The operation of unmanned aerial vehicles (UAV) imposes various challenges on radio spectrum management to achieve safe operation, efficient spectrum utilization, and coexistence with legacy wireless networks. Current spectrum schemes have limitations when applied to UAV networks due to the dynamic nature of UAV networks that require adaptive spectrum decisions and robust schemes that provide seamless and reliable services. Existing surveys mostly focus on UAV applications, channel models, and security challenges, with a lack of studies on spectrum management in the context of UAV networks. Further, current spectrum efforts focus on terrestrial networks that feature fixed infrastructure and less dynamicity as compared to UAV networks. This motivates the need to revisit existing approaches and identify suitable schemes that allow for the rapid integration of UAVs with existing wireless technologies. Motivated by this observation, this article presents a comprehensive survey on spectrum management for UAV operations. It identifies suitable management schemes that align with UAV features and requirements to enable safe and efficient usage of the radio spectrum. The article assumes coexistence with prevalent wireless technologies that occupy the spectrum. It first presents the ruling from policymakers and regulators and discusses operation bands and radio interfaces. It then introduces deployment scenarios (applications and architectures) as standalone or heterogeneous networks. This is followed by a systematic structure for the management tools that employ deterministic, opportunistic, and competitive schemes. In addition, network monitoring, patrolling, and enforcement schemes are identified. The survey also specifies key tools that can be leveraged for spectrum management solutions such as optimization and blockchain. Finally, it recognizes open research directions and challenges that need to be tackled to advance UAV communications.

Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.

Network function virtualization (NFV) technologies are being rapidly adopted across many enterprise and service provider networks to help replace costly, proprietary hardware systems. This paradigm runs virtual “softwarized” networking functions on low-cost off-the-shelf servers and can be used to construct highly-customized service function chains (SFC) for clients. NFV also offers many advantages at the network edge, and hence newer studies have started to look at SFC deployment in specialized fog computing domains, i.e., to support clients with stringent delay and localized contextual service needs. As these trends accelerate, there is a growing need to further address service survivability in these settings, particularly for clients with stringent/rapid failure recovery requirements. Hence this paper presents novel “on-demand” heuristic solutions and batch sorting methodologies for SFC service mapping and protection in fog domains. These schemes take into account key end-user client and operator concerns, including service delay, bandwidth, and node/link resource parameters.

Network function virtualization (NFV) uses commodity servers to run “softwarized” networking device functions and replace costly, proprietary hardware systems. This paradigm is now being used to build and deploy highly-customized service function chains (SFC) for clients. However most related studies have only considered function placement in cloud-based networks. There is now a growing need for NFV service chaining in more specialized fog networks to support users with stringent delay and localized contextual data needs. Hence this letter presents novel fog-based SFC mapping schemes that take into account delay, bandwidth, and node resource parameters.

Network function virtualization (NFV) requires service function chain (SFC) provisioning on cloud or fog servers. Now fog nodes offers short provisioning times as compared to the cloud, albeit limited resources. This makes fog nodes vulnerable to saturation if users demand computation-intensive services, where offloading to the cloud is prohibited due to the prolonged delays. Hence, this letter proposes a novel SFC provisioning scheme for fog paradigms that achieves load balancing (LB) and redistribution between heavily- and lightly-loaded nodes, without exceeding delay bounds. The scheme features high admission rates and reduced delays versus prominent solutions such as standalone delay or load minimization methods.

The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.