In congested Wi-Fi, sending less does not guarantee lower latency. Based on measurements on commodity Wi-Fi routers in the wild, sparse microflows can suffer bulk-like tail latency even at negligible load. This latency is driven by MAC-level contention dynamics rather than a flow's sending rate, rendering rate-based congestion control ineffective. We present Concord, a Wi-Fi MAC mechanism that makes burst airtime an explicit control signal and penalizes excessive medium holding. Concord operates entirely within the Wi-Fi driver, requires no flow classification, no client or protocol changes, and incurs only O(1) work per burst. Concord shows that controlling medium-holding time, rather than transmission rate, is key to tail latency in WLANs. With four saturated downlink contenders, Concord reduces the 99.9th-percentile enqueue-to-ACK latency of 100 B microflows from 298/461 ms (IEEE baseline / vendor bursting) to 42 ms without sacrificing bulk throughput. For interactive workloads (cloud gaming), it cuts 99.9th-percentile latency from 231/441 ms to 92 ms and reduces starvation by up to 10× versus the default IEEE stack.

The channel state information (CSI) acquisition plays a pivotal role in cell-free (CF) massive multiple-input-multi-output (mMIMO) systems. However, conventional pilot-based channel estimation incurs prohibitive overhead costs as user density and mobility increase. To address this, we propose a multi-slot alternating estimation–prediction (MAEP) framework, which leverages temporal correlation to predict future CSI directly and thereby drastically reduce pilot overhead. The efficacy of the proposed framework hinges on prediction accuracy. Inspired by the remarkable modeling capabilities of large language models (LLMs) and their demonstrated efficacy in cross-modal applications, we introduce an LLM-driven channel predictor termed frequency-temporal alignment with LLM (FTAlign-LLM). FTAlign-LLM bridges the modality gap between CSI and the LLM’s feature space through three key components:(i) a multi-scale CSI attention (MSCA) network for extracting rich spatiotemporal features across frequency and delay domains, (ii) a frequency–temporal feature fusion (FTFF) network that fuses these features and aligns them with the LLM’s feature space, and (iii) the utilization of parameter-efficient fine-tuning for LLM adaptation. Extensive results demonstrate that FTAlign-LLM significantly outperforms benchmarks in prediction accuracy. Concurrently, the MAEP framework achieves substantial improvements in sum spectral efficiency, particularly when a large number of access points are deployed in CF mMIMO systems.

Semantic communication (SemCom) has recently emerged as a transformative paradigm for next-generation wireless systems, aiming to enable task-oriented information transmission. However, existing SemCom networks rely on cell-centric architectures, where users, particularly those at the cell edge, suffer from severe inter-cell interference and poor channel conditions. These issues undermine semantic accuracy, induce intolerable delay, and ultimately hinder the fulfillment of quality-of-service (QoS) requirements. To overcome these limitations, we propose the user-centric SemCom (UCSC) network, a novel architecture that integrates user-centric networks with semantic-aware transmission. In UCSC, each user is served by a dedicated cooperative access point group, enabling effective interference mitigation and reliable semantic delivery, even at the cell edge. To fully exploit the potential of UCSC, we formulate a joint optimization problem for semantic information extraction and resource allocation, aiming to minimize total energy consumption under constraints on semantic similarity and delay. By decomposing the original problem into three tractable subproblems, semantic information extraction, computational capacity control, and power allocation, we propose an alternating optimization algorithm to efficiently solve it. Simulation results demonstrate that UCSC, combined with the proposed algorithm, achieves significant performance improvements in reducing energy consumption compared with cell-centric SemCom networks, especially under stringent QoS requirements.

Segment Routing over IPv6 (SRv6) gives operators explicit path control and alleviates network congestion, making it a compelling technique for traffic engineering (TE). Yet two practical hurdles slow adoption. First, a one-shot upgrade of every traditional device is prohibitively expensive, so operators must prioritize which devices to upgrade. Second, the Segment Routing Header (SRH) increases packet size; if TE algorithms ignore this overhead, they will underestimate link load and may cause congestion in practice. We address both challenges with DRL-TE, an algorithm that couples deep reinforcement learning (DRL) with a lightweight local search (LS) step to minimize the network’s maximum link utilization (MLU). DRL-TE first identifies the smallest set of critical devices whose upgrade yields the largest drop in MLU, enabling hybrid IP/SRv6 networks to approach optimal performance with minimal investment. It then computes SRH-aware routes, and the DRL agent, augmented by a fast LS refinement, rapidly reduces MLU even under traffic variation. Experiments on an 11-node hardware testbed and three larger simulated topologies show that upgrading about 30% of devices allows DRL-TE to match fully upgraded networks and reduce MLU by up to 34% compared with existing algorithms. DRL-TE also maintains high performance under link failures and traffic variations, offering a cost-effective and robust path toward incremental SRv6 deployment.

The limited-capacity fronthaul link between access points (APs) and central processing unit (CPU), along with signal distortions caused by hardware impairments at users and APs, lead to unreliable transmission, hindering the achievement of ultra-reliable low-latency communication (URLLC) in cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems. In this paper, we investigate the performance of URLLC in CF mMIMO systems with limited fronthaul capacity and hardware impairments. First, a low-complexity fronthaul rate allocation scheme is proposed using rate-distortion theory in the considered system. Then, we derive the distribution of signal-to-interference-plus noise ratio (SINR) and analyze the delay violation probability (DVP) based on stochastic network calculus. Furthermore, a closed-form upper bound expression for DVP is also derived. To fulfill the requirements of next-generation URLLC, which demand additional key performance indicators, we further investigate effective energy efficiency and derive a closed-form lower bound, defined as the ratio of effective capacity to total power consumption. Extensive numerical results verify the accuracy of our derivations and indicate that a large deployment of APs can compensate for the reliability degradation caused by limited fronthaul capacity and hardware impairments. Additionally, the number of APs must be appropriately configured to ensure low latency and high reliability while avoiding energy waste.

More refined resource management and Quality of Service (QoS) provisioning is a critical goal of wireless communication technologies. In this paper, we propose a novel Business-Centric Network (BCN) aimed at enabling scalable QoS provisioning, based on a cross-layer framework that captures the relationship between application, transport parameters, and channels. We investigate both continuous flow and event-driven flow models, presenting key QoS metrics such as throughput, delay, and reliability. By jointly considering power and bandwidth allocation, transmission parameters, and AP network topology across layers, we optimize weighted resource efficiency with statistical QoS provisioning. To address the coupling among parameters, we propose a novel deep reinforcement learning (DRL) framework, which is Collaborative Optimization among Heterogeneous Actors with Experience Sharing (COHA-ES). Power and sub-channel (SC) Actors representing multiple APs are jointly optimized under the unified guidance of a common critic. Additionally, we introduce a novel multithreaded experience-sharing mechanism to accelerate training and enhance rewards. Extensive comparative experiments validate the effectiveness of our DRL framework in terms of convergence and efficiency. Moreover, comparative analyses demonstrate the comprehensive advantages of the BCN structure in enhancing both spectral and energy efficiency.

Embodied AI calls for a reliable, cross-modal object recognition that deeply mines High-Quality (HQ) object appearance (i.e., visual information) and touch details (i.e., haptic information). While in real-world scenarios, cross-modal data is usually degraded due to data acquisition and delivery in complex environments. In this paper, we propose a Robust Visual-Haptic recognition (RobustVisH) model that identifies Low-Quality (LQ) visual-haptic data with transmission distortion for the first time. First, we introduce the WIreless Transmission Interference-based Multi-modal benchmark (WITIM) as a visual-haptic dataset under transmission interference. In particular, the dataset consists of WITIM/AU and WITIM/PHAC-2, in which the original signals are obtained from AU and PHAC-2, respectively. Second, we design a trainable weighted fusion and a Transformer encoder based on the bi-directional self-attention mechanism, enabling RobustVisH to form and learn fused visual-haptic features after modality-specific one-dimensional feature encoding. Third, we employ a covariate shift paradigm, transferring knowledge of RobustVisH from HQ data to LQ data, thereby increasing its robustness against transmission-interference inputs. Experimental results demonstrate that the proposed RobustVisH improves the accuracy of the state-of-the-art method by 2.06% and 9.28% on WITIM/AU and WITIM/PHAC-2, respectively. Source code is available at: https://github.com/lylibylily/RobustVisH.

Massive connections of diverse mission-critical devices have sparked people’s envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently necessitates large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) framework tailored to accommodate xURLLC’s critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity alternating optimization algorithm for efficient optimization. Extensive simulations demonstrate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture accommodates larger-scale access and improves total ETR by 15.3% and 41.91%, respectively.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) has been considered as a potential technology for Beyond 5G. However, the performance of CF mMIMO systems has not been thoroughly studied. Most existing analytical studies on CF mMIMO systems rely on deriving average performance metrics. The statistical characteristics of the signal-to-interference-plus-noise ratio (SINR), which capture the tail behavior of SINR, are crucial for metrics such as outage probability and for emerging mission-critical applications that emphasize extreme and rare events, but have not been thoroughly investigated. In this paper, we aim to obtain the distribution of SINR in CF mMIMO systems. Considering a downlink CF mMIMO system with pilot contamination, we first give the closed-form expression of the SINR. Based on our analytical work on the two components of the SINR, i.e., desired signal and interference-plus-noise, we then derive the probability density function and cumulative distribution function of the SINR under maximum ratio transmission (MRT) and full-pilot zero-forcing (FZF) precoding, respectively. Subsequently, the closed-form expressions for two more sophisticated performance metrics, i.e., ergodic rate and outage probability, are obtained. Finally, we perform Monte Carlo simulations to validate our analytical work. Numerous numerical results demonstrate the effectiveness of the derived SINR distribution, ergodic rate, and outage probability.

