The dense deployment of Integrated Access and Backhaul (IAB) networks exacerbates spectrum consumption. This paper aims to enhance Spectrum Efficiency (SE) in IAB networks through multi-hop Quality of Service (QoS) analysis and network interference decomposition. We propose a multi-hop delay QoS analysis method that increases computational efficiency and accuracy, thus preventing spectrum over-allocation. We introduce a Transformer-based Interference Path Loss Assessment Neural Network (TIPA-NN) to tackle the issue of inadequate interference information in complex IAB networks, ensuring efficient spectrum reuse. The simulation results show that the proposed QoS analysis method effectively approximates delay unreliability probability across varying hop counts, demonstrating good scalability. The minimum service rate derived supports diverse QoS requirements in multi-hop scenarios. Our algorithm guarantees QoS and enhances SE in IAB networks, outperforming baselines and exhibiting topology-agnostic adaptability. Notably, there is a minimum of 25.03% reduction in subcarrier consumption compared to existing approaches, while ensuring improved SE.

In this article, based on martingale theory, we investigate the problem of maximum throughput in hybrid energy harvesting wireless communication systems (EH-WCSs) under energy storage and delay (or backlog) constraints. Specifically, the energy supply and data transmission of the hybrid EH-WCS are modeled as two queuing systems. For the first energy supply queueing system, we construct corresponding martingales for each type of energy harvesting (EH) process and the system’s energy consumption process. Leveraging the multiplicativity of martingales, the stochastic characteristics of the hybrid EH process are described in the martingale domain. On this foundation, a closed-form expression for the energy depletion probability bound (EDPB) under various energy storage constraints is derived. In the second data transmission queueing system, to capture the impact of channel fading on the system’s service, we map the arrival and service processes to the signal-to-noise ratio (SNR) domain and construct the corresponding martingales. A martingale parameter is proposed that connects the martingales of the arrival and service processes with the system’s EDPB. Based on this, the closed-form expressions for the delay violation probability bound and backlog violation probability bound are derived. Utilizing these derived performance bounds, we address the maximum throughput optimization problems under the energy storage and delay (or backlog) constraints. Furthermore, we instantiate a scenario and provide guidance on the impact of resource allocation on maximum throughput through simulation and validation, offering insights for achieving green communication networks.

End-to-end (E2E) reliability provisioning is critical for network slice customization. Leveraging martingale theory, we construct an analysis framework of the reliability with regard to E2E latency for the multihop system, where service processes provided by wireless nodes and networking nodes are heterogeneous. Based on the Wald martingale constructions of arrival processes and service processes, tandem service descriptor is defined to embody the features of tandem service mode for the targeted flow. Arrival bias and service bias are derived to describe the fluctuations of arrival and service processes, respectively. Relying on the Doob maximum inequality of martingales, a tight upper bound of the unreliability regard to E2E latency is captured. Packet duplication and hot-backup parallel transmission patterns are adopted for network reliability enhancement. A reliability decomposition and bandwidth abstraction algorithm is proposed, which achieves the decoupling between the statistical reliability requirement and the desired bandwidth of each node. Slice instantiation is performed in the access network and the core network, respectively. In the wireless access network, a long-term stochastic optimization problem is formulated with reliability requirements constraint. Lyapunov drift-plus-penalty (DPP) optimization theory is explored to transform the intractable long-term optimization problem into dynamic evolutionary per-slot optimization problems. We yield the closed-form solutions of user scheduling and power allocation. In the core network, the forwarding rates in the networking nodes for the targeted flow are obtained.

Carrier sense multiple access (CSMA) provides a feasible way to support machine type communications (MTC). However, in CSMA how to guarantee the diverse delay QoS efficiently is unsolved because the relationship between the back-off factor and delay QoS is unexploited. In this paper, we propose a new CSMA-type scheme named QoS-aware Local-Information-driven Multiple Access (LIMA) to handle this problem. The LIMA is formulated as a throughput maximization problem subject to the statistical delay QoS including the delay bound and delay bound violation probability. The diverse statistical delay QoS constraints are decomposed constraint into a series of the parameter adjusting period (PAP)-progressive QoS constraints in the time scale. Based on the effective capacity, the relationship between the back-off factor (defined as the average of back-off duration) and statistical delay QoS is studied. Then, each PAP-progressive QoS constraint is explicitly expressed in a simple form. Finally, a distributed algorithm is developed to find the optimal back-off factors based on local information. The global optimality of the proposed solution is theoretically proved. The salient feature of LIMA is that it can satisfy the diverse delay QoS requirements in a distributed way without signaling overhead for traffic information of other devises and scheduling information from the coordinator/access point (AP).

Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator.

Ultrareliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) are two types of services with delay Quality-of-Service (QoS) demands. Considering the random and sporadic URLLC packets arrival feature, slicing puncture is believed to be the suitable method to support coexistence communication scenario of eMBB and URLLC terminals. However, slicing puncture in a short time is not trivial, and needs accurate wireless resource scheduling. At the same time, it may damage the QoS of eMBB service. All of these make the QoS guaranteed scheduling problem more challenging. In this article, we investigate the bandwidth, power allocations, slice puncturing problems to find the way satisfying services’ individual QoS demands. For the scheduling of eMBB service, we formulate a joint optimization problem for bandwidth and power allocations with long-term constraints of queues backlog. To solve this problem, we utilize the Lyapunov drift-plus-penalty (DPP) method to establish the relationship between the long-term constraints and the short-term optimization problem, which means that the long-term constraints are gradually satisfied by the proposed strategy at each step. We further divide the short-term optimization problem into two subproblems and adopt a block coordinate descent (BCD) algorithm to reduce the computation complexity. Then, we put forward a one-to-one matching method to solve the integer programming in resource block (RB) allocation and slicing puncture problems. Numerical results demonstrate that the proposed dynamic resource allocation and puncturing strategy (DRAPS) can solve the scheduling problem of eMBB and URLLC services in the presence of multiple randomness of channel-state information (CSI) and URLLC packet arrival.

The optimal design of the energy-efficient multiple-input multiple-output (MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is an important but unsolved problem. For such a system, we propose a novel absorbing-Markov-chain-based analysis framework to shed light on the puzzling relationship between the delay and reliability, as well as to quantify the system energy efficiency. We derive the transition probabilities of the absorbing Markov chain considering the Rayleigh fading, the channel estimation error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access, the ACK-enabled retransmissions within the delay bound and the interactions among these technical ingredients. Then, the delay-constrained reliability and the system energy efficiency are derived based on the absorbing Markov chain formulated. Finally, we study the optimal number of user equipments (UEs) and the optimal number of receiving antennas that maximize the system energy efficiency, while satisfying the reliability and latency requirements of URLLC simultaneously. Simulation results demonstrate the accuracy of our theoretical analysis and the effectiveness of massive MIMO in supporting large-scale URLLC systems.

Intelligent reflecting surfaces (IRS) can improve the physical layer security (PLS) by providing a controllable wireless environment. In this paper, we propose a novel PLS technique with the help of IRS implemented by an intelligent mirror array for the visible light communication (VLC) system. First, for the IRS aided VLC system containing an access point (AP), a legitimate user and an eavesdropper, the IRS channel gain and a lower bound of the achievable secrecy rate are derived. Further, to enhance the IRS channel gain of the legitimate user while restricting the IRS channel gain of the eavesdropper, we formulate an achievable secrecy rate maximization problem for the proposed IRS-aided PLS technique to find the optimal orientations of mirrors. Since the sensitivity of mirrors’ orientations on the IRS channel gain makes the optimization problem hard to solve, we transform the original problem into a reflected spot position optimization problem and solve it by a particle swarm optimization (PSO) algorithm. Our simulation results show that secrecy performance can be significantly improved by adding an IRS in a VLC system.

In next-generation wireless networks, providing secure transmission and delay guarantees are two critical goals. However, either of them requires a concession on the transmission rate. In this article, we consider a visible light network consisting of multiple access points and multiple users. Our first objective is to mathematically evaluate the achievable rate under constraints on delay and security. The second objective is to provide a cell formation with customized statistical delay and security guarantees for each user. First, we propose a user-centric design called secure cell formation, in which artificial noise is considered, and flexible user scheduling is determined. Then, based on the effective capacity theory, we derive the statistical-delay-constrained secrecy rate and formulate the cell formation problem as a stochastic optimization problem (OP). Further, based on the Lyapunov optimization theory, we transform the stochastic OP into a series of evolutionary per-slot drift-plus-penalty OPs. Finally, a modified particle swarm optimization algorithm and an interference graph-based user-centric scheduling algorithm are proposed to solve the OPs. We obtain a dynamic independent set of scheduled users as well as secure cell formation parameters. Simulation results show that the proposed algorithm can achieve a better delay-constrained secrecy rate than the existing cell formation approaches.

