scholarly journals Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Khush Bakht ◽  
Furqan Jameel ◽  
Zain Ali ◽  
Wali Ullah Khan ◽  
Imran Khan ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Power Allocation ◽  
Heterogeneous Networks ◽  
Cognitive Radios ◽  
Limiting Factors ◽  
Dual Decomposition ◽  
Joint Power ◽  
Decomposition Approach ◽  
Spectrum Scarcity ◽  
The Individual

The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.

scholarly journals Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

2021 ◽  
Vol 11 (9) ◽  
pp. 4135
Author(s):  
Chi-Kai Hsieh ◽  
Kun-Lin Chan ◽  
Feng-Tsun Chien
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Power Allocation ◽  
Heterogeneous Networks ◽  
User Association ◽  
Joint Power ◽  
Continuous Space ◽  
Wireless Backhaul ◽  
Continuous Actions ◽  
Efficient Power

This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs.

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.

scholarly journals Energy-Efficient Joint Base Station Switching and Power Allocation for Smart Grid Based Hybrid-Powered CoMP-Enabled HetNet

2021 ◽  
Vol 13 (8) ◽  
pp. 213
Author(s):  
Shornalatha Euttamarajah ◽  
Yin Hoe Ng ◽  
Chee Keong Tan
Keyword(s):  
Wireless Networks ◽  
Power Allocation ◽  
Low Complexity ◽  
Base Station ◽  
Design Criterion ◽  
Combinatorial Optimisation ◽  
Dual Decomposition ◽  
Hybrid Energy ◽  
Data Intensive ◽  
Bs Switching

With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO2 emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach’s method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15–20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method.

scholarly journals Joint Power Allocation and User Scheduling for Device-to-Device-Enabled Heterogeneous Networks With Non-Orthogonal Multiple Access

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 62657-62671 ◽  
Author(s):  
Jiaqi Liu ◽  
Gang Wu ◽  
Sa Xiao ◽  
Xiangwei Zhou ◽  
Geoffrey Ye Li ◽  
...  
Keyword(s):  
Power Allocation ◽  
Heterogeneous Networks ◽  
Multiple Access ◽  
User Scheduling ◽  
Joint Power ◽  
Device To Device

Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks with Imperfect Spectrum Sensing

2013 ◽  
Vol 846-847 ◽  
pp. 635-642
Author(s):  
Er Qing Zhang ◽  
Si Xing Yin ◽  
Liang Yin ◽  
Shu Fang Li
Keyword(s):  
Cognitive Radio ◽  
Power Allocation ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Rapid Development ◽  
Cognitive Radios ◽  
System Throughput ◽  
Dual Decomposition ◽  
Imperfect Spectrum Sensing ◽  
Efficient Power

With the rapid development of wireless network technologies and proliferation of related services such as multimedia applications, demands for wireless spectrum resources keep rising. Cognitive radio (CR) is a novel approach for better utilization of the scarce, already packed but highly underutilized radio spectrum. Meanwhile, exclusive functionalities such as spectrum sensing make energy efficiency (EE) a crucial issue in Cognitive Radios (CRs). In this paper, we focus on the energy-efficient power allocation for OFDM-based CRs with imperfect spectrum sensing. The EE maximization for secondary users (SUs) is formulated as a nonlinear fractional programming problem taking into account imperfect spectrum sensing such as miss detection and false alarm. Then by transforming the original problem into a parameter programming, the optimal power allocation is derived with the bisection search (BS) method and dual decomposition method (DDM). Simulation results illustrate the significant performance improvement of our scheme compared to an existing one with objective of maximizing system throughput rather than EE.

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