scholarly journals A Joint Subcarrier Assignment and Power Allocation with Statistical Delay QoS Provisioning in Full-Duplex OFDMA Systems

2018 ◽  
Vol 17 ◽  
pp. 01009
Author(s):  
Shuai Yan ◽  
Xuefen Chi ◽  
Linlin Zhao ◽  
Yuhong Zhu
Keyword(s):  
Integer Programming ◽  
Power Allocation ◽  
Programming Problem ◽  
Allocation Problem ◽  
Full Duplex ◽  
Mixed Integer ◽  
Effective Capacity ◽  
Integer Programming Problem ◽  
Statistical Delay ◽  
Subcarrier Assignment

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

scholarly journals Optimizing the Spectrum and Power Allocation for D2D-Enabled Communication Systems Using DC Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guomei Gan ◽  
Yanhu Huang ◽  
Qiang Wang
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Dc Programming ◽  
Base Station ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Resource Allocation Problem ◽  
Subcarrier Assignment

Device to device (D2D) communication has recently attracted a lot of attentions since it can significantly improve the system throughput and reduce the energy consumption. Indeed, the devices can communicate with each other in a D2D system, and the base station (BS) can share the spectrum with D2D users, which can efficiently improve the spectrum and energy efficiency. Nevertheless, spectrum sharing also raises the difficulty of resource allocation owing to the serious cochannel interference. To reduce the interference, the transmit power of the D2D pairs and BS to cellular users should be further optimized. In this paper, we consider the resource allocation problem of D2D networks involving the power allocation and subcarrier assignment. The resource allocation problem is formulated as a mixed integer programming problem which is difficult to solve. To reduce the computational complexity, the original problem is decomposed as two subproblems in terms of the subcarrier assignment and power allocation. For the subcarrier assignment problem, the particle swarm optimization (PSO) is adopted to solve it since the subcarrier assignment is an integer optimization problem, and it is difficult to be tackled using the traditional optimization approach. When the subcarrier assignment is fixed, there are only the power allocation variables in the original resource allocation problem. The difference of convex functions (DC) programming is adopted to solve the power allocation problem. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme of D2D networks.

scholarly journals Distributionally robust chance constrained svm model with $\ell_2$-Wasserstein distance

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Qing Ma ◽  
Yanjun Wang
Keyword(s):  
Integer Programming ◽  
Programming Problem ◽  
Numerical Experiments ◽  
Wasserstein Distance ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Discrete Form ◽  
Svm Model ◽  
Distributionally Robust ◽  
Mixed Integer Programming Problem

<p style='text-indent:20px;'>In this paper, we propose a distributionally robust chance-constrained SVM model with <inline-formula><tex-math id="M1">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein ambiguity. We present equivalent formulations of distributionally robust chance constraints based on <inline-formula><tex-math id="M2">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein ambiguity. In terms of this method, the distributionally robust chance-constrained SVM model can be transformed into a solvable linear 0-1 mixed integer programming problem when the <inline-formula><tex-math id="M3">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein distance is discrete form. The DRCC-SVM model could be transformed into a tractable 0-1 mixed-integer SOCP programming problem for the continuous case. Finally, numerical experiments are given to illustrate the effectiveness and feasibility of our model.</p>

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