous statistical delay-bounded QoS constraints over 5G mobile wireless networks.

In order to support delay-bounded multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been designed to jointly guarantee statistically delay-bounded video transmissions over different time-varying wireless channels, simultaneously. In addition, with short transmission delay requirements for the multimedia data transmissions, the traditional Shannon's capacity is no longer appropriate to characterize the maximum achievable data transmission rate given the block error probability. Towards this end, the finite blocklength coding (FBC) technique has been developed for reliable delay-bounded 5G multimedia mobile wireless networks. Recent results have derived the throughput of FBC subject to statistical delay-bounded QoS constraints given the block error probability. However, it is challenging to characterize the effective capacity for multimedia data transmissions over fading channels while guaranteeing statistical delay-bounded QoS constraints in the finite blocklength regime. To effectively remedy the above-mentioned deficiencies, we propose FBC based cross-layer design while guaranteeing statistical delay-bounded QoS requirements over 5G multimedia mobile wireless networks. In particular, we establish and analyze FBC based wireless network models. Given statistical delay-bounded QoS constraints, we formulate and solve the e-effective-capacity optimization problem with FBC. Also conducted is a set of simulations which validate and evaluate our proposed FBC scheme under statistical delay-bounded QoS constraints.

To support delay-bounded multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been designed to jointly guarantee statistically delaybounded video transmissions over different time-varying wireless channels, simultaneously. On the other hand, energy harvesting (EH) wireless channels/networks have received a great deal of research attention recently to address the issue of terminals that need to refresh their energy supplies remotely. An EH device is considered as a rechargeable battery which stores the incoming energy from various external sources. As one of the 5G promising technologies, the study of EH systems has brought many new challenges, such as how to characterize the wireless channels for EH systems using finite blocklength coding (FBC). In addition, given blocklength n, error probability e, and c-effective capacity in the finite blocklength regime, the achievable data transmission rate under statistical delay-bounded and error-rate bounded QoS requirements for EH based systems still remains as a challenging and open problem. To overcome the aforementioned problems, we propose FBC based cross-layer design for EH systems in supporting statistical delay-bounded and error-rate bounded QoS requirements over 5G mobile wireless networks. In particular, we establish and analyze FBC based EH system models. Given statistical delay-bounded and error-rate bounded QoS constraints, we derive an approximate lower bound on the data transmission rate in terms of the effective capacity for our proposed EH system in the finite blocklength regime. Also conducted is a set of simulations that validate and evaluate our proposed FBC based EH system under statistical delay-bounded and error-rate bounded QoS constraints.

With the recent developments in energy harvesting (EH) technologies, mobile devices are able to support the wireless multimedia services by harvesting energy from various external sources. As one of the promising technologies to solve the energy scarcity problem, EH schemes have brought many new challenges due to the stochastic nature of the wireless channel and the harvested energy in supporting the statistical quality-of-service (QoS) provisionings. On the other hand, the full-duplex spectrum sensing (FD-SS) scheme has been designed to improve the spectrum efficiency while significantly enhancing the system performance over cognitive radio networks (CRNs). However, due to the unknown dynamics of the channel state information and the energy state information, it is challenging to design the efficient EH based power allocation policies for all the users with different operating modes while guaranteeing the statistical delay-bounded QoS constraints. To overcome the aforementioned problems, in this paper we develop the collaborative learning system model by choosing the optimal operation strategies and power allocation policies through learning from the EH process while satisfying the heterogeneous statistical delay-bounded QoS constraints over CRNs. In particular, we establish and analyze the FD-SS based EH system models over CRNs. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the max-min fairness effective-capacity optimization problem for the battery-free EH based CRNs. Then, we apply the collaborative learning algorithm for deriving the optimal joint EH based mode selection and power allocation schemes. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative learning based EH scheme outperforms the other existing schemes under the heterogeneous statistical delay-bounded QoS constraints over CRNs.

With the exponentially increasing demand for wireless multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) provisioning has been proven to be able to effectively guarantee the multimedia data transmissions over highly time-varying wireless channels. On the other hand, many research efforts have been focused on various 5G-promising candidate techniques, such as device-to-device (D2D) caching based communications to offload cellar traffics and address the data explosion problem for the next generation wireless networks. Furthermore, in order to enhance the reliability of video delivery without causing too much interference to other mobile users, researchers have applied the coordinated joint transmission techniques for collaborative D2D caching scheme, which enables mobile users to share popular multimedia files within a D2D communication group instead of downloading from remote backhaul networks. Towards this end, one of the key issues lies in the power allocation problems subject to the heterogeneous statistical delay-bounded QoS requirements for the collaborative D2D caching over edge-computing networks. To overcome the aforementioned challenges, in this paper we propose the collaborative D2D caching model and D2D communication schemes over edge-computing networks. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the effective-capacity optimization problem for our proposed collaborative D2D caching schemes over edge-computing networks. Then, we develop the collaborative D2D-cache matching algorithms by using bipartite graph technique for selecting D2D-caching users to maximize the effective capacity. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative D2D caching schemes outperform the other existing schemes under heterogeneous statistical delay-bounded QoS constraints.

