利用 MOSDS-DQN 缓解蜂窝连接无人机的蜂窝间干扰

IF 7.4 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI:10.1109/TCCN.2023.3307940
Liyana Adilla Binti Burhanuddin;Xiaonan Liu;Yansha Deng;Maged Elkashlan;Arumugam Nallanathan
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引用次数: 0

摘要

在 5G 及以后,无人机作为新的空中移动用户被集成到蜂窝网络中,以支持许多应用,并为基站(BS)提供更高的视距(LoS)传输概率。然而,由于为地面用户(TUE)和无人机提供服务的基站的频率带宽和频谱资源重用有限,会对 TUE 造成严重的下行链路干扰,尤其是在网络负载较重时。因此,本文研究了城市地区无人机和地面用户的无线连接性能,并引入了下行链路小区间干扰协调机制。然后,我们提出了自适应小区静音干扰和资源分配调度方案。我们提出了值函数近似解决方案(VFA)、Tabular-Q 和 Deep-Q 网络(DQN),以最大化 TUE 的长期网络吞吐量,同时保证无人机的数据速率要求。随着 UAV 和 TUE 数量的增加以及无线环境的动态变化,我们进一步提出了静音优化方案和动态时频调度(MOSDS)算法,以提高 UAV 和 TUE 的吞吐量和满意度。仿真结果表明,所提出的算法使无人机和 TUE 网络的吞吐量提高了 80%,并减轻了它们之间的干扰。此外,与 DQN 算法相比,所提出的 MOSDS-DQN 提高了 18%。
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Inter-Cell Interference Mitigation for Cellular-Connected UAVs Using MOSDS-DQN
In 5G and beyond, UAVs are integrated into cellular networks as new aerial mobile users to support many applications and provide higher probability of line-of-sight (LoS) transmission to base stations (BSs). Nevertheless, due to limited frequency bandwidth and spectrum resource reuse when BSs serving terrestrial users (TUEs) and UAVs, it causes severe downlink interference to TUEs, especially when the network has a heavy load. Thus, in this paper, we study the performance of radio connectivity of UAVs and TUEs in an urban area and introduce a downlink inter-cell interference coordination mechanism. Then, we propose adaptive cell muting interference and resource allocation scheduling schemes. A value function approximation solution (VFA), Tabular-Q, and Deep-Q Network (DQN) are proposed to maximize the long-term network throughput of TUEs while guaranteeing the data rate requirements of UAVs. With increasing number of UAVs and TUEs and dynamic wireless environment, we further propose a Muting Optimization Scheme and Dynamic time-frequency Scheduling (MOSDS) algorithm to increase throughput and satisfactory level for both UAVs and TUEs. Simulation results show that the proposed algorithms achieve 80% performance improvement of throughput of UAV and TUE networks and mitigate the interference among them. Also, the proposed MOSDS-DQN shows 18% improvement compared to the DQN algorithm.
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
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