论文标题
通过深度强化学习的碰撞解决，以便在机器型通信中随机访问
Collision Resolution with Deep Reinforcement Learning for Random Access in Machine-Type Communication
论文作者
论文摘要
免费赠款随机访问（RA）技术适用于机器型通信（MTC）网络，但它们需要适应MTC流量，这与人类类型的通信不同。常规的RA协议，例如插入式Aloha的指数向后（EB）方案（EB）方案遭受大量碰撞的影响，它们不直接适用于MTC流量模型。在这项工作中，我们建议将多代理深Q-NETWORK（DQN）与参数共享使用，以查找网络中所有机器类型设备（MTD）应用的单个策略来解决碰撞。此外，我们考虑所有设备常见的二进制广播反馈，以减少开销。我们将提议的DQN-RA方案的性能与最多500 MTD的EB计划进行比较，并表明所提出的方案优于EB策略，并在吞吐量，延迟和碰撞率之间提供了更好的平衡。
Grant-free random access (RA) techniques are suitable for machine-type communication (MTC) networks but they need to be adaptive to the MTC traffic, which is different from the human-type communication. Conventional RA protocols such as exponential backoff (EB) schemes for slotted-ALOHA suffer from a high number of collisions and they are not directly applicable to the MTC traffic models. In this work, we propose to use multi-agent deep Q-network (DQN) with parameter sharing to find a single policy applied to all machine-type devices (MTDs) in the network to resolve collisions. Moreover, we consider binary broadcast feedback common to all devices to reduce signalling overhead. We compare the performance of our proposed DQN-RA scheme with EB schemes for up to 500 MTDs and show that the proposed scheme outperforms EB policies and provides a better balance between throughput, delay and collision rate
