Power assignment strategies and traffic control for wireless multimedia DS-CDMA systems

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.

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this paper, we consider a fifth generation (5 G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

The paper considers discrete multitone (DMT) modulation for multiuser communications when multiple users are supported by the same system, a zero-padding redundancy is employed at the transmitter output, and linear redundancy removal is used at the channel output. These users may have differing quality of service (QoS) requirements, as quantified by bit rate and symbol-error rate specifications, and are each assigned an equal number of subchannels. Our goal is to minimize the transmitted power, given the QoS specifications and subject to the knowledge of the channel and the second-order statistics of the colored interference at the receiver input. In particular, we find an optimum bit-loading scheme that distributes the bit rate transmitted across the various subchannels belonging to each user, and, subject to this bit allocation, we determine the precise subchannels assigned to each user, the optimum transceiver characterized by the input/output block transforms, and the redundancy removal operation. A major conclusion is that even though the optimum bit-rate allocation differs from the single-user case, the optimum transceiver does not. Further, it is determined entirely by the channel and interference conditions, and is unaffected by the QoS requirements.

The joint design of the multi-stream in MIMO relay sigle/multiple user system is investigated. Under the inter-stream interference, a constrained optimization problem with respect to the unknown relay precoder matrix is formulated with a goal of minimizing the total relay transmit power subject to quality of service (QoS) requirement on each data stream. As the original opitimization problem is non-convex, the matrix expression of the cost function and constrained requirement is converting to a scalar optimization problem and the optimal relay precoder and receive filter are obtained. The optimal design of the multiple user relay system under QoS requirement is also provided. By using a reasonable approximation and converting to the generalized Rayleigh form for the cost function, a suboptimal relay precoder matrix for QoS optimization with relay transmit power constraint is obtained. Simulation results demonstrate that for single user model, the minimize relay power will increase with Signal to Interference plus Noise Ratio(SINR) constraint value becoming larger, and for multiple users model, more users in our algorithm will make the MSE gain more obvious as increasing transmitting SNR.

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.

We consider a Web server that can provide differentiated services to clients with different quality of service (QoS) requirements. The Web server can provide N/spl ges/1 classes of proportional-delay differentiated services (PDDS) to heterogeneous clients. An operator can specify fixed performance spacings between classes, namely, r/sub i,i+1/>1, for i=1,..., N-1. Requests in class i+1 are guaranteed to have an average waiting time which is 1/r/sub i,i+1/ of the average waiting time of class i requests. With PDDS, we can provide consistent performance spacings over a wide range of system loading and this simplifies many pricing issues. In addition, each client can specify a maximum average waiting time requirement to be guaranteed by the PDDS-enabled Web server. We show that, in general, the problem of assigning clients to service classes in order to optimize system efficacy is NP-complete. We propose two efficient admission control algorithms so that a Web server can provide the QoS guarantees and, at the same time, classify each client to its "lowest" admissible class, resulting in lowest usage cost for the admitted client. We also consider how to perform end-point dynamic adaptation such that admitted clients can submit requests at a lower class and further reduce their usage costs without violating their QoS requirements. We propose two dynamic adaptation algorithms: one is server-based and the other is client-based. The client-based adaptation is distributed and is based on a noncooperative game technique. We carry out experiments to illustrate the effectiveness of these algorithms under different utility functions and traffic arrival patterns (e.g., Poisson, MMPP, and Pareto). We report extensive experimental results to illustrate the effectiveness of our proposed algorithms.

Effective support of real‐time multimedia applications in wireless access networks, viz. cellular networks and wireless LANs, requires a dynamic bandwidth adaptation framework where the bandwidth of an ongoing call is continuously monitored and adjusted. Since bandwidth is a scarce resource in wireless networking, it needs to be carefully allocated amidst competing connections with different Quality of Service (QoS) requirements. In this paper, we propose a new framework called QoS‐adaptive multimedia wireless access (QoS‐AMWA) for supporting heterogeneous traffic with different QoS requirements in wireless cellular networks. The QoS‐AMWA framework combines the following components: (i) a threshold‐based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold‐type connection admission control algorithm, and (iii) a bandwidth adaptation algorithm that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. The framework can be modeled as a multi‐dimensional Markov chain, and therefore, a product‐form solution is provided. The QoS metrics—new call blocking probability (NCBP), handoff call dropping probability (HCDB), and degradation probability (DP)—are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability and maintaining the bandwidth utilization efficiently. Copyright © 2006 John Wiley & Sons, Ltd.

