The integration of unmanned aerial vehicles (UAVs) into spectrum sensing cognitive communication networks can offer many benefits for massive connectivity services in 5G communications and beyond; hence, this work analyses the performance of non-orthogonal multiple access-based cognitive UAV-assisted ultra-reliable and low-latency communications (URLLCs) and massive machine-type communication (mMTC) services. An mMTC service requires better energy efficiency and connection probability, whereas a URLLC service requires minimising the latency. In particular, a cognitive UAV operates as an aerial secondary transmitter to a ground base station by sharing the unlicensed wireless spectrum. To address these issues, we derive the analytical expressions of throughput, energy efficiency, and latency for mMTC/URLLC-UAV device. We also formulate an optimisation problem of energy efficiency maximisation to satisfy the needs of URLLC latency and mMTC throughput and solve it using the Lagrangian method and the Karush–Kuhn–Tucker conditions. The algorithm is presented by jointly optimising the transmission powers of the mMTC and URLLC users. The derived expressions and algorithm are then used to evaluate the performance of the proposed system model. The numerical results show that the proposed algorithm improves the energy efficiency and satisfies the latency requirement of the mMTC/URLLC-UAV device.

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Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

Unmanned aerial vehicle (UAV) is a promising technology to serve as a mission-critical communication, where human operation or intervention is risky, dangerous, impossible or critical in terms of time and cost. The performance of the UAV depends highly on the operating radio environment e.g. multipath shadowing and fading, and spectrum availability. Therefore, a cognitive radio-based UAV is considered to utilize the radio spectrum opportunistically, and minimize the network congestion during a disaster on preallocated channel or degradation of the quality of service. In this paper, we have presented an analytical model for cognitive UAV downlink communication between the UAV and the ground nodes. This model aims to maximize the throughput and improve the energy efficiency by minimizing the power consumption for the UAVs. Specifically, we developed two optimization approaches to address the energy efficiency with and without the throughput of the user as constraints. Simulation results for the energy efficiency without constraints show that $$11.16\%$$
 and $$6.43\%$$
 performance improvement can be achieved for throughput, and energy efficiency respectively in comparison to unoptimized one. Similar behavior has observed with the energy efficiency with constraints and a significant improvement has achieved in throughput performance e.g. $$64.02\%$$
 with the cost of energy.

An optimization based approach to enhance the throughput and energy efficiency for cognitive unmanned aerial vehicle networks

Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme is considered to serve enhanced mobile broadband communication (eMBB) and massive machine-type communication (mMTC) users, in which the transmission delay for both users is derived. To optimize the delay in this system, we formulated an optimization problem aimed at minimizing the processing delay of eMBB and mMTC users by jointly optimizing the transmit power of the users’ information, considering the constraints of the transmit power of the secondary network. The numerical results demonstrate that the proposed Rosen’s gradient projection algorithm can considerably minimize the processing delay for a CUAV with a fixed position compared with a CUAV with a predetermined trajectory.

Delay Optimization in Mobile Edge Computing: Cognitive UAV-Assisted eMBB and mMTC Services

Unmanned aerial vehicle (UAV)-based communication system design has already attracted substantial interest due to UAV flexibility in deployment, cost effectiveness, and in-built line-of-sight air-to-ground channels. However, there is a persistent issue of security threats associated with the broadcast nature of UAV: physical layer security (PLS) can be introduced to enhance the secrecy performance in that regard. In the system proposed here, the combined effect of 3D fractional-order Liu chaotic system and 3D fractional-order Li chaotic system is introduced to enhance the PLS of the UAV-to-ground communication network. In addition, to reduce multi-user interference (MUI), efficient orthogonal variable spreading factor (OVSF) codes are integrated with a zero forcing (ZF) scheme. Moreover, to improve bit error rate (BER), a combined MUI signal discrimination and ZF signal detection scheme is introduced, strengthened by various channel coding techniques with multi-user beamforming weighting. The numerical outcomes establish the efficacy of the proposed system in terms of PLS and improvement of data rate with signal-to-interference-plus-noise ratio. This approach achieved an out-of-band emission reduction of 119 dB. Furthermore, an improved BER of <inline-formula> <tex-math notation="LaTeX">$1 \times 10^{-4}$ </tex-math></inline-formula> is achieved in 16-QAM for a signal-to-noise ratio of 6 dB. 

/pdf/uav-aided-transceiver-design-for-secure-downlink-ow-dfts-2mpu0z0v.pdf

UAV-Aided Transceiver Design for Secure Downlink OW-DFTs-OFDM System: A Multi-User mmWave Application

EEOMA: End-to-end oriented management architecture for 6G-enabled drone communications

Performance analysis of cognitive unmanned aerial vehicle

It has become very essential to address the limited spectrum capacity and their efficient utilization to support the increasing number of Internet of Things devices. When it comes to medical infrastructure, it becomes very imperative for medical devices to communicate with the base station. In such situations, communication over the wireless medium must provide optimized throughput (data rate) with effectual energy usage, which will ensure precise medical feedback by the responsible staff. Taking into account, it is necessary to operate wireless communication precisely at a higher frequency with more substantial bandwidth and low latency. Cognitive Radio (CR) is traditionally a viable choice, where it identifies and utilizes the vacant spectrum, thus maximizing the primary user's capacity and achieving spectral efficiency. To ensure such outcomes, the Non-Orthogonal Multiple Access (NOMA) techniques have proven to deliver an effective solution to the increasing number of devices with unimpaired performance, especially when the communication shifts towards a higher frequency band such as the mmWave band. In this chapter, IoT based CR network in uplink communication is proposed alongside employing NOMA techniques for optimal throughput, and energy efficiency for a medical infrastructure. Numerical results show that effectual throughput and energy efficiency for a High Reliable Communication (HRC) device and Moderate Reliable Communication (MRC) device improve over 83.13% and 73.95%, respectively and their corresponding energy efficacy values show vast improvement (83.11% and 73.96% respectively). Likewise, for interference case both the throughput and the energy efficiency improve approximately over 93% for all devices.

Cognitive IoT based Health Monitoring Scheme using Non-Orthogonal Multiple Access.

Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system’s energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09896864.pdf

Ashiqur Rahman Rahul

Papers

An optimization based approach to enhance the throughput and energy efficiency for cognitive unmanned aerial vehicle networks

Performance analysis of cognitive unmanned aerial vehicle

Cognitive IoT based Health Monitoring Scheme using Non-Orthogonal Multiple Access.

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis