Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks

Efficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power control and channel allocation is an NP-hard problem and the research is still in the preliminary stage. In this paper, we propose a novel joint approach based on long short-term memory deep Q network (LSTM-DQN). Our objective is to obtain the channel allocation schemes of the access points (APs) and the power control strategies of the secondary users (SUs). Specifically, the received signal strength information (RSSI) collected by the microbase stations is used as the input of LSTM-DQN. In this way, the collection of RSSI can be shared between users. After the training is completed, the APs are capable of selecting channels with small interference while the SUs may access the authorized channels in an underlay operation mode without knowing any knowledge about the primary users (PUs). Experimental results show that the channels are allocated to the APs with a lower probability of collision. Moreover, the SUs can adjust their power control strategies quickly to avoid the harmful interference to the PUs when the environment parameters change randomly. Consequently, the overall performance of CRNs and the utilization of spectrum resources are improved significantly compared to existing popular solutions.