Stringent Quality Of Service Requirements Research Articles

A Decentralized Collaborative Learning Approach in 5G+ Core Networks

The 5G+ networks are going through extensive overhauling at both the radio access network (RAN) and core network (CN) to truly support the envisioned 5G+ use cases with ever so increasingly stringent quality of service requirements. Although many academic and industrial advancements have been realized, there are still needs for innovative changes vital for 5G+ connectivity, applications, and services. Consequently, the 3GPP is ushering in the next paradigm shift for CNs. CNs, which consist of several network functions (NFs) working cooperatively to service a RAN, are expected to exploit artificial intelligence (AI) in the form of an all-seeing-eye AI engine known as the Network Data Analytics Function (NWDAF). Its purpose is to optimize and automate network operation and management. However, accurate analytics require extensive machine learning (ML) training and as such, in this article, we present a distributed collaborative ML approach to facilitate said training. Accordingly, we present two variations of a distributed NWDAF architecture to enhance NF data localization, improve security, reduce control overhead during model training, shorten the training time, and finally, enhance the accuracy of the trained models by virtue of local testing on real-time network data. The first architecture distributes the global model training by exploiting the unique learning domain of each local model for each network function instance in a federated learning-based scheme while the second partitions the actual training network between multiple entities for each network function in a split learning-based scheme.

Energy Efficient Power Allocation in Massive MIMO Based on Parameterized Deep DQN

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2% and 108.5% improvement over DQN methods and the RL method, respectively, in handling large-scale multi-agent problems in M-MIMO networks.

QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios

The advent of the Internet of Things (IoT) paradigm to intensive domains, such as industry, is a key enabler for the automation of critical, real-world processes. The strict Quality-of-Service (QoS) requirements of these domains make low-latency computing paradigms, such as fog computing, very attractive for meeting these requirements. Moreover, the requirements of scalability and flexibility in the underlying network communications motivate the use of software-defined networking (SDN) in the infrastructure. To enable these fog-SDN environments, fog nodes (FNs) that have both computing and SDN capabilities can be deployed, thus easing the deployment of fog in SDN networks. However, the exact placement of these FNs is key to the latency of the hosts that make use of them, and thus, must be carefully assessed to meet the stringent QoS requirements of critical, time-strict IoT applications. This article focuses on this FN placement problem by formalizing it and solving it through both optimal and approximated methods, including comparisons with state-of-the-art benchmarks. In particular, we analyze the performance of each of these methods in terms of latency and execution time in both SDN Internet topologies and Industrial IoT infrastructures. Our proposed heuristic provides placements with near-optimal latencies, with smaller optimality gaps than the benchmark, and computes them in tractable times.

Cooperative Resource Scheduling for Time-Sensitive Services in an Integrated XGS-PON and Wi-Fi 6 Network

10-Gbps symmetrical passive optical network (XGS-PON) and Wi-Fi 6 are two key technologies to provide gigabit access for the fifth-generation fixed network (F5G). However, how to integrate XGS-PON and Wi-Fi 6 to meet F5G’s stringent quality of service requirements is still a challenge. In this letter, we propose a cooperative resource scheduling scheme, in which a communication tunnel is established to achieve a low end-to-end (E2E) delay by sharing medium access control information between XGS-PON and Wi-Fi 6. Simulation results show that the proposed scheme can significantly reduce the E2E delay for time-sentinel services by up to 50% without sacrificing the performance of non-time-sensitive services.

Admission Control and Scheduling of Isochronous Traffic With Guard Time in IEEE 802.11ad MAC

An upsurge of low latency and bandwidth hungry applications such as virtual reality, augmented reality and availability of unlicensed spectrum in the millimeter wave band at 60 GHz have led to standardization of the new generation WiFi systems such as IEEE 802.11ad and 802.11ay. Due to the stringent Quality of Service requirement of those applications, IEEE 802.11ad/ay have introduced contention free channel access called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Service Period</i> . One type of user traffic supported by IEEE 802.11ad is isochronous traffic, which is essentially periodic traffic that requires certain channel time to be allocated before its period ends. In an earlier work, we presented three Admission Control Algorithms (ACAs) which admit isochronous requests to achieve the above goals. One of these ACAs, the proportional fair allocation admission control (PFAAC), offers the best tradeoff across different performance metrics. But it did not consider guard time (GT) overhead, which is essential in a practical system. In this paper, we present two methods to compute upper bounds on GT overhead. We evaluate performance of the modified PFAAC with the two methods and PFAAC with no GT overhead. The modified PFAAC with the method that uses a tighter upper bound on GT overhead, provides the best performance.

