Source-linked AI summary
The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cells
Irem Bor-Yaliniz, Halim Yanikomeroglu
TL;DR
Fixed terrestrial RAN locations struggle with unpredictable supply-demand mismatches, motivating opportunistic multi-tier drone-cells. The paper proposes DMF using cloud computing, SDN, and NFV, and a multitenancy case study reports lower cost while serving more users.
Problem
Terrestrial RAN and HetNet deployments are relatively rigid and may not match hard-to-predict, temporary traffic demand without costly over-engineering.
Method
The paper proposes DMF, which uses cloud big-data intelligence with SDN and NFV to reconfigure and manage drone-cell networks.
Results
The multitenancy case study found that sharing a drone-cell reduced its cost by a factor of two and increased total served users approximately 1.5 times.
Takeaways & Limitations
Multi-tier drone-cell networks provide a radio-access paradigm for bringing wireless supply to demand across space and time.
Abstract
from arXiv · showhide
In cellular networks, the locations of the radio access network (RAN) elements are determined mainly based on the long-term traffic behaviour. However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load,demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable. The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch. However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping up with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution). In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (BSs), i.e., drone-BSs, in 5G networks. In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets. The variety of equipment, and non-rigid placement options allow utilizing multitier drone-cell networks to serve diversified demands. Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks. We investigate the advancements promised by drone-cells, and discuss the challenges associated with their operation and management. We propose a drone-cell management framework (DMF) benefiting from the synergy among software defined networking (SDN), network functions virtualization (NFV), and cloud-computing. We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multitenancy cellular networks.
I. INTRODUCTION
Drone-cells address the mismatch between rigid terrestrial RAN placement and unpredictable, temporary traffic demand by providing flexible, opportunistic network support. The paper proposes DMF, combining cloud intelligence with SDN and NFV to configure and manage these dynamic nodes.
- Flexible equipment and placement options make multi-tier drone-cell networks adaptable to changing connectivity needs.The paper identifies radical flexibility as their main advantage and notes support for RATs including mmWave and FSO.
- Drone-cells dynamically deploy BSs with different power levels and RATs to improve alignment between wireless supply and user demand.They target unexpected, temporary, and spatially inhomogeneous demand such as disasters, traffic congestion, and concerts.
- Existing LTE configuration and self-organizing capabilities may not adequately integrate and remove drone-cells during temporary service periods.Dynamic nodes require seamless network integration and disintegration, motivating more flexible management mechanisms.
- The paper proposes a drone-cell management framework that uses cloud big-data intelligence with SDN and NFV to reconfigure the network.The framework is presented alongside business and information models for future wireless networks.
II. DESCRIPTIONS, OPPORTUNITIES, AND CHALLENGES
Drone-cells are low-altitude aerial access nodes whose mobility and configurable characteristics support temporary, unexpected, and critical connectivity demands. Their flexibility also introduces substantial design, operation, and management challenges.
- A drone-BS is a low-altitude unmanned aerial vehicle equipped with transceivers, while a drone-cell is its corresponding coverage area.Coverage varies with altitude, location, power, RATs, antenna directivity, drone type, and environment.
- Drone-cells support agility and resilience in temporary, unexpected, and critical scenarios, including congestion, emergencies, and hard-to-reach sensing deployments.Mobility can bring a cell toward user or device clusters and exploit proximity and potential LOS connectivity.
- Critical applications may require high data rates, high reliability, or low energy consumption, while emergency connectivity can help prevent serious losses.The paper gives emergency communications and telecontrol as examples of demanding applications.
- The versatility of drone-cells creates significant design, operation, and management challenges.
A. Challenges of drone-cells
Drone-cell design must match aircraft, payload, access technology, and deployment scenario while supporting diverse temporary and resilient networking uses. The paper describes several node configurations and application categories for efficient design.
- 1) Efficient design: Drone-BS designs must balance hovering, endurance, turbulence robustness, antenna span, and transmission energy requirements.The paper suggests hybrid aircraft combining vertical take-off, collapsible wings, MIMO antennas, and solar energy harvesting.
