基于不完美CSI的水声自适应功率分配算法

doi:10.12305/j.issn.1001-506X.2023.09.34

摘要/Abstract

摘要：

水声信道面临带宽资源有限、环境复杂的问题, 为提高水下通信速率, 基于水声传感器网络的海洋应用提出自适应通信的需求。传统基于简单信噪比指标的自适应资源分配算法无法准确表述衰落信道的统计特征, 利用强化学习和卷积神经网络预测信道的方法虽然可以提高一定信道状态信息(channel state information, CSI)的准确性, 但这种方法需要长期的观测和大量的训练样本, 不符合水声环境的实际情况。对比, 构建了一种中继放大转发协作正交频分复用(orthogonal frequency division multiplexing, OFDM)通信的模型, 解决了由于节点漂浮导致直接通信传输效率变低的问题，并提出一种在时延反馈CSI中基于OFDM的自适应功率比特分配算法, 利用条件概率表征不完美的CSI, 调整自适应通信参数, 进行遍历容量最大化建模。仿真结果表明, 该算法实现功率和比特的联合自适应分配, 平均传输速率指标优于直接反馈CSI的功率分配算法, 虽然略低于采用马尔可夫链预测信道的方法, 但结合算法复杂度来看, 所提算法更简单, 更适合能量有限的水声传感器网络。

关键词: 水声传感器网络, 中继协作正交频分复用通信系统, 不完美信道状态信息, 自适应功率比特分配

Abstract:

The underwater acoustic channel faces the problems of limited bandwidth and complex environment.To improve the underwater communication rate, the marine application using the underwater acoustic sensor networks need adaptive communication.The traditional resource allocation algorithm using signal-noise ratio cannot accurately express the statistical characteristics of the fading channel.Although the method of reinforcement learning and convolutional neural network to predict the channel can improve accuracy of channel state information(CSI), it requires long observation and massive training samples, it is unsuitable for underwater acoustic environment. Thus, a relay amplification and forwarding cooperative orthogonal frequency division multiplexing(OFDM) communication model is constructed to solve the problem of low transmission efficiency of direct communication due to floating nodes. And an adaptive resource allocation algorithm based on OFDM in delay feedback CSI is proposed, which uses conditional probability to characterize imperfect CSI to adjust adaptive communication parameters, and conducts ergodic capacity maximization modeling. The simulation results show that it realizes joint adaptive allocation of power and bits, and the averager transmission rate is better than the power allocation algorithm with direct feedback of CSI, the proposed algorithm is more suitable for the energy-limited underwater acoustic sensor networks.

Key words: underwater acoustic sensor networks, relay cooperative orthogonal frequency division multi-plexing (OFDM) communication system, imperfect channel state information (CSI), adaptive power bit allocation

中图分类号:

TP393

金志刚, 尹欢, 苏毅珊. 基于不完美CSI的水声自适应功率分配算法[J]. 系统工程与电子技术, 2023, 45(9): 2941-2948.

Zhigang JIN, Huan YIN, Yishan SU. Underwater acoustic adaptive power allocation algorithm based on imperfect CSI[J]. Systems Engineering and Electronics, 2023, 45(9): 2941-2948.

