PERFORMANCE ANALYSIS OF EXP-BET ALGORITHM FOR TRIPLE PLAY SERVICES IN LTE SYSTEM

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

The integration of multiple Radio Access Technologies (RATs) of licensed or unlicensed bands is considered as a cost-efficient way to greatly increase network capacity of mobile networks. In this paper, we propose a Smart Aggregated RAT Access (SARA) strategy with aim to maximize network throughput while meeting diverse traffic Quality of Service (QoS) requirements. We consider a scenario where users with different QoS requirements access to the Heterogeneous Network (HetNet) with coexisting Cellular-WiFi. In order to maximize network resource utilization in such a complex and dynamic environment, we exploit multi-agent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual users based on sensing dynamic channel states and traffic characteristics. We first use Nash Q-learning to provide a set of feasible RAT access strategies, and then employ Monte-Carlo (MCTS) based Q-learning to perform resource allocation which tries to maximize system throughput while meeting traffic QoS requirements. Numerical results reveal that the network access capacity can be maximized while meeting traffic QoS requirements with limited number of searches by using our proposed SARA. Compared with traditional WiFi offloading schemes, SARA can significantly improve system resource utilization and capacity while guaranteeing QoS requirements of UEs.

We present an approach to Service Provisioning with Quality of Service (QoS) requirements in ATM networks. The ATM network provides services to users every $\widehat{T}$ units of time. Each service is characterized by its one way route, its input traffic constraints and its provided QoS. The input traffic constraints consist of the maximum input rate and an upper bound on the burstiness curve. The QoS provided is defined by the maximum percentage of cell loss along the route and the maximum end‐to‐end cell delay. The service provisioning problem is defined as follows: determine the amount, price and required resources (bandwidth and buffers) for each type of service, that maximize a social welfare function that consists of the network’s revenue and the users’ surplus. This problem is solved every $\widehat{T}$ units of time and the allocation is made only over the available resources, that is, those that are not used by connections that are still active. We prove the existence of a solution to the Service Provisioning Problem and we also suggest an iterative procedure that interprets the solution. The network adjusts the prices to maximize the welfare function and also to guarantee that the allocated resources do not exceed the available resources. Based on these prices, users request a new allocation that minimizes their cost and the network adjusts again the prices based on the new allocation. The above procedure has the following features: (i) the network needs to know only the average request rate for each type of service, their route and the resources requested by the users; (ii) users need to know only their private information (input traffic constraints and QoS requirements), their route, and the prices for resources announced by the network.

A 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) system uses the concept of two-tier heterogeneous networks (HetNets), where low-power and short-range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network capacity. However, co-channel interference is one of the major issues that need to be resolved for the successful deployment of HetNets. To overcome this problem, fractional frequency reuse (FFR) schemes have been proposed that can efficiently utilize the available spectrum. Nevertheless, these schemes waste limited frequency resources owing to their static allocation and lack of following QoS requirements, network loading conditions, and service priority of users. In this paper, a QoS-based dynamic FFR (QoS-DFFR) scheme is proposed that efficiently allocates the non-occupied center-zone frequency bands, i.e., bonus bandwidth (BBW), to cell-edge users by considering their QoS requirements. Consequently, the proposed QoS-DFFR scheme can optimize cell-edge user throughput and sector throughput and reduce co-channel interference by dynamically allocating the BBW to the most demanding cell-edge users. The proposed QoS-DFFR scheme improves performance because of its ability to dynamically allocate the limited portion of the frequency bands based on the service priorities of users. The system-level simulation results show that the proposed QoS-DFFR scheme performs remarkably well in a HetNet environment. Compared with the usual FFR schemes, the proposed scheme almost doubles the cell-edge user's throughput and reduces the user's packet loss rate.

Long‐term evolution (LTE) is a promising last mile access candidate technology for the smart‐metering communication architecture. However, when the mobile LTE network is used to support smart meters (SMs), the quality‐of‐service (QoS) requirements of the smart‐metering traffic as well as all the other typical mobile network traffic need to be ensured. This becomes problematic when the network users generate diverse traffic types that have different QoS requirements. Therefore, in this paper, we propose a dynamic bandwidth scheduling algorithm to ensure the required QoS of various traffic types arising from both SMs and mobile users. Our proposed dynamic bandwidth allocation algorithm integrates two schedulers that are designed for periodic and emergency SM traffic situations that have different SM traffic intensities and QoS requirements. Designing of two schedulers provides the advantages of leveraging the particular traffic characteristics of these two diverse operational situations and achieving the maximum use of resources to ensure QoS requirements. In addition, to alleviate potential problems created by simultaneous emergency SM traffic, we also propose a method that deploys a random delay for SM packet transmissions. We analyse the delay and packet drop ratio of diverse traffic types when both the LTE base station scheduler and the SMs deploy our proposed methods under either periodic or emergency SM traffic conditions in the smart grid. Our results show that our proposed mechanisms are capable of satisfying the QoS requirements of both mobile users and SMs under diverse traffic conditions.