- 1) Efficient design: Payload selection is as important as aircraft mechanics because drone-BSs have limited volume, weight, and energy.Payload configurations can vary according to the scenario.
- 1) Efficient design: Drone-relays can use lighter payloads and potentially lower power, reducing drone-cell CAPEX and OPEX through lower node weight.
- 1) Efficient design: Multi-tier drone-cells can serve rural areas, malfunctioning BSs, high-mobility users, congestion, temporary events, and coverage blind spots.The figure associates these scenarios with macro-, pico-, and femto-drone-cell tiers.
- 1) Efficient design: Temporary, unexpected, and critical events may require different solutions, including opportunistic drone-BS deployment instead of permanent over-engineering.For critical emergency operations, the paper notes benefits beyond revenue, such as saving lives.
2) Backhaul/fronthaul connection:
Wireless backhaul and fronthaul are unavoidable for multi-tier drone-cell networks, but promising FSO and mmWave links face reliability and coverage constraints. Placement must therefore respond quickly to network demand while accounting for altitude, location, and trajectory.
- 2) Backhaul/fronthaul connection:: Wireless backhaul and fronthaul are inevitable for multi-tier drone-cell networks.FSO and mmWave offer high rates and low spectrum cost, but their reliability and coverage are limited in inclement weather.
- 2) Backhaul/fronthaul connection:: Drone-cell placement must rapidly determine altitude, location, and trajectory from network demands rather than long-term traffic behavior.For RAN congestion, the target benefit is to offload users from the congested cell.
B. Challenges of multi-tier drone-cell networks
Multi-tier drone-cell networks face air-to-ground channel, interference, coordination, planning, and integration challenges. Effective operation requires centralized information, programmable resources, and software-based control.
- Physical layer signal processing: Air-to-ground channels vary with environmental and aircraft conditions, making robust signaling under strict drone-BS energy constraints difficult.Channel characteristics differ across users and can change with temperature, wind, foliage, environment, aircraft, and speed.
- Interference dynamics: Drone-cell mobility and proximity create co-channel, Doppler, and intercarrier interference that require advanced interference management.High-frequency RATs such as mmWave are especially affected by Doppler-induced intercarrier interference.
- Cooperation among drone-cells: Dynamic drone-cell networks require cooperation to manage radio resources, user mobility, handovers, power, resource allocation, and collisions.Cooperation supports efficiency as drone-cells and users change location.
- Infrastructure decision and planning: Drone-cell assets and deployment scale must be selected according to weather, service type, area size, target benefit, and service duration.A drone-cell is tailored to the target benefit rather than being a one-size-fits-all solution.
- Capabilities for integration: Integration requires centralized global information, programmable network resources, and efficient control for seamless drone-cell integration and disintegration.Cloud computing and big data support centralized intelligence, NFV supports programmability, and SDN supports automatic software-based control.
A. Enabling Technologies for DMF
The enabling technologies for DMF combine cloud-based data processing with centralized resources to support network-wide monitoring and decision making for drone-cells.
- Enabling Technologies for DMF: Cloud computing and big data analysis provide centralized computing, storage, monitoring, and decision making for efficient drone-cell utilization.A drone-cell cloud is intended to use centralized resources efficiently and economically.
2) Network Functions Virtualization:
NFV virtualizes network functions on general-purpose infrastructure, while SDN supplies centralized control and reconfiguration for integrating and managing drone-cells.
- Network Functions Virtualization: NFV virtualizes gateways, inspection modules, firewalls, and other network functions to create a programmable structure for seamless drone-cell integration.Virtualized functions run on general-purpose servers, standard storage devices, and switches.
- Network Functions Virtualization: SDN separates control and data planes to provide centralized network control, global visibility, reconfiguration, VNF orchestration, and cellular resource management.A centralized SDN controller can support radio-resource and mobility management, including load balancing.
B. Business and Information Models of DMF
DMF’s business and information models distribute ownership, operation, and service roles across network participants while collecting operational data in the cloud. The model also recognizes near-term realism constraints and potential revenue opportunities.
- Business model: Future cellular networks may divide roles among infrastructure providers, MVNOs, service providers, and potentially a separate drone-InP.The proposed model assigns physical resources to InPs and virtual-network operation to MVNOs for delivering SP services.
- Information model: DMF collects more granular but distributed information in the cloud for processing and subsequent SDN and NFV orchestration.Table II organizes information by type, source, and usage before the results are used to integrate drone-cells.
- Challenges and opportunities: Isolated business roles may be unrealistic in the near future, although specialization among InP, MVNO, and SP roles could mature over time.The model may introduce distinct pricing strategies and QoS monitoring requirements for drone-cell operations.
- Challenges and opportunities: Despite complexity and expense, drone-cell operations may increase revenues through leaner terrestrial networks, high-priority services, and continuity during unpredictable high-density traffic.The cited opportunities include public safety and coverage continuity where infrastructure is relatively insufficient.
C. Challenges for DMF Implementation
DMF implementation faces unresolved NFV, SDN, and cloud-management challenges. Real-time centralized processing cannot handle every operational need, so autonomous drone capabilities remain necessary.
- NFV challenges include drone-cell slicing, MVNO traffic isolation, virtual-function migration, resource management, and scheduling.
- SDN must address controller scalability, efficient programming of new paths, and communication with diverse virtual network entities.
- Cloud deployment must centralize distributed data, provide security, determine sharing levels under regulations, and supply processing power for massive data.
- Real-time data collection and processing for turbulence, collision avoidance, and user-mobility tracking is infeasible, so DMF provides guidelines rather than replacing highly autonomous drones.
IV. A CASE STUDY: 3-D PLACEMENT OF A DRONE-CELL VIA DMF
The case study formulates DMF-based 3-D drone-cell placement for congestion relief, multi-tenancy, energy, and content objectives. Simulations show DMF can distribute service equally between MVNOs without reducing the total number of served users, while broader multi-tier deployment remains more complex.
- Placement objectives: DMF formulates drone-cell placement to serve congested users while considering multi-tenancy, energy-critical users, and content demands.The objective also incorporates QoS, location constraints, capacity, and weighted benefits.
- DMF operation: DMF placement uses cloud guidance and SDN/NFV reconfiguration to integrate drone-cells with virtual MVNO networks.The management cycle collects global data, creates intelligence, guides operation, and reconfigures network functions before drone-cell assistance.
- Optimization model: The placement problem constrains QoS, allowable 3-D locations, drone-cell capacity, and binary user-service decisions.Allowable locations can reflect regulations and line-of-sight links to backhaul or fronthaul nodes.
- Experimental setup: In the multi-tenancy experiment, 24 users cannot be served by the terrestrial HetNet, and two identical MVNOs share the congested macrocell.The study assumes one InP, two MVNOs, equal QoS requirements, and equal service targets v1 = v2 = 12.
- Results: 10 users are served in both multi-tenancy placements, but DMF serves 5 users from each MVNO instead of an uneven 4-versus-6 allocation.Single-tenancy serves only 6 users because MVNO2 users are excluded; DMF preserves the multi-tenancy total while equalizing service.
- Scope and limitations: The study demonstrates one drone-cell in 3-D, whereas multi-tier networks additionally require attention to interference, density, cooperation, and green networking.The authors also note that covered users only implicitly indicate injected capacity, enhanced coverage, and reduced retransmission time.
V. CONCLUSION
The paper presents multi-tier drone-cells as a flexible alternative to costly ultra-dense deployment for sporadic, unpredictable demand. It proposes DMF to operate drone-cells efficiently in multi-tenant networks.
- Fig. 3 shows that tenancy policies and MVNO service regulation change the drone-BS’s three-dimensional placement.
- Multi-tier drone-cells bring wireless-network supply to demand in space and time, avoiding infrastructure that would otherwise be under-utilized.
- Drone-cell operation is complex because wireless networks are designed mainly for mobile users rather than mobile base stations.
- DMF provides an efficient management framework for drone-cells and is demonstrated in a multi-tenancy case study.