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Fifth Generation (5G) networks are envisioned to be fully autonomous in accordance to the ETSI-defined Zero touch network and Service Management (ZSM) concept. To this end, purpose-specific Machine Learning (ML) models can be used to manage and control physical as well as virtual network resources in a way that is fully compliant to slice Service Level Agreements (SLAs), while also boosting the revenue of the underlying physical network operator(s). This is because specially designed and trained ML models can be both proactive and very effective against slice management issues that can induce significant SLA penalties or runtime costs. However, reaching that point is very challenging. 5G networks will be highly dynamic and complex, offering a large scale of heterogeneous, sophisticated and resource-demanding 5G services as network slices. This raises a need for a well-defined, generic and step-wise roadmap to designing, building and deploying efficient ML models as collaborative components of what can be defined as Cognitive Network and Slice Management (CNSM) 5G systems. To address this need, we take a use case-driven approach to design and present a novel Integrated Methodology for CNSM in virtualized 5G networks based on a concrete eHealth use case, and elaborate on it to derive a generic approach for 5G slice management use cases. The three fundamental components that comprise our proposed methodology include (i) a 5G Cognitive Workflow model that conditions everything from the design up to the final deployment of ML models; (ii) a Four-stage approach to Cognitive Slice Management with an emphasis on anomaly detection; and (iii) a Proactive Control Scheme for the collaboration of different ML models targeting different slice life-cycle management problems., Comment: 22 pages, 4 figures, 5 author bios and photos

5G new radio (NR) introduced flexible numerology to provide the necessary flexibility for multiplexing the communication of heterogeneous services on a shared channel. One of the fundamental challenges of 5G NR is to develop resource allocation schemes to efficiently exploit such flexibility to optimize resource allocation of ultra-reliable low-latency communications (URLLC) in coexistence with enhanced mobile broadband (eMBB) while ensuring their colliding performance requirements. To address this challenge, we present a new formulation of 5G NR resource allocation to accommodate eMBB and URLLC services, by considering their interplay. The objective of the formulated problem is to meet throughput quality of service (QoS) requirements probabilistically when the optimal global solution is infeasible. To this end, we express the problem as an integer linear program and consider two formulations with hard and soft URLLC throughput constraints. Furthermore, we propose a low-complexity two-step heuristic approach, inspired by an instance of bin packing optimization, to provide a trade-off between resource allocation efficiency and computational complexity. Finally, performance results are provided and demonstrate that the proposed approaches can provide low-complexity resource allocation solutions, in balance with the performance targets of the services.

In Long Term Evolution-Advanced (LTE-A) networks, different aspects of radio resource scheduling such as fairness and Quality of Service (QoS) assurance must be provided for heterogeneous traffic, having different characteristics. However, the ever-growing number of mobile devices sharing the limited radio resources leads to the high cost and difficulty for information acquisition and computations in resource scheduling process. In the present paper, we propose a proportional knapsack scheduling approach for fairness- and QoS-aware downlink transmission of all different service classes in LTE-A networks. Moreover, we assess the computational cost created by the uncertainty and lack of information on user operations, and propose a Gaussian-based approach for ranking the bearers in presence of limited information and computational capabilities. This approach is particularly suitable for emerging next generation wireless networks to support a wide range of applications with huge number of users. The results indicate that significant advantages are achieved both in terms of QoS and fairness and that it is a scalable solution for overload states of the network.

In Long Term Evolution-Advanced (LTE-A) networks, different aspects of radio resource scheduling such as fairness and Quality of Service (QoS) assurance must be provided for heterogeneous traffic, having different characteristics. However, the ever-growing number of mobile devices sharing the limited radio resources leads to the high cost and difficulty for information acquisition and computations in resource scheduling process. In the present paper, we propose a proportional knapsack scheduling approach for fairness- and QoS-aware downlink transmission of all different service classes in LTE-A networks. Moreover, we assess the computational cost created by the uncertainty and lack of information on user operations, and propose a Gaussian-based approach for ranking the bearers in presence of limited information and computational capabilities. This approach is particularly suitable for emerging next generation wireless networks to support a wide range of applications with huge number of users. The results indicate that significant advantages are achieved both in terms of QoS and fairness and that it is a scalable solution for overload states of the network.

