With the rapid proliferation of wireless communication systems, accurate Start-of-Packet (SOP) detection is essential for efficient packet reception and decoding. Conventional correlation-based techniques struggle under low signal-to-noise ratio (SNR) and phase/frequency offsets. This paper proposes a Proximity-based Support Vector Machine (P-SVM), a novel SVM-based approach that incorporates Proximity-based weighting and a Distance adjustment function to enhance robustness under such impairments. Evaluated using both simulations and SDR-based experiments, P-SVM consistently outperforms conventional and state-of-the-art methods in Probability of detection, Mean Absolute Error, and Probability of false alarm. Moreover, P-SVM offers reduced implementation complexity, making it suitable for practical deployment.

Internet of Things (IoT) communication in rural areas faces significant challenges due to limited infrastructure and power constraints, making long-range communication difficult. To address this, we propose a novel noncoherent distributed transmission of chirp spread spectrum (NCDT-CSS) waveform that enables power-efficient, long-range communication by allowing multiple asynchronous transmitters to send the same data. Unlike existing methods, our NCDT-CSS framework uses a least squares (LS)-based detector to extract symbols from the complex received signal without relying on intricate synchronization algorithms. The process involves estimating time, frequency, phase offsets, and channel conditions, followed by symbol detection using single-window (SW) and two-window (TW) LS-based detectors. Simulation results show that the NCDT-CSS framework, combined with an LS-based detector, offers superior bit error rate (BER) performance, contributing to an extended communication range. Additionally, hardware implementation validates the model’s effectiveness, demonstrating enhanced data rates at a lower spreading factor (SF). These results confirm that the NCDT-CSS system is a robust solution for IoT communication in rural areas, providing low-power long-range capabilities with enhanced data rates.

Unmanned aerial vehicles (UAVs) have emerged as a specular technology that can assist the terrestrial base stations. However, the battery limitation of UAV inhibits the system performance by decreasing the overall lifespan of coverage provided by the UAV, driving the necessity of replacement and recharging. Thus, the energy-depleted UAV must be returned to a charging station and be replaced by a fully charged UAV to increase the service span. Therefore, this paper presents a novel framework of UAV replacement to maintain coverage continuity in a UAV-assisted wireless communication system when a serving UAV runs out of energy. Our objective during this replacement process is to maximize the minimum achievable throughput to the UAV-served ground users by jointly optimizing the three-dimensional (3D) multi-UAV trajectory and resources allocated to the users from the individual UAVs. The formulated problem is non-convex for which an efficient algorithm based on successive convex approximation and alternating optimization is proposed. Numerical results provide insights into the UAV trajectories and the effectiveness of the proposed scheme compared to the existing benchmark schemes.

This paper presents a peer conscious opportunistic network coded cooperation (PC-O-NCC) system that exploits multi-user diversity (MUD) gain in a simple network coded cooperation (NCC) based network. It prioritizes sources with better channel conditions by granting them earlier access than the other competing nodes unlike the prevalent time division multiple access (TDMA) technique used widely in any NCC network. This improves the outage performance of the overall system. To prioritise sources with better channel conditions, a novel timer-based MAC protocol is proposed. The proposed protocol is designed such that it aims to reduce collisions by taking the network load into account along with channel conditions while generating the timer values. It also minimizes the power consumption in performing clear channel assessment (CCA) by sources which is a costly affair for battery-operated devices. The simulation results show that the proposed algorithm improves the outage performance while keeping the latency to a minimum value. The improvement in the outage performance can prove to be important in taking key decisions which becomes crucial in disaster management, drone assisted scenarios or intelligent transportation systems.

In this paper, we present a novel framework to maintain coverage continuity in a unmanned aerial vehicle (UAV)-assisted wireless communication system, when a serving UAV runs out of energy. Service continuity is maintained by launching another fully charged UAV to replace the existing serving UAV. This replacement process must ensure maximal coverage to all ground users. Our objective during this replacement process is to maximize the achievable sum rate of all ground users by jointly optimizing the three-dimensional (3D) multi-UAVs trajectory and resource allocation to the users from the individual UAVs. This is carried out in the presence of the UAV’s constraints on velocity, collision avoidance, and energy availability while considering a more practical and accurate probabilistic line-of-sight (LoS) channel model. This results in a non-convex optimization problem for which an efficient iterative algorithm based on successive convex approximation and alternating optimization is proposed. Numerical results are provided to obtain insights on the UAV trajectories and the effectiveness of the proposed scheme compared to the existing benchmark schemes is shown.

In an unmanned aerial vehicle (UAV)-enabled Inter-net of Thing (IoT) network, the transmission rate of an IoT node from the aerial base station (ABS) is determined by the signal-to-noise ratio (SNR). In general, the characterization of the ABS-to-node (A2N) channel is determined by its instantaneous SNR. The previous works reported in this direction have considered the average SNR. This study fills the gap in understanding how fading affects the network’s performance. In this paper, we consider a UAV-enabled IoT network, where multiple ABSs are deployed to serve the IoT nodes. We model the A2N channel based on the instantaneous channel; we consider a generalized fading model, the Nakagami-m model for the A2N link, to provide a more generic analysis. Through analytical insights, we obtain the exact and closed-form approximate expression of the association probability of IoT node with the ABS and infer the capacity enhancement obtained by considering fading compared to the average SNR scenario. The average improvement in ergodic capacity is around 10% compared to the average fading scenario. Furthermore, the results show that considering fading is beneficial for low-altitude scenarios, as it provides a significant increase in the node association probability compared to the average fading scenario.