In an era of uncertain business tendencies, realizing profitable optical networks is emerging as the most urgent task for optical operators. This task is aggravated by the need to support quality of service (QoS) differentiation, especially with the continuous development of new applications, each having different QoS requirements. This letter proposes to tackle these challenges through the definition of a novel QoS-aware connection setup strategy. The proposed setup approach enables network operators to ensure both efficient utilization of network resources and connection blocking probability differentiation. Resource efficiency is achieved via event-driven rerouting, whereas differentiated blocking probability is realized by preemption. Specifically, high priority connections are equipped with the capability of preempting lower priority connections, which through event-driven rerouting experience smaller blocking probability. Performance of this novel connection setup strategy is analyzed in this letter by simulating its operation in the context of the National Science Foundation Network (NSFNET). The obtained simulation results show that the proposed strategy can minimize the overall blocking probability while achieving QoS differentiation.

This paper investigates a multi-unmanned aerial vehicle (UAV) enabled wireless communication, where a number of ground nodes (GNs) are scheduled to communicate with UAVs in the presence of jammers with imperfect location information. Considering different quality of service (QoS) requirements for a wide range of applications, we aim to improve the minimum throughput, the average throughput, and the delay-constrained minimum throughput of all GNs, respectively, via the joint design of UAVs' trajectories, GNs' scheduling and power allocation. However, the formulated optimization problems are difficult to solve due to the non-convex and combinatorial nature. To overcome this difficulty, we propose two block coordinate descent (BCD) based algorithms to solve them sub-optimally with the aid of slack variables, successive convex approximation (SCA) technique and S-procedure. Numerical results show that our proposed algorithms outperforms the benchmark algorithms and offers a considerable gain in the view of different QoS requirements, giving a certain practical significance.

Applications running on the same Wireless Sensor Network (WSN) platform usually have different Quality of Service (QoS) requirements. Two basic requirements are low delay and high data integrity. However, in most situations, these two requirements cannot be satisfied simultaneously. In this paper, based on the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential</i> in physics, we propose IDDR, a multi-path dynamic routing algorithm, to resolve this conflict. By constructing a virtual hybrid potential field, IDDR separates packets of applications with different QoS requirements according to the weight assigned to each packet, and routes them towards the sink through different paths to improve the data fidelity for integrity-sensitive applications as well as reduce the end-to-end delay for delay-sensitive ones. Using the Lyapunov drift technique, we prove that IDDR is stable. Simulation results demonstrate that IDDR provides data integrity and delay differentiated services.

Although cloud storage service (CSS) is convenient to access data, the cost, security, and performance are the issues to users who consider to adopt CSS. For example, CSS could be unavailable due to technical issues or natural disaster. In order to avoid single point of failure, many users duplicate data into multiple CSS providers (CSPs). However, this will increase cost; decrease security; and is difficult to maintain data consistency. In this paper, we propose a QoS-aware Distributed Cloud Storage Service (QDCSS) which aims to provide users with the appropriate CSS according to their quality of service (QoS) requirements. In QDCSS, the data will be encoded into code words by applying erasure code and then the code words will be smartly distributed to multiple CSPs to meet users' QoS requirements. Compared with data duplication, the numerical analysis show that QdCSS can reduce cost by 73% and improve security by 63%. Moreover, the simulation results show that QDCSS can adapt to different users' QoS requirements.

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.

Edge computing is a new computing paradigm after cloud computing. Edge service scheduling and composition (ESC) has been well recognized as a convenient and flexible way of services sharing and integrating in industrial application fields. The ESC aims at selecting a set of existing edge service candidates with different Quality of Service (QoS) attributes (e.g., price), then compositing them to accomplish a complex task to meet users' QoS requirements, where each edge service may have multiple functionally equivalent, but different QoS metrics. A grand research challenge of the ESC based on QoS is to select proper service candidates to maximize QoS of the composited edge service and meanwhile meet the global QoS requirements. In this article, we focus on this challenge and propose a hierarchical solution for ESC. Specifically, we classify service candidates into specified grades based on their QoS attributes, and use hierarchical model to address service selection problem. Furthermore, in order to expand the flexibility of our approach, we design a near-optimal solution by decomposing the global end-to-end QoS constraints into local QoS constraints and adopting local service selecting and updating strategy based on the hierarchical model. The results of simulation-based experiments are conducted to show our approach is more efficient and valid compared to other existing approaches.

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.

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.