The growing variety and consumption of the mobile services throughout the cellular networks lead to various challenging issues in radio resource scheduling. To have an apparent perspective over the resource scheduling in real implementation of the next generation cellular networks, it is essential to consider sequences of alternating overload and normal states of the traffic, occurring much in the system. In this paper, we do a performance study of three overload-state schedulers by implementing such a network environment and exploiting the advantages and drawbacks of the compared algorithms. This performance study through the simulation results reveals that the existing overload-state resource scheduling schemes do not satisfy the fifth generation (5G) mobile network's requirements to be more optimized in hard real time fashion. Then, open challenges and potential research directions for resource management in future 5G mobile networks are presented at the end.

The growing variety and consumption of the mobile services throughout the Long Term Evolution (LTE) mobile networks lead to various challenging issues in radio resource scheduling. One of the main challenges in LTE resource management is how to allocate the scare available bandwidth, especially in overload states of the network, to the services which have different characteristics and quality requirements. In this paper, we study the optimal solution to LTE downlink resource allocation problem which has already been cast as the fractional knapsack optimization problem. The performance of this solution is investigated by a wide range of demands from different classes of services, over sequences of alternating overload and normal states of the network. The simulation results demonstrate that the proposed method supplies diverse quality requirements of different service classes in compromise with data rate enhancement. System performance has been evaluated in terms of loss, delay and throughput in comparison with reference algorithms.

The Long Term Evolution (LTE) as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore, scheduling of all flows from various applications in overload states in which the requested amount of bandwidth exceeds the limited available spectrum resources is a challenging issue. Accordingly, in this paper, a greedy algorithm is presented to evaluate user candidates which are waiting for scheduling and select an optimal set of the users to maximize system performance, without exceeding available bandwidth capacity. The greedy-knapsack algorithm is defined as an optimal solution to the resource allocation problem, formulated based on the fractional knapsack problem. A compromise between throughput and QoS provisioning is obtained by proposing a class-based ranking function, which is a combination of throughput and QoS related parameters defined for each application. The simulation results show that the proposed method provides high performance in terms of throughput, loss and delay for different classes of QoS over the existing ones, especially under overload traffic., Comment: Wireless Networks, 2015

The growing variety and consumption of the mobile services throughout the cellular networks lead to various challenging issues in radio resource scheduling. To have an apparent perspective over the resource scheduling in real implementation of the next generation cellular networks, it is essential to consider sequences of alternating overload and normal states of the traffic, occurring much in the system. In this paper, we do a performance study of three overload-state schedulers by implementing such a network environment and exploiting the advantages and drawbacks of the compared algorithms. This performance study through the simulation results reveals that the existing overload-state resource scheduling schemes do not satisfy the fifth generation (5G) mobile network's requirements to be more optimized in hard real time fashion. Then, open challenges and potential research directions for resource management in future 5G mobile networks are presented at the end.

The growing variety and consumption of the mobile services throughout the Long Term Evolution (LTE) mobile networks lead to various challenging issues in radio resource scheduling. One of the main challenges in LTE resource management is how to allocate the scare available bandwidth, especially in overload states of the network, to the services which have different characteristics and quality requirements. In this paper, we study the optimal solution to LTE downlink resource allocation problem which has already been cast as the fractional knapsack optimization problem. The performance of this solution is investigated by a wide range of demands from different classes of services, over sequences of alternating overload and normal states of the network. The simulation results demonstrate that the proposed method supplies diverse quality requirements of different service classes in compromise with data rate enhancement. System performance has been evaluated in terms of loss, delay and throughput in comparison with reference algorithms.

In WLANs, with the existence of multi-rate capability, the Distributed Coordination Function (DCF) of MAC layer protocol equalizes the throughput of all the stations regardless of their own link rate. This equalization leads to the Performance Anomaly of IEEE 802.11 where the throughput of the station with the higher data transmission rate is decreased as much as that of the lower rate station. The objective of this study was to provide fair proportional throughput for TCP flows of competing stations in multi-rate IEEE 802.11 WLANs infrastructure. This research considers the scenarios when there are other kinds of unfairness along with per-rate unfairness. In this work, Rated Window and Packet Size Differentiation schemes are proposed by adjusting window and packet size according to the availability of buffer size in the access point and transmission rates of each flow. We validate the proposed methods by means of simulation and compare the results of the total throughput and fairness index with previous methods. The results show that the proposed methods generate fair service in terms of proportional throughput among wireless stations having different numbers and directions of flow with various data transmission rates. By getting appropriate fairness among stations in WLAN infrastructure, a Wireless Internet Service Provider (WISP) can significantly increase its revenue by providing various appropriate service plans.

