As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP Long Term Evolution (LTE) and envisioned to be an essential component of 5th generation (5G) mobile networks. The key feature of NOMA is to serve multiple users at the same time/frequency/code, but with different power levels, which yields a significant spectral efficiency gain over conventional orthogonal MA. The article provides a systematic treatment of this newly emerging technology, from its combination with multiple-input multiple-output (MIMO) technologies, to cooperative NOMA, as well as the interplay between NOMA and cognitive radio. This article also reviews the state of the art in the standardization activities concerning the implementation of NOMA in LTE and 5G networks.

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Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide betterperforming and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.

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Decision making for self-driving cars is usually tackled by manually encoding rules from drivers' behaviors or imitating drivers' manipulation using supervised learning techniques. Both of them rely on mass driving data to cover all possible driving scenarios. This paper presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labeled driving data. This method comprehensively considers both high-level maneuver selection and low-level motion control in both lateral and longitudinal directions. We firstly decompose the driving tasks into three maneuvers, including driving in lane, right lane change and left lane change, and learn the sub-policy for each maneuver. Then, a master policy is learned to choose the maneuver policy to be executed in the current state. All policies including master policy and maneuver policies are represented by fully-connected neural networks and trained by using asynchronous parallel reinforcement learners (APRL), which builds a mapping from the sensory outputs to driving decisions. Different state spaces and reward functions are designed for each maneuver. We apply this method to a highway driving scenario, which demonstrates that it can realize smooth and safe decision making for self-driving cars.