A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Coexistence Signal Recognition. <s> Wireless network deployments in industry often grow organically with new technologies added over time, among which many use the non-licensed spectrum to avoid licensing costs. As a result, technologies competing for the same spectrum end up deployed in the same area, causing coexistence problems to manifest themselves at a later stage. To avoid unexpected performance degradation, there is a need to evaluate the impact of additional wireless technologies on an existing network before the actual deployment. This paper proposes to simplify the impact assessment by emulating the signals of the potential wireless network with a single software-defined radio. To evaluate the emulator’s performance, the impact of Bluetooth on Wi-Fi technology is considered as the reference scenario. A series of real-life experiments with configurable traffic load and network scale are conducted to estimate the impact of Bluetooth network on a Wi-Fi link, and the corresponding measurements are repeated with the emulated Bluetooth signals. To the best of the authors’ knowledge, we are the first to propose such a solution, and it is shown that the use of our emulator gives a reliable indication of the expected impact at the location of the Wi-Fi link. As such, this paper provides an important step toward a simple, cost efficient, and reliable solution, to assess the impact of a wireless network prior to its deployment. <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Coexistence Signal Recognition. <s> In this paper, a home Internet-of-Things system is analyzed by dividing it into four layers, i.e., the node layer, gateway layer, service layer, and open layer. The gateway layer, which supports a variety of wireless technologies and is the core of home wireless networks access unit, together with the node layer constitutes the home wireless network. A gateway prototype following the proposed architecture has been implemented. A testbed of an interference-aware wireless network which includes the gateway prototype has also been created for testing its user interaction performances. The experimental results show that both Wi-Fi and Bluetooth have an impact on the ZigBee communication. Considering the complex scene of home and building, ZigBee multihop communications are set to reduce the packet loss probability. In addition, an event-level-based transmission control strategy is proposed, in which the packet loss probability of wireless network is reduced by controlling the transmission priority of different levels of monitoring events, and optimizing the ZigBee wireless network channel occupancy. Note to Practitioners —The IoT in home and building automation has become a hot topic, and embedded intelligent home gateway is an important part of intelligent home system which mainly responsible for data exchange and protocol conversion between the node layer and the user client tier. In a wireless home network, intelligent home gateway mainly supports the wireless interfaces of Wi-Fi, Bluetooth, and ZigBee, and all of these wireless technologies work at the frequency band of 2.4 GHz, and among them, ZigBee technology is generally in the continuous working state. Hence, the adverse impact of Wi-Fi and Bluetooth on ZigBee wireless network is purpose for evaluation, which relating to the popularization of home wireless networks. This paper proposes a multi-interface gateway design method, the ZigBee networking process, an interaction principle for interference verification, and an ELT-based interactive control mechanism, respectively. A testbed of an interference-aware wireless network which includes the gateway prototype has also been created for performance evaluation. Node-to-node spacing, packet loss error and energy consumption performance tests have been done to realize the optimization of deployment and distribution of the home networking nodes. The results of this thesis lay a good foundation for further study and promotion of smart home network system. <s> BIB002 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Coexistence Signal Recognition. <s> Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity, and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of the signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity tradeoff for the IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1, and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches. <s> BIB003
Signal monitoring and interference management under the condition of multisignal coexistence have attracted much attention recently BIB002 BIB001 . With the advent of 5G and the IoT commercialization, crowded spectrum resources are likely to lead to an overlap of multiple signals. Moreover, the increasing types of signals and diverse wireless networks will undoubtedly bring challenges on the signal recognition in the coexistence environment BIB003 . Predictably, the recognition of homogeneous and heterogeneous signal in an overlapped bandwidth may be a novel development trend of signal recognition and highly effective and accurate DL algorithms will create a new centre.