Ultra-reliable low-latency communication (URLLC) has emerged as a key service class for enabling next-generation mission-critical applications. However, the increasingly stringent quality-of-service requirements pose significant challenges for URLLC under traditional bit-level communication (BitCom), especially in handling rare and extreme events that are likely to occur in low signal-to-noise ratio (SNR) conditions and resource-constrained wireless environments. Semantic communication (SemCom), by compressing source information toward task-relevant content, demonstrates clear advantages over BitCom in low SNR regimes and under limited resource availability. Furthermore, SemCom inherently conveys task-specific meanings, making it well-suited for mission-critical applications in machine-to-machine communications. Motivated by these observations, we investigate the performance of URLLC under the SemCom paradigm, and derive a closed-form upper bound on the delay violation probability (DVP) using stochastic network calculus. To further exploit the strengths of both SemCom and BitCom, we propose a hybrid semantic and bit communication paradigm (HSBP). To ensure URLLC performance under HSBP, we first conduct a theoretical performance analysis and, based on this analysis, propose a bisection-based bandwidth allocation algorithm to minimize the maximum DVP. Extensive simulation results validate the accuracy of the derived upper bound and demonstrate that the proposed HSBP paradigm outperforms pure SemCom and pure BitCom in terms of DVP.

As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.

Millimeter-wave$\!$(mmWave) technology has emerged as a pivotal catalyst for unleashing the full potential of 360° virtual reality (VR). Nonetheless, the explosive growth of VR services, combined with the quality-of-service (QoS) provisioning issues of mmWave, poses formidable challenges in wireless resource allocation for mmWave-enabled 360° VR. In this paper, we propose an innovative 360° VR streaming architecture that addresses three underexplored issues: overlapping fields-of-view (FoVs), statistical QoS provisioning (SQP), and loss-tolerant active data discarding. Specifically, we first design an overlapping FoV-based optimal joint unicast and multicast (JUM) task assignment scheme, which significantly conserves wireless resources by implementing non-redundant task allocation. Leveraging stochastic network calculus (SNC), we develop a comprehensive SNC-based SQP theoretical framework from delay and rate perspectives. Additionally, we propose two corresponding optimal adaptive joint resource allocation and active-discarding (ADAPT-JRAAD) transmission schemes to minimize resource consumption while guaranteeing SQP performance from delay and rate perspectives, respectively. Extensive simulations demonstrate the outstanding performance of the designed optimal JUM task assignment scheme in conserving wireless resources. Moreover, comprehensive comparisons against six benchmarks validate the superiority of the proposed two ADAPT-JRAAD schemes in resource utilization, flexible rate control, and robust queue management.

Industry 5.0 and beyond networks have driven the emergence of numerous mission-critical services exemplified by neXt-generation ultra-reliable low-latency communication (xURLLC). To guarantee low-latency requirements, xURLLC heavily relies on short-blocklength packets with sporadic arrival traffic. As a disruptive multi-access technique, rate-splitting multiple access (RSMA) has emerged as a promising avenue to enhance the quality of service (QoS) and flexibly manage interference for xURLLC. In this paper, we study an innovative RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT) (RSMA-xURLLC-IIoT) network, which takes into account imperfect CSI and finite blocklength (FBL) regimes. We develop a novel theoretical framework leveraging stochastic network calculus (SNC) aimed at revealing insights into statistical QoS provisioning (SQP). Building upon this framework, we formulate the SQP-driven short-packet size maximization and transmit power minimization problems, aiming to guarantee the SQP performance to delay, decoding, and reliability while maximizing the short-packet size and minimizing the transmit power, respectively. By exploiting Monte-Carlo methods, we have thoroughly validated the reliability of the developed theoretical framework. Moreover, through extensive comparison analysis with state-of-the-art multi-access techniques, including non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), we have demonstrated the superior performance gains achieved by the proposed network.

Cell-free (CF) massive multiple-input multiple-output (mMIMO), which has been considered as a promising technology to achieve ultra-reliable low latency communications (URLLC), emphasizes extreme and rare events instead of well studied average performance. In this paper, we analyze the statistical delay performance in uplink CF mMIMO-aided URLLC systems, specifically characterizing the tail of the delay distribution. Firstly, we derive the SINR distribution in a user-centric uplink CF mMIMO system using moment-matching techniques and log-normal approximation. Subsequently, the average decoding error probability (DEP) with finite blocklength coding is studied based on the derived distribution, and an upper bound on the delay violation probability (DVP) is analyzed using stochastic network calculus (SNC). To provide a clearer representation of the bound on DVP, a closed-form upper bound for the average DEP is derived. Furthermore, we propose a rate adaptive scheme based on SNC to minimize the DVP. Numerous numerical results validate the accuracy of the derived SINR distribution and the upper bound on average DEP. Besides, CF mMIMO systems exhibit significant gains compared to mMIMO systems in terms of statistical delay performance, as the distributed deployment of numerous APs can effectively compensate for performance degradation caused by interference and increased arrival rates.

User-centric strategies in cell-free (CF) massive multiple-input multiple-output (mMIMO)-aided ultra-reliable low-latency communication (URLLC) systems can significantly reduce backhaul pressure to ensure low latency. Furthermore, appropriate AP selection algorithms can guarantee reliable QoS support for URLLC. However, existing AP selection algorithms in CF mMIMO-aided URLLC systems are designed solely based on large-scale fading information, which is a heuristic algorithm that makes it difficult to guarantee performance. In this paper, we consider an uplink user-centric CF mMIMO-enabled URLLC system and formulate a weighted sum rate maximum problem by jointly optimizing AP selection and power control. Since the problem is non-convex, we first decompose the original problem into a series of subproblems by the log-function transformation and fractional programming. Subsequently, we divide each subproblem into two parts for iterative solutions: one part deals with AP selection with fixed power allocation which can be solved using linear conic relaxation, and the other part involves power control with fixed AP allocation, and can be directly solved. Finally, the simulation results validate that our proposed algorithm converges rapidly, and has significant performance improvement compared to the benchmark algorithms.

Serverless computing is gaining popularity due to its "pay-per-use" model and simplified management. Benefiting from the common features of Internet of Things (IoT), the adaptation of serverless in edge computing is extensively researched. To protect nodes in serverless edge computing from potential attacks, blockchain technology plays a crucial role. Considering that the computing time of a single serverless function can be less than the time for a blockchain to generate a block, the blockchain component requires significant computing and bandwidth resources to ensure the generation efficiency of blocks matches the execution efficiency of serverless functions. However, existing studies on minimizing the completion time (a.k.a. makespan) of application workflows in serverless edge computing have never considered the impact brought by blockchain, leading to an imbalance in the resource allocation between the computing and blockchain components. To address this, we propose a Heterogeneous Event-driven Scheduling (HEDS) mechanism, which employs a fine-grained CPU al-location scheme and an event-driven approach, to make the adjustment of resource allocation between the computing and blockchain components more flexible and expeditious, leading to improving the utilization of both computing and bandwidth resources. Meanwhile, a Multi-agent Double Deep Q-network (MADDQN) algorithm is proposed to help agents minimize the makespan while balancing the resource consumption in the computing and blockchain components. Simulation results demonstrate that HEDS with MADDQN outperforms existing algorithms in terms of average makespan over different numbers of functions and nodes while satisfying the latency requirements of block generation.

Integrated sensing and communication (ISAC) is an emerging technology in next-generation communication networks. However, the communication performance of the ISAC system may be severely affected by interference from the radar system if the sensing task has demanding performance requirements. In this paper, we exploit device-to-device (D2D) communication to improve communication capacity of the ISAC system. The ISAC system in a single cell D2D assisted-network is investigated, where the base station (BS) performs target sensing and communication with multiple cellular user equipments (CUEs) as well as D2D user equipments (DUEs) simultaneously communicating with other DUEs by multiplexing the same frequency resource. To achieve the optimal communication performance in such D2D-assisted ISAC system, a joint beamforming and power control problem is formulated with the goal to maximize the sum rate while guaranteeing the performance requirements of radar sensing. Due to the non-convexity of the problem, we transform the origin problem into a relaxation form, then, a joint beamforming and D2D power control algorithm is proposed to obtain the solution efficiently. Particularly, a zero-forcing (ZF) beamforming scheme to eliminate the interference from the BS on DUEs is also proposed. Extensive numerical simulations demonstrated that with the assistance of the D2D communications, our proposed algorithm significantly outperforms the baseline schemes in terms of the system sum rate.

Cell-free (CF) massive multiple-input multiple-output (mMIMO), characterized by macro-diversity and spatial sparsity, has been considered as a potential technology to support ultra-reliable low-latency communication (URLLC). The average performance has been comprehensively investigated for URLLC in CF mMIMO systems. However, URLLC places its central focus on extreme and rare events, requiring statistical quality of service (QoS) provisioning in CF mMIMO systems. In this paper, we model the statistical QoS provisioning constraints for URLLC in a CF mMIMO system based on extreme value theory (EVT), i.e., delay violation probability boundary and statistical properties of extreme queue values. Based on our analytical work, a power control optimization problem with long-term URLLC constraints is formulated, aiming at minimizing energy consumption. Then, Lyapunov optimization is utilized to decompose this long-term stochastic optimization problem into a series of short-term deterministic problems. Since the short-term problems are non-convex and intractable, a learning-based hyper-heuristic algorithm, consisting of a high-level strategy and multiple low-level heuristics, is proposed. Numerical results verify the effectiveness of parameterizing URLLC in the CF mMIMO system based on EVT and demonstrate that the proposed algorithm outperforms benchmark algorithms in both average delay and delay fluctuations, achieving statistical QoS provisioning.