With the help of big data analytics, predictive resource allocation (PRA) techniques for video on demand (VoD) have been recognized as promising methods to save time-frequency resources, for a number of VoD packets can be transmitted in good channels in advance to avoid the predicted transmissions in bad channel conditions. With the increasing demands on a fantastic user quality of experience, a smooth playback and a low start-up delay are of equal importance to the emerging VoD with high fidelity, which inevitably leads to a critical delay requirement of VoD packets. However, the issue of resource estimation with quality of service (QoS) requirements is still an unsolved puzzle in PRA. In this paper, we propose a martingales-based physical resource block (PRB) abstraction method, where the random characteristics of the service process are embedded in the minimum PRB consumption. Based on the method, a proactive QoS-guaranteed reconfiguration algorithm is developed to optimize the multi-user delivery pattern applied in the prediction window, aiming to maximize spectrum efficiency. In this algorithm, since the delay sensitivity of VoD content transmitted in advance is dulled compared with the original VoD stream, we divide the original VoD slice into two sub-slices and derive a three-dimensional delivery pattern. The gain of resource saving and the capability of QoS guarantee brought by the reconfiguration have been demonstrated by the simulation results.

Based on martingale theory, the authors explore a novel method to analyse the bound of delay violation probability for aggregate traffic in this study. The proposed arrival model, four‐state Markov modulated multinomial process, describes the behaviour of mixed service flows, which depicts the packet arrival process of two interweaved services and highlights the arrival characteristics of the batch, bursty, and heterogeneous. The traffic of data service is modelled by the interrupted multinomial process to depict the randomness and discontinuity. Relying on martingale theory, arrival‐supermartingales provide a model for integrating multiple heterogeneous services. They construct a supermartingale process of the backlog in the buffer, which enables the analysis of delay for the queuing system with multiple arrivals and a single server. Finally, the delay of aggregate traffic is featured by the parameters of its corresponding supermartingales. Based on the stopping time theory of martingales, the delay violation probability bound is derived, which matches the simulation results well and is tight.

Enhanced mobile broadband (eMBB) is a major scenario in the prospective fifth generation (5G) wireless communications. Achieving high user experienced data rate (UEDR) is one of vital goals of eMBB, which needs to consume massive bandwidth. Considering the limited radio frequency (RF) spectrum resource has got heavily congested, we explore the multi-packet reception (MPR) aided visible light communications (VLCs) system to support eMBB services in this paper. MPR can efficiently alleviate collisions for VLC system and enhance UEDR. However, the implementation of MPR in random access system is challenging because of dynamic and unpredictable transmissions of eMBB terminals. For fully utilizing MPR capability and promoting UEDR maximally, we design a two-period frame structure including the access request period (ARP) and the data transmission period (DTP). UEDR emphasizes not only the data rate measured in physical layer but also the network operation behaviour experienced by users which connects closely with quality of service (QoS). In this paper, effective capacity (EC) is employed to depict UEDRs for eMBB terminals for the first time, which provides an efficient link model to evaluate data rates with consideration of statistical delay QoS. Then, the aggregate UEDR is derived and defined as a system metric, which reflects the transmission features of both two periods. We formulate the hybrid access algorithm for eMBB terminals with heterogeneous QoS requirements as an aggregate UEDR maximization problem and find the optimum access probability (for ARP) and optimal resource allocation strategy (for DTP) via the particle swarm optimization-initialization intervention (PSO-II) algorithm.

