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.

Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm

2016 ◽  
Vol 12 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Haider AlSabbagh
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Base Stations ◽  
Resource Allocation Problem ◽  
Effective Parameters

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

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 Energy consumption index minimized resource allocation in hybrid energy multiuser OFDM system with distributed antennas

2018 ◽  
Vol 17 ◽  
pp. 03015
Author(s):  
Huanhuan MAO ◽  
Pengcheng Zhu ◽  
Jiamin Li
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Energy Harvesting ◽  
Power Allocation ◽  
Base Station ◽  
Mixed Integer ◽  
Subcarrier Allocation ◽  
Ofdm System ◽  
Consumption Index ◽  
Distributed Antennas

Energy harvesting is one of the promising option for realization of green communication and has been a growing concern recently. In this paper, we address the downlink resource allocation in OFDM system with distributed antennas with hybrid power supply base station, where energy harvesting and non-renewable power sources are used complementarily. A joint subcarrier and power allocation problem is formulated for minimizing the net Energy Consumption Index (ECI) with system Quality of Service (QoS) and bit error rates constraint. The problem is a 0-1 mixed integer nonlinear programming problem due to the binary subcarrier allocation variable. To solve the problem, we design an algorithm based on Lagrange relaxation method and fraction programming which optimizes the power allocation and subcarrier allocation iteratively in two nests. Simulation results show that the proposed algorithm converges in a small number of iterations and can improve net ECI of system greatly.

scholarly journals Resource Allocation for Intelligent Reflecting Surface Enabled Heterogeneous Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Zhijin Qin ◽  
Yu Zhao ◽  
Guoquan Li ◽  
Guan Gui ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Phase Shift ◽  
Power Allocation ◽  
Heterogeneous Networks ◽  
Communication Systems ◽  
Base Station ◽  
Small Cell ◽  
Joint Power ◽  
User Scenario ◽  
Reflecting Surface

Intelligent reflecting surface (IRS)-enabled communication systems provide higher system capacity and spectral efficiency by reflecting the incident signals from transmitters in a low-cost passive reflecting way. However, it poses new challenges in resource allocation due to surrounding interference and phase shift, especially when IRS is employed in heterogeneous networks (HetNets). In this paper, a joint power allocation and phase shift optimization problem is studied for the downlink IRS-enabled HetNet, in which the IRS is deployed to enhance the communications between small cell users (SCUs) and associated base station (BS). The signal-to-interference-plus-noise ratio (SINR) received at the SCU is maximized by jointly optimizing the transmit power of the small-cell BS and the phase shift of the IRS, subject to the constraints on the minimum SINR requirement of the macro-cell user (MCU) and the phase shift. Although the formulated problem is non-convex, we develop an optimal power allocation and the IRS's passive array coefficient solution for the single-user scenario. For the multi-user scenario, we propose an iterative algorithm to maximize the total rates of SCUs for obtaining a suboptimal solution by an alternating iteration manner, where the sum of multiple-ratio fractional programming problem is converted into a convex semidefinite program (SDP) problem. Simulation results show that the proposed algorithm significantly improves the achieved transmission rates of SCUs compared to the case without the IRS.

scholarly journals Resource Allocation for Intelligent Reflecting Surface Enabled Heterogeneous Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Zhijin Qin ◽  
Yu Zhao ◽  
Guoquan Li ◽  
Guan Gui ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Phase Shift ◽  
Power Allocation ◽  
Heterogeneous Networks ◽  
Communication Systems ◽  
Base Station ◽  
Small Cell ◽  
Joint Power ◽  
User Scenario ◽  
Reflecting Surface

Intelligent reflecting surface (IRS)-enabled communication systems provide higher system capacity and spectral efficiency by reflecting the incident signals from transmitters in a low-cost passive reflecting way. However, it poses new challenges in resource allocation due to surrounding interference and phase shift, especially when IRS is employed in heterogeneous networks (HetNets). In this paper, a joint power allocation and phase shift optimization problem is studied for the downlink IRS-enabled HetNet, in which the IRS is deployed to enhance the communications between small cell users (SCUs) and associated base station (BS). The signal-to-interference-plus-noise ratio (SINR) received at the SCU is maximized by jointly optimizing the transmit power of the small-cell BS and the phase shift of the IRS, subject to the constraints on the minimum SINR requirement of the macro-cell user (MCU) and the phase shift. Although the formulated problem is non-convex, we develop an optimal power allocation and the IRS's passive array coefficient solution for the single-user scenario. For the multi-user scenario, we propose an iterative algorithm to maximize the total rates of SCUs for obtaining a suboptimal solution by an alternating iteration manner, where the sum of multiple-ratio fractional programming problem is converted into a convex semidefinite program (SDP) problem. Simulation results show that the proposed algorithm significantly improves the achieved transmission rates of SCUs compared to the case without the IRS.

Optimized Base Station Sleeping and Renewable Energy Procurement Scheme Using PSO

2017 ◽  
Vol 8 (1) ◽  
pp. 54-73
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Wireless Networks ◽  
Renewable Energy ◽  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Green Communication

Energy efficiency of the wireless networks has drawn more and more attentions due to the requirement of the green communication. The base station sleeping strategy and resource allocation can effectively improve the energy efficiency for the wireless networks. Meanwhile, renewable energy is important to decrease the carbon emission. In this paper, the authors propose a joint BS sleeping strategy, resource allocation and renewable energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment and energy procurement. Numerical results illustrate the effectiveness of their proposed scheme and algorithm.

Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

2018 ◽  
pp. 357-378
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Swarm Optimization ◽  
Improve Energy Efficiency ◽  
Dual Domain ◽  
Mixed Integer Programming Problem ◽  
Subcarrier Assignment

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

2022 ◽  
pp. 465-486
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Swarm Optimization ◽  
Improve Energy Efficiency ◽  
Dual Domain ◽  
Mixed Integer Programming Problem ◽  
Subcarrier Assignment

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

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