The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.

In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an long-term delay minimization problem by jointly optimizing task offloading, power allocation, and computing resource allocation in UCMEC. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at least 176.99% and reduce the long-term average total delay by at least 16.36% compared to traditional cellular-based MEC.

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay and caching cost.

In this paper, fundamentals and performance tradeoffs of next-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem, which is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE), have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems, and the EP-EC maximization problem is proven to be equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework can reduce computational complexity compared to reference schemes and provide various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE.

Inter-user interference and pilot contamination are the major obstacles limiting the performance of cell-free massive multiple-input multiple-output (CF mMIMO)-aided ultra-reliable low-latency communication (URLLC) systems. In this paper, user grouping is utilized to address these issues, by allocating users to groups based on frequency band division, preventing interference among users within different groups, and eliminating pilot contamination between different groups. We consider an uplink CF mMIMO-aided URLLC system with user grouping and derive the lower bound for the ergodic rate. Due to the limited blocklength of each group necessitating pilot reuse, a weight sum rate (WSR) maximum problem is formulated by jointly optimizing user grouping, pilot assignment, and power control. We propose a user grouping scheme based on graph theory, where iteratively searching for specific negative loops in the weighted directed graph can approach the optimal user grouping matrix. As the user grouping matrix updates at each iteration, we update the pilot assignment matrix based on graph theory and employ logarithmic function approximation and fractional programming for power control updates. Numerous numerical results demonstrate the effectiveness of user grouping, and the proposed algorithm improves WSR by 25% compared to the non-grouping algorithm, while outperforming other benchmark algorithms.

Segment Routing over IPv6 (SRv6) is a promising efficient technology for traffic engineering (TE). As transitioning from a traditional distributed network to a full SRv6 network faces technical and economic challenges, partially deploying SRv6 has attracted much attention from academic communities. Many TE research attempts have been made on SRv6 deployment and routing optimization, among which Deep Reinforcement Learning (DRL) based algorithms have shown their advantages over traditional algorithms. However, with the incremental deployment of SRv6 nodes, training costs for DRL as well as solution space of routing optimization increase significantly, which obstructs the application of DRL-based algorithms in practice. To address this issue, we propose a DRL-based SRv6 deployment and routing optimization (SDRO) algorithm, with the TE objective of minimizing the maximum link utilization. In SDRO, the DRL agent is only trained once on a full SRv6 network and then used for different SRv6 deployment ratios. Hence, training costs can be obviously reduced. To reduce the solution space of routing optimization, the DRL agent performs routing pre-optimization on a portion of the traffic before routing is finally optimized by the Linear Programming method. By doing so, the execution time for routing optimization can be greatly reduced. Besides, for the issue of frequent link failures in the network, SDRO leverages the generalization of Graph Neural Networks to improve its robustness. Simulation results demonstrate that SDRO outperforms existing algorithms under different SRv6 deployment ratios and link failures, and completes routing optimization in a few seconds.

In this paper, we propose FoVCast, a 360° virtual reality (VR) video transmission framework, to implement efficient and robust performance for users with heterogeneous channels. In FoVCast, the overlapped field of views (FoVs) requested by multiple users are transmitted in the broadcast mode to improve the utilization efficiency of wireless bandwidth. To combat heterogeneous wireless broadcast environments, robust video transmission is exploited to provide users with received quality proportional to their channel conditions. The basic idea is to replace nonlinear quantization and coding with linear transform and power scaling to achieve graceful performance over heterogeneous channels. Furthermore, a joint power allocation and subcarrier matching problem is formulated to minimize the distortion of overlapped FoVs. To solve the problem efficiently, an auction-based collaborative optimization algorithm is proposed. Simulation results demonstrate that the proposed scheme can achieve significant performance gains in terms of peak signal-to-noise ratio (PSNR) compared with baseline schemes.

Reconfigurable intelligent surface (RIS)-enhanced secure wireless system has captured widespread concern recently. However, there is still a lack of research on the evaluation of security threats from the illegal RIS (IRIS) deployed by malicious eavesdroppers. In this paper, we investigate the signal leakage brought by IRIS in typical RIS-enhanced millimeter wave (mmWave) multiple-input multiple-output (MIMO) wiretap system. We firstly illustrate the detriment and challenges of IRIS with respect to system secrecy rate (SR) competition. The existence of IRIS aggravates the difficulty in detecting behaviors of the eavesdropper, making it infeasible to globally solve the SR competition problem. Therefore, we propose an artificial noise (AN)-based interference scheme to moderate the security degradation caused by IRIS. Specifically, the transmit precoder, receiver combiner and RIS discrete phase shifts are jointly designed by exploiting the interpolation search method and sparsity of mmWave channels for the sake of generating more AN. Simulation results demonstrate the non-negligible SR degradation caused by IRIS and validate the effective security enhancement of the proposed scheme. Besides, we emphatically analyze specific system factors that should be weighed to better combat IRIS especially when IRIS is powerful.

With the widely deployed video sensors, Internet of Video Things (IoVT) has emerged as a new paradigm of Internet of Things (IoT). Due to limited computing capacity of video sensors and multi-device wireless environments in IoVT, uplink video transmission faces challenges brought by complex coding and heterogeneous channel conditions. To combat these challenges, we propose a multi-device uplink uncoded video transmission scheme (MDUcast). Different from traditional encoded video transmission systems, MDUcast performs efficient linear operations instead of complex coding to reduce computing requirements on video sensors as well as guarantee the reconstructed video quality proportional to channel conditions in heterogenous environments. Furthermore, in MDUcast, an optimal power allocation strategy and a subcarrier scheduling algorithm based on matching theory are proposed to approach the near-optimal performance for multi-device uplink transmission, where both channel diversity and video content diversity are exploited. Simulation results demonstrate that MDUcast outperforms conventional Softcast and Parcast in terms of peak signal-to-noise ratio under various scenarios.

In view of the stringent requirements for delay in 5G usage scenarios, the existing congestion control schemes based on terminal measurement and evaluation cannot meet the requirements due to their reactive nature and blindness to links. To this end, we propose a buffer state-driven rate control mechanism in 5G networks, namely active queue management (AQM) based buffer delay guarantee (AQM-BDG) to explicitly adjust the sending rate of the source. Specifically, the rate mismatch and buffer state in Radio Link Control (RLC) layer are comprehensively considered, which timely grasp the dynamics of the buffer to obtain the direction and urgency of rate adjustment. Then the incoming packets are marked proportionally as "increase" or "decrease" to adjust the sending rate. Meanwhile, the adjustment quantum is reallocated among the different Service Data Flows (SDFs) that are mapped to the same Data Radio Bearer (DRB), so that every SDF can obtain the throughput share according to the preset priority, no matter what rate it starts at. Experimental results demonstrate that AQM-BDG significantly outperforms classical congestion control schemes on network utility by achieving relatively high throughput while bounding queuing delay within the threshold.

User-centric network (UCN) has been considered as a promising technology to improve the throughput of users, especially users at the cell edge, where each user is connected to and served by a group of access points. With the rapid growth of user’s uplink traffic demands, energy consumption has become an important issue in uplink UCN as user’s battery capacity is limited. To address this issue, in this research, we develop a reconfigurable intelligent surface (RIS)-enhanced uplink UCN, where RIS is exploited to improve the signal quality of uplink transmission for users with virtually no extra energy consumption. To approach an optimal energy efficiency, we jointly perform reflect beamforming at RISs and uplink power control at users. This is formulated into a non-convex and intractable energy efficiency maximization problem. To solve it effectively, we decompose it into two subproblems and carry out the optimization alternately. That is, we design the reflect beamforming matrices at RISs through fractional programming and an uplink power control at users through constructing a surrogate function via majorization-minimization algorithm. Numerical results demonstrate that the proposed algorithm could achieve significant gain both on energy efficiency and spectral efficiency compared to the benchmark algorithm.

In millimeter wave heterogeneous networks with integrated access and backhaul (mABHetNets), a considerable part of spectrum resources are occupied by the backhaul link, which limits the performance of the access link. In order to overcome such backhaul “ spectrum occupancy ”, we introduce cache into mABHetNets. Caching popular files at small base stations (SBSs) can offload the backhaul traffic and transfer spectrum from the backhaul link to the access link. To approach the optimal performance of cache-enabled mABHetNets, we first analyze the signal-to-interference-plus-noise ratio distribution and derive the average potential throughput (APT) expression by stochastic geometric tools. Then, based on our analytical work, we formulate a joint optimization problem of cache decision and spectrum partition to maximize the APT. Inspired by the block coordinate descent (BCD) method, we propose a joint cache decision, spectrum partition and power allocation (JCSPA) algorithm to approach the optimal solution. Numerical results show the effectiveness and enhancement of the proposed algorithm. Besides, we verify the APT under different parameters and find that the introduction of cache facilitates the transfer of backhaul spectrum to access link. Jointly deploying appropriate caching capacity at SBSs and performing specified spectrum partition can bring up about 90% APT gain in mABHetNets.

As a new development of Internet of Things (IoT), Internet of Video Things (IoVT) emerges to provide novel services based on video sensing, transmission, storage and analysis. However, IoVT also imposes massive computation and transmission on mobile edge computing (MEC) systems with a large amount of offloaded video data. To address this issue, in this paper, we propose a non-orthogonal multiple access (NOMA) assisted multi-MEC IoVT system. Although NOMA-assisted MEC systems have been proposed in existing studies, non-negligible inter-device interference caused by NOMA transmission has not been investigated, which is much more serious in multi-MEC IoVT systems. To combat them, we perform joint optimization on grouping and offloading in the proposed NOMA-assisted multi-MEC IoVT system. To achieve optimal performance, a utility minimization problem is formulated where the definition of utility leverages critical performance metrics including energy consumption and delay. We prove that this problem can be modeled as an exact potential game. Then, a selection algorithm is proposed to find the optimal strategies for each IoVT device by obtaining the Nash equilibrium. Specifically, power allocation for IoVT devices can be handled by a computation resource minimization problem. Simulation results demonstrate that the proposed algorithm achieves significant performance gains compared with existing schemes, i.e., at least 34.8% reduction in total utility and 14.5% less power consumption.