One of the key features in machine type communications (MTCs), especially for MTCs embracing ultrareliable and low latency communications (URLLCs), is the short packet transmission with finite block-length. Both feature of short packet transmissions and requirement on supporting low latency communications or URLLCs pose challenges to radio access design for MTC networks in fifth generation. In this paper, for the MTC networks with delay-insensitive/URLLC terminals co-existence scenarios, we propose the energy-efficient differentiated ALOHA (D-ALOHA) random access algorithms, which simultaneously achieve energy efficient and satisfy the requirements of URLLC in a distributed mode. First, we abstract a service model of an MTC terminal with joint consideration of the achievable rate for short packet transmissions, the truncated channel inverse scheme, and the D-ALOHA scheme. Based on the formulated service model, then we derive the martingale parameters of service process for each terminal, and analyze the delay-bound violation probability of a terminal via the optimum stopping theory for martingales. The martingales-based delay analysis is verified via simulations. Finally, we formulate our energy-efficient D-ALOHA algorithm as an energy efficiency maximization problem with martingales-based statistical delayquality of service constraints, and the resultant optimization problem is solved by the invasive weed optimization-differential evolution (IWO-DE) algorithm. Furthermore, we propose a suboptimal but low-complexity energy-efficient D-ALOHA scheme, which neglects the distance difference of delay-insensitive terminals, such that the same access probability is imposed on these terminals for reducing the computational complexity of IWO-DE. Simulation results demonstrate that the energy efficiency performance of our D-ALOHA algorithms is favorite, and our D-ALOHA can achieve URLLCs in case of short duration of a slot.

There is a paucity of random access protocols designed for alleviating collisions in visible light communication (VLC) systems, where carrier sensing is hard to achieve due to the directionality of light. To resolve the problem of collisions, we adopt the successive interference cancellation (SIC) algorithm to enable the coordinator to simultaneously communicate with multiple devices, which is referred to as the multi-packet reception (MPR) capability. However, the MPR capability could be fully utilized only when random access algorithms are properly designed. Considering the characteristics of the SIC aided random access VLC system, we propose a novel effective capacity (EC)-based ALOHA-like distributed random access algorithm for MPR-aided uplink VLC systems having heterogeneous quality-of-service (QoS) guarantees. First, we model the VLC network as a conflict graph and derive the EC for each device. Then, we formulate the VLC QoS-guaranteed random access problem as a saturation throughput maximization problem subject to multiple statistical QoS constraints. Finally, the resultant non-concave optimization problem is solved by a memetic search algorithm relying on invasive weed optimization and differential evolution. We demonstrate that our derived EC expression matches the Monte Carlo simulation results accurately, and the performance of our proposed algorithms is competitive.

This paper proposes a channel error adaptive FEC algorithm to balance the conflict between the QoS of video transmission and the bandwidth utilization ratio in wireless IP networks. Two analytic models are derived, one is playable frame rate in MPEG video stream, another is effective utilization ratio of FEC. Based on these analytic models, quality of video stream and effective utilization ratio of FEC under different network conditions (presented by packet loss probability in this paper) are predicted. Then optimal amount of redundant packets is calculated and decided, which makes both the quality of video stream and the effective utilization ratio of FEC approximate their maximum. The simulation results show that the proposed algorithm improves system performance in both the QoS of wireless video transmission and the utilization of bandwidth.

This paper presents a comprehensive system modeling and analysis approach for both predicting and controlling queuing delay at an expected value under multi-class traffic in a single buffer. This approach could effectively enhance QoS delivery for delay sensitive applications. Six major contributions are given in the paper: (1) a discrete-time analytical model is developed for capturing multi-class traffic with binomial distribution; (2) a control strategy with dynamic queue thresholds is used in simulation experiments to control the delay at a specified value within the buffer; (3) the feasibility of the system is validated by comparing theoretical analysis with simulation scenarios; (4) the arrival rate can be adjusted for each forthcoming time window during the simulation with multi-packet sources; (5) statistical evaluation is performed to show both efficiency and accuracy of the analytical and simulation results; (6) a graphical user interface is developed that can provide flexible configuration for the simulation and validate input values.

Delay is arguably one of the most vital Quality of Service (QoS) metrics in computer networks since it has many implications for real time multimedia applications. The congestion and queuing delay of Internet core routers is one of the most important fundamental research topics which plays a vital role in determination of the overall end-to-end delay. In previous works, the control algorithm proposed and modelled in the discrete-time domain, by controlling the value of the threshold within the queue buffer to achieve the required delay, is based on a single arrival in one time slot. In this paper, a new traffic model with the ability to capture the multiple arrivals in a single time slot is described and its application for restraining the delay to a pre-defined value within the network core router is developed. The feasibility of the system is examined using both theoretical analysis and simulation. A statistical evaluation is performed to show both efficiency and accuracy of the results acquired throughout the simulation.