Joint BS-User Association, Power Allocation, and User-Side Interference Cancellation in Cell-free Heterogeneous Networks

2017 ◽  
Vol 65 (2) ◽  
pp. 335-345 ◽  
Author(s):  
An Liu ◽  
Vincent K. N. Lau
Keyword(s):  
Power Allocation ◽  
Heterogeneous Networks ◽  
Interference Cancellation ◽  
User Association

scholarly journals An Optimal Joint User Association and Power Allocation Algorithm for Secrecy Information Transmission in Heterogeneous Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Chai ◽  
Mingxue Chen ◽  
Qianbin Chen ◽  
Yuanpeng Gao
Keyword(s):  
Energy Efficiency ◽  
Information Transmission ◽  
Power Allocation ◽  
Heterogeneous Networks ◽  
Optimization Problem ◽  
Transmission Rate ◽  
Rapid Development ◽  
Base Stations ◽  
User Association ◽  
Efficient Manner

In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets). In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs) to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE). By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M) algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.

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.

Sign in / Sign up

Export Citation Format

Share Document