State-Aware Resource Allocation for Wireless Closed-Loop Control Systems

Wireless closed-loop control is of major significance for future industrial manufacturing. However, control applications pose stringent quality of service requirements for reliable operation. Contrary to traditional ultra-reliable low-latency communications design goals such as low packet loss rates and low latency, research results in the domain of networked control systems (NCS) state that depending on the sampling period, control applications inherently tolerate a few consecutive packet losses. This translates into a better-suited metric to capture control application requirements and therefore a more conclusive design goal for wireless networks: ensuring a maximum age of information (AoI). With a Markov modeling approach, we propose to exploit the tolerance through a novel dynamic multi-connectivity scheme that we term state-aware resource allocation (SARA), which temporally negatively correlates packet losses, thus avoiding long packet loss sequences. Through statistical multiplexing, SARA enables a mean time to failure (MTTF) in the order of years while keeping the per-agent average channel usage close to one, also in a multi-agent setting with competition for resources. Compared with static dual-connectivity, the MTTF can be increased 100-fold whereas the number of required channels reduces by 40%. Our approach also statistically guarantees system-wide AoI distributions, which aid to ensure control performance.

User association for load balancing in coordinated multipoint green HetNets: A Quasi-Newton-based approach

The demand for high capacity network services with stringent quality of service requirements is at a rapidly accelerating rate due to the exponential rise in the numbers of mobile-connected devices. This demand has motivated the use of the heterogeneous network (HetNet) architectures. However, even though small-cell base-stations have relatively low power consumption, the overall aggregate power consumption of a dense HetNet is significant. Due to high inter-cell-interference and imbalanced loads in dense HetNets with conventional user association techniques, cell-edge users perceive dramatically less quality of service than their cell-center counterparts. The use of a Coordinated Multipoint (CoMP) association can augment the service perceived by cell-edge users by allowing a single user to be jointly served by two base-stations. In this work, we propose a load balancing scheme for CoMP-enabled HetNets with hybrid energy supplies that jointly optimizes user latency and green energy utilization. The proposed scheme employs a fractional solution to the user association problem to decide CoMP transmission for cell-edge users, ultimately improving their data rates. Performance evaluations of the proposed scheme show a reduction in latency of 79% and on-grid power consumption by 99% compared to conventional user association schemes that associate users based on the maximum received signal strength. Furthermore, an improvement in the network sum-rate for cell-edge users by 24% has been achieved compared to the traditional association scheme and as much as 40% over other existing schemes.

Survey on radio resource allocation in long‐term evolution‐vehicle

AbstractThe main goal behind radio resource management (RRM), in any conventional wireless networks, is to efficiently utilize the available resources. Critical and safety communications have very stringent quality of service requirements on reliability and availability. Consequently, the efficient use of resources becomes more compelling when it deals with this type of application. In this context, cellular vehicle‐to‐everything (C‐V2X) communications, enabled by cellular device‐to‐device (D2D), have recently drawn much attention. This is due to their applicability to road safety and traffic efficiency applications, which require information exchange among road users and road‐side entities. This emerging technology, referred hereinafter as LTE‐V or C‐V2X, supports two different communication modes: 3 and 4. Both of the two modes enable direct vehicle‐to‐vehicle (V2V) communications via PC5 interface. However, they differ on how the radio resources are allocated. Several approaches and algorithms are proposed in the literature to address the resource allocation issue. In this paper, a state of the art of the radio resource allocation strategies was made for LTE‐V technology and a qualitative description was carried out of these different schemes. We proposed a classification of these strategies into different categories. This classification identified the different strengths and weaknesses of each proposal. Up to our knowledge, several surveys concerning resource allocation are published in the context of LTE but very few of them focuses on both RRM and vehicular networks.