图/表 12

图1

图2

图3

图4

图5

表1

表2

图6

图7

图8

图9

图10

参考文献 29

1	张育芝, 张效民, 王安义, 等. 水声通信网络信道建模与仿真研究进展[J]. 科学技术与工程, 2021, 21 (4): 1249- 1261. doi: 10.3969/j.issn.1671-1815.2021.04.002
	ZHANG Y Z , ZHANG X M , WANG A Y. , et al. Progress in channel modeling and simulation of underwater acoustic communication network[J]. Science Technology and Engineering, 2021, 21 (4): 1249- 1261. doi: 10.3969/j.issn.1671-1815.2021.04.002
2	EGBEWANDE A L , BOUSQUET J F , BARCLAY D R . The effect of directional ambient noise on an underwater acoustic link in shallow environments[J]. IEEE Journal of Oceanic Engineering, 2022, 47 (4): 1188- 1202. doi: 10.1109/JOE.2022.3178816
3	李加林, 沈满洪, 马仁锋, 等. 海洋生态文明建设背景下的海洋资源经济与海洋战略[J]. 自然资源学报, 2022, 37 (4): 829- 849.
	LI J L , SHEN M H , MA R F , et al. Marine resource economy and marine strategy under the background of marine ecological civilization construction[J]. Journal of Natural Resources, 2022, 37 (4): 829- 849.
4	FATTAH S , GANI A , AHME D I , et al. A survey on underwater wireless sensor networks: requirements, taxonomy, recent advances, and open research challenges[J]. Sensors, 2020, 20 (18): 5393. doi: 10.3390/s20185393
5	YAN H L, MA T L, PAN C Y, et al. Statistical analysis of time-varying channel for underwater acoustic communication and network[C]//Proc. of the International Conference on Frontiers of Information Technology, 2021: 55-60.
6	MEULEN E . Transmission of information in a terminal discrete memoryless channel[J]. Technology Report, 1968, 59 (4): 2442- 2458.
7	COVER T , GAMAL A E . Capacity theorems for the relay channels[J]. Transactions on Information Theory, 1979, 25 (5): 572- 584. doi: 10.1109/TIT.1979.1056084
8	MOHAMMADI Z. Modified relay node placement in dense 3D underwater acoustic sensor networks[C]//Proc. of the 9th Iranian Joint Congress on Fuzzy and Intelligent Systems, 2022.
9	ZHANG Y , VENKATESAN R , DOBRE O A , et al. Efficient estimation and prediction for sparse time-varying underwater acoustic channels[J]. IEEE Journal of Oceanic Engineering, 2020, 45 (3): 1112- 1125. doi: 10.1109/JOE.2019.2911446
30	王安义, 余龙, 张育芝. 基于马尔可夫状态空间的水声正交频分复用技术资源分配[J]. 科学技术与工程, 2018, 18 (32): 195- 199.
	WANG A Y , YU L , ZHANG Y Z . Resource allocation of underwater acoustic orthogonal frequency division multiplexing based on Markov state space[J]. Science Technology and Engineering, 2018, 18 (32): 195- 199.
10	QASEM Z A . Real signal DHT-OFDM with index modulation for underwater acoustic communication[J]. IEEE Journal of Oceanic Engineering, 2022, 1- 14.
11	WAN L , ZHU J , CHENG E , et al. Joint CFO, gridless channel estimation and data detection for underwater acoustic OFDM systems[J]. IEEE Journal of Oceanic Engineering, 2022, 47 (4): 1215- 1230. doi: 10.1109/JOE.2022.3162025
12	NGUYEN T N, PHAN H A, CAO V L. Pilot enrichment methods for improving quality of received signal in underwater acoustic OFDM systems[C]//Proc. of the International Confe-rence on Advanced Technologies for Communications, 2022: 401-406.
13	WAN L , ZHOU H , XU X K , et al. Adaptive modulation and coding for underwater acoustic OFDM[J]. IEEE Journal of Oceanic Engineering, 2015, 40 (2): 327- 336. doi: 10.1109/JOE.2014.2323365
14	BARUA S, RONG Y, MORDHOL S, et al. Adaptive modulation for underwater acoustic OFDM communication[C]//Proc. of the OCEANS Marseille, 2019.
