具有对数遗憾的学习和认知介质访问分布式算法
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret
摘要：
研究了多个次用户认知网络中的分布式学习和信道接入问题。信道的可用性统计数据最初是未知的次要用户和估计使用感测决策。二级用户之间没有明确的信息交换或事先协议。我们提出了分布式学习和访问的策略，这些策略在自我发挥的情况下实现了顺序最优的认知系统吞吐量（成功的二次传输的数量），即，当在所有次要用户处实现时。同样，我们的策略最大限度地减少了分布式学习和访问中的遗憾。我们首先考虑的情况下，二级用户的数量是已知的政策，并证明总遗憾是对数的传输时隙的数量。我们的分布式学习和访问策略通过与任何一致良好的学习和访问策略下的渐进下限进行比较，实现了订单最优遗憾。然后，我们考虑的情况下，二级用户的数量是固定的，但未知的，并估计通过反馈。我们提出了一个政策，在这种情况下，其渐近和遗憾的增长略快于对数的传输时隙的数量。
英文摘要：
The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the regret in distributed learning and access. We first consider the scenario when the number of secondary users is known to the policy, and prove that the total regret is logarithmic in the number of transmission slots. Our distributed learning and access policy achieves order-optimal regret by comparing to an asymptotic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated through feedback. We propose a policy in this scenario whose asymptotic sum regret which grows slightly faster than logarithmic in the number of transmission slots.
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