In WLANs, with the existence of multi-rate capability, the Distributed Coordination Function (DCF) of MAC layer protocol equalizes the throughput of all the stations regardless of their own link rate. This equalization leads to the Performance Anomaly of IEEE 802.11 where the throughput of the station with the higher data transmission rate is decreased as much as that of the lower rate station. The objective of this study was to provide fair proportional throughput for TCP flows of competing stations in multi-rate IEEE 802.11 WLANs infrastructure. This research considers the scenarios when there are other kinds of unfairness along with per-rate unfairness. In this work, Rated Window and Packet Size Differentiation schemes are proposed by adjusting window and packet size according to the availability of buffer size in the access point and transmission rates of each flow. We validate the proposed methods by means of simulation and compare the results of the total throughput and fairness index with previous methods. The results show that the proposed methods generate fair service in terms of proportional throughput among wireless stations having different numbers and directions of flow with various data transmission rates. By getting appropriate fairness among stations in WLAN infrastructure, a Wireless Internet Service Provider (WISP) can significantly increase its revenue by providing various appropriate service plans.

In LTE multi-service communication system, a trade-off between QoS assurance and fairness is a challenging issue, since the QoS provisioning at the cost of starving users in low service demand classes is not favorable for the operator. In this paper, we adopt the time-domain Knapsack algorithm and fine tune it to provide fair resource allocation while support QoS requirements in LTE downlink scheduling system when the bearers are from different classes of service, having different QoS characteristics. We demonstrate that more efficient performance can be achieved in two aspects of fairness and QoS provisioning in terms of normalized throughput, and packet loss and delay rate, which are evaluated using simulation results.

In LTE systems, multicast services must be delivered efficiently in response to the need for strong QoS support. However, each class of quality services has its own requirements to be satisfied. These quality constraints limit the scheduling flexibility, and the LTE downlink resource allocating algorithms need to assimilate these constraints while trying to maximize system performance in terms of fairness and throughput. This paper addresses this fundamental problem of LTE downlink scheduling by adopting the time-domain Knapsack algorithm over the traffic overload patterns. We fine tune the Knapsack algorithm, to overcome this problem and improve system performance objectives. We demonstrate that more efficient performance can be achieved in terms of fairness index and system throughput, which is evaluated using simulation results.

In LTE systems, multicast services must be delivered efficiently in response to the need for strong QoS support. However, each class of quality services has its own requirements to be satisfied. These quality constraints limit the scheduling flexibility, and the LTE downlink resource allocating algorithms need to assimilate these constraints while trying to maximize system performance in terms of fairness and throughput. This paper addresses this fundamental problem of LTE downlink scheduling by adopting the time-domain Knapsack algorithm over the traffic overload patterns. We fine tune the Knapsack algorithm, to overcome this problem and improve system performance objectives. We demonstrate that more efficient performance can be achieved in terms of fairness index and system throughput, which is evaluated using simulation results.

In LTE multi-service communication system, a trade-off between QoS assurance and fairness is a challenging issue, since the QoS provisioning at the cost of starving users in low service demand classes is not favorable for the operator. In this paper, we adopt the time-domain Knapsack algorithm and fine tune it to provide fair resource allocation while support QoS requirements in LTE downlink scheduling system when the bearers are from different classes of service, having different QoS characteristics. We demonstrate that more efficient performance can be achieved in two aspects of fairness and QoS provisioning in terms of normalized throughput, and packet loss and delay rate, which are evaluated using simulation results.

The Long Term Evolution (LTE) as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore scheduling of all flows from various applications by the same strategy of resource allocation would not be efficient. Accordingly it is necessary to design new scheduling algorithms by considering the Quality of Service (QoS) requirements defined for each application. In this regard, this paper will provide a brief overview of previously reported schedulers for QoS support in LTE cellular networks by taking into account the various important QoS parameters such as delay, packet loss ratio and data rate. The resource distribution problem in LTE networks and solutions from the numerous numbers of previous resource allocation approaches are outlined and compared through a comparative table. The future direction for solving these problems will be stated. Overall this study summarises the current state of knowledge on the QoS-oriented scheduling algorithms for LTE networks.

The Long Term Evolution (LTE) as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore scheduling of all flows from various applications by the same strategy of resource allocation would not be efficient. Accordingly it is necessary to design new scheduling algorithms by considering the Quality of Service (QoS) requirements defined for each application. In this regard, this paper will provide a brief overview of previously reported schedulers for QoS support in LTE cellular networks by taking into account the various important QoS parameters such as delay, packet loss ratio and data rate. The resource distribution problem in LTE networks and solutions from the numerous numbers of previous resource allocation approaches are outlined and compared through a comparative table. The future direction for solving these problems will be stated. Overall this study summarises the current state of knowledge on the QoS-oriented scheduling algorithms for LTE networks.