In tactical networks, link fluctuations and node movements make operation management a difficult problem. Software-defined networking (SDN) has the ability to manage wired networks, but for tactical networks, SDN faces challenges arising from unreliable transmission and inefficient operation of wireless links. Moreover, protocols in wireless tactical networks are heterogeneous to implement different tasks, while the existing Openflow protocol cannot handle such protocol heterogeneity. In this paper, we proposed a reliable dynamic control architecture based on protocol-oblivious forwarding (POF) in the wireless tactical network, which can increase the reliability of the control plane and the communication performance between switches of the data plane. Based on the proposed architecture, we customized a routing protocol, which can make data forwarding more efficient. Finally, we build a test platform based on Raspberry Pi to validate the architecture of the proposed architecture. The results demonstrate the proposed architecture achieves desired performance in dynamic tactical environments.

Adaptive bitrate (ABR) algorithms have been considered as efficient ways to improve the performance of video streaming by adaptively selecting appropriate video transmission bitrate. However, traditional ABR algorithms show poor adaptability to complex and dynamic networks, thus limit user’s quality of experience (QoE). In the case of multi-user streaming, multiple users simultaneously requesting video from the server often compete for a bottleneck link. The bandwidth sharing problem should also be carefully considered, otherwise it will result in poor fairness and insufficient bandwidth utilization. To address these issues, this paper proposes a multi-user adaptive bitrate algorithm (MUABR) based on multi-agent reinforcement learning. MUABR takes the overall QoE of multi-user as the optimization goal, designs neural network structure and training method by reinforcement learning, so as to solve the problem of bitrate selection and poor adaptability when performing video streaming. Furthermore, the bandwidth allocation strategy adopted in MUABR ensures fairness to a certain extent and improves bandwidth utilization. Comprehensive experimental results show that MUABR has reached the desired goal. Compared with Pensieve and Festive algorithms, MUABR can improve the average QoE of users by about 7.5%-24.6%.

As a promising technology with disruptive innovation, user-centric network can meet the data traffic demand and user service demand in the future mobile networks. It completely transforms the traditional cell-centric paradigm into user-centric paradigm, requiring the deployment of a large number of access points (APs). However, the massive deployment of APs leads to issues such as high hardware cost, huge power consumption and complex interference management. Reconfigurable intelligent surfaces (RIS), as another promising technology, can expand signal coverage, suppress interference, reduce hardware cost and power consumption. Inspired by this, we propose a novel RIS assisted user-centric network that uses RIS to replace some low utilization APs to reduce cost as well as power consumption, and deploy more RIS to achieve energy-efficient user-centric communication. In order to take full advantages of RIS, we jointly optimize passive beamforming at RIS and power control at AP to maximize the energy efficiency of the network. Since this problem is intractable and non-convex, an effectively alternating optimization algorithm, capitalizing on fractional programming and successive lower-bound maximization is proposed. The simulation results verify that the proposed algorithm outperforms reference algorithms in terms of energy efficiency and sum rate.

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions. In LensCast, a transmission distortion minimization problem is formulated with the consideration of video chunk scheduling, path matching and power allocation, which is an intractable mixed integer non-linear programming (MINLP) problem. The solution of this MINLP problem is converted into resource allocation (i.e., joint path matching and power allocation) plus chunk scheduling. First, resource allocation is investigated with given chunk scheduling results. By analyzing the optimality of the resource allocation problem, a winner-takes-all assignment is obtained to guide resource allocation. After that, a greedy water-filling algorithm is proposed as a near-optimal solution. Second, we propose a low-complexity chunk scheduling algorithm to schedule chunks for each transmission. Simulation results demonstrate that the proposed LensCast achieves an improved performance in terms of both peak signal-to-noise ratio and visual quality comparing with reference schemes.

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNE-RFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks.

The reliability of current virtual reality (VR) delivery is low due to the limited resources on VR head-mounted displays (HMDs) and the transmission rate bottleneck of sub-6 GHz networks. In this paper, we propose a dual-connectivity sub-6 GHz and mmWave heterogeneous network architecture empowered by mobile edge capability. The core idea of the proposed architecture is to utilize the complementary advantages of sub-6 GHz links and mmWave links to conduct a collaborative edge resource design, which aims to improve the reliability of VR delivery. From the perspective of stochastic geometry, we analyze the reliability of VR delivery and theoretically demonstrate that sub-6 GHz links can be used to enhance the reliability of VR delivery despite the large mmWave bandwidth. Based on our analytical work, we formulate a joint caching and computing optimization problem with the goal to maximize the reliability of VR delivery. By analyzing the coupling caching and computing strategies at HMDs, sub-6 GHz and mmWave base stations (BSs), we further transform the problem into a multiple-choice multi-dimension knapsack problem. A best-first branch and bound algorithm and a difference of convex programming algorithm are proposed to obtain the optimal and sub-optimal solution, respectively. Numerical simulations demonstrate the performance improvement using the proposed algorithms, and reveal that caching more monocular videos at sub-6 GHz BSs and more stereoscopic videos at mmWave BSs can improve the VR delivery reliability efficiently.

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

Integrated terrestrial-satellite networks (ITSNs) are the promising trends of future networks. However, there exist great challenges when performing video multicast in ITSNs due to the strong heterogeneity of users in comprehensive satellite coverage and the inter-system complex co-channel interference. To overcome these, we make the first attempt to propose an efficient cooperative robust video multicast (CRVM-ITSN) framework in ITSNs. The basic idea is to leverage the non-orthogonal multiple access technique in the cooperative transmission of ITSNs to achieve high-efficiency robust video multicast. The desired robust multicast performance realizes that the recovered video quality can adapt to diverse channel conditions of users. In CRVM-ITSN, to achieve the optimal cooperative transmission performance, power allocation and chunk scheduling of video data are jointly formulated as a distortion minimization problem, which is a non-convex mixed integer non-linear programming problem. To solve it, we design a provably convergent optimal algorithm by converting it to be convex. Besides, based on the theorem on optimal chunks selection of satellite cooperative transmission, a low-complexity chunk grouping algorithm is proposed to accelerate the optimal algorithm. Simulation results have demonstrated the superiority of proposed CRVM-ITSN against existing reference schemes, achieving about 4.1dB more gains in the recovered video quality.

Intelligent reflecting surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider a multi-IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable non-convex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 147% higher sum rate compared with the benchmark and 116% higher energy efficiency compared with amplify-and-forward relay.

In network function virtualization enabled networks with dynamic traffic, virtual network function (VNF) migration has been considered as an effective way to improve quality of service as well as resource utilization. However, due to time-varying network traffic, designing a fast and accurate VNF migration algorithm is still a great challenge. To address this issue, in this paper, we exploit the temporal convolutional network (TCN) to predict traffic flow for VNF migration decision in a fast and accurate manner. Based on the predicted results, we define a metric, i.e., migration index, to represent the load trend of each node in the network. A fast and efficient heuristic VNF migration algorithm is then proposed based on the migration index, with the goal to minimize the total migration cost in a time period. Extensive simulations are carried out to validate the effectiveness of TCN for traffic prediction. The results demonstrate that the proposed VNF migration algorithm can reduce the total migration cost up to 20% compared with existing algorithms.

Mobile edge computing provides users with a cloud environment close to the edge of the wireless network, supporting the computing intensive applications that have low latency requirements. The combination of offloading with the wireless communication brings new challenges. This paper investigates the service caching problem during the long-term service offloading in the user-centric wireless network. To meet the time-varying service demands of a typical user, a cooperative service caching strategy in the unit of the base station (BS) cluster is proposed. We formulate the caching problem as a time-averaged completion delay minimization problem and transform it into time-decoupled instantaneous problems with a virtual caching cost queue at first. Then we propose a distributed algorithm which is based on the consensus-sharing alternating direction method of multipliers to solve each instantaneous problem. The simulations validate that the proposed online distributed service caching algorithm can achieve the optimal time-averaged completion delay of offloading tasks with the smallest caching cost in the unit of a BS cluster.

In recent years, with the explosive growth of visual sensors and a large number of related video applications in Internet of Things (IoT), massive video data is generated by IoT devices. Since the volume of video data is far greater than traditional data in IoT, it is challenging to ensure high Quality of Service (QoS) for video uplinking in IoT. To address this challenge, we integrate non-orthogonal multiple access (NOMA) and scalable video coding (SVC) in IoT. To improve the video quality, we formulate a power allocation problem to maximize the average QoS in the proposed integrated system. Due to that the problem is non-convex, we transform it into a monotonic problem based on its hidden monotonicity. Then a power allocation algorithm based on polyblock outer approximation is proposed to solve the problem effectively. Finally, simulation results demonstrate that the proposed algorithm outperforms existing OMA and NOMA based schemes for video uplinking in IoT in terms of QoS and energy efficiency.

Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios.

