基于上下文学习的电力物联网接入控制方法

doi:10.11959/j.issn.1000-436x.2021062

Abstract

Abstract:

In view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was based on reinforcement learning and fast uplink grant technology, considering active state and dormant state of terminals, and the optimization objective was to maximize the total network energy efficiency under the long-term constraint of terminal access service quality requirements.Lyapunov optimization was used to decouple the long-term optimization objective and constraint, and the long-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems, which could be solved by the terminal state-aware upper confidence bound algorithm.The simulation results show that CLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.Compared with the traditional fast uplink grant, CLAC can improve the average energy efficiency by 48.11%, increase the proportion of terminals meeting access service quality requirements by 54.95%, and reduce the average queue backlog by 83.83%.

Key words: 6G, power IoT, massive terminal access, context-aware learning, fast uplink grant

CLC Number:

TN915

Zhenyu ZHOU, Zehan JIA, Haijun LIAO, Xiongwen ZHAO, Lei ZHANG. Context-aware learning-based access control method for power IoT[J]. Journal on Communications, 2021, 42(3): 150-159.

Figures/Tables 7

References 24

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参数	含义
J/个	基站/边缘服务器数量
K/个	终端数量
M/个	基站可授权的终端数量
T/个	时隙数量
τ/s	时隙长度
A_k/个	到达终端u_k的子任务数量
ρ/bit	子任务大小
q_k	终端u_k存储在本地缓存区的任务数据队列
Q_k/bit	终端u_k存储在本地缓存区的数据队列积压
U_k,j/bit	终端u_k传输到基站s_j的任务数据量
U_k/bit	终端u_k的吞吐量
a_k	活跃指示变量，表示终端u_k处于活跃态或休眠态
x_{k, j}	授权指示变量，表示终端u_k是否获取基站s_j许可
γ_k,j/dB	数据传输信噪比
P_TX,k/W	终端u_k的传输功率
g_k,j/dB	终端u_k与基站s_j之间的信道增益
B_k,j/kHz	终端u_k与基站s_j之间的传输带宽
N₀/(W·Hz^-1)	噪声功率谱密度
E_{k, j}/J	终端u_k接入基站s_j的传输能耗
EE_k,j/(bit·J^-1)	终端u_k接入基站s_j的传输能量效率
η_k	终端u_k的接入服务质量约束
X_{k, T}/个	T个时隙内，终端u_k获得基站授权的总时隙数
T _k′/个	T个时隙内，终端u_k处于活跃态的总时隙数
F_k	终端u_k的接入服务质量需求赤字虚拟队列积压
θ_{k, j}	终端u_k接入基站s_j时的能量效率和接入性能的加权和
V_{k, j}	基站s_j对终端u_k性能的估计值
P_k	预测终端u_k的活跃概率
z_{k, j}(t)	终端u_k被基站s_j授权的累积回报
n_{k, j}(t)/个	终端u_k处于活跃态且被基站s_j授权的总时隙数
t'k/个	终端u_k处于活跃态的总时隙数
?	算法探索权重
V_EE	能量效率权重
V_F	接入性能权重

参数	取值
J	3
K	3 000
M	300
T	500
τ/s	1
A_k	[4,6]
ρ/bit	10⁴
P_TX,k/W	0.1
B_k,j/kHz	200
N₀ /(W·Hz^-1)	10^-12
V_EE	5
V_F	5
?	0.2

Context-aware learning-based access control method for power IoT

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