Energy-spectrum efficiency trade-off for multi-cognitive relay network with decode-and-forward full-duplex maximum energy harvesting

doi:10.11772/j.issn.1001-9081.2023040534

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1202-1208.DOI: 10.11772/j.issn.1001-9081.2023040534

• Network and communications • Previous Articles

Energy-spectrum efficiency trade-off for multi-cognitive relay network with decode-and-forward full-duplex maximum energy harvesting

Zhipeng MAO¹^,², Runhe QIU¹^,²()

^1.College of Information Science and Technology，Donghua University，Shanghai 201620，China
^2.Engineering Research Center of Digitized Textile & Apparel Technology，Ministry of Education，Shanghai 201620，China

Received:2023-05-06 Revised:2023-07-28 Accepted:2023-08-07 Online:2024-04-22 Published:2024-04-10
Contact: Runhe QIU
About author:MAO Zhipeng， born in 2000， M. S. candidate. His research interests include cognitive relay network.
QIU Runhe， born in 1961， Ph. D.， professor. His research interests include communication and information system， cognitive wireless network， cognitive cooperative relay network， wireless remote monitoring system.
Supported by:
Natural Science Foundation of Shanghai(20ZR1400700)

解码转发全双工能量收集最大的多认知中继网络能效谱效权衡

毛志鹏¹^,², 仇润鹤¹^,²()

^1.东华大学信息科学与技术学院，上海 201620
^2.数字化纺织服装技术教育部工程研究中心，上海 201620

通讯作者: 仇润鹤
作者简介:毛志鹏（2000—），男，江西抚州人，硕士研究生，主要研究方向：认知中继网络
仇润鹤（1961—），男，上海人，教授，博士，主要研究方向：通信与信息系统、认知无线网络、认知协作中继网络、无线远程监控系统。qiurh@dhu.edu.cn
基金资助:
上海市自然科学基金资助项目(20ZR1400700)

Abstract

Abstract:

In a full-duplex multi-cognitive relay network supported by Simultaneous Wireless Information and Power Transfer （SWIPT）， in order to maximize energy-spectrum efficiency， the relay with the maximum energy harvesting was selected for decoding and forwarding， thus forming an energy-spectrum efficiency trade-off optimization problem. The problem was transformed into a convex optimization problem by variable transformation and concave-convex process optimization method. When the trade-off factor was 0， the optimization problem was equivalent to the optimization problem of maximizing the Spectrum Efficiency （SE）. When the trade-off factor was 1， the optimization problem was equivalent to the problem of minimizing the energy consumed by the system. In order to solve this optimization problem， an improved algorithm that could directly obtain the trade-off factor for maximizing Energy Efficiency （EE） was proposed， which was optimized by combining the source node transmit power and the power split factor. The proposed algorithm was divided into two steps. First， the power split factor was fixed， and the source node transmit power and trade-off factor that made the EE optimal were obtained. Then， the optimal source node transmit power was fixed， and the optimal power split factor was obtained by using the relationship between energy-spectrum efficiency and power split factor. Through simulation experimental results， it is found that the relay network with the maximum energy harvesting is better in EE and SE than the network composed of other relays. Compared with the method of only optimizing the transmit power， the proposed algorithm increases the EE by more than 63%， and increases the SE by more than 30%； its EE and SE are almost the same as the exhaustive method， and the proposed algorithm converges faster.

Key words: full-duplex, cognitive relay, energy harvesting, energy-spectrum efficiency, trade-off factor

摘要：

在无线携能（SWIPT）支持下的全双工多认知中继网络中，为使能效谱效最大，选择能量收集最大的中继进行解码转发，以此形成一个能效谱效权衡最优化问题，采用变量变换和凹凸过程优化方法将该问题转换为凸优化问题。当权衡因子为0时，该优化问题等价于使谱效（SE）最大的优化问题；当权衡因子为1时，该优化问题等价于使系统消耗的能量最小问题。为求解该优化问题，提出一种可以直接得到使能效（EE）最大的权衡因子的改进算法，通过联合源节点发射功率和功率分割因子进行优化。所提算法分为两步：首先固定功率分割因子，得到使EE最优的源节点发射功率和权衡因子；其次，固定最优的源节点发射功率，利用能效谱效与功率分割因子之间的关系，求得最优的功率分割因子。仿真实验结果表明，相较于剩余其他中继组成的网络，能量收集最大的中继网络在EE和SE上都更优。在能效谱效方面，与只优化发射功率的方法相比，所提算法的EE提高了63%以上，SE提高了30%以上；所提算法EE和SE与穷举法相差不大，同时算法收敛较快。

关键词: 全双工, 认知中继, 能量收集, 能效谱效, 权衡因子

CLC Number:

TN925

Zhipeng MAO, Runhe QIU. Energy-spectrum efficiency trade-off for multi-cognitive relay network with decode-and-forward full-duplex maximum energy harvesting[J]. Journal of Computer Applications, 2024, 44(4): 1202-1208.

