Optimal Auction for Resource Allocation in Wireless Virtualization: A Deep Learning Approach Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation problem is a main challenge for wireless virtualization for which auction approaches have been widely used. However, for most existing auctionbased allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenuemaximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenueoptimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, we apply the deep learning techniques. Specifically, we construct a multilayer feedforward neural network based on the analysis of optimal auction design. The neural network adopts usersu0027 bids as the input and the allocation rule and conditional payment rule for the users as the output. The training set of this neural network is the usersu0027 valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality and incentive compatibility. Finally, simulation results demonstrate the effectiveness of the proposed scheme

GANpowered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demandaware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial networkpowered deep distributional Q network (GANDDQN) to learn the actionvalue distribution driven by minimizing the discrepancy between the estimated actionvalue distribution and the target actionvalue distribution. We put forward a rewardclipping mechanism to stabilize GANDDQN training against the effects of widelyspanning utility values. Moreover, we further develop Dueling GANDDQN, which uses a specially designed dueling generator, to learn the actionvalue distribution by estimating the statevalue distribution and the action advantage function. Finally, we verify the performance of the proposed GANDDQN and Dueling GANDDQN algorithms through extensive simulations.