The statistical delay-bounded quality-of-service (QoS) theory has been developed to efficiently support multimedia transmissions over 5G wireless networks. On the other hand, unlike in Shannon's information-theoretic formalism requiring infinite blocklength, finite blocklength coding (FBC) has recently emerged for error control in the non-asymptotic regime, guaranteeing stringent statistical QoS requirements in terms of both latency and reliability for ultra-reliable low-latency communications (URLLC) in 5G services. Moreover, integrated with FBC, millimeter wave (mmWave) massive multi-input multi-output (m-MIMO) schemes have been designed to significantly improve the performance in guaranteeing delay/error-rate bounded QoS. However, due to the complexity of modeling and solving the optimization problems over mmWave m-MIMO fading channels in the non-asymptotic error-control regime, it is challenging to derive an optimal resource allocation policy for maximizing the ε-effective capacity to guarantee statistical delay/error-rate bounded QoS. To overcome the above problems, in this paper we propose heterogeneous statistical-QoS driven resource allocation policies for mmWave m-MIMO based 5G wireless networks in both asymptotic and non-asymptotic regimes. In particular, we develop an mmWave m-MIMO based 5G wireless networks model to optimize the effective capacity for our proposed schemes. Our simulations show that our proposed schemes outperform the existing schemes in guaranteeing heterogeneous statistical delay/error-rate bounded QoS.

In order to effectively guarantee the statistically delay-bounded multimedia services over time-varying wireless channels, the statistical quality-of-service (QoS) technique has been developed over 5G big data mobile wireless networks. On the other hand, as one of the 5G-promising techniques, the wireless caching enabled WiFi offloading technique is shown to be powerful in addressing the data explosion problem and alleviating the network congestion problem in macrocells. Consequently, challenges have been imposed in applying the cooperative caching schemes for maximizing the successful playback probability under statistical delay-bounded QoS constraints. To effectively overcome the above-mentioned problems, we propose the statistical QoS-driven power allocation scheme through applying the cooperative caching enabled WiFi offloading system over 5G big data mobile wireless networks. In particular, under the Nakagami- \textit{m} fading model, we establish the system models for cooperative caching and wireless transmissions. Given the statistical QoS constraints, we derive and analyze the aggregate effective capacity and the successful playback probability under our developed optimal power allocation policies for the QoS-driven cooperative caching enabled WiFi offloading. Also conducted is a set of simulations which analyze and show the priority of our proposed cooperative caching enabled WiFi offloading scheme, compared with the schemes without cooperative caching in terms of effective capacity under statistical QoS constraints over 5G big data mobile wireless networks.

To support the multimedia services over 5G mobile wireless networks, the heterogeneous statistical quality- of-service (QoS) technique has been designed to jointly guarantee the statistically delay-bounded video transmissions over different time-varying wireless channels, simultaneously. On the other hand, as one of the 5G-promising candidate techniques, device-to-device (D2D) technique has been shown to improve both energy efficiency and spectrum efficiency for multimedia communications. However, overuse of the D2D transmissions may cause the unnecessary interferences to the original base-station oriented cellular networks. Consequently, under heterogeneous statistical delay- bounded QoS constraints, in-network caching techniques, clustering algorithms, and resource allocation policies have been proposed for D2D cluster-caching based 5G multimedia wireless networks with new opportunities and challenges. To effectively overcome the above-mentioned challenges, we propose the heterogeneous statistical QoS-driven resource allocation scheme through applying the D2D cluster-caching based system. In particular, under the Nakagami-m fading model, we establish the system models for the dynamic D2D clustering based video stream sharing and the wireless transmissions. Given the heterogeneous statistical QoS constraints, we derive and analyze the aggregate effective capacity under our developed optimal resource allocation policies for the heterogeneous QoS-driven D2D cluster-caching based 5G multimedia mobile wireless networks. Also conducted is a set of simulations which validate and evaluate our proposed D2D cluster-caching based scheme, compared with the other existing schemes in terms of effective capacity under heterogeneous statistical QoS constraints.