毛志鹏, 仇润鹤. 解码转发全双工能量收集最大的多认知中继网络能效谱效权衡[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1202-1208.

Figures/Tables 11

Fig. 1 Full-duplex multi-cognitive relay network

Tab. 1 Definition of variables

变量	定义
$S$	次级网络源节点
$R m$	中继节点
$D$	次级网络目的节点
$P S$	主网络源节点
$R$	收集能量最大的中继
$P S$	源节点 $S$ 的发射功率
$P m a x$	源节点 $S$ 最大的发射功率
$P R m$	中继节点 $R m$ 的发射功率， $m ∈ {1,2, …, M}$
$P R$	中继节点 $R$ 的发射功率
$h S R m 2$	源节点 $S$ 与中继 $R m$ 之间的信道增益
$h S R 2$	源节点 $S$ 与中继 $R$ 之间的信道增益
$h R m D 2$	中继 $R m$ 与目的节点 $D$ 之间的信道增益
$h R D 2$	中继 $R$ 与目的节点 $D$ 之间的信道增益
$h r r 2$	中继节点剩余自干扰的信道增益，每个中继相同
$h s p 2$	源节点 $S$ 与源节点 $P S$ 之间的信道增益
$h r p 2$	中继 $R$ 与源节点 $P S$ 之间的信道增益
$h r m p 2$	中继 $R m$ 与源节点 $P S$ 之间的信道增益
$ρ$	功率分割因子， $ρ ∈ (0,1)$
$η$	能量转换效率
$t$	传输时间
$Q E E$	次级网络能效
$Q S E$	次级网络谱效
$I t h$	次级网络对主网络的干扰阈值
$n$	迭代次数
$w$	权衡因子
$w E E$	使能效最大的权衡因子

Tab. 1 Definition of variables

变量	定义
$S$	次级网络源节点
$R m$	中继节点
$D$	次级网络目的节点
$P S$	主网络源节点
$R$	收集能量最大的中继
$P S$	源节点 $S$ 的发射功率
$P m a x$	源节点 $S$ 最大的发射功率
$P R m$	中继节点 $R m$ 的发射功率， $m ∈ {1,2, …, M}$
$P R$	中继节点 $R$ 的发射功率
$h S R m 2$	源节点 $S$ 与中继 $R m$ 之间的信道增益
$h S R 2$	源节点 $S$ 与中继 $R$ 之间的信道增益
$h R m D 2$	中继 $R m$ 与目的节点 $D$ 之间的信道增益
$h R D 2$	中继 $R$ 与目的节点 $D$ 之间的信道增益
$h r r 2$	中继节点剩余自干扰的信道增益，每个中继相同
$h s p 2$	源节点 $S$ 与源节点 $P S$ 之间的信道增益
$h r p 2$	中继 $R$ 与源节点 $P S$ 之间的信道增益
$h r m p 2$	中继 $R m$ 与源节点 $P S$ 之间的信道增益
$ρ$	功率分割因子， $ρ ∈ (0,1)$
$η$	能量转换效率
$t$	传输时间
$Q E E$	次级网络能效
$Q S E$	次级网络谱效
$I t h$	次级网络对主网络的干扰阈值
$n$	迭代次数
$w$	权衡因子
$w E E$	使能效最大的权衡因子

Fig. 2 Relationship between power split factor and γSR and γRD

Tab. 2 Simulation parameters

参数	参数值
信道衰弱因子 $α$	2
自干扰信道增益 $h r r 2$	0.235
能量转换效率 $η$	0.5
静态电路消耗的功率 $P c$	5 dBm
高斯白噪声的方差 $σ 2$	20 dBm
源节点最大发射功率 $P m a x$	3 W
最小传输速 $C m i n$	0.01 bit/s
干扰阈值 $I t h$	4

Tab. 2 Simulation parameters

参数	参数值
信道衰弱因子 $α$	2
自干扰信道增益 $h r r 2$	0.235
能量转换效率 $η$	0.5
静态电路消耗的功率 $P c$	5 dBm
高斯白噪声的方差 $σ 2$	20 dBm
源节点最大发射功率 $P m a x$	3 W
最小传输速 $C m i n$	0.01 bit/s
干扰阈值 $I t h$	4

Fig. 3 Relationship between network spectrum efficiency and transmitting power of source node under different relay conditions

Fig. 4 Relationship between network energy efficiency and transmitting power of source node under different relay conditions

Fig. 5 System energy efficiency and spectrum efficiency under different power split factors

Fig. 6 Relationship between convergence of system energy efficiency and number of iterations

Fig. 7 Relationship between energy-spectrum efficiency of system and tradeoff factor

Fig. 8 Energy efficiency under different optimization schemes

Fig. 9 Spectrum efficiency under different optimization schemes

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Energy-spectrum efficiency trade-off for multi-cognitive relay network with decode-and-forward full-duplex maximum energy harvesting

解码转发全双工能量收集最大的多认知中继网络能效谱效权衡

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Figures/Tables 11

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