To cope with rapid growth of video services, we propose a non-orthogonal multiple access (NOMA) based scalable video multicast (NOMA-SVM) framework for mobile networks, by exploiting NOMA's specific potential in scalable video multicast transmission. We consider statistical channels, instead of channels with perfect estimation, in the proposed NOMA-SVM framework in order to capture the realistic channel behaviors. As quality of experience (QoE) is a better metric than throughput for video transmission, QoE-driven power allocation is performed among multiple video layers in the proposed NOMA-SVM framework, in which users can decode video with quality proportional to their channel conditions. Specifically, we formulate the power allocation problem with the goal to maximize the average QoE over all users while guaranteeing the basic services of these users. To solve such a non-convex discrete problem, an optimal algorithm is developed based on the hidden monotonicity of the problem. A suboptimal algorithm is also proposed with much lower complexity in order to meet the practical needs. Simulation results show that the proposed algorithms outperform existing orthogonal multiple access (OMA) and NOMA based algorithms under various multicast scenarios in terms of QoE.

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.

In multi-cell non-orthogonal multiple access (NOMA) systems, designing an appropriate user grouping strategy is an open problem due to diverse quality of service (QoS) requirements and inter-cell interference. In this paper, we exploit both game theory and graph theory to study QoS-aware user grouping strategies, aiming at minimizing power consumption in downlink multi-cell NOMA systems. Under different QoS requirements, we derive the optimal successive interference cancellation (SIC) decoding order with inter-cell interference, which is different from existing SIC decoding order of increasing channel gains, and obtain the corresponding power allocation strategy. Based on this, the exact potential game model of the user grouping strategies adopted by multiple cells is formulated. We prove that, in this game, the problem for each player to find a grouping strategy can be converted into the problem of searching for specific negative loops in the graph composed of users. Bellman-Ford algorithm is expanded to find these negative loops. Furthermore, we design a greedy based suboptimal strategy to approach the optimal solution with polynomial time. Extensive simulations confirm the effectiveness of grouping users with consideration of QoS and inter-cell interference, and show that the proposed strategies can considerably reduce total power consumption comparing with reference strategies.

Non-orthogonal multiple access (NOMA) is promising to enable growing video services with requirements of massive traffic and low latency. In this paper, we investigate a novel multi-user NOMA system design for video delivery. Different from data delivery considered in existing NOMA systems, video distortion is taken into consideration for the design and optimization of the proposed scheme. We first propose a cross-layer scalable video delivery scheme over NOMA wireless networks based on given user grouping. By adopting a semi-analytical rate distortion model for encoded video sequences and investigating the physical layer model for the multi-carrier NOMA network, the distortion-aware power allocation problem is formulated with the goal to minimize the system end-to-end distortion with quality-of-service requirements. To solve such a non-convex problem, we design an efficient algorithm by exploiting its hidden monotonic property to approximate the optimal solution. Inspired by the decoding order in NOMA, a fast algorithm is developed by combining water-filling and greedy strategies to further tame the computational complexity. Simulations results have verified the advantages of the proposed cross-layer scheme with distortion-aware power allocation algorithms, against two existing NOMA schemes and an OMA scheme.

In millimeter wave (mmWave) systems, the advanced lens antenna array can effectively reduce the radio frequency chains cost. However, the mmWave signal is still vulnerable to blocking obstacles and suffers from severe path loss. To address this problem, we propose an intelligent reflecting surface (IRS) enhanced multi-user mmWave communication system with lens antenna array. Moreover, we attempt to optimize energy efficiency in the proposed system. An energy efficiency maximization problem is formulated where the transmit beamforming at base station and the reflect beamforming at IRSs are jointly considered. To solve this non-convex problem, we propose an algorithm based on the alternating optimization technique. In the proposed algorithm, the transmit beamforming is handled by the sequential convex approximation method and the reflect beamforming is optimized based on the quadratic transform method. Our simulation results show that the proposed algorithm can achieve significant energy efficiency improvement under various scenarios.

In this paper, we investigate an intelligent reflect surface (IRS) assisted multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to alleviate the blockage effect and enhance the performance of the mmWave system. Considering the impact of IRS on user association, we formulate a sum rate maximization problem by jointly optimizing the passive beamforming at IRS and user association, which is an intractable non-convex problem. Then an alternating optimization algorithm is proposed to solve the problem efficiently. In the proposed algorithm, passive beamforming at IRS is optimized by utilizing the fractional programming method and user association is solved through the network optimization based auction algorithm. We provide numerical comparisons between the proposed algorithm and different reference algorithms. Simulation results demonstrate that the proposed algorithm can achieve significant gains in the sum rate of all users.

Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization. However, the lack of future network information leads to non-optimal solutions, which motivates us to study reinforcement learning based SFC migration from a long-term perspective. In this paper, we formulate the SFC migration problem as a minimization problem with the objective of total network operation cost under constraints of users' quality of service. We firstly design a deep Q-network based algorithm to solve single SFC migration problem, which can adjust migration strategy online without knowing future information. Further, a novel multi-agent cooperative framework, called MSDF, is proposed to address the challenge of considering multiple SFC migration on the basis of single SFC migration. MSDF reduces the complexity thus accelerates the convergence speed, especially in large scale networks. Experimental results demonstrate that MSDF outperforms typical heuristic algorithms under various scenarios.

Index modulation (IM) brings the reduction of power consumption and complexity of transmitter to classical multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, due to the introduction of IM, the complexity of the detector at receiver is greatly increased. Furthermore, the detector also requires the channel state information at receiver (CSIR), which leads to high system overhead. To tackle these challenges, in this paper, we introduce deep learning (DL) in designing a non-iterative detector. Specifically, based on the structural sparsity of the transmitted signal in IM aided MIMO-OFDM (IM-MIMOOFDM) systems, we first formulate the detection process as a sparse reconstruction problem. Then, a DL based detector called IMNet, which combines two subnets with the traditional least square method, is designed to recover the transmitted signal. To the best of our knowledge, this is the first attempt that designs the DL based detector for IM-MIMO-OFDM systems. Finally, to verify the adaptability and robustness of IMNet, simulations are carried out with consideration of correlated MIMO channels and imperfect CSIR. The simulation results demonstrate that the proposed IMNet outperforms existing detectors in terms of bit error rate and computational complexity under various scenarios.

Edge caching has been regarded as an effective way to relieve backhaul pressure as well as reduce service delivery delay. In this paper, we attempt to improve user’s quality of experience (QoE) of video services in cache-enabled heterogeneous networks (HetNets). Scalable video coding (SVC) based video services are considered and hence the proper video quality can be selected for each user according to its channel conditions. Intuitively, caching video layers that cannot be delivered to users leads to the waste of limited cache resource. Based on this observation, we formulate the joint video quality selection and caching problem, with the goal to maximize user’s QoE. Furthermore, we propose a two-fold dynamic programming algorithm to approach the optimal video quality selection and caching strategies. Finally, the simulation results demonstrate that the proposed strategies can utilize the limited cache space more effectively and achieve more QoE gains compared to existing caching strategies.

Recently, a mmWave-based access and backhaul integration heterogeneous networks (HetNets) architecture (mABHetNets) has been envisioned to provide high wireless capacity. Since the access link and the backhaul link share the same mm-wave spectral resource, a large spectrum bandwidth is occupied by the backhaul link, which hinders the wireless access capacity improvement. To overcome the backhaul spectrum occupation problem and improve the network throughput in the existing mABHetNets, we introduce the cache at base stations (BSs). In detail, by caching popular files at small base stations (SBSs), mABHetNets can offload the backhaul link traffic and transfer the redundant backhaul spectrum to the access link to increase the network throughput. However, introducing cache in SBSs will also incur additional power consumption and reduce the transmission power, which can lower the network throughput. In this paper, we investigate spectrum partition between the access link and the backhaul link as well as cache allocation to improve the network throughput in mABHetNets. With the stochastic geometry tool, we develop an analytical framework to characterize cache-enabled mABHetNets and obtain the signal-to-interference-plus-noise ratio (SINR) distributions in line-of-sight (LoS) and non-line-of-sight (NLoS) paths. Then we utilize the SINR distribution to derive the average potential throughput (APT). Extensive numerical results show that introducing cache can bring up to 80% APT gain to the existing mABHetNets.

Recently, pseudo-analog transmission based on SoftCast has been proposed to improve the received quality of video/image by eliminating the cliff effect in traditional digital transmission. In this paper, we propose a Compressed Pseudo-analog Transmission System (ComPaTS) for remote sensing images over bandwidth-constrained wireless channels. This novel scheme is developed based on the observation that the inherent dropping strategy in pseudo-analog transmission is impractical for remote sensing images in which the transmission bandwidth is generally insufficient. In ComPaTS, to guarantee the content diversity gain under pseudo-analog transmission, block-based Compressive Sensing (CS) is applied to the wavelet domain of each remote sensing image, where the sampling ratio is proportional to the importance of different blocks. The main work of ComPaTS is to leverage the sampling ratio in block-based CS and the resource allocation in pseudo-analog transmission in order to minimize system distortion. Two components of system distortion, i.e., source distortion and channel distortion are analyzed respectively. To characterize the coupling relationship between these two different types of distortion, a joint bandwidth-power distortion optimization problem is formulated. Furthermore, we also propose a two-stage allocation algorithm to solve the problem efficiently. The simulation results demonstrate that the proposed ComPaTS scheme significantly outperforms reference schemes in terms of peak signal-to-noise ratio under different bandwidth-constrained scenarios.

For video streaming, Adaptive BitRate (ABR) algorithms are usually used to improve end-to-end user’s Quality of Experience (QoE). Many of the state-of-the-art ABR algorithms are based on simplified models, leading to conservative predictions of real situations. To optimize the QoE in complex end-to-end transmission environments, ABR algorithms based on Deep Reinforcement Learning (DRL) has shown a great improvement compared to traditional algorithms. However, the slow convergence of existing DRL-based ABR algorithms limits the QoE performance under dynamic video streaming environments. In this paper, we propose Fastconv, a novel DRL-based ABR algorithm that has a fast convergence speed to ensure a satisfactory QoE performance. Our work can be mainly divided into two parts. First, we preprocess the input data with large fluctuation in order to obtain the steady input and reduce the indeterminacy of convergence. Second, in order to reduce the structural complexity of the neural network itself and the number of parameters, we propose a neural network architecture based on multiplexed convolution kernel. Experiment results based on a real traced mobile dataset have demonstrated that Fastconv outperforms both the traditional and DRL-based ABR algorithms in terms of QoE.