As a critical technique to support the multimedia services - the major traffic in cognitive radio networks (CRNs), the statistical quality-of-service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, in modern CRNs, cooperative spectrum sensing is shown to be able to greatly improve the sensing performance in cognitive radio networks. However, secondary users belonging to different service providers tend to be selfish and allocate their resources independently. Accordingly, challenges have been raised in applying the cooperative game to maximize the total network utility. To effectively overcome the above-mentioned challenges, we propose the QoS-driven power allocation scheme implementing the cooperative spectrum sensing game over CRNs. In particular, under the Nakagami-m channel model, we establish the cooperative spectrum sensing system model. Given the statistical QoS constraints, we analyze the effective capacity and Markov chain model for different sensing scenarios. We propose the QoS-aware cooperative power control game for cooperative spectrum sensing system over CRNs. Also conducted is a set of simulations which evaluate the system performance and show that our proposed resource allocation policy can achieve the optimality under the statistical delay-bounded QoS constraints over cooperative spectrum sensing CRNs.

To support the emerging next generation wireless networks, researchers have made a great deal of efforts in investigating promising techniques in multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the two 5G-promising candidate techniques, the multiple-input and multiple-output (MIMO) based full-duplex (FD) and device-to-device (D2D) can also significantly enhance the performance of statistical QoS for time- sensitive traffics over the 5G mobile wireless networks. However, how to efficiently integrate these advanced techniques in supporting statistical QoS impose many new challenges not met before. To effectively overcome the difficulties, in this paper we propose the QoS-driven power adaptation scheme by applying Quadrature- OFDMA (Q-OFDMA) to implement MIMO FD D2D based multimedia services in 5G mobile wireless networks. In particular, under the Nakagami-m channel model, we establish the PHY-layer Q-OFDMA system model and FD D2D model. Given the statistical QoS constraint, we derive and analyze the effective capacity under our proposed optimal power adaptation policy over 5G mobile wireless networks. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of self-interference cancellation to efficiently implement the statistical QoS over 5G mobile wireless networks.

With the explosive development of the next era for mobile wireless networks, there has been a lot of studies in the promising techniques for multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G-promising techniques, multiple input multiple output-orthogonal frequency-division multiple access (MIMO-OFDMA) based cognitive radio schemes are proposed to significantly improve the system capacity while mitigate the interference for future dynamic spectrum access networks. However, due to the heterogeneity caused by different links of simultaneous traffics over the wireless relay, supporting diverse delay-bounded QoS guarantees for MIMO-OFDMA based cognitive radio networks (CRNs) imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this paper we propose the heterogeneous QoS-driven resource allocation scheme by applying the MIMO-OFDMA based relaying scheme over CRNs. In particular, under the Nakagami-m fading model, we establish the MIMO-OFDMA based system model. Then, given the heterogeneous statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal power-allocation policies for the MIMO-OFDMA based CRNs. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of effective capacity to efficiently implement the heterogeneous statistical QoS over MIMO-OFDMA based CRNs.

To support the emerging next era of mobile wireless networks, researchers have made a great deal of efforts in investigating promising techniques in multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G- promising techniques, the full-duplex (FD) technique can also significantly enhance the performance of statistical QoS for real-time traffic over 5G mobile wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless FD relay links, supporting diverse delay-bounded QoS guarantees for wireless FD relay networks imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this paper we propose the heterogeneous QoS-driven resource adaptation scheme by applying the full-duplex relaying scheme. In particular, under the Nakagami-m fading model, we establish the system model for the decode and forward (DF) protocol based FD relay system. Then, we propose the FD based relay selection model. Given the heterogeneous statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal power-adaptation policies for the FD relays over 5G mobile wireless networks. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of self- interference cancellation to efficiently implement the heterogeneous statistical QoS over FD relay networks.

As a critical technique to support the multimedia services - the major traffic in cooperative cognitive radio networks (CRNs), the statistical quality-of-service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time- varying wireless channels. On the other hand, in modern cooperative CRNs, the full-duplex spectrum sensing (FD-SS) scheme is designed to be a promising candidate technique for fully utilizing the channel spectrum while significantly enhancing the performance. However, how to efficiently integrate the FD-SS technique in supporting statistical QoS over cooperative CRNs imposes many new challenges not met before. To effectively overcome the above-mentioned difficulties, we propose the QoS-driven resource allocation scheme to implement FD-SS based multimedia services in cooperative CRNs. In particular, under the Nakagami-m channel model, we establish the cooperative spectrum sharing system model. We develop the FD-SS scheme and derive the probabilities of miss detection and false alarm for the proposed FD-SS scheme over cooperative CRNs. Given the statistical QoS constraints, we analyze the effective capacity and our proposed optimal resource allocation policy using the proposed FD-SS architecture over cooperative CRNs. Also conducted is a set of simulations which evaluate the system performance and show that our proposed resource allocation policy can achieve the optimality under the statistical delay-bounded QoS constraints over cooperative CRNs.