E2bird: Enhanced Elastic Batch for Improving Responsiveness and Throughput of Deep Learning Services

We aim to tackle existing problems about deep learning serving on GPUs in the view of the system. GPUs have been widely adopted to serve online deep learning-based services that have stringent QoS(Quality-of-Service) requirements. However, emerging deep learning serving systems often result in poor responsiveness and low throughput of the inferences that damage user experience and increase the number of GPUs required to host an online service. Our investigation shows that the poor batching operation and the lack of data transfer-computation overlap are the root causes of the poor responsiveness and low throughput. To this end, we propose E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird, a deep learning serving system that is comprised of a GPU-resident memory pool, a multi-granularity inference engine, and an elastic batch scheduler. The memory pool eliminates the unnecessary waiting of the batching operation and enables data transfer-computation overlap. The inference engine enables concurrent execution of different batches, improving the GPU resource utilization. The batch scheduler organizes inferences elasticallyto guarantee the QoS. Our experimental results on an Nvidia Titan RTXGPU show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird reduces the response latency of inferences by up to 82.4 percent and improves the throughput by up to 62.8 percent while guaranteeing the QoS target compared with TensorFlow Serving.

Enabling technologies for low-latency service migration in 5G transport networks [Invited

The fifth generation (5G) mobile communications system is envisioned to serve various mission-critical services such as industrial automation, cloud robotics, and safety-critical vehicular communications. To satisfy the stringent end-to-end latency requirement of these services, fog computing has been regarded as a promising technology to be integrated into 5G networks, in which computing, storage, and network functions are provisioned close to end users, thus significantly reducing the latency caused in transport networks. However, in the context of fog-computing-enabled 5G networks, the high mobility feature of users brings critical challenges to satisfy the stringent quality of service requirements. To address this issue, service migration, which transmits the associated services from the current fog server to the target one to follow the users’ travel trace and keep the service continuity, has been considered. However, service migration cannot always be completed immediately and may lead to a situation where users experience a loss of service access. In this regard, low-latency service migration plays a key role to reduce the negative effects on services being migrated. In this paper, the factors that affect the performance of service migration are analyzed. To enable low-latency service migration, three main enabling technologies are reviewed, including migration strategies, low-latency, and high-capacity mobile backhaul network design, and adaptive resource allocation. Based on a summary of the reviewed technologies, we conclude that dynamic resource allocation is the worthiest one to research. Therefore, we carry out a use case, where reinforcement learning (RL) is adopted for autonomous bandwidth allocation in support of low-latency service migration in a dynamic traffic environment and evaluate its performance compared to two benchmarks. The simulation demonstrates that the RL-based algorithm is able to self-adapt to a dynamic traffic environment and gets converged performance, which has an obviously smaller impact on non-migration traffic than the two benchmarks while keeping the migration success probability high. Meanwhile, unlike the benchmarks, the RL-based method shows performance fluctuations before getting converged, which may cause unstable system performance. It calls for future research on advanced smart policies that can get convergence quickly, particularly for handling the migration of latency-sensitive services in 5G transport networks.

IEEE 802.11n/ac Üzerinden Çoklu Video Yayını Analizi ve Değerlendirilmesi

There is an ever-growing interest for video applications. It is well-known that video applications have very stringent quality of service (QoS) requirements and require high data rates. To meet ever growing demands for mobile video streaming applications and stringent QoS requirements, it is required to optimise the current networks. IEEE 802.11 is one of the most widely deployed networks in the world. Therefore, this paper investigates the performance of multicast video streaming over IEEE 802.11n/ac in a mobile outdoor scenario. To this end, an end-to-end system level simulator is developed to evaluate the performance of the perceived video quality at the receiver in terms of peak signal to noise ratio (PSNR) and resource efficiency in terms of total transmission times for different application layer - forward error correction (AL-FEC) and medium access control (MAC) layer parameters. Results show that without AL-FEC protection the legacy IEEE 802.11n/ac provides very poor quality of experience. However, the perceived video quality can be significantly improved by adding some extra repair symbols. Since video applications have very challenging QoS requirements, changing only MAC parameters are unable to meet these QoS requirements thus additional AL-FEC protection is required. Moreover, it is observed that with the use of the frame aggregate mechanism at the MAC layer, the total transmission times can be significantly reduced and hence the resource consumptions.