To alleviate severe inter-user interference in Non-orthogonal multiple access (NOMA) systems, in practice, several users are grouped together for NOMA transmission and orthogonal resources are allocated among different groups. In this paper, we investigate uplink NOMA scenarios with multiple base stations, where user association is jointly considered with user grouping. It is worth noting that a user prefers to be associated with the base station where it can be assigned into a group with less inter-user interference. Furthermore, different users have different quality-of-service (QoS) requirements. Based on these observations, we firstly quantitatively analyze the extra transmit power that a user brings to the others in the same group under QoS constraints, i.e., externalities, and derive an externality function to indicate interference among users in uplink NOMA. Then, we obtain a game model for the joint user association, grouping and power allocation (JAGP) problem in uplink NOMA systems and prove that it is an exact potential game, with the goal to minimize total transmit power. Furthermore, we prove the existence of Nash Equilibrium (NE) as well as the finite improvement property (FIP) of this game. In addition, the NE of this game is proved to be Pareto-optimal. A heuristic algorithm is proposed to find the solution to the JAGP problem by achieving the NE of the proposed game, which convergence is guaranteed by FIP. Simulation results show that the proposed algorithm can considerably reduce the total transmit power compared with existing schemes.

Recently, in order to achieve performance improvement in scenarios where the channel is either unknown, or too complex for an analytical description, Neural Network (NN) based channel estimation is introduced in Orthogonal Frequency Division Multiplexing (OFDM) systems. However, this kind of learning method is not reliable enough when the conditions of online deployment of the common NNs are not consistent with the channel models used in the training stage. Furthermore, common NNs need plenty of data as well as time to be trained, which is not suitable for the OFDM communication network with time varying channels. To tackle these challenges, we propose a novel meta learning based channel estimation approach called RoemNet. The most distinctive characteristic of RoemNet is that it involves a meta-learner that can learn from the environment of different channels. With the update of meta-learner, RoemNet is robust enough to solve new channel learning tasks using only a small number of pilots. Furthermore, RoemNet can alleviate the effect of Doppler spread and significantly improve the Bit Error Ratio (BER) performance under different channel environments. Experiment results demonstrate that the proposed RoemNet outperforms existing channel estimation methods including existing learning methods under various scenarios.

To meet the performance and complexity requirements from practical deployment of non-orthogonal multiple access (NOMA) systems, several users are grouped together for NOMA transmission while orthogonal resources are allocated among groups. User grouping strategies have significant impact on the power consumption and system performance. However, existing related studies divide users into groups based on channel conditions, where diverse quality of service (QoS) and interference have not been considered. In this paper, we focus on the interference-aware user grouping strategy in NOMA systems, aiming at minimizing power consumption with QoS constraints. We define a power consumption and externality (PCE) function for each user to represent the power consumption involved by this user to satisfy its QoS requirement as well as interference that this user brings to others in the same group. Then, we extend the definition of PCE to multi-user scenarios and convert the user grouping problem into the problem of searching for specific negative loops in the graph. Bellman-Ford algorithm is extended to find these negative loops. Furthermore, a greedy suboptimal algorithm is proposed to approach the solution within polynomial time. Simulation results show that the proposed algorithms can considerably reduce the total power consumption compared with existing strategies.

Non-orthogonal multiple access (NOMA) has shown potential for scalable multicast of video data. However, one key drawback for NOMA-based video multicast is the limited number of layers allowed by the embedded successive interference cancellation algorithm, failing to meet satisfaction of heterogeneous receivers. We propose a novel receiver-driven superposed video multicast (Supcast) scheme by integrating Softcast, an analog-like transmission scheme, into the NOMA-based system to achieve high bandwidth efficiency as well as gradual decoding quality proportional to channel conditions at receivers. Although Softcast allows gradual performance by directly transmitting power-scaled transformation coefficients of frames, it suffers performance degradation due to discarding coefficients under insufficient bandwidth and its power allocation strategy cannot be directly applied in NOMA due to interference. In Supcast, coefficients are grouped into chunks, which are basic units for power allocation and superposition scheduling. By bisecting chunks into base-layer chunks and enhanced-layer chunks, the joint power allocation and chunk scheduling is formulated as a distortion minimization problem. A two-stage power allocation strategy and a near-optimal low-complexity algorithm for chunk scheduling based on the matching theory are proposed. Simulation results have shown the advantage of Supcast against Softcast as well as the reference scheme in NOMA under various practical scenarios.

To cope with rapid growth of video traffic and energy consumption in mobile networks, we propose a spatial modulation (SM) and non-orthogonal multiple access (NOMA) integrated system for multi-user video transmission (SNIS-MUVT). SNIS-MUVT attempts to exploit the benefits from SM and NOMA to achieve high energy efficiency and high throughput. The main contribution of SNIS-MUVT lies in cross-layer design for video-aware transmission and quality of experience (QoE)-driven power allocation. With scalable video coding, base layers (BLs) are transmitted in the spatial domain while enhancement layers (ELs) are transmitted in the signal domain. In the spatial domain, layered SM is performed to support multi-user transmission according to the required BL rates and channel conditions of different users. In the signal domain, ELs are first extracted based on the estimated channel capacity. Then, the extracted ELs are handled by unequal error protection according to their importance before superposed for NOMA transmission. We formulate the QoE-driven power allocation problem in SNIS-MUVT. Both optimal algorithm based on the hidden monotonicity of the problem and suboptimal iterative algorithm with polynomial time are proposed. Finally, simulation results demonstrate that the proposed SNIS-MUVT outperforms existing SM and SM-NOMA integrated schemes in terms of QoE under various scenarios.

Enabling caching capabilities in dense small-cell networks (DSCNs) has a direct impact on file delivery delay and power consumption. Most of the existing works studied these two performance metrics separately in cache-enabled DSCNs. However, file delivery delay and power consumption are coupled with each other and cannot be minimized simultaneously. In this paper, we investigate the optimal tradeoff between these two performance metrics. First, we formulate the joint file delivery delay and power consumption optimization (JDPO) problem where power control, user association, and file placement are jointly considered. Then, we convert it into a form that can be handled by the generalized Benders decomposition (GBD). With GBD, we decompose the converted JDPO problem into two smaller problems, i.e., the primal problem related to power control and the master problem related to user association and file placement. An iterative algorithm is proposed and proved to be an $\epsilon $ -optimal, in which the primal problem and the master problem are solved iteratively to approach the optimal solution. To further reduce the complexity of the master problem, an accelerated algorithm based on semi-definite relaxation is proposed. Finally, the simulation results demonstrate that the proposed algorithm can approach the optimal tradeoff between file delivery delay and power consumption.

Fiber-Wireless (FiWi) access networks have been widely deployed due to the complementary advantages of high-capacity fiber backhaul and ubiquitous wireless front end. To meet the increasing demands for bandwidth-hungry applications, access points (APs) are densely deployed and new wireless network standards have been published for higher data rates. Hence, fiber backhaul in FiWi access networks is still facing the incoming bandwidth capacity crunch. In this paper, we involve caches in FiWi access networks to cope with fiber backhaul bottleneck and enhance the network throughput. On the other hand, power consumption is an important issue in wireless access networks. As both power budget in wireless access networks and bandwidth of fiber backhaul are constrained, it is challenging to properly leverage power for caching and that for wireless transmission to achieve optimal system performance. To address this challenge, we formulate the downlink wireless access throughput maximization problem by joint consideration of power allocation and caching strategy in FiWi access networks. To solve the problem, firstly, we propose a volume adjustable backhaul-constrained waterfilling method (VABWF) to derive the expression of optimal wireless transmission power allocation. Then, we reformulate the problem as a multiple-choice knapsack problem (MCKP) and propose a dynamic programming algorithm to find the optimal solution of the MCKP problem. Simulation results show that the proposed algorithm significantly outperforms existing algorithms in terms of system throughput under different FiWi access network scenarios.

Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However, traditional optimization based ICIC schemes suffer from severe performance degradation with complex interference pattern. To address this issue, we propose a Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework for ICIC in wireless networks. DRL- DPT overcomes the main obstacles in applying reinforcement learning and deep learning in wireless networks, i.e. continuous state space, continuous action space and convergence. Firstly, a Deep Neural Network (DNN) is involved as the actor to obtain deterministic power control actions in continuous space. Then, to guarantee the convergence, an online training process is presented, which makes use of a dedicated reward function as the target rule and a policy gradient descent algorithm to adjust DNN weights. Experimental results show that the proposed DRL-DPT framework consistently outperforms existing schemes in terms of energy efficiency and throughput under different wireless interference scenarios. More specifically, it improves up to 15% of energy efficiency with faster convergence rate.