To support the emerging next generation wireless networks, researchers have made a great deal of efforts in investigating various promising techniques, such as full-duplex (FD) multiple-input and multiple-output (MIMO) technique and device-to-device (D2D) communications. FD MIMO technique can practically achieve the theoretical doubling of throughput if the self-interference can be efficiently cancelled. And D2D communication is designed and implemented to significantly enhance the FD communication performance by effectively reducing the overall interference and lowering transmit power over ad hoc networks. However, how to efficiently cancel the selfinterference induced by the FD MIMO transmissions under D2D communications has imposed many new challenges. To overcome the above-mentioned problems, we propose the adaptive power control policy for maximizing channel capacity over FD D2D QOFDMA ad hoc networks. In particular, under the Nakagami-m channel model, we establish the system model for the spatial multiplexing oriented Q-OFDMA system, and apply the selfinterference suppression techniques for FD model. We derive and analyze the energy efficiency for FD system over ad hoc networks. Then, we develop the adaptive power control policy for maximizing the MIMO channel capacity under our proposed spatial multiplexing based Q-OFDMA system over ad hoc networks. Also conducted is a set of simulations which show that our proposed scheme outperform the other existing schemes in terms of energy efficiency and self-interference cancellation over ad hoc networks.

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to the required coverage rate constraint due to their extensive power consumption for data transmissions and inter-sensor interference over time-varying wireless channels in the 3D WCSNs. To overcome the above-mentioned problems, in this paper we propose the multi-hop cooperative multi-input-multi-output and orthogonal frequency-division multiplexing (MIMO-OFDM) based energy-efficient and interference-mitigating scheme for the 3D clustered WCSNs with the minimum target-object coverage rate constraint. We propose to integrate the cooperative MIMO-OFDM with the new low energy adaptive clustering hierarchy (NEW LEACH) algorithm to increase the spatial diversity of wireless channels, reducing the transmitted power with the constraints of bandwidth and energy in multi-hop WCSNs. In particular, applying the NEW LEACH architecture and using the Nakagami-m model, we develop the cooperative MIMO-OFDM based scheme to implement the energy-efficient and interference-mitigating wireless communications over our multi-hop 3D clustered WCSNs. Then, we model and analyze the performance of our proposed cooperative MIMO-OFDM scheme. Also conducted is a set of simulations which show that our proposed scheme outperform the other existing schemes in terms of energy efficiency and interference mitigation over multi-hop 3D WCSNs.

With fast advances in camera sensor devices and wide applications of wireless camera sensor networks (WCSNs), optimizing the tradeoff between power consumption and coverage rate of WCSNs attracts a great deal of research attention. In contrast to 2D WCSNs, 3D WCSNs capture more accurate and comprehensive information for surveillant applications. The percolation theory has been proved to be powerful and effective in characterizing the exposure path prevention using 2D WCSNs. While percolation theory can be potentially extended into 3D WCSNs to improve the power and coverage performances, there are still many new challenges remaining unsolved. On the other hand, the clustering algorithm is widely cited as an efficient power saving and interference mitigation technique for WCSNs. However, how to integrate the clustering technique with 3D percolation theory in WCSNs is still an open problem. To overcome the aforementioned challenges, in this paper we propose the 3D percolation theory-based exposure-path prevention scheme for optimizing the tradeoff between power consumption and coverage rate over clustered WCSNs. First, we apply and extend the bond-percolation theory to derive the optimal density of camera sensors deployed in 3D WCSNs subject to the minimum exposure-path prevention probability constraint. Then, we apply the mutual entropy to analyze the dependency among 3D neighboring camera sensors, justifying the bond-percolation theory in 3D WCSNs. Finally, we apply the new low energy adaptive clustering hierarchy (LEACH) architecture into our 3D WCSNs for power saving and interference mitigation. The conducted extensive simulations show that our proposed schemes outperform the other existing schemes in optimizing the tradeoff between power consumption and coverage rate over 3D WCSNs.