Quality-oriented Rate Control and Resource Allocation in Time-Varying OFDMA Networks

Guo, Yashuang; Yang, Qinghai; Kwak, Kyung Sup

doi:10.1109/tvt.2016.2575920

Cited by 43 publications

(33 citation statements)

References 34 publications

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“…It can be observed that the satisfaction of D2D pairs and the average network delay increase as V increases. The reason is consistent with the second and third properties of the snapshot based algorithm [10] is denoted as the baseline algorithm to maximize instantaneous satisfaction of all D2D pairs for the purpose of comparison. Typically, the baseline algorithm only considers the service demands of users in short-term while ignoring the power constraint and stability of the network in longterm.…”

Section: Resultssupporting

confidence: 56%

“…Assume that there are M = 4 D2D pairs for data transmission in each time slot, and the corresponding number of subchannels is set as 4. Detailed parameters are summarized in Table 1 [10,34,35]. Figure 2 shows the queue length of the data queue Q m (t) and the virtual power queue Z m (t) versus time slots, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Various constraints of network reliability, transmission delay, and power consumption have been taken into consideration. The difference between [10] and our work is that instead of solving the large-scale network optimization problem directly, we develop a low-complexity power allocation algorithm based on ADMM.…”

Section: Related Workmentioning

confidence: 99%

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Joint rate control and power allocation for low-latency reliable D2D-based relay network

Wang

et al. 2019

J Wireless Com Network

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Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve the cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (ADMM) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate that the proposed algorithm can reach 99.9% of the optimal satisfaction of D2D pairs with lower average network delay compared to the baseline algorithm. Furthermore, the convergence time of the ADMM-based power allocation algorithm is only about 1.7% of that by using the CVX toolbox.

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Section: Resultssupporting

confidence: 56%

Section: Resultsmentioning

confidence: 99%

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Joint rate control and power allocation for low-latency reliable D2D-based relay network

Wang

et al. 2019

J Wireless Com Network

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“…Time slots on subchannel can be arranged according to the priority of transmission [16,17]. Other resources like data rate and power allocation can be adaptive with both time-varying channel and user's quality requirement [18].…”

Section: Introductionmentioning

confidence: 99%

“…For resource allocation in cloud and wireless communication, most researches commonly formulate the issue into optimization problems with the subjects of resource limitations, for example, energy consumption [18,20,21], bandwidth [22], financial cost [23], processing time [24], utility function [25], secrecy outage probability [26], and caching [27] The assumption of these approaches is that the resource requirements of different clients or traffic should be explicit, while the summation of total resource is beyond the limitation. In the circumstance of UAV cloud, these approaches are not suitable anymore because the model is different.…”

Section: Introductionmentioning

confidence: 99%

BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

Gao

et al. 2018

Journal of Sensors

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Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters' self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption.

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