With the explosive increment in mobile users and mobile devices, nowadays network infrastructures are foreseen to be much more struggle in handling the vast mobile video traffic in the network. To meet this challenge, small-cell base stations (SBSs) are introduced in heterogeneous networks (HetNets) to support high data rate services in the fronthaul. However, HetNets still suffer from backhaul bandwidth shortage. Caching is a promising way to relieve the bandwidth burden on the backhaul. However it may fail in improving user's quality of experience (QoE) in video services. The reason is that, without a corresponding power allocation strategy in the fronthaul, users are unlikely to be allocated sufficient power to receive the higher quality version of videos, which in return leads to the waste of the limited caching space. On the other hand, the distinct layered feature of scalable video encoding (SVC) videos allows us to do layer-wise caching to further promote the cache utilization efficiency. Thereupon, a nature idea is to combine the power allocation strategy and the layer-wise caching strategy to effectively utilize the limited power and cache space of SBSs, so that the limited bandwidth of backhaul can be saved while user's QoE can be improved. In this work, we take the layered feature of SVC videos into account to jointly optimize these two strategies, aiming to maximize user's QoE. A simulated annealing based algorithm is proposed to solve the formulated problem. Simulation results show that, by doing so, the cache-hit ratio is increased while the limited backhaul bandwidth can be saved. Moreover, user's QoE has been significantly improved compared to traditional caching strategies.

Recently, non-orthogonal multiple access (NOMA) has been proposed to improve spectral efficiency greatly over conventional orthogonal multiple access (OMA). However, it is still challenging to implement NOMA into transmission of video. In this research, we investigate video broadcasting design over NOMA systems. When video transmission is considered, quality of experience (QoE) is a better metric for NOMA design than throughput. Therefore, we design a QoE-driven scalable video broadcasting (SVB) scheme over NOMA systems. To achieve scalable transmission performance, power allocation is optimized among multiple video layers, which are requested by users with different channel conditions. Specifically, we formulate the power allocation problem to maxmize the system overall QoE while guaranteeing the basic service of users. Although this problem is non-convex and discontinuous, an optimal algorithm is proposed based on the hidden monotonicity of the problem. Simulation results show that the proposed SVB scheme over NOMA systems outperforms existing OMA and NOMA based schemes in various broadcast scenarios in terms of QoE.

Recently, non-orthogonal multiple access (NOMA) has been proposed to achieve higher spectral efficiency over conventional orthogonal multiple access. Although it has the potential to meet increasing demands of video services, it is still challenging to provide high performance video streaming. In this research, we investigate, for the first time, a multi-user NOMA system design for video transmission. Various NOMA systems have been proposed for data transmission in terms of throughput or reliability. However, the perceived quality, or the quality-of-experience of users, is more critical for video transmission. Based on this observation, we design a quality-driven scalable video transmission framework with cross-layer support for multiuser NOMA. To enable low complexity multi-user NOMA operations, a novel user grouping strategy is proposed. The key features in the proposed framework include the integration of the quality model for encoded video with the physical layer model for NOMA transmission, and the formulation of multiuser NOMA-based video transmission as a quality-driven power allocation problem. As the problem is non-concave, a global optimal algorithm based on the hidden monotonic property and a suboptimal algorithm with polynomial time complexity are developed. Simulation results show that the proposed multi-user NOMA system outperforms existing schemes in various video delivery scenarios.

Recently, a novel uncoded transmission system called SoftCast has attracted great interest from academia, which can eliminate the cliff effect in digital transmission. Since the whole system is linear, SoftCast is intrinsically scalable to channel conditions. However, the resolution heterogeneity in the uncoded video broadcast system has not been well studied as far as we know. In this paper, we propose a Spatial Scalability Enabled Soft Video Broadcast (SSESVB) system based on SoftCast, aim to accommodate diverse users with heterogeneous channel conditions and heterogeneous resolutions simultaneously. Existing solutions for the resolution heterogeneity issue are previously designed for digital source coding, which cannot fully exploit performance gains from uncoded transmission. Instead, in the proposed SSESVB system, a novel spatial decomposition method based on linear projection is specially designed for uncoded transmission. Then a power distortion optimization problem is formulated and solved with the goal to minimize the average distortion of multiple users with heterogeneous channel conditions and heterogeneous resolutions. The simulation results demonstrate that SSESVB achieves more than 5.8 dB gain over compared schemes (i.e., SoftCast, Discrete Wavelet Transform (DWT) based method, and Scalable Video Coding (SVC)) in terms of average Peak Signal-to-Noise Ratio (PSNR) under heterogeneous scenarios.

In the context of Service Function Chain (SFC), SFC Mapping (SFCM) problem has a decisive impact on the resource utilization efficiency of physical networks, where node resources and link resources are concerned. However, most existing work on the SFCM problem has not taken into account difference in the amount of node resources and link resources. This kind of difference might result in some nodes that cannot be mapped because of insufficient link resources around these nodes. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by reducing resource fragmentation in physical networks, where SFCM is performed. First and most importantly, we propose a metric called Resource Fragmentation Degree (RFD) to quantify resource fragmentation. The basic idea behind RFD is that the resource availability of a node is determined by the residual link resources around the node. Based on RFD, we formulate the SFCM problem with goal of minimizing resource fragmentation. Furthermore, we also propose an efficient online heuristic to find the optimal mapping strategy. Simulation results show that much more SFC requests can be accepted by reducing resource fragmentation in physical networks and the proposed algorithm achieves more than 25% higher acceptance ratio compared with existing algorithms.

With global coverage abilities, satellite networks are expected to provide users with ubiquitous data services. However, routing in satellite networks faces more challenges due to the satellite movement. Fortunately, the satellite movement can be predicted by orbit calculation. Based on this characteristic, a lot of existing routing algorithms use Temporal Graph Model (TGM) to calculate instantaneous satellite network topologies at discrete times as priori knowledge for routing decision. In this case, high computation cost will be involved. In this paper, we propose a novel Temporal Netgrid Model (TNM) to represent the time-varying satellite network topology. In TNM, the whole space is divided into small cubes (i.e. netgrids) and then satellites can be located by netgrids instead of coordinates. By doing so, TNM reduces the computation complexity from O(N2) to O(N2) compared with TGM. Furthermore, an Earliest Arrival Space Routing (EASR) algorithm is proposed, which attempt to find the earliest arrival paths from the source node to any other reachable nodes with low computation cost. Simulations are performed to validate the effectiveness of the proposed routing algorithm. Results show that EASR algorithm achieves a significant reduction in computation complexity as well as acceptable routing performance in terms of data delivery ratio.

With ability to eliminate the cliff effect and achieve graceful degradation, analog-like transmission such as SoftCast has become a hot research issue for robust image/video delivery over wireless channels. However, it has not been well studied in the case of bandwidth compression, which usually occurs in bandwidth-constrained wireless environments such as satellite communication scenarios. In this paper, we propose an analoglike robust transmission scheme based on Compressive Sensing (CS) for satellite image delivery over bandwidth-constrained wireless channels. The motivation for integration of CS in the proposed scheme lies in the fact that the simple dropping strategy is not optimal for satellite image transmission when bandwidth is insufficient. Considering high information entropy and rich structure information properties of satellite images, we perform an amplitude offset operation and the block-based CS (BCS) procedure to improve the energy efficiency and meet the bandwidth budget respectively. We analyze the system distortion of the proposed scheme and formulate the distortion minimization problem as a resource allocation problem. Then, we propose an efficient two-step strategy to find the optimal solution for bandwidth and power allocation. The simulation results show that the proposed scheme achieves up to 5.5dB gain over the state-of-the-art transmission schemes for satellite image delivery.

With the development of satellite communication technologies and user requirements for pervasive network access, there is a trend to integrate satellites into the terrestrial network infrastructure. Such kind of satellite-terrestrial network is often used for content delivery services as satellites are with wide-area coverage. In terrestrial networks such as Internet, in-network caching has been proved to be an effective method to improve the network performance in terms of throughput and delay. Based on this observation, we involve caches in the satellite-terrestrial networks. Particularly, a two-layer caching model is proposed for content delivery, where caches placed in the ground stations constitute the first caching layer and caches deployed in the satellite forms the second one. On the satellite, to make full use of the broadcast advantage, we set a window to aggregate the requests for the same files from ground stations. These requests will be served by one satellite broadcasting when the aggregation window expires. Our goal is to minimize the downlink and uplink satellite bandwidth consumption, which requires joint caching optimization between the satellite and ground stations. We formulate the joint caching optimization problem as a nonlinear integer programming problem. Furthermore, a caching strategy based on the genetic algorithm is proposed to solve the problem efficiently. The simulation results show that the proposed caching strategy significantly outperforms content popularity based and random caching strategies in terms of satellite bandwidth consumption.

Due to the dense deployment of low-cost small base stations (SBSs), the serious interference among small cells has become a great challenge, which will decrease the system throughput significantly. Most of traditional time-domain inter-cell interference coordination (ICIC) schemes use almost blank subframes (ABS) to completely avoid the inter-cell interference. We observe that users can suffer a certain amount of interference as long as their quality of service (QoS) is satisfied. Therefore, we can improve the system throughput by reducing the transmission power instead of using ABS. Based on the above fact, we propose a power control based time-domain ICIC scheme in dense small cell networks (DSCNs) with the objective to maximize the downlink system throughput as well as guarantee users' QoS. In the proposed scheme, each SBS adopts an adaptive ABS ratio according to its traffic load condition. Then, a Q-learning based algorithm is proposed to get the optimal transmission power of the ABS subframes. Simulation results demonstrate the significant advantages of the proposed scheme over the baseline schemes in the existing literature, in terms of downlink system throughput in DSCNs.

We propose an adaptive hybrid digital-analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital-analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios.

Due to the quantization errors introduced by source coding, the transmission performance in digital communication systems might remain constant when the channel quality gets better, which is referred to as the quality saturation effect. To combat this effect, hybrid digital-analog (HDA) transmission is proposed, where the quantization errors are transmitted in an analog mode to achieve graceful improvement for better channels. In this paper, we focus on HDA transmission in wireless relay networks (WRNs). We propose a power allocation scheme for HDA transmission in a three-node WRN (3-WRN) to eliminate the quality saturation effect and improve the distortion performance. It should be emphasized that HDA transmission involves not only digital-analog power allocation but also source-relay power allocation. Therefore, the joint power allocation is considered. We formulate the joint power allocation problem as a nonlinear fractional programming problem. Then, we propose an effective algorithm to search the solution and obtain the optimal distortion performance. Finally, we conduct simulations to validate our work. The results show that the proposed joint power allocation scheme outperforms existing schemes in terms of end-to-end distortion under various channel conditions.

