## 5 Paradigms of AI for Physical Layer Optimization and System Design
### Applications of AI in Secure Resource Allocation  
Resource management is a crucial issue for 5G networks, including inter-cell resource block allocation, computing power and energy allocation, available spectrum and communication channels, massive MIMO user clustering and beamforming resource allocation in distributed network structures [163; 164; 165]. Secure resource allocation in B5G networks is a more challenging and multi-objective performance optimization problem. The essential task of resource allocation in 5G networks is conducted to predict future service requests, mobility and the location of users. Since the emergence of new technologies and services in 5G that typically have limited power and restricted processing and are delay-sensitive, it has become useful to match these multi-objective requirements with minimum valuable resource costs, such as power and energy. The existing secure resource management and utilization schemes that have been widely adopted in traditional design concepts are based on end-to-end performance metrics, with less focus on multi-dimensional wireless networks. This requires the help of AI and other intelligent computing technologies to overcome these gaps in the existing schemes. The application of AI and ML techniques has great potential in these areas. In this subsection, we consider typical multi-dimensional downlink resource allocation in a multi-cell, multi-user OFDMA-based scenario, as shown in Figure 11. In this framework, since the OFDM resource block (RB) distribution to various users within the  
Figure 10: An illustration of a MIMO channel autoencoder. same cell is orthogonal, the intra-cell interference signals are removed over wireless channels. The detection of OFDM symbols at the receiver usually depends on a receiver that uses maximum throughput estimation for evaluation, but this method is very sensitive to CSI errors and the accuracy of the model itself. The design of RB allocation for users is mostly carried out using non-linear programming, which involves non-deterministic polynomial (NP) optimization problems [153]. Nevertheless, the high complexity of this optimization method is prohibitively expensive; hence, it is hard to apply this method in practical scenarios. Therefore, it is evident that the traditional method for reducing computational cost is becoming less reliable. By applying AI to this NP problem, resource allocation can be further improved compared to existing resource management schemes. More specifically, reinforcement learning approaches (e.g., the Q-learning algorithm) can help to allocate resources efficiently. The Q-learning algorithm was designed with the goal of maximizing the entire system performance by avoiding the allocation of similar RBs to neighboring cells that are located close to boundaries. The main motivation for applying Q-learning to RB allocation in downlink OFDM systems was discussed in [153]. According to [149], the Q-learning scheme can solve the NP combinatorial optimization problems that are hard to handle in the existing static partitioning of networks. Intuitively, the Q-learning algorithm allows an agent to interactively learn the optimal allocation policy by sharing resources with neighboring cells. The main strategies include: (1) the allocation of free RBs to users in the same cell by assigning higher SIRs to the users; (2) the interactive updates of the allocated RBs for each user, based on the Bellman equation [166], in order to improve the system throughput performance; (3) the avoidance of allocating the same RB that has been assigned to users in neighboring cells. Consequently, by employing the Q-learning algorithm, a system can maximize its overall capacity by adjusting the RB allocation of each user. Additionally, the Q-learning approach promotes the fair allocation of user power since the involved users cannot acquire essential SIR from neighboring cells.