论文标题
超越5G网络中URLLC的预测干扰管理算法
A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks
论文作者
论文摘要
减轻干扰是无线系统中的主要设计挑战，尤其是在超级可靠的低延迟通信（URLLC）服务的背景下。传统的基于平均的干扰管理方案不适合URLLC，因为它们无法准确捕获干扰分布的尾部信息。这封信提出了一种新颖的干扰预测算法，该算法考虑了整个干扰分布而不是均值。关键思想是将干扰变化建模为离散状态空间离散时间马尔可夫链。然后，使用状态过渡概率矩阵来估计状态进化，并相应地分配无线电资源。发现该计划的计划可满足目标可靠性要求在低延迟的单弹性传输系统中，考虑到现实的系统假设，而具有完美干扰知识的最佳案例的资源仅比最佳案例高约25％。
Interference mitigation is a major design challenge in wireless systems,especially in the context of ultra-reliable low-latency communication (URLLC) services. Conventional average-based interference management schemes are not suitable for URLLC as they do not accurately capture the tail information of the interference distribution. This letter proposes a novel interference prediction algorithm that considers the entire interference distribution instead of only the mean. The key idea is to model the interference variation as a discrete state space discrete-time Markov chain. The state transition probability matrix is then used to estimate the state evolution in time, and allocate radio resources accordingly. The proposed scheme is found to meet the target reliability requirements in a low-latency single-shot transmission system considering realistic system assumptions, while requiring only ~25% more resources than the optimum case with perfect interference knowledge.