In cellular/WLAN integrated networks, WLAN APs are usually deployed to offload the heavy traffic of cellular networks. Due to the inherent asymmetry of downlink and uplink in WLAN, competitions increase rapidly along with uplink traffic being offloaded to WLAN, which degrades system performance significantly. In this paper, we propose a new access scheme, i.e. uplink traffic cellular-first access (UTCFA) scheme. By offloading uplink traffic within WLAN coverage to contention-free cellular networks as much as possible, UTCFA scheme improves WLAN channel utilization significantly. We derive the throughput produced by UTCFA by theoretical analysis and perform extensive simulations to validate these theoretical results. Particularly, we find that the well-known WLAN-first access scheme is a special case of the proposed scheme. The simulation results also show that UTCFA can significantly improve the system performance and provide better support for quality of service guaranteed services.

Studies have shown that the size of the transmitted packet has a significant impact on the performance of wireless networks, especially those in challenged environments such as Delay Tolerant Networks (DTNs) where bandwidth is a precious resource. In order to improve the end-to-end goodput in DTNs, segment size at the convergence layer and bundle size at the bundle layer are jointly optimized in this paper. Different from existing work that only performs experimentations to find the optimal packet size in single-hop DTNs, our work focuses on the theoretic analysis and formulates the relationship between packet size at two layers (i.e., the convergence layer and the bundle layer) and the performance metrics (i.e., delay, goodput) of single-hop and multi-hop DTNs. The analysis not only confirms the existence of the optimal packet size, but also quantitatively shows that the optimal packet size is determined by the number of hops, wireless channel conditions in DTNs. Furthermore, a novel Packet Size Optimization Algorithm (PASO) is proposed, in which segment size and bundle size are jointly adjusted to maximize the goodput in multi-hop DTNs. Compared with schemes where joint packet size optimization is not considered, PASO can achieve average 15% more goodput in typical DTN environments where channel conditions are usually assumed to be poor.

Virtual Network Embedding (VNE) is one of the critical techniques in network virtualization. In this paper, we attempt to improve the performance of VNE in terms of acceptance ratio of Virtual Network (VN) requests by considering resource fragmentation in a substrate network. Different from existing work, we involve a new metric called Resource Fragmentation Degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The definition of RFD is derived from the conception of connectivity in graph theory. Actually, RFD of a node (or a link) is only determined by residual resources at its neighbor nodes and adjacent links. Thus it can be derived efficiently. For a node (or a link), maximum RFD is reached when either all of its neighbor nodes or all of its adjacent links are running out of resources. In this case, resources at the node (or the link) are entirely fragmented. With consideration of RFD, we then formulate the problem of VNE as a mixed integer program. The optimization objective includes minimizing fragmented resources indicated by RFD in the substrate network. Finally, an online algorithm called VNE-RFD is proposed to solve the problem. Simulation results show that VNE-RFD can effectively reduce resource fragmentation and thus accept more VN requests compared with some existing algorithms.

This paper proposes an adaptive hybrid digital-analog video transmission scheme to eliminate cliff effect and improve user experience in wireless mobile networks. To achieve graceful degradation performance, each Group of Pictures (GOP) is transformed and efficiently compressed into a reference frame and several Motion Vector (MV) frames with Motion-Compensated Temporal Filtering (MCTF). Then, the critical reference frame is transmitted as base data in a reliable digital mode. MV frames in units of chunks are transmitted as enhancement data in an analog mode where power allocation is optimized among chunks based on their entropy. To combat channel fading, each chunk in MV frames is further divided into subbands with consideration of the channel coherence time. Then, Adaptive Power Distortion Optimization (APDO) with channel prediction is performed among the subbands within each chunk to provide error resilience with adaptive scaling factors varying with channel conditions. Simulation results show that the proposed scheme outperforms Softcast (about 5.6 ~ 10.3dB) and Parcast (about 0.9 ~ 6.1dB) in terms of Peak Signal-to-Noise Ratio (PSNR) under various degrees of channel fading. Furthermore, the proposed scheme also works effectively when coping with channel fluctuation in high mobility scenarios.

Accurate modeling of multimedia traffic is important for optimizing resource allocation such that Quality of Service (QoS) can be most satisfied with constrained resource in wireless networks. Multimedia traffic is demonstrated to exhibit self- similar nature. We take account of this kind of characteristic and propose a comprehensive analytical model subject to variable capacity in wireless networks from the viewpoint of queueing theory. Different from existing work on QoS analysis of self-similar traffic, we focus on practical multimedia traffic where packet size is variable. In the proposed analytical model, the self-similar behavior of multimedia traffic is simulated by the fractional Brownian motion (fBm). Furthermore, packet size is assumed to follow the heavy-tailed distribution (e.g. Pareto distribution). Finally, the closed-form expressions for QoS metrics (i.e. the tail distribution of queueing delay and packet loss probability) for self-similar traffic with variable packet size are derived. Our performance evaluation shows that the results from our analysis are consistent with those from simulations. Also, the effect of packet size on QoS metrics is carefully explored.

In the pioneering works, network coding has shown great potential to improve the performance of wireless networks. However, most of them are implemented under specific traffic pattern. Since various traffic patterns will affect the promising gains of network coding, in this paper, we study how traffic pattern affects the benefits of general practical network coding scheme-Multi-hop Network Coding (MNC) in wireless networks. First, we analyze the idiosyncratic properties of MNC and raise two practical issues of MNC neglected before: iteration coding and path division. Subsequently, we put forward traffic factors to describe some characteristics of realistic traffic flows, and then qualitatively analyze the relations between the traffic factors and the gains of MNC. In addition, throughput boundary with MNC is formulated in the perspective of flows. Our evaluation also shows that particular MNC should be applied according to different traffic patterns to maximize network throughput and reduce the systematic overhead.

For media streaming, throughput are inadequate to fully determine media playback quality at receivers. Packet loss behavior is also crucial in designing a high performance media streaming system. In this paper, we carry out analysis on packet loss behavior when network coding is performed at the Base Station (BS) with limited buffer in a wireless broadcast network. Two types of packet loss at the BS are considered, namely packet loss at the input introduced by buffer overflow (Lin), and packet loss at the output due to channel errors and late arrivals (Lout). Our analysis is based on a bulk-service queue model denoted as M/G(a, b)/l/B. The major contribution of this research is the successful derivation of the probability distribution of queue states at an arbitrary time by applying the supplementary variable technique. Given particular channel conditions, this probability distribution enables us to obtain the relationship between two types of packet loss (Lin,Lout) and the supportable arrival rates at the BS. Then, the maximum allowable arrival rate when Linand Loutare constrained can be determined. This is critical for the design of appropriate congestion control for media streaming at the BS. Extensive simulations have been performed to demonstrate that the results from our analysis are consistent with that from simulations.

In this paper, we propose a novel analytical framework for Roadside-to-Vehicle (R2V) media streaming to investigate the interruption probability when playback is performed at a vehicle. The results from such analysis can be used to design an R2V media streaming system with minimum interruption probability. We first introduce a general time-dependent queue model G(t)/G/1 to describe the playout buffer at the vehicle. This model characterizes the dynamic wireless channel conditions in a vehicular environment with time-varying packet arrival intervals. Based on the statistically monotonic behavior of the channel rates as the vehicle moves towards and away from a roadside Access Point (AP), we are able to derive the upper and lower bounds of the interruption probability through a time-dependent queuing analysis with diffusion approximation when the startup state is given. The parameters of startup state include startup delay and the number of cached packets in the playout buffer before playback. We divide the period during which the vehicle is guaranteed with low playback interruption probability into small time intervals. This way, the analysis of the G(t)/G/1 system can be decomposed into a series of transient analysis of stationary G/G/1 systems in a sequential fashion. For each stationary G/G/1 system, diffusion approximation with specified initial conditions is applied to obtain the transient queue length and the interruption probability of the playback. This scheme can be easily implemented since it only requires limited statistical information about fading channel and the playback statistics, such as mean and variance of the rate. The proposed analytical framework has been validated to show accurate characterization of the R2V media streaming system with extensive simulations.

In urban areas such as street blocks, media-rich services are delivering to moving vehicles through Access Points (APs) deployed on the roadside. To increase the capacity of wireless links for these services, a roadside-to-vehicle communication system based on a novel Stateful Scheduling with Network Coding (SSNC) strategy is proposed. A major innovation of SSNC is that it enables the scheduling on the roadside AP to fully utilize the states of received data from vehicles for an enhanced performance. Furthermore, a set of mechanisms are proposed to ensure reliable transmission in such highly dynamic and error-prone wireless channels. Extensive simulations show that SSNC achieves an increased throughput gain between 1.2 and 2.0 at various conditions.

The fast handovers for mobile IPv6 protocol has been proposed to improve handover latency due to mobile IPv6 procedures. However, in practice, the performance of fast handovers suffers from problems such as the predictive latency and reactive loss. To address these problems, in this paper, cross-layer interactions between the link layer and the network layer are introduced. First, we propose a network controlled link layer trigger on the mobile node to reduce the predictive latency. Moreover, the network controlled link layer trigger can also configure a reduced set of potential channels during scanning. Further, we propose a link triggered buffering mechanism on the access router to alleviate packet loss in reactive fast handover. Experiments in an IEEE 802.11 based test-bed show that the proposed schemes improve the performance of fast handovers in terms of handover latency and packet loss.