Generalized processor sharing: Characterization of the admissible region and selection of optimal weights

This paper studies an important problem in the IEEE 802.11 distributed coordination function (DCF)-based wireless local area network (WLAN): how well can the network support quality of service (QoS). Specifically, this paper analyzes the network's performance in terms of maximum protocol capacity or throughput, delay, and packet loss rate. Although the performance of the 802.11 protocol, such as throughput or delay, has been extensively studied in the saturated case, it is demonstrated that maximum protocol capacity can only be achieved in the nonsaturated case and is almost independent of the number of active nodes. By analyzing packet delay, consisting of medium access control (MAC) service time and waiting time, accurate estimates were derived for delay and delay variation when the throughput increases from zero to the maximum value. Packet loss rate is also given for the nonsaturated case. Furthermore, it is shown that the channel busyness ratio provides precise and robust information about the current network status, which can be utilized to facilitate QoS provisioning. The authors have conducted a comprehensive simulation study to verify their analytical results and to tune the 802.11 to work at the optimal point with maximum throughput and low delay and packet loss rate. The simulation results show that by controlling the total traffic rate, the original 802.11 protocol can support strict QoS requirements, such as those required by voice over Internet protocol (VoIP) or streaming video, and at the same time achieve high channel utilization.

The traditional ant routing protocol provides an efficient way for dynamic routing in continuously changing networks. Due to their social behaviour, ants tend to find optimized paths based on network status information such as the path length and capacity. However, the conventional ant routing protocol tends to satisfy acceptable Quality of Service (QoS) requirements for the whole network traffic. The major problem is that different traffic types request different QoS requirements, and the traditional ant routing lacks the ability of providing different levels of QoS parameters for these different traffic types. In this paper, a novel technology is proposed to satisfy the requirements of different traffic types which vary in terms of bandwidth, latency and security. Oyster Optics Technology has been used to introduce the security as a new significant QoS factor.

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360°) video streaming. The proposed solution is deployed in a highly dynamic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate.

IEEE 802.16 is a standardisation for a broadband wireless access in metropolitan area networks (MAN). Since the IEEE 802.16 standard defines the concrete quality of service (QoS) requirement, a scheduling scheme is necessary to achieve the QoS requirement. Many scheduling schemes are proposed with the purpose of throughput optimisation and fairness enhancement, however, few scheduling schemes support the delay requirement. In this study, the authors propose a new scheduling scheme reflecting the delay requirement. Specifically, the authors add the delay requirement term in the proportional fair scheduling scheme and the scheduling parameters are optimised with respect to the QoS requirement. Therefore the QoS requirement is achieved without the excessive resource consumption.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multiobjective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.

A third‐generation partnership project long‐term evolution system uses the concept of a 2‐tier heterogeneous network, where low‐power and short‐range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network throughput. However, cochannel deployment gives rise to the problem of cross‐tier interference that significantly deteriorates the performance of the cellular network. The coordinated multipoint (CoMP) transmission scheme is proposed as a promising solution to alleviate cross‐tier interference by doing cooperation among the neighbor base stations. In this paper, we propose a QoS priority‐based coordinated scheduling and hybrid spectrum access (QoS‐CSaHSA) scheme for downlink CoMP transmission in 2‐tier networks. The proposed QoS‐CSaHSA scheme dynamically reduces the femtocells' power requirements by considering the neighbor cell interference, and it also balances the macrocell load by switching the femtocells to HA mode. Moreover, it can reduce unnecessary muting of the base stations because it only enables the coordinated scheduling CoMP when there is no other possibility to reduce the cross‐tier interference. System‐level simulation results prove the validity of the proposed QoS‐CSaHSA scheme in a heterogeneous network environment. Compared with the existing schemes that operate the femtocells in a closed‐subscriber group without considering users' QoS requirements, the proposed scheme almost doubles the cell‐edge user throughput and reduces the packet loss rate and call‐blocking probability.

The purpose of this paper is to study quality of service (QoS) management and performance monitoring issues in B-ISDN. A QoS framework is proposed in order to satisfy the user's service quality requirements. The basic idea of this QoS framework is that the user's QoS requirement should be represented in terms of objectives set to QoS parameters which are in turn translated into asynchronous transfer mode (ATM) network performance (NP) parameters objectives; then, meeting NP parameters objectives ensures that the user's QoS requirements are fulfilled. Performance monitoring mechanisms based on operation and maintenance (OAM) cells flows in the ATM layer and estimation methods, which are both part of a QoS framework, are reviewed. We focus in particular on the definition and estimation of NP parameters in the case of non-conforming cells transmitted on an ATM connection. Finally, a QoS management scenario for the ATM layer is proposed. >

The growing emergence of a variety of network applications have stringent Quality of Service (QoS) requirements which cannot be always met by the best-effort Internet. To provide QoS, several QoS frameworks have been explored over last two decades, but none of them has been truly successful and globally implemented. This paper proposes QOF, a novel QoS framework based on the OpenFlow. It uses the superior control capabilities of OpenFlow network to transmit business having QoS requirements. On receiving QoS requirements of an application, QOF employs network status information provided by the measurement system (bandwidth, delay, packet loss), then uses QoS routing algorithm implemented by path computation module to guarantee QoS. Finally, QOF is simulated and verified, simulation results show that QOF can find a path satisfying QoS requirements of application according to network status information, and can improve resource usage.