No Arabic abstract
We consider a virtualized RAN architecture for 5G networks where the Remote Units are connected to a central unit via a mid-haul. To support high data rates, the midhaul is realized with a Passive Optical Network (PON). In this architecture, the data are stored at the central unit until the scheduler decides to transmit it through the mid-haul to an appropriate remote unit, and then over the air at the same slot. We study an optimal scheduling problem that arises in this context. This problem has two key features. First, multiple cells must be scheduled simultaneously for efficient operation. Second, the interplay between the time-varying wireless interface rates and the fixed capacity PON needs to be handled efficiently. In this paper, we take a comprehensive look at this resource allocation problem by formulating it as a utility-maximization problem. Using combinatorial techniques, we derive useful structural properties of the optimal allocation and utilize these results to design polynomial-time approximation algorithms and a pseudopolynomial-time optimal algorithm. Finally, we numerically compare the performance of the proposed algorithms to heuristics which are natural generalizations of the ubiquitous Proportional Fair algorithm.
Software-defined networking (SDN) is the concept of decoupling the control and data planes to create a flexible and agile network, assisted by a central controller. However, the performance of SDN highly depends on the limitations in the fronthaul which are inadequately discussed in the existing literature. In this paper, a fronthaul-aware software-defined resource allocation mechanism is proposed for 5G wireless networks with in-band wireless fronthaul constraints. Considering the fronthaul capacity, the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium (CCE) and incentivizing base stations (BSs) to locally optimize their decisions to ensure mobile users (MUs) quality-of-service (QoS) requirements. By marrying tools from Lyapunov stochastic optimization and game theory, we propose a two-timescale approach where the controller gives recommendations, i.e., sub-carriers with low interference, in a long-timescale whereas BSs schedule their own MUs and allocate the available resources in every time slot. Numerical results show considerable throughput enhancements and delay reductions over a non-SDN network baseline.
Various legacy and emerging industrial control applications create the requirement of periodic and time-sensitive communication (TSC) for 5G/6G networks. State-of-the-art semi-persistent scheduling (SPS) techniques fall short of meeting the requirements of this type of critical traffic due to periodicity misalignment between assignments and arriving packets that lead to significant waiting delays. To tackle this challenge, we develop a novel recursive periodicity shifting (RPS)-SPS scheme that provides an optimal scheduling policy by recursively aligning the period of assignments until the timing mismatch is minimized. RPS can be realized in 5G wireless networks with minimal modifications to the scheduling framework. Performance evaluation shows the effectiveness of the proposed scheme in terms of minimizing misalignment delay with arbitrary traffic periodicity.
In order to meet the ever-increasing demand for high throughput in WiFi networks, the IEEE 802.11ax (11ax) standard introduces orthogonal frequency division multiple access (OFDMA). In this letter, we address the station-resource unit scheduling problem in downlink OFDMA of 11ax subject to minimum throughput requirements. To deal with the infeasible instances of the constrained problem, we propose a novel scheduling policy based on weighted max-min fairness, which maximizes the minimum fraction between the achievable and minimum required throughputs. Thus, the proposed policy has a well-defined behavior even when the throughput constraints cannot be fulfilled. Numerical results showcase the merits of our approach over the popular proportional fairness and constrained sum-rate maximization strategies.
Fifth Generation (5G) wireless networks are designed to meet various end-user Quality of Service (QoS) requirements through high data rates (typically of Gbps order) and low latencies. Coupled with Fog and Mobile Edge Computing (MEC), 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale Artificial Intelligence (AI)-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low and highly dense locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of Unmanned Aerial Vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (Drones as a Service) and routing to and from end-users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide wide range of complex authenticated service and data availability. Particular attention is paid tocomparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as Federated Learning.
In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks, i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing, i.e., providing a reduced protection bandwidth with respect to the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and Forward Error Correction (FEC) for allocating rightsize spectrum resources to network slices. Our numerical evaluation over realistic case-study network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization.