2020/02/15 by Sherif ElAzzouni, ElAzzouni, Sherif, Eylem Ekici +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Mathematics #Networking and Internet Architecture (cs.NI) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2002.06474
openalex publication_date 2020/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The emergence of bandwidth-intensive latency-critical traffic in 5G Networks,\nsuch as Virtual Reality, has motivated interest in wireless resource allocation\nproblems for flows with hard-deadlines. Attempting to solve this problem brings\nabout two challenges: (i) The flow arrival and the channel state are not known\nto the Base Station (BS) apriori, thus, the allocation decisions need to be\nmade online. (ii) Wireless resource allocation algorithms that attempt to\nmaximize a reward will likely be unfair, causing unacceptable service for some\nusers. We model the problem as an online convex optimization problem. We\npropose a primal-dual Deadline-Oblivious (DO) algorithm, and show it is\napproximately 3.6-competitive. Furthermore, we show via simulations that our\nalgorithm tracks the prescient offline solution very closely, significantly\noutperforming several existing algorithms. In the second part, we impose a\nstochastic constraint on the allocation, requiring a guarantee that each user\nachieves a certain timely throughput (amount of traffic delivered within the\ndeadline over a period of time). We propose the Long-term Fair Deadline\nOblivious (LFDO) algorithm for that setup. We combine the Lyapunov framework\nwith analysis of online algorithms, to show that LFDO retains the\nhigh-performance of DO, while satisfying the long-term stochastic constraints.\n