Drone small cells (DSCs) can provide on-demand air-to-ground wireless communications in various unexpected situations, such as traffic jam or natural disasters. However, a DSC needs to face the challenges such as severe co-channel interference, limited battery capacity, and fast topology changes. Aiming to improve energy efficiency of DSCs and quality of services of customers, this paper presents a learning-based multiple drone management (LDM) framework by controlling the transmission power and the 3-dimension location of DSCs based on location data, and reference signal received power of users. Since the labeled throughput data are typically not available in emergency situations, we develop unsupervised learning DSC management techniques: 1) affinity propagation interference management scheme to mitigate interference and energy consumption, and 2) K-means position adjustment to adjust the new 3-dimension positions of drones. Our numerical results show that the proposed LDM framework combining with affinity propagation clustering and k-means clustering can enhance the energy efficiency of DSCs by 25% and the signal-to-interference-plus-noise ratio of ground users by 56%, respectively.
Ultra‐dense small cell (UDSC) network will play a key role to cope with the capacity issue for 5G cellular mobile systems and beyond because future broadband mobile applications require high‐speed transmission and low latency. Nevertheless, deploying small cell deployment will need to face the challenges of severe interference and excessive energy consumption in very dynamic environments. To address these issues, in this article we introduce a data‐driven resource management (DDRM) framework combined with power control, channel rearrangement, and dynamic antenna clustering (DAC). In particular, an unsupervised learning affinity propagation clustering is applied to UDSC to identify cluster heads, i.e. the cell causing most serious interference to its neighboring cochannel cells. Then, these cluster heads will lower its transmission power. We call this learning‐based interference management approach as affinity propagation power control (APPC). We show that DDRM with APPC can improve energy efficiency and reduce interference for UDSC, while taking account of cell switching on/off, transmission power adjustment, and traffic loads simultaneously.
A multifunction phased array radar must search and track suspicious targets in its surveillance space in a real-time fashion. With inefficient scheduling implementations in many traditional systems, much radar resource is wasted with a very limited performance gain. This paper targets one of the most important issues in the design of modern phased array radars: real-time dwell scheduling. We formalize the typical workload of a modern phased array radar and propose a rate-based approach to schedule radar dwells in a real-time fashion. We show how to reserve radar resources to guarantee the minimum radar operation without sacrificing the stability of the system. The strength of our approach is verified by a series of simulation experiments based on a real phased array radar for air defense frigates [9]. A significant improvement in the performance of phased array radars was shown.
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