Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems.

This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive.

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security.

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies.

A Game-Theoretic Framework to Preserve Location Information Privacy in Location-based Service Applications.

Recently, the growing ubiquity of location-based service (LBS) technology has increased the likelihood of users' privacy breaches due to the exposure of their real-life information to untrusted third parties. Extensive use of such LBS applications allows untrusted third-party adversarial entities to collect large quantities of information regarding users' locations over time, along with their identities. Due to the high risk of private information leakage using resource-constrained smart mobile devices, most LBS users may not be adequately encouraged to access all LBS applications.