15	PELEKANAKIS K, CAZZANTI L. On adaptive modulation for low SNR underwater acoustic communications[C]//Proc. of the OCEANS MTS/IEEE Charleston, 2018.
16	ZHANG Y Z , HUANG Y , WAN L , et al. Adaptive OFDMA with partial CSI for downlink underwater acoustic communications[J]. Journal of Communications and Networks, 2016, 18 (3): 387- 396. doi: 10.1109/JCN.2016.000054
17	WANG H S, FENG W, XU W, et al. Measuring channel state information by underwater acoustic gliders[C]//Proc. of the IEEE/CIC International Conference on Communications in China, 2021: 304-308.
18	QIAO G, LIU L, MA L. Analysis of outdated channel state information in underwater acoustic downlink OFDMA system[C]//Proc. of the OCEANS-MTS/IEEE Kobe Techno-Oceans, 2018.
19	CAO Y , SHI W G , SUN L J , et al. Channel state information-based ranging for underwater acoustic sensor networks[J]. IEEE Trans. on Wireless Communications, 2021, 20 (2): 1293- 1307. doi: 10.1109/TWC.2020.3032589
20	POTTIER A, TOMASI B. Online adaptive power allocation and channel state feedback strategies in underwater acoustic networks[C]//Proc. of the OCEANS 2021: San Diego-Porto, 2021.
21	ZHANG R X , MA X L , WANG D Q , et al. Adaptive coding and bit-power loading algorithms for underwater acoustic transmissions[J]. IEEE Trans. on Wireless Communications, 2021, 20 (9): 5798- 5811. doi: 10.1109/TWC.2021.3070363
22	LIU L , CAI L , MA L , et al. Channel state information prediction for adaptive underwater acoustic downlink OFDMA system: deep neural networks based approach[J]. IEEE Trans. on Vehicular Technology, 2021, 70 (9): 9063- 9076. doi: 10.1109/TVT.2021.3099797
23	WANG H , LI Y M , QIAN J B . Self-adaptive resource allocation in underwater acoustic interference channel: a reinforcement learning approach[J]. IEEE Internet of Things Journal, 2020, 7 (4): 2816- 2827. doi: 10.1109/JIOT.2019.2962915
24	GHANNADREZAII H, MACDONALD J, BOUSQUET J, et al. Channel quality prediction for adaptive underwater acoustic communication[C]//Proc. of the 5th Underwater Communications and Networking Conference, 2021.
25	XU H B, LI X G, LI G S. Underwater acoustic channel tracking based on parameter fault detection algorithm[C]//Proc. of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference, 2019: 469-472.
26	LE K N . Fundamentals of acute relay CSI severity[J]. IEEE Trans. on Wireless Communications, 2020, 19 (11): 7653- 7662. doi: 10.1109/TWC.2020.3014935
27	HU Y F, TAO J, JIANG M, et al. Improved dynamic compressive sensing based channel estimation for single-carrier underwater acoustic communication[C]//Proc. of the OES China Ocean Acoustics, 2021: 655-659.
28	段乐峥. 基于BELLHOP的水声信道时变模型[J]. 电子世界, 2014, (9): 105- 112.
	DUAN L Z . Time-varying model of underwater acoustic channel based on BELLHOP[J]. Electronic World, 2014, (9): 105- 112.
29	WONG I C , EVANS B L . Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge[J]. IEEE Trans. on Communications, 2009, 57 (1): 232- 241. doi: 10.1109/TCOMM.2009.0901.060546

模式	调制方式	阈值区间/dB
1	BPSK	SNR≤5
2	4QAM	5＜SNR≤9
3	8QAM	9＜SNR≤14
4	16QAM	14＜SNR

实验参数	取值
水深/m	5 000
通信半径/m	2 000
声速/(m/s)	1 500
海面波动幅度/m	±1
通信距离变化/m	±2
子载波总数量	64
水下节点数量	50
带宽/kHz	6(21~27)

[1]	金志刚, 段晨旭, 羊秋玲, 苏毅珊. 基于水下云边协同架构的珊瑚礁监测新机制[J]. 系统工程与电子技术, 2022, 44(12): 3829-3836.
[2]	金志刚, 冀智华, 苏毅珊. 基于深度可调节节点的水声网络部署优化算法[J]. 系统工程与电子技术, 2019, 41(1): 203-207.
[3]	赵旦峰，梁明珅，段晋珏. 水声网络中喷泉码的应用研究现状与发展前景[J]. 系统工程与电子技术, 2014, 36(9): 1838-1843.