Enabling seamless multimedia wireless access through QoS‐based bandwidth adaptation

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.

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.

Multi-threshold bandwidth reservation scheme of an integrated voice/data wireless network

Bayesian optimization has been used for the global optimization of communication parameters of Vehicular Ad Hoc Networks (VANETs) for safety applications with stringent quality of service (QoS) requirements. However, the effectiveness of the methodology relies on an accurate analytic model for querying a distribution over functions, which is not practical. Furthermore, incorporating QoS requirements as constraints into the search process is cumbersome, timeconsuming, and even unreliable. In this paper, we present a new approach to the constrained Bayesian optimization of IEEE 802.11 based VANETs for safety messaging with the help of deep learning neural networks (DLNNs). First, we design and train a DLNN using data collected from the analytic models or channel measurements to approximate a mapping from the search parameter space to the QoS metrics. The QoS constraints are naturally incorporated into the DLNN through preprocessing data pairs that cannot meet the QoS requirements. Then, the Bayesian optimization is conducted to find the optimal communication parameters for the best channel usage on the condition that all QoS requirements are met. Accordingly, experiments are carried out on the Google Colab platform where the impact of DLNN structure, data sampling rate, and other optimization parameters are investigated. In comparison to other optimization approaches, utilizing a DLNN in the Bayesian optimization process is more time efficient and flexible.

Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.

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.

In this paper we propose a general utility-oriented adaptive Quality of Service (QoS) model for wireless networks and establish a framework for formulating the bandwidth allocation problem for users with time-varying links. For slow link variations, it is inadequate to only employ low-level adaptive mechanisms at the symbol or packet level, such as error correetion coding or swapping packet transmission opportunities. To improve bandwidth utilization and satisfy users' QoS requirements, high-level adaptive mechanisms working at larger time scale is needed. We propose an adaptive bandwidth allocation scheme, which is capable of providing QoS guarantees, ensuring long-term fairness, and achieving high bandwidth utilization. A finite-state Markov channel model (FSMC) is used to model wireless links.

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.

To guarantee the real-time transmission for time-sensitive traffic, we need to take delay-bound quality-of-service (QoS) into account when designing the wireless cellular networks. The traditional powerful QoS guarantee technique, called the homogeneous statistical QoS provisioning, assumes that the QoS provisioning of each link can be individually processed. In order to further significantly increase the global system throughput, the available resources of all links need to be jointly controlled, which is typically suitable for downlink transmissions over wireless cellular networks. Under this setup, we need to consider the diverse delay-bound QoS provisionings for different links at the same time, which represents the new heterogeneous statistical QoS provisioning framework and imposes many new challenges not encountered before in wireless networks. To overcome these problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for high-speed downlink transmissions in wireless cellular networks. In particular, we formulate the optimization problem to maximize the downlink throughput subject to heterogeneous statistical delay-bound QoS requirements. For solving this optimization problem, we develop the heterogeneous-QoS-driven power allocation scheme to derive the closed-form solutions which can maximize the global system throughput while guaranteeing the heterogeneous delay-bound QoS for the entire wireless cellular networks. We show that the heterogeneous-QoS-driven power allocation provides more generic framework for downlink transmissions. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can achieve the global optimization of wireless resource efficiency, thus significantly increasing the global system throughput as compared with the homogeneous statistical delay-bound QoS provisioning schemes.

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.

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.

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.

We propose a wireless multimedia DS-CDMA system with dynamic power assignment (DPA) for adaptive traffic control. The system supports service groups with different quality of service (QoS) requirements specified in terms of both transmission bit rate and bit error rate (BER). The optimal received power level for each service group is obtained by minimizing a cost function consisting of the weighted sum of the blocking rates of different service groups subject to their QoS requirements. The performance is compared to a similar system with fixed power assignment (FPA) in which the received power level for each service group is assigned based only on its transmission bit rate. Simulation results show that DPA algorithm can support much higher offered traffic loads, on merit of the blocking probability. By implementing the cost function with different weighting coefficients, we can assign different blocking probabilities to different service groups to cope with the demands in the system. The DPA algorithm therefore plays the role of a movable blocking boundary in the system which can accommodate multimedia traffic and priority-based capacity access.

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.

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.