QoE-Oriented Resource Allocation Design Coping with Time-Varying Demands in Wireless Communication Networks

Abstract

Efficiently utilizing the network resources to minimize the operation costs while satisfying customer's Qualityof-Experience (QoE) related requirement as well as dynamic demands is a challenging task of all network operators. This paper aims to develop a stationary capacity allocation method that anticipates time-varying demand and keeps the network operating under constraints on a stochastic blocking probability. Queuing delay requirement is also regarded as an QoE-oriented practical design. Employing an approximation of time-varying queuing model and continuous time Markov chain (CTMC) for queue length, the technical designs are stated as a convex stochastic optimization based on which a dynamic capacity allocation is proposed by using Lagrangian and gradient descent searching method. Numerical studies confirm that our proposed framework can efficiently and dynamically allocate optimal capacity for a blocking probability of less than 1% and the probability of violating the queuing-delay requirement is less than 5%.

QoE-Aware Cost-Minimizing Bandwidth Renting for Satellite-as-a-Service enabled Multiple-Beam SATCOM Systems

“…Normally, 𝑓 (𝑋) is a monotonic increasing function with respect to 𝑋 which reflects the idea of 1 It is worth noting that the results can be applied to other SaaS-enabled Non-geostationary satellite (NGSO) systems with multiple satellites as well.…”

“…We examine a SaaS platform in a multi-beam GEO satellite system where the overall available bandwidth of a GEO satellite is owned by an IP that can be rented by SPs 1 The study focuses on a scenario where a specific SP 2 rents a time-varying amount of bandwidth from the IP to provide broadband services to multiple users randomly distributed across 𝐵 beams. Let 𝑊 (𝑡)…”

“…Consider 𝑆 data-flows corresponding to 𝑆 services tending to access each of 𝐵 beams as shown in Figure 3. We further assume that the flow 𝑠 carrying data packets of 𝐿 𝑠 -bit length comes to W (1) k (t)…”

“…Similarly, the complexity due to equation ( 23) is the order of O (𝐶 + 𝐶𝐾). One assumes that implementing Algorithm 1 required ℓ (1) iterations to get convergence and obtain the solution, the overall complexity of the algorithm taking the highest degree polynomial becomes…”

“…This novel paradigm can enable IPs to access new markets and it can release SPs from investing a huge amount of money in order to build dedicated satellites [5]. Such a model comprises infrastructure providers (IPs) -the owners of the SATCOM systems, and the service providers (SPs), which may rent flexible RF bandwidth from the IPs to provide different services to their end users [1], [3], [6], [7]. SATCOM services are also shifting from broadcast transmission to on-demand traffic going through the networks because of different applications such as aviation, marine, manufacturing, and IoT services, including voice, web browsing, video streaming, live presentations, VoIP, social media, file transfers, etc [8].…”