In this article, we aim to study an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication system where a rotary-wing UAV travels from the initial to the final location to communicate with multiple IoT ground devices. The limited onboard energy of the UAV poses a constraint to the overall system’s performance. UAV’s energy consumption is majorly based on its kinematics, i.e., UAV’s velocity and acceleration. Therefore, in this work, we maximize the sum user throughput by jointly optimizing the 3-D UAV trajectory, and velocity-time profile in the presence of onboard energy, velocity, acceleration, and completion time constraints. Noting the nonconvexity of the optimization problem, the original problem is decoupled into two subproblems. First, the trajectory is optimized considering the velocity constraint, while in the second subproblem, the velocity and time optimization in each time slot is carried out. Simulation results show insights on the UAV trajectory and velocity-time profile with the variation in the onboard energy availability. In addition, we demonstrate the superior performance of the proposed approach in comparison to the benchmark schemes.

In this paper, we study and optimize the trajectory for unmanned aerial vehicle (UAV) to provide fifth-generation (5G) cellular service to users in a given area. We consider a single UAV launched from the fixed initial to the final location, during which it serves the ground users that are distributed in a circular field. We introduced an optimization problem to minimize the average outage probability of the system by optimizing the three-dimensional (3D) trajectory of the UAV. As this problem is nonconvex, we proposed an efficient approach that involves two steps, firstly it frames a new problem to obtain a globally optimal location in 3D space for minimal average outage probability. This problem is shown to be conditionally convex and an efficient algorithm is proposed to obtain a globally optimal location within an acceptable tolerance. Thereafter, a sub-optimal solution is proposed for the original problem. Simulation results provide useful insights into the sub-optimal trajectory of UAV and show that our proposed approach on an average provides 24% outage improvement over the benchmark scheme. This improvement is further enhanced with the increase in the velocity of UAV.

Unmanned Aerial Vehicle (UAV) deployment as an aerial base station in fifth-generation (5G) communication system has emerged as a promising technology to provide seamless communication in a geographical region. The UAV’s high mobility potential offers additional degrees of freedom for effective deployment. Therefore, the three-dimensional (3D) deployment of UAV is one of the key challenges in UAV-assisted communication systems. In this paper, we address the problem of UAV deployment in 3D space to provide on-demand coverage to the ground users to maximize the sum rate. We consider realistic UAV-ground channel model derived from extensive experiments. The problem formulated is non-convex. To obtain the optimal location, we approximate the rate expression to identify the concave regions and propose a low-complexity solution by applying alternating optimization. Through simulation results, we provide valuable insights into the low-complexity solution.

Ultra reliable low latency communication (URLLC) is one of the main goals in 5G. URLLC requires highly reliable and low latency transmission of safety critical and control packets. Slotted ALOHA forms one of the widely used access schemes in cellular communication. In this paper, we study the performance of URLLC packet transmissions utilizing slotted ALOHA and propose a prioritized access mechanism to provide low latency and high reliability transmission of delay critical packets. We consider two types of traffic, namely, regular traffic and URLLC traffic. URLLC packets have stringent delay requirements, thus, they are provided high priority access to the channel. Via analytical formulation, we obtain the packet delay distribution function for both URLLC and regular packets and formulate an optimization problem to maximize the reliability of regular packets given that the URLLC packets meet their required reliability threshold. Results show that by enabling prioritized access to URLLC packets, their reliability requirements can be met, while at the same time, regular packet transmission can make most use of the remaining channel.

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out an accounting for multiple scenarios representing the real-world services, shows that MaxZ performs substantially better than the state-of-the-art alternatives and consistently close to the optimum.

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

In energy‐constrained cognitive radio networks (CRNs), the choice on single channel access versus switched multichannel access is critical for energy saving and sustainable network operation. In this study, the authors study and compare the energy efficiency of switched (probabilistic) multichannel access ( p MCA) and fixed single‐channel access (SCA) in CRNs. In p MCA, a secondary user (SU) switches channel with certain probability whenever it encounters a busy channel. In SCA on the other hand, the SU stays on the same channel for its usage and waits for its availability. Via an analytical framework the authors derive the channel utilisation and energy efficiency of the two schemes. From the results the authors examine the primary user (PU) traffic dependent optimum switching probability in p MCA and the regime of PU activity dynamics where SCA outperforms p MCA.

In this paper, we propose a class of energy-efficient dynamic spectrum access (DSA) protocols for secondary user (SU) communication over a single primary user (PU) channel. The proposed variants of DSA can be optimized with respect to different backoff strategies and SU packet lengths. Via Markov chain models and numerical analysis, we derive the optimal SU packet length and inter-sensing time for optimal SU performance in the DSA variants. We evaluate the protocol performance in terms of SU goodput, SU energy efficiency, and PU collision ratio. Adaptability of the proposed SU operation protocol in practical scenarios is tested over cellular GSM band as well as under real-time video over IP based PU traffic in ISM band. Our performance studies demonstrate that the proposed protocols offer significantly high channel utilization while keeping the PU collisions below an acceptable threshold. The proposed protocol operation is also outlined, where the protocol adapts to the changing PU traffic load for optimized performance.