Cross‐layer resource allocation for critical MTC coexistent with human‐type communications in LTE: a two‐sided matching approach

Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine-type communications (MTC) that require strict quality-of-service (QoS) guarantees. Therefore, ultra-reliable and low-latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human-type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross-layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally-efficient solution of the problem, they formulate it as a two-sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close-to-optimal performance of the matching-based scheduling scheme and its superiority to other existing techniques.

A Review of Cognitive Radio Smart Grid Communication Infrastructure Systems

The cognitive smart grid (SG) communication paradigm aims to mitigate quality of service (QoS) issues in obsolete communication architecture associated with the conventional electrical grid. This paradigm entails the integration of advanced information and communication technologies (ICTs) into power grids, enabling a two-way flow of information. However, due to the exponential increase in wireless applications and services, also driven by the deployment of the Internet of Things (IoT) smart devices, SG communication systems are expected to handle large volumes of data. As a result, the operation of SG networks is confronted with the major challenge of managing and processing data in a reliable and secure manner. The existing works in the literature proposed architectures with the objective to mitigate the underlying QoS issues such as latency, bandwidth, data congestion, energy efficiency, etc. In addition, a variety of communication technologies have been analyzed for their capacity to support stringent QoS requirements for diverse SGs environments. This notwithstanding, a standard architecture designed to mitigate the aforementioned issues for SG networks remains a work-in-progress. The main objective of this paper is to investigate the emerging technologies such as cognitive radio networks (CRNs) as part of the Fifth-Generation (5G) mobile technology for reliable communication in SG networks. Furthermore, a hybrid architecture based on the combination of fog computing and cloud computing is proposed. In this architecture, real-time latency-sensitive information is given high priority, with fog edge based servers deployed in close proximity to home area networks (HANs) for preprocessing and analyzing of information collected from smart IoT devices. In comparison to the recent works in the literature, which are mainly based on CRNs and 5G separately, the proposed architecture in this paper incorporates the combination of CRNs and 5G for reliable and efficient communication in SG networks.

Lightweight and secure cipher scheme for multi-homed systems

Multiple access technologies allow a network to connect to two or more communication networks efficiently, to enhance system performance. They are more robust and reliable than legacy technologies, which allow the connection to one network at a time. In this paper, a new and efficient dynamic and key-dependent cipher scheme is designed to secure multi-homed devices, in real-time, while preserving their main functionality and meeting the stringent Quality of Service requirements. The proposed scheme consists of a dynamic key derivation technique that mixes together a dynamic nonce and a secret key. The dynamic nonce is extracted from multiple channels, after establishing a new session. The proposed approach is based on simple operations such as substitution and permutation, to enhance the network robustness against different types of attacks. The dynamicity is based on the random nature of physical channels and as such, data confidentiality, integrity and source authentication are achieved with the least amount of resources, delay and computational complexity. The implementation of the proposed cipher scheme is appropriate for small and resource-constrained devices including IoT devices and mobile phones (battery-constrained devices). Theoretical and simulation results confirm that the cipher scheme has a better efficiency than other recent schemes, which require multiple rounds. In addition, a cryptanalysis discussion is provided towards proving that the proposed cipher is capable of resisting and preventing the different existing cryptanalysis and security attacks.

Reliable Video Multicasting over WLANs

As is known reliable multicast transmission is not addressed in WLANs. Multicast packets are sent over wireless channels without using medium access control (MAC) layer automatic repeat request (ARQ) mechanism. Therefore, packet error rates will be very high. This paper investigates reliable video multicasting, which became a very popular application, over WLANs. To this end, application layer forward error correction (AL-FEC) codes based on Raptor Q can be used as a means to provide reliable delivery of the multicast video encoded with the H.264/Advanced video coding (AVC) codec. Since video streaming applications have very stringent Quality of Service requirements (low delay and error free transmission of packets), the Raptor Q and the H.264/AVC parameters have to be determined depending on the QoS of the video. Therefore, in this work an advanced cross-layer simulator was developed to analyse the performance of the end-to-end system. Simulation results show that using AL-FEC significantly improve the received video quality, i.e., the mean peak signal to noise ratio (PSNR) is improved over 34 dB. Further that it is shown that frequently inserting intra-frame results in poor video quality. Therefore, it is suggested to send intra frame at longer intervals.

