| Popis: |
The emergence of bandwidth-intensive latency-critical traffic in 5G Networks, such as Virtual Reality, has motivated interest in wireless resource allocation problems for flows with hard-deadlines. Attempting to solve this problem brings about two challenges: (i) The flow arrival and the channel state are not known to the Base Station (BS) apriori, thus, the allocation decisions need to be made online. (ii) Wireless resource allocation algorithms that attempt to maximize a reward will likely be unfair, causing unacceptable service for some users. We model the problem as an online convex optimization problem. We propose a primal-dual Deadline-Oblivious (DO) algorithm, and show it is approximately 3.6-competitive. Furthermore, we show via simulations that our algorithm tracks the prescient offline solution very closely, significantly outperforming several existing algorithms. In the second part, we impose a stochastic constraint on the allocation, requiring a guarantee that each user achieves a certain timely throughput (amount of traffic delivered within the deadline over a period of time). We propose the Long-term Fair Deadline Oblivious (LFDO) algorithm for that setup. We combine the Lyapunov framework with analysis of online algorithms, to show that LFDO retains the high-performance of DO, while satisfying the long-term stochastic constraints. |