Heterogeneous Statistical-QoS Driven Resource Allocation Over mmWave Massive-MIMO Based 5G Mobile Wireless Networks in the Non-Asymptotic Regime

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

A Feasibility Study of IEEE 802.11 HCCA for Low-Latency Applications

Existing optical and wireless networks designed to support today’s services may not be suitable for the emerging services, such as machine-to-machine applications of the Internet-of-Things paradigm and tactile-haptic applications of the Tactile Internet paradigm. Some of these applications must adhere to stringent quality of service requirements, especially an ultra-low end-to-end latency constraint of approximately 1 ms. In this paper, we investigate the feasibility of the wireless local area network (WLAN) infrastructure, specifically the IEEE 802.11 HCF Controlled Channel Access (HCCA) media access control (MAC) protocol, in supporting low-latency applications. The scenario considered is tactile-haptic data transmission between body-worn data collection devices and a wireless access point. In particular, we focus on uplink direction rather than downlink direction, because uplink data transmission forms a latency bottleneck of this particular network. In this paper, we first formulate an analytical model of the HCCA using a queuing theory to evaluate the average uplink latency. We then carry out global sensitivity analyses to understand the implications of various timing and network parameters on the uplink latency. Using these insights, we propose a novel strategic parameter selection (SPS) algorithm that effectively reduces uplink latency of the IEEE 802.11 WLAN. This paper provides insights into WLAN design such that latency-sensitive applications can be supported in the near future.

Optimal Cross-Layer Resource Allocation for Critical MTC Traffic in Mixed LTE Networks

Machine-type communications (MTC) play an important role in implementing and enabling the Internet of Things. Long -term evolution (LTE) is a strong candidate technology for the interconnection of the MTC devices. However, to optimize LTE for MTC purposes, several issues need to be addressed. This is due to the different and diverse quality of service (QoS) requirements of MTC compared to those of human-to-human (H2H) communications. In particular, critical-MTC pose many challenges to radio resource management in LTE. They have stringent QoS requirements that need to be considered without sacrificing the QoS of the H2H traffic. In this paper, we formulate the resource allocation optimization problem from a cross-layer design perspective to consider both the QoS requirements of critical-MTC and those of H2H communications. We propose methods to handle the optimization problem to reduce the computational complexity of the optimal solution. Additionally, the performance of the proposed resource allocation algorithms is evaluated analytically. Moreover, more computationally efficient algorithms are proposed to practically implement the resource allocation process in several operational cases. Finally, the computational complexity of the proposed algorithms is analyzed. The simulations results show the superiority of the proposed algorithms and methods compared to other techniques from the literature.

Exploiting efficiency of ultra-dense wavelength switched network for carrying metro network traffic

In the 5G era, metro optical networks would need to meet more stringent quality of service requirements. They would have to operate with high spectral efficiency but with low latency and low power consumption. For this, we introduced a new paradigm based on the elastic optical network, called ultra-dense wavelength switched network (UD-WSN) in Zhang et al. (Proceedings of the Asia Communications and Photonics Conference, 2016), Shen et al. (EEE Commun Mag 56(2):189–195, 2017), and Zhou et al. (IEEE/OSA J Lightw Technol 35(11):2063–2069, 2016). UD-WSN was verified to be efficient in terms of system cost, spectrum efficiency, power consumption, and service connection latency when compared to other popular architectures, such as pure optical transport networks (OTN) and conventional OTN over dense wavelength division multiplexing networks. This motivates us to explore further enhancements to UD-WSN in this paper for even better performance. Specifically, we consider a UD-WSN architecture without aggregation OTN switches to evaluate how the system cost can be reduced further by trading off the system performance. We also propose to implement partial OTN switching within the UD-WSN to exploit the benefit of traffic grooming to lower the system cost even further. Finally, we also implement spectrum defragmentation to improve the spectrum utilization of the system. These schemes are studied through simulations